Warning: Permanently added '2620:52:3:1:dead:beef:cafe:c158' (ED25519) to the list of known hosts. You can reproduce this build on your computer by running: sudo dnf install copr-rpmbuild /usr/bin/copr-rpmbuild --verbose --drop-resultdir --task-url https://copr.fedorainfracloud.org/backend/get-build-task/9101332-fedora-rawhide-x86_64 --chroot fedora-rawhide-x86_64 Version: 1.3 PID: 8569 Logging PID: 8570 Task: {'allow_user_ssh': False, 'appstream': False, 'background': True, 'build_id': 9101332, 'buildroot_pkgs': [], 'chroot': 'fedora-rawhide-x86_64', 'enable_net': False, 'fedora_review': False, 'git_hash': '635f83b74bd4e4710a949d97c9a4976584c5bc38', 'git_repo': 'https://copr-dist-git.fedorainfracloud.org/git/@python/python3.14-b1/python-imbalanced-learn', 'isolation': 'default', 'memory_reqs': 2048, 'package_name': 'python-imbalanced-learn', 'package_version': '0.13.0-2', 'project_dirname': 'python3.14-b1', 'project_name': 'python3.14-b1', 'project_owner': '@python', 'repo_priority': 9, 'repos': [{'baseurl': 'https://download.copr.fedorainfracloud.org/results/@python/python3.14-b1/fedora-rawhide-x86_64/', 'id': 'copr_base', 'name': 'Copr repository', 'priority': 9}, {'baseurl': 'http://kojipkgs.fedoraproject.org/repos/rawhide/latest/$basearch/', 'id': 'http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch', 'name': 'Additional repo http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch'}], 'sandbox': '@python/python3.14-b1--ksurma', 'source_json': {}, 'source_type': None, 'ssh_public_keys': None, 'storage': 0, 'submitter': 'ksurma', 'tags': [], 'task_id': '9101332-fedora-rawhide-x86_64', 'timeout': 18000, 'uses_devel_repo': False, 'with_opts': [], 'without_opts': []} Running: git clone https://copr-dist-git.fedorainfracloud.org/git/@python/python3.14-b1/python-imbalanced-learn /var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn --depth 500 --no-single-branch --recursive cmd: ['git', 'clone', 'https://copr-dist-git.fedorainfracloud.org/git/@python/python3.14-b1/python-imbalanced-learn', '/var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn', '--depth', '500', '--no-single-branch', '--recursive'] cwd: . rc: 0 stdout: stderr: Cloning into '/var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn'... Running: git checkout 635f83b74bd4e4710a949d97c9a4976584c5bc38 -- cmd: ['git', 'checkout', '635f83b74bd4e4710a949d97c9a4976584c5bc38', '--'] cwd: /var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn rc: 0 stdout: stderr: Note: switching to '635f83b74bd4e4710a949d97c9a4976584c5bc38'. You are in 'detached HEAD' state. You can look around, make experimental changes and commit them, and you can discard any commits you make in this state without impacting any branches by switching back to a branch. If you want to create a new branch to retain commits you create, you may do so (now or later) by using -c with the switch command. Example: git switch -c Or undo this operation with: git switch - Turn off this advice by setting config variable advice.detachedHead to false HEAD is now at 635f83b automatic import of python-imbalanced-learn Running: dist-git-client sources cmd: ['dist-git-client', 'sources'] cwd: /var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn rc: 0 stdout: stderr: INFO: Reading stdout from command: git rev-parse --abbrev-ref HEAD INFO: Reading stdout from command: git rev-parse HEAD INFO: Reading sources specification file: sources /usr/bin/tail: /var/lib/copr-rpmbuild/main.log: file truncated INFO: Calling: curl -H Pragma: -o imbalanced-learn-0.13.0.tar.gz --location --connect-timeout 60 --retry 3 --retry-delay 10 --remote-time --show-error --fail --retry-all-errors https://copr-dist-git.fedorainfracloud.org/repo/pkgs/@python/python3.14-b1/python-imbalanced-learn/imbalanced-learn-0.13.0.tar.gz/md5/ae4f8a011727877d7038a7263b98c8b6/imbalanced-learn-0.13.0.tar.gz % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 511k 100 511k 0 0 6593k 0 --:--:-- --:--:-- --:--:-- 6639k INFO: Reading stdout from command: md5sum imbalanced-learn-0.13.0.tar.gz Running (timeout=18000): unbuffer mock --spec /var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn/python-imbalanced-learn.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1748529486.082522 -r /var/lib/copr-rpmbuild/results/configs/child.cfg INFO: mock.py version 6.1 starting (python version = 3.13.0, NVR = mock-6.1-1.fc41), args: /usr/libexec/mock/mock --spec /var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn/python-imbalanced-learn.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1748529486.082522 -r /var/lib/copr-rpmbuild/results/configs/child.cfg Start(bootstrap): init plugins INFO: tmpfs initialized INFO: selinux enabled INFO: chroot_scan: initialized INFO: compress_logs: initialized Finish(bootstrap): init plugins Start: init plugins INFO: tmpfs initialized INFO: selinux enabled INFO: chroot_scan: initialized INFO: compress_logs: initialized Finish: init plugins INFO: Signal handler active Start: run INFO: Start(/var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn/python-imbalanced-learn.spec) Config(fedora-rawhide-x86_64) Start: clean chroot Finish: clean chroot Mock Version: 6.1 INFO: Mock Version: 6.1 Start(bootstrap): chroot init INFO: mounting tmpfs at /var/lib/mock/fedora-rawhide-x86_64-bootstrap-1748529486.082522/root. INFO: calling preinit hooks INFO: enabled root cache INFO: enabled package manager cache Start(bootstrap): cleaning package manager metadata Finish(bootstrap): cleaning package manager metadata INFO: Guessed host environment type: unknown INFO: Using container image: registry.fedoraproject.org/fedora:rawhide INFO: Pulling image: registry.fedoraproject.org/fedora:rawhide INFO: Tagging container image as mock-bootstrap-2f04132a-ace5-4d8a-a21d-a2c54c759d8e INFO: Checking that 15d08b8587d237ac5162867223c37b51dc20254429b3e7a3b72e8a422100d3f1 image matches host's architecture INFO: Copy content of container 15d08b8587d237ac5162867223c37b51dc20254429b3e7a3b72e8a422100d3f1 to /var/lib/mock/fedora-rawhide-x86_64-bootstrap-1748529486.082522/root INFO: mounting 15d08b8587d237ac5162867223c37b51dc20254429b3e7a3b72e8a422100d3f1 with podman image mount INFO: image 15d08b8587d237ac5162867223c37b51dc20254429b3e7a3b72e8a422100d3f1 as /var/lib/containers/storage/overlay/0f8dda24b95468405258d5176b7ee6c3d0a63d0f5a96f5f6c46c5f95438e640f/merged INFO: umounting image 15d08b8587d237ac5162867223c37b51dc20254429b3e7a3b72e8a422100d3f1 (/var/lib/containers/storage/overlay/0f8dda24b95468405258d5176b7ee6c3d0a63d0f5a96f5f6c46c5f95438e640f/merged) with podman image umount INFO: Removing image mock-bootstrap-2f04132a-ace5-4d8a-a21d-a2c54c759d8e INFO: Package manager dnf5 detected and used (fallback) INFO: Not updating bootstrap chroot, bootstrap_image_ready=True Start(bootstrap): creating root cache Finish(bootstrap): creating root cache Finish(bootstrap): chroot init Start: chroot init INFO: mounting tmpfs at /var/lib/mock/fedora-rawhide-x86_64-1748529486.082522/root. INFO: calling preinit hooks INFO: enabled root cache INFO: enabled package manager cache Start: cleaning package manager metadata Finish: cleaning package manager metadata INFO: enabled HW Info plugin INFO: Package manager dnf5 detected and used (direct choice) INFO: Buildroot is handled by package management downloaded with a bootstrap image: rpm-5.99.90-5.fc43.x86_64 rpm-sequoia-1.8.0-1.fc43.x86_64 dnf5-5.2.13.1-2.fc43.x86_64 dnf5-plugins-5.2.13.1-2.fc43.x86_64 Start: installing minimal buildroot with dnf5 Updating and loading repositories: fedora 100% | 10.5 MiB/s | 21.7 MiB | 00m02s Copr repository 100% | 33.1 MiB/s | 5.9 MiB | 00m00s Additional repo http_kojipkgs_fedorapr 100% | 22.2 MiB/s | 14.4 MiB | 00m01s Repositories loaded. 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http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 989.0 B popt x86_64 1.19-8.fc42 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 132.8 KiB publicsuffix-list-dafsa noarch 20250116-1.fc42 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 68.5 KiB pyproject-srpm-macros noarch 1.18.1-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.9 KiB python-srpm-macros noarch 3.14-5.fc43 copr_base 51.7 KiB qt5-srpm-macros noarch 5.15.17-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 500.0 B qt6-srpm-macros noarch 6.9.0-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 464.0 B readline x86_64 8.2-13.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 485.0 KiB rpm x86_64 5.99.90-5.fc43 copr_base 3.1 MiB rpm-build-libs x86_64 5.99.90-5.fc43 copr_base 264.4 KiB rpm-libs x86_64 5.99.90-5.fc43 copr_base 929.8 KiB rpm-sequoia x86_64 1.8.0-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 2.5 MiB rpm-sign-libs x86_64 5.99.90-5.fc43 copr_base 39.7 KiB rust-srpm-macros noarch 26.3-4.fc42 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 4.8 KiB setup noarch 2.15.0-25.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 725.0 KiB sqlite-libs x86_64 3.49.2-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.5 MiB systemd-libs x86_64 257.5-5.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 2.2 MiB systemd-standalone-sysusers x86_64 257.5-5.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 277.3 KiB tpm2-tss x86_64 4.1.3-7.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.6 MiB tree-sitter-srpm-macros noarch 0.2.4-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 7.5 KiB util-linux-core x86_64 2.41-2.fc43 copr_base 1.4 MiB xxhash-libs x86_64 0.8.3-2.fc42 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 90.2 KiB xz-libs x86_64 1:5.8.1-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 217.8 KiB zig-srpm-macros noarch 1-4.fc42 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.1 KiB zip x86_64 3.0-43.fc42 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 698.5 KiB zlib-ng-compat x86_64 2.2.4-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 137.6 KiB zstd x86_64 1.5.7-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.7 MiB Installing groups: Buildsystem building group Transaction Summary: Installing: 168 packages Total size of inbound packages is 58 MiB. Need to download 58 MiB. After this operation, 197 MiB extra will be used (install 197 MiB, remove 0 B). [ 1/168] bzip2-0:1.0.8-20.fc42.x86_64 100% | 520.7 KiB/s | 52.1 KiB | 00m00s [ 2/168] bash-0:5.2.37-3.fc43.x86_64 100% | 8.2 MiB/s | 1.8 MiB | 00m00s [ 3/168] cpio-0:2.15-2.fc41.x86_64 100% | 2.3 MiB/s | 285.2 KiB | 00m00s [ 4/168] coreutils-0:9.7-3.fc43.x86_64 100% | 4.7 MiB/s | 1.1 MiB | 00m00s [ 5/168] fedora-release-common-0:43-0. 100% | 1.1 MiB/s | 25.9 KiB | 00m00s [ 6/168] findutils-1:4.10.0-5.fc42.x86 100% | 10.8 MiB/s | 542.7 KiB | 00m00s [ 7/168] glibc-minimal-langpack-0:2.41 100% | 1.4 MiB/s | 26.6 KiB | 00m00s [ 8/168] grep-0:3.12-1.fc43.x86_64 100% | 7.9 MiB/s | 289.5 KiB | 00m00s [ 9/168] diffutils-0:3.12-2.fc43.x86_6 100% | 3.0 MiB/s | 384.5 KiB | 00m00s [ 10/168] gzip-0:1.13-3.fc42.x86_64 100% | 6.2 MiB/s | 164.2 KiB | 00m00s [ 11/168] patch-0:2.8-1.fc43.x86_64 100% | 6.5 MiB/s | 113.7 KiB | 00m00s [ 12/168] info-0:7.2-3.fc42.x86_64 100% | 3.4 MiB/s | 183.8 KiB | 00m00s [ 13/168] redhat-rpm-config-0:343-5.fc4 100% | 3.2 MiB/s | 73.1 KiB | 00m00s [ 14/168] gawk-0:5.3.2-1.fc43.x86_64 100% | 6.7 MiB/s | 1.1 MiB | 00m00s [ 15/168] sed-0:4.9-4.fc42.x86_64 100% | 8.4 MiB/s | 308.8 KiB | 00m00s [ 16/168] rpm-build-0:5.99.90-5.fc43.x8 100% | 1.8 MiB/s | 126.0 KiB | 00m00s [ 17/168] unzip-0:6.0-66.fc42.x86_64 100% | 4.9 MiB/s | 184.6 KiB | 00m00s [ 18/168] tar-2:1.35-5.fc42.x86_64 100% | 12.1 MiB/s | 853.9 KiB | 00m00s [ 19/168] which-0:2.23-1.fc42.x86_64 100% | 2.1 MiB/s | 41.7 KiB | 00m00s [ 20/168] util-linux-0:2.41-2.fc43.x86_ 100% | 18.7 MiB/s | 1.1 MiB | 00m00s [ 21/168] xz-1:5.8.1-1.fc43.x86_64 100% | 7.8 MiB/s | 556.6 KiB | 00m00s [ 22/168] libblkid-0:2.41-2.fc43.x86_64 100% | 5.5 MiB/s | 123.4 KiB | 00m00s [ 23/168] libfdisk-0:2.41-2.fc43.x86_64 100% | 7.1 MiB/s | 160.9 KiB | 00m00s [ 24/168] liblastlog2-0:2.41-2.fc43.x86 100% | 2.5 MiB/s | 23.2 KiB | 00m00s [ 25/168] libmount-0:2.41-2.fc43.x86_64 100% | 7.9 MiB/s | 162.7 KiB | 00m00s [ 26/168] libsmartcols-0:2.41-2.fc43.x8 100% | 3.7 MiB/s | 84.4 KiB | 00m00s [ 27/168] libuuid-0:2.41-2.fc43.x86_64 100% | 2.6 MiB/s | 26.7 KiB | 00m00s [ 28/168] util-linux-core-0:2.41-2.fc43 100% | 18.6 MiB/s | 533.2 KiB | 00m00s [ 29/168] shadow-utils-2:4.17.4-1.fc43. 100% | 3.5 MiB/s | 1.3 MiB | 00m00s [ 30/168] xz-libs-1:5.8.1-1.fc43.x86_64 100% | 2.6 MiB/s | 113.0 KiB | 00m00s [ 31/168] bzip2-libs-0:1.0.8-20.fc42.x8 100% | 1.5 MiB/s | 43.6 KiB | 00m00s [ 32/168] libacl-0:2.3.2-3.fc42.x86_64 100% | 1.2 MiB/s | 23.0 KiB | 00m00s [ 33/168] libselinux-0:3.8-1.fc43.x86_6 100% | 4.7 MiB/s | 97.2 KiB | 00m00s [ 34/168] audit-libs-0:4.0.4-1.fc43.x86 100% | 6.0 MiB/s | 129.8 KiB | 00m00s [ 35/168] coreutils-common-0:9.7-3.fc43 100% | 5.8 MiB/s | 2.1 MiB | 00m00s [ 36/168] glibc-0:2.41.9000-14.fc43.x86 100% | 12.0 MiB/s | 2.2 MiB | 00m00s [ 37/168] libeconf-0:0.7.6-1.fc43.x86_6 100% | 2.0 MiB/s | 35.2 KiB | 00m00s [ 38/168] libsemanage-0:3.8-1.fc43.x86_ 100% | 6.4 MiB/s | 123.9 KiB | 00m00s [ 39/168] libxcrypt-0:4.4.38-7.fc43.x86 100% | 5.0 MiB/s | 127.2 KiB | 00m00s [ 40/168] pam-libs-0:1.7.0-4.fc42.x86_6 100% | 2.4 MiB/s | 58.3 KiB | 00m00s [ 41/168] setup-0:2.15.0-25.fc43.noarch 100% | 6.2 MiB/s | 151.5 KiB | 00m00s [ 42/168] ansible-srpm-macros-0:1-17.1. 100% | 1.2 MiB/s | 20.3 KiB | 00m00s [ 43/168] build-reproducibility-srpm-ma 100% | 730.2 KiB/s | 11.7 KiB | 00m00s [ 44/168] dwz-0:0.15-9.fc42.x86_64 100% | 5.5 MiB/s | 135.7 KiB | 00m00s [ 45/168] efi-srpm-macros-0:6-3.fc43.no 100% | 1.3 MiB/s | 22.5 KiB | 00m00s [ 46/168] file-0:5.46-1.fc43.x86_64 100% | 5.3 MiB/s | 49.3 KiB | 00m00s [ 47/168] filesystem-0:3.18-44.fc43.x86 100% | 3.9 MiB/s | 1.3 MiB | 00m00s [ 48/168] filesystem-srpm-macros-0:3.18 100% | 1.4 MiB/s | 26.0 KiB | 00m00s [ 49/168] glibc-gconv-extra-0:2.41.9000 100% | 5.3 MiB/s | 1.5 MiB | 00m00s [ 50/168] fonts-srpm-macros-1:2.0.5-22. 100% | 1.7 MiB/s | 27.2 KiB | 00m00s [ 51/168] forge-srpm-macros-0:0.4.0-2.f 100% | 1.1 MiB/s | 19.9 KiB | 00m00s [ 52/168] fpc-srpm-macros-0:1.3-14.fc42 100% | 501.2 KiB/s | 8.0 KiB | 00m00s [ 53/168] ghc-srpm-macros-0:1.9.2-2.fc4 100% | 572.3 KiB/s | 9.2 KiB | 00m00s [ 54/168] gnat-srpm-macros-0:6-7.fc42.n 100% | 506.5 KiB/s | 8.6 KiB | 00m00s [ 55/168] go-srpm-macros-0:3.6.0-7.fc43 100% | 1.1 MiB/s | 27.6 KiB | 00m00s [ 56/168] kernel-srpm-macros-0:1.0-25.f 100% | 470.1 KiB/s | 9.9 KiB | 00m00s [ 57/168] lua-srpm-macros-0:1-15.fc42.n 100% | 557.3 KiB/s | 8.9 KiB | 00m00s [ 58/168] ocaml-srpm-macros-0:10-4.fc42 100% | 575.3 KiB/s | 9.2 KiB | 00m00s [ 59/168] file-libs-0:5.46-1.fc43.x86_6 100% | 2.9 MiB/s | 850.1 KiB | 00m00s [ 60/168] openblas-srpm-macros-0:2-19.f 100% | 456.8 KiB/s | 7.8 KiB | 00m00s [ 61/168] package-notes-srpm-macros-0:0 100% | 544.7 KiB/s | 9.3 KiB | 00m00s [ 62/168] perl-srpm-macros-0:1-57.fc42. 100% | 531.6 KiB/s | 8.5 KiB | 00m00s [ 63/168] pyproject-srpm-macros-0:1.18. 100% | 869.1 KiB/s | 13.9 KiB | 00m00s [ 64/168] python-srpm-macros-0:3.14-5.f 100% | 2.6 MiB/s | 23.8 KiB | 00m00s [ 65/168] qt5-srpm-macros-0:5.15.17-1.f 100% | 512.9 KiB/s | 8.7 KiB | 00m00s [ 66/168] qt6-srpm-macros-0:6.9.0-2.fc4 100% | 586.4 KiB/s | 9.4 KiB | 00m00s [ 67/168] rpm-0:5.99.90-5.fc43.x86_64 100% | 19.8 MiB/s | 526.9 KiB | 00m00s [ 68/168] tree-sitter-srpm-macros-0:0.2 100% | 622.7 KiB/s | 12.5 KiB | 00m00s [ 69/168] rust-srpm-macros-0:26.3-4.fc4 100% | 585.3 KiB/s | 11.7 KiB | 00m00s [ 70/168] zig-srpm-macros-0:1-4.fc42.no 100% | 484.9 KiB/s | 8.2 KiB | 00m00s [ 71/168] libattr-0:2.5.2-5.fc42.x86_64 100% | 1.0 MiB/s | 17.1 KiB | 00m00s [ 72/168] zip-0:3.0-43.fc42.x86_64 100% | 7.8 MiB/s | 263.5 KiB | 00m00s [ 73/168] ed-0:1.21-2.fc42.x86_64 100% | 2.4 MiB/s | 82.0 KiB | 00m00s [ 74/168] ncurses-libs-0:6.5-5.20250125 100% | 8.4 MiB/s | 335.0 KiB | 00m00s [ 75/168] libgcc-0:15.1.1-2.fc43.x86_64 100% | 5.2 MiB/s | 127.5 KiB | 00m00s [ 76/168] glibc-common-0:2.41.9000-14.f 100% | 5.4 MiB/s | 308.5 KiB | 00m00s [ 77/168] pcre2-0:10.45-1.fc43.x86_64 100% | 3.6 MiB/s | 262.8 KiB | 00m00s [ 78/168] gmp-1:6.3.0-3.fc43.x86_64 100% | 8.3 MiB/s | 322.2 KiB | 00m00s [ 79/168] fedora-repos-0:43-0.2.noarch 100% | 576.8 KiB/s | 9.2 KiB | 00m00s [ 80/168] mpfr-0:4.2.2-1.fc43.x86_64 100% | 5.1 MiB/s | 346.7 KiB | 00m00s [ 81/168] readline-0:8.2-13.fc43.x86_64 100% | 3.7 MiB/s | 212.9 KiB | 00m00s [ 82/168] elfutils-libelf-0:0.193-2.fc4 100% | 8.1 MiB/s | 207.9 KiB | 00m00s [ 83/168] libcap-0:2.76-1.fc43.x86_64 100% | 2.7 MiB/s | 86.9 KiB | 00m00s [ 84/168] systemd-libs-0:257.5-5.fc43.x 100% | 7.0 MiB/s | 788.8 KiB | 00m00s [ 85/168] zlib-ng-compat-0:2.2.4-2.fc43 100% | 3.0 MiB/s | 79.1 KiB | 00m00s [ 86/168] libcap-ng-0:0.8.5-4.fc43.x86_ 100% | 3.6 MiB/s | 32.7 KiB | 00m00s [ 87/168] openssl-libs-1:3.5.0-3.fc43.x 100% | 15.6 MiB/s | 2.6 MiB | 00m00s [ 88/168] add-determinism-0:0.6.0-1.fc4 100% | 5.7 MiB/s | 918.3 KiB | 00m00s [ 89/168] debugedit-0:5.1-6.fc43.x86_64 100% | 4.0 MiB/s | 78.7 KiB | 00m00s [ 90/168] elfutils-0:0.193-2.fc43.x86_6 100% | 13.2 MiB/s | 566.3 KiB | 00m00s [ 91/168] libarchive-0:3.7.7-4.fc43.x86 100% | 5.6 MiB/s | 411.7 KiB | 00m00s [ 92/168] pkgconf-pkg-config-0:2.3.0-2. 100% | 583.8 KiB/s | 9.9 KiB | 00m00s [ 93/168] libstdc++-0:15.1.1-2.fc43.x86 100% | 13.5 MiB/s | 912.8 KiB | 00m00s [ 94/168] popt-0:1.19-8.fc42.x86_64 100% | 2.4 MiB/s | 59.4 KiB | 00m00s [ 95/168] rpm-build-libs-0:5.99.90-5.fc 100% | 5.4 MiB/s | 128.0 KiB | 00m00s [ 96/168] rpm-libs-0:5.99.90-5.fc43.x86 100% | 16.3 MiB/s | 400.7 KiB | 00m00s [ 97/168] zstd-0:1.5.7-1.fc43.x86_64 100% | 11.0 MiB/s | 485.8 KiB | 00m00s [ 98/168] curl-0:8.14.0-1.fc43.x86_64 100% | 5.6 MiB/s | 234.0 KiB | 00m00s [ 99/168] libsepol-0:3.8-1.fc42.x86_64 100% | 10.6 MiB/s | 348.9 KiB | 00m00s [100/168] lz4-libs-0:1.10.0-2.fc42.x86_ 100% | 4.0 MiB/s | 78.1 KiB | 00m00s [101/168] pkgconf-0:2.3.0-2.fc42.x86_64 100% | 2.6 MiB/s | 44.9 KiB | 00m00s [102/168] pkgconf-m4-0:2.3.0-2.fc42.noa 100% | 711.8 KiB/s | 14.2 KiB | 00m00s [103/168] libpkgconf-0:2.3.0-2.fc42.x86 100% | 2.2 MiB/s | 38.4 KiB | 00m00s [104/168] sqlite-libs-0:3.49.2-1.fc43.x 100% | 6.8 MiB/s | 759.5 KiB | 00m00s [105/168] pcre2-syntax-0:10.45-1.fc43.n 100% | 5.8 MiB/s | 161.7 KiB | 00m00s [106/168] crypto-policies-0:20250402-2. 100% | 4.2 MiB/s | 73.8 KiB | 00m00s [107/168] ncurses-base-0:6.5-5.20250125 100% | 3.4 MiB/s | 63.5 KiB | 00m00s [108/168] ca-certificates-0:2024.2.69_v 100% | 8.2 MiB/s | 945.0 KiB | 00m00s [109/168] libxml2-0:2.12.10-1.fc43.x86_ 100% | 7.5 MiB/s | 692.0 KiB | 00m00s [110/168] libzstd-0:1.5.7-1.fc43.x86_64 100% | 7.7 MiB/s | 314.8 KiB | 00m00s [111/168] fedora-repos-rawhide-0:43-0.2 100% | 550.2 KiB/s | 8.8 KiB | 00m00s [112/168] fedora-gpg-keys-0:43-0.2.noar 100% | 4.6 MiB/s | 125.8 KiB | 00m00s [113/168] elfutils-debuginfod-client-0: 100% | 2.3 MiB/s | 47.0 KiB | 00m00s [114/168] libffi-0:3.4.8-1.fc43.x86_64 100% | 2.3 MiB/s | 40.5 KiB | 00m00s [115/168] elfutils-libs-0:0.193-2.fc43. 100% | 6.4 MiB/s | 270.2 KiB | 00m00s [116/168] binutils-0:2.44-3.fc43.x86_64 100% | 10.5 MiB/s | 5.8 MiB | 00m01s [117/168] alternatives-0:1.33-1.fc43.x8 100% | 2.1 MiB/s | 40.5 KiB | 00m00s [118/168] p11-kit-trust-0:0.25.5-8.fc43 100% | 4.0 MiB/s | 132.4 KiB | 00m00s [119/168] p11-kit-0:0.25.5-8.fc43.x86_6 100% | 8.6 MiB/s | 474.6 KiB | 00m00s [120/168] jansson-0:2.14-2.fc42.x86_64 100% | 2.5 MiB/s | 45.7 KiB | 00m00s [121/168] lua-libs-0:5.4.7-3.fc43.x86_6 100% | 5.1 MiB/s | 130.4 KiB | 00m00s [122/168] libgomp-0:15.1.1-2.fc43.x86_6 100% | 7.0 MiB/s | 365.2 KiB | 00m00s [123/168] rpm-sign-libs-0:5.99.90-5.fc4 100% | 531.1 KiB/s | 29.7 KiB | 00m00s [124/168] libtasn1-0:4.20.0-1.fc43.x86_ 100% | 3.7 MiB/s | 75.0 KiB | 00m00s [125/168] rpm-sequoia-0:1.8.0-1.fc43.x8 100% | 10.8 MiB/s | 938.8 KiB | 00m00s [126/168] elfutils-default-yama-scope-0 100% | 699.2 KiB/s | 12.6 KiB | 00m00s [127/168] json-c-0:0.18-2.fc42.x86_64 100% | 2.4 MiB/s | 44.9 KiB | 00m00s [128/168] ima-evm-utils-libs-0:1.6.2-5. 100% | 1.7 MiB/s | 29.5 KiB | 00m00s [129/168] libfsverity-0:1.6-2.fc42.x86_ 100% | 1.1 MiB/s | 18.8 KiB | 00m00s [130/168] gpgverify-0:2.1-3.fc43.noarch 100% | 565.9 KiB/s | 10.8 KiB | 00m00s [131/168] tpm2-tss-0:4.1.3-7.fc43.x86_6 100% | 9.5 MiB/s | 418.8 KiB | 00m00s [132/168] gnupg2-dirmngr-0:2.4.8-2.fc43 100% | 7.5 MiB/s | 274.8 KiB | 00m00s [133/168] gnupg2-gpgconf-0:2.4.8-2.fc43 100% | 4.7 MiB/s | 115.2 KiB | 00m00s [134/168] gnupg2-gpg-agent-0:2.4.8-2.fc 100% | 8.1 MiB/s | 273.0 KiB | 00m00s [135/168] gnupg2-keyboxd-0:2.4.8-2.fc43 100% | 5.1 MiB/s | 94.8 KiB | 00m00s [136/168] gnupg2-verify-0:2.4.8-2.fc43. 100% | 7.0 MiB/s | 171.3 KiB | 00m00s [137/168] libassuan-0:2.5.7-3.fc42.x86_ 100% | 3.9 MiB/s | 67.6 KiB | 00m00s [138/168] gnupg2-0:2.4.8-2.fc43.x86_64 100% | 9.9 MiB/s | 1.6 MiB | 00m00s [139/168] libgpg-error-0:1.55-1.fc43.x8 100% | 7.1 MiB/s | 238.9 KiB | 00m00s [140/168] libgcrypt-0:1.11.1-1.fc43.x86 100% | 11.9 MiB/s | 596.1 KiB | 00m00s [141/168] npth-0:1.8-2.fc42.x86_64 100% | 1.5 MiB/s | 25.8 KiB | 00m00s [142/168] libusb1-0:1.0.28-2.fc43.x86_6 100% | 4.6 MiB/s | 79.3 KiB | 00m00s [143/168] libksba-0:1.6.7-3.fc42.x86_64 100% | 6.3 MiB/s | 162.0 KiB | 00m00s [144/168] openldap-0:2.6.9-5.fc43.x86_6 100% | 7.9 MiB/s | 258.6 KiB | 00m00s [145/168] libevent-0:2.1.12-15.fc42.x86 100% | 7.7 MiB/s | 260.2 KiB | 00m00s [146/168] libtool-ltdl-0:2.5.4-4.fc42.x 100% | 2.2 MiB/s | 36.2 KiB | 00m00s [147/168] gnutls-0:3.8.9-5.fc43.x86_64 100% | 13.7 MiB/s | 1.2 MiB | 00m00s [148/168] cyrus-sasl-lib-0:2.1.28-30.fc 100% | 9.7 MiB/s | 793.5 KiB | 00m00s [149/168] libidn2-0:2.3.8-1.fc43.x86_64 100% | 6.6 MiB/s | 168.9 KiB | 00m00s [150/168] gdbm-libs-1:1.23-9.fc42.x86_6 100% | 3.1 MiB/s | 57.0 KiB | 00m00s [151/168] libunistring-0:1.1-9.fc42.x86 100% | 10.6 MiB/s | 542.5 KiB | 00m00s [152/168] fedora-release-0:43-0.16.noar 100% | 928.8 KiB/s | 14.9 KiB | 00m00s [153/168] nettle-0:3.10.1-1.fc43.x86_64 100% | 8.5 MiB/s | 424.6 KiB | 00m00s [154/168] fedora-release-identity-basic 100% | 976.9 KiB/s | 15.6 KiB | 00m00s [155/168] systemd-standalone-sysusers-0 100% | 5.2 MiB/s | 133.8 KiB | 00m00s [156/168] libcurl-0:8.14.0-1.fc43.x86_6 100% | 9.8 MiB/s | 399.7 KiB | 00m00s [157/168] xxhash-libs-0:0.8.3-2.fc42.x8 100% | 2.2 MiB/s | 39.1 KiB | 00m00s [158/168] krb5-libs-0:1.21.3-5.fc42.x86 100% | 11.4 MiB/s | 760.6 KiB | 00m00s [159/168] libnghttp2-0:1.65.0-1.fc43.x8 100% | 3.9 MiB/s | 72.6 KiB | 00m00s [160/168] libpsl-0:0.21.5-5.fc42.x86_64 100% | 3.5 MiB/s | 64.0 KiB | 00m00s [161/168] libssh-0:0.11.1-4.fc42.x86_64 100% | 8.1 MiB/s | 233.3 KiB | 00m00s [162/168] keyutils-libs-0:1.6.3-5.fc42. 100% | 1.7 MiB/s | 31.5 KiB | 00m00s [163/168] libcom_err-0:1.47.2-3.fc42.x8 100% | 1.6 MiB/s | 26.9 KiB | 00m00s [164/168] gdb-minimal-0:16.3-1.fc43.x86 100% | 20.7 MiB/s | 4.4 MiB | 00m00s [165/168] libverto-0:0.3.2-10.fc42.x86_ 100% | 1.3 MiB/s | 20.8 KiB | 00m00s [166/168] libbrotli-0:1.1.0-6.fc43.x86_ 100% | 1.9 MiB/s | 339.8 KiB | 00m00s [167/168] libssh-config-0:0.11.1-4.fc42 100% | 529.6 KiB/s | 9.0 KiB | 00m00s [168/168] publicsuffix-list-dafsa-0:202 100% | 3.4 MiB/s | 58.8 KiB | 00m00s -------------------------------------------------------------------------------- [168/168] Total 100% | 19.4 MiB/s | 58.0 MiB | 00m03s Running transaction [ 1/170] Verify package files 100% | 1.5 KiB/s | 168.0 B | 00m00s >>> Running pre-transaction scriptlet: filesystem-0:3.18-44.fc43.x86_64 >>> Finished pre-transaction scriptlet: filesystem-0:3.18-44.fc43.x86_64 >>> [RPM] /var/lib/mock/fedora-rawhide-x86_64-1748529486.082522/root/var/cache/d [ 2/170] Prepare transaction 100% | 2.0 KiB/s | 168.0 B | 00m00s [ 3/170] Installing libgcc-0:15.1.1-2. 100% | 131.0 MiB/s | 268.3 KiB | 00m00s [ 4/170] Installing publicsuffix-list- 100% | 67.6 MiB/s | 69.2 KiB | 00m00s [ 5/170] Installing libssh-config-0:0. 100% | 0.0 B/s | 816.0 B | 00m00s [ 6/170] Installing fedora-release-ide 100% | 0.0 B/s | 920.0 B | 00m00s [ 7/170] Installing fedora-repos-rawhi 100% | 0.0 B/s | 2.4 KiB | 00m00s [ 8/170] Installing fedora-gpg-keys-0: 100% | 19.1 MiB/s | 175.9 KiB | 00m00s [ 9/170] Installing fedora-repos-0:43- 100% | 0.0 B/s | 5.7 KiB | 00m00s [ 10/170] Installing fedora-release-com 100% | 12.1 MiB/s | 24.7 KiB | 00m00s [ 11/170] Installing fedora-release-0:4 100% | 7.6 KiB/s | 124.0 B | 00m00s >>> Running sysusers scriptlet: setup-0:2.15.0-25.fc43.noarch >>> Finished sysusers scriptlet: setup-0:2.15.0-25.fc43.noarch >>> Scriptlet output: >>> Creating group 'adm' with GID 4. >>> Creating group 'audio' with GID 63. >>> Creating group 'cdrom' with GID 11. >>> Creating group 'clock' with GID 103. >>> Creating group 'dialout' with GID 18. >>> Creating group 'disk' with GID 6. >>> Creating group 'floppy' with GID 19. >>> Creating group 'ftp' with GID 50. >>> Creating group 'games' with GID 20. >>> Creating group 'input' with GID 104. >>> Creating group 'kmem' with GID 9. >>> Creating group 'kvm' with GID 36. >>> Creating group 'lock' with GID 54. >>> Creating group 'lp' with GID 7. >>> Creating group 'mail' with GID 12. >>> Creating group 'man' with GID 15. >>> Creating group 'mem' with GID 8. >>> Creating group 'nobody' with GID 65534. >>> Creating group 'render' with GID 105. >>> Creating group 'root' with GID 0. >>> Creating group 'sgx' with GID 106. >>> Creating group 'sys' with GID 3. >>> Creating group 'tape' with GID 33. >>> Creating group 'tty' with GID 5. >>> Creating group 'users' with GID 100. >>> Creating group 'utmp' with GID 22. >>> Creating group 'video' with GID 39. >>> Creating group 'wheel' with GID 10. >>> Creating user 'adm' (adm) with UID 3 and GID 4. >>> Creating group 'bin' with GID 1. >>> Creating user 'bin' (bin) with UID 1 and GID 1. >>> Creating group 'daemon' with GID 2. >>> Creating user 'daemon' (daemon) with UID 2 and GID 2. >>> Creating user 'ftp' (FTP User) with UID 14 and GID 50. >>> Creating user 'games' (games) with UID 12 and GID 100. >>> Creating user 'halt' (halt) with UID 7 and GID 0. >>> Creating user 'lp' (lp) with UID 4 and GID 7. >>> Creating user 'mail' (mail) with UID 8 and GID 12. >>> Creating user 'nobody' (Kernel Overflow User) with UID 65534 and GID 65534. >>> Creating user 'operator' (operator) with UID 11 and GID 0. >>> Creating user 'root' (Super User) with UID 0 and GID 0. >>> Creating user 'shutdown' (shutdown) with UID 6 and GID 0. >>> Creating user 'sync' (sync) with UID 5 and GID 0. >>> [ 12/170] Installing setup-0:2.15.0-25. 100% | 44.6 MiB/s | 730.6 KiB | 00m00s >>> [RPM] /etc/hosts created as /etc/hosts.rpmnew [ 13/170] Installing filesystem-0:3.18- 100% | 1.5 MiB/s | 212.5 KiB | 00m00s [ 14/170] Installing ncurses-base-0:6.5 100% | 38.2 MiB/s | 352.2 KiB | 00m00s [ 15/170] Installing bash-0:5.2.37-3.fc 100% | 204.5 MiB/s | 8.2 MiB | 00m00s [ 16/170] Installing glibc-common-0:2.4 100% | 60.0 MiB/s | 1.0 MiB | 00m00s [ 17/170] Installing glibc-gconv-extra- 100% | 152.3 MiB/s | 7.3 MiB | 00m00s [ 18/170] Installing glibc-0:2.41.9000- 100% | 148.5 MiB/s | 6.7 MiB | 00m00s [ 19/170] Installing ncurses-libs-0:6.5 100% | 186.1 MiB/s | 952.8 KiB | 00m00s [ 20/170] Installing glibc-minimal-lang 100% | 0.0 B/s | 124.0 B | 00m00s [ 21/170] Installing zlib-ng-compat-0:2 100% | 135.2 MiB/s | 138.4 KiB | 00m00s [ 22/170] Installing bzip2-libs-0:1.0.8 100% | 83.7 MiB/s | 85.7 KiB | 00m00s [ 23/170] Installing libgpg-error-0:1.5 100% | 56.2 MiB/s | 921.1 KiB | 00m00s [ 24/170] Installing libstdc++-0:15.1.1 100% | 283.6 MiB/s | 2.8 MiB | 00m00s [ 25/170] Installing xz-libs-1:5.8.1-1. 100% | 213.8 MiB/s | 218.9 KiB | 00m00s [ 26/170] Installing libassuan-0:2.5.7- 100% | 165.6 MiB/s | 169.6 KiB | 00m00s [ 27/170] Installing libgcrypt-0:1.11.1 100% | 262.5 MiB/s | 1.6 MiB | 00m00s [ 28/170] Installing readline-0:8.2-13. 100% | 237.8 MiB/s | 487.1 KiB | 00m00s [ 29/170] Installing libuuid-0:2.41-2.f 100% | 37.6 MiB/s | 38.5 KiB | 00m00s [ 30/170] Installing gmp-1:6.3.0-3.fc43 100% | 267.4 MiB/s | 821.5 KiB | 00m00s [ 31/170] Installing popt-0:1.19-8.fc42 100% | 34.0 MiB/s | 139.4 KiB | 00m00s [ 32/170] Installing npth-0:1.8-2.fc42. 100% | 49.5 MiB/s | 50.7 KiB | 00m00s [ 33/170] Installing libblkid-0:2.41-2. 100% | 257.2 MiB/s | 263.4 KiB | 00m00s [ 34/170] Installing libxcrypt-0:4.4.38 100% | 140.2 MiB/s | 287.2 KiB | 00m00s [ 35/170] Installing sqlite-libs-0:3.49 100% | 252.1 MiB/s | 1.5 MiB | 00m00s [ 36/170] Installing libzstd-0:1.5.7-1. 100% | 263.4 MiB/s | 809.1 KiB | 00m00s [ 37/170] Installing elfutils-libelf-0: 100% | 291.6 MiB/s | 1.2 MiB | 00m00s [ 38/170] Installing gnupg2-gpgconf-0:2 100% | 20.5 MiB/s | 252.1 KiB | 00m00s [ 39/170] Installing libattr-0:2.5.2-5. 100% | 27.4 MiB/s | 28.1 KiB | 00m00s [ 40/170] Installing libacl-0:2.3.2-3.f 100% | 38.2 MiB/s | 39.2 KiB | 00m00s [ 41/170] Installing libtasn1-0:4.20.0- 100% | 173.9 MiB/s | 178.1 KiB | 00m00s [ 42/170] Installing libunistring-0:1.1 100% | 287.8 MiB/s | 1.7 MiB | 00m00s [ 43/170] Installing libidn2-0:2.3.8-1. 100% | 28.7 MiB/s | 558.7 KiB | 00m00s [ 44/170] Installing crypto-policies-0: 100% | 16.3 MiB/s | 166.6 KiB | 00m00s [ 45/170] Installing dwz-0:0.15-9.fc42. 100% | 20.4 MiB/s | 292.4 KiB | 00m00s [ 46/170] Installing mpfr-0:4.2.2-1.fc4 100% | 202.7 MiB/s | 830.4 KiB | 00m00s [ 47/170] Installing gawk-0:5.3.2-1.fc4 100% | 86.5 MiB/s | 1.8 MiB | 00m00s [ 48/170] Installing libksba-0:1.6.7-3. 100% | 197.8 MiB/s | 405.1 KiB | 00m00s [ 49/170] Installing unzip-0:6.0-66.fc4 100% | 27.5 MiB/s | 393.8 KiB | 00m00s [ 50/170] Installing file-libs-0:5.46-1 100% | 474.3 MiB/s | 11.9 MiB | 00m00s [ 51/170] Installing file-0:5.46-1.fc43 100% | 8.3 MiB/s | 101.7 KiB | 00m00s [ 52/170] Installing libsmartcols-0:2.4 100% | 177.3 MiB/s | 181.6 KiB | 00m00s [ 53/170] Installing libeconf-0:0.7.6-1 100% | 64.7 MiB/s | 66.2 KiB | 00m00s [ 54/170] Installing libcap-ng-0:0.8.5- 100% | 69.2 MiB/s | 70.8 KiB | 00m00s [ 55/170] Installing audit-libs-0:4.0.4 100% | 172.5 MiB/s | 353.3 KiB | 00m00s [ 56/170] Installing pam-libs-0:1.7.0-4 100% | 63.1 MiB/s | 129.1 KiB | 00m00s [ 57/170] Installing libcap-0:2.76-1.fc 100% | 16.1 MiB/s | 214.3 KiB | 00m00s [ 58/170] Installing systemd-libs-0:257 100% | 248.0 MiB/s | 2.2 MiB | 00m00s [ 59/170] Installing libsepol-0:3.8-1.f 100% | 269.2 MiB/s | 827.0 KiB | 00m00s [ 60/170] Installing lz4-libs-0:1.10.0- 100% | 154.7 MiB/s | 158.5 KiB | 00m00s [ 61/170] Installing libffi-0:3.4.8-1.f 100% | 82.4 MiB/s | 84.3 KiB | 00m00s [ 62/170] Installing p11-kit-0:0.25.5-8 100% | 87.4 MiB/s | 2.2 MiB | 00m00s [ 63/170] Installing alternatives-0:1.3 100% | 5.7 MiB/s | 63.8 KiB | 00m00s [ 64/170] Installing p11-kit-trust-0:0. 100% | 13.8 MiB/s | 397.1 KiB | 00m00s [ 65/170] Installing lua-libs-0:5.4.7-3 100% | 135.8 MiB/s | 278.1 KiB | 00m00s [ 66/170] Installing json-c-0:0.18-2.fc 100% | 85.9 MiB/s | 88.0 KiB | 00m00s [ 67/170] Installing zstd-0:1.5.7-1.fc4 100% | 100.6 MiB/s | 1.7 MiB | 00m00s [ 68/170] Installing libusb1-0:1.0.28-2 100% | 168.7 MiB/s | 172.7 KiB | 00m00s [ 69/170] Installing zip-0:3.0-43.fc42. 100% | 49.0 MiB/s | 702.4 KiB | 00m00s [ 70/170] Installing gnupg2-keyboxd-0:2 100% | 14.1 MiB/s | 202.7 KiB | 00m00s [ 71/170] Installing libpsl-0:0.21.5-5. 100% | 75.7 MiB/s | 77.5 KiB | 00m00s [ 72/170] Installing liblastlog2-0:2.41 100% | 34.9 MiB/s | 35.8 KiB | 00m00s [ 73/170] Installing libfdisk-0:2.41-2. 100% | 184.3 MiB/s | 377.5 KiB | 00m00s [ 74/170] Installing gnupg2-verify-0:2. 100% | 24.4 MiB/s | 349.9 KiB | 00m00s [ 75/170] Installing nettle-0:3.10.1-1. 100% | 193.8 MiB/s | 793.6 KiB | 00m00s [ 76/170] Installing gnutls-0:3.8.9-5.f 100% | 255.3 MiB/s | 3.6 MiB | 00m00s [ 77/170] Installing libxml2-0:2.12.10- 100% | 94.7 MiB/s | 1.7 MiB | 00m00s [ 78/170] Installing bzip2-0:1.0.8-20.f 100% | 8.5 MiB/s | 103.8 KiB | 00m00s [ 79/170] Installing add-determinism-0: 100% | 129.8 MiB/s | 2.5 MiB | 00m00s [ 80/170] Installing build-reproducibil 100% | 0.0 B/s | 1.0 KiB | 00m00s [ 81/170] Installing cpio-0:2.15-2.fc41 100% | 61.1 MiB/s | 1.1 MiB | 00m00s [ 82/170] Installing diffutils-0:3.12-2 100% | 82.2 MiB/s | 1.6 MiB | 00m00s [ 83/170] Installing ed-0:1.21-2.fc42.x 100% | 12.1 MiB/s | 148.8 KiB | 00m00s [ 84/170] Installing patch-0:2.8-1.fc43 100% | 18.6 MiB/s | 228.3 KiB | 00m00s [ 85/170] Installing libpkgconf-0:2.3.0 100% | 77.4 MiB/s | 79.2 KiB | 00m00s [ 86/170] Installing pkgconf-0:2.3.0-2. 100% | 7.4 MiB/s | 91.0 KiB | 00m00s [ 87/170] Installing jansson-0:2.14-2.f 100% | 92.2 MiB/s | 94.4 KiB | 00m00s [ 88/170] Installing libgomp-0:15.1.1-2 100% | 263.9 MiB/s | 540.5 KiB | 00m00s [ 89/170] Installing libtool-ltdl-0:2.5 100% | 69.6 MiB/s | 71.2 KiB | 00m00s [ 90/170] Installing gdbm-libs-1:1.23-9 100% | 128.5 MiB/s | 131.6 KiB | 00m00s [ 91/170] Installing cyrus-sasl-lib-0:2 100% | 121.3 MiB/s | 2.3 MiB | 00m00s [ 92/170] Installing xxhash-libs-0:0.8. 100% | 89.4 MiB/s | 91.6 KiB | 00m00s [ 93/170] Installing libbrotli-0:1.1.0- 100% | 204.0 MiB/s | 835.6 KiB | 00m00s [ 94/170] Installing libnghttp2-0:1.65. 100% | 159.5 MiB/s | 163.3 KiB | 00m00s [ 95/170] Installing keyutils-libs-0:1. 100% | 58.3 MiB/s | 59.7 KiB | 00m00s [ 96/170] Installing libcom_err-0:1.47. 100% | 66.6 MiB/s | 68.2 KiB | 00m00s [ 97/170] Installing libverto-0:0.3.2-1 100% | 26.6 MiB/s | 27.2 KiB | 00m00s [ 98/170] Installing filesystem-srpm-ma 100% | 38.0 MiB/s | 38.9 KiB | 00m00s [ 99/170] Installing elfutils-default-y 100% | 157.2 KiB/s | 2.0 KiB | 00m00s [100/170] Installing elfutils-libs-0:0. 100% | 167.3 MiB/s | 685.2 KiB | 00m00s [101/170] Installing pcre2-syntax-0:10. 100% | 135.0 MiB/s | 276.4 KiB | 00m00s [102/170] Installing pcre2-0:10.45-1.fc 100% | 227.6 MiB/s | 699.1 KiB | 00m00s [103/170] Installing libselinux-0:3.8-1 100% | 94.9 MiB/s | 194.3 KiB | 00m00s [104/170] Installing grep-0:3.12-1.fc43 100% | 47.7 MiB/s | 1.0 MiB | 00m00s [105/170] Installing findutils-1:4.10.0 100% | 89.2 MiB/s | 1.9 MiB | 00m00s [106/170] Installing sed-0:4.9-4.fc42.x 100% | 47.0 MiB/s | 865.5 KiB | 00m00s [107/170] Installing xz-1:5.8.1-1.fc43. 100% | 60.5 MiB/s | 1.3 MiB | 00m00s [108/170] Installing libmount-0:2.41-2. 100% | 182.4 MiB/s | 373.7 KiB | 00m00s [109/170] Installing util-linux-core-0: 100% | 66.9 MiB/s | 1.5 MiB | 00m00s [110/170] Installing tar-2:1.35-5.fc42. 100% | 123.4 MiB/s | 3.0 MiB | 00m00s [111/170] Installing libsemanage-0:3.8- 100% | 149.5 MiB/s | 306.2 KiB | 00m00s [112/170] Installing systemd-standalone 100% | 22.6 MiB/s | 277.8 KiB | 00m00s [113/170] Installing pkgconf-m4-0:2.3.0 100% | 0.0 B/s | 14.8 KiB | 00m00s [114/170] Installing pkgconf-pkg-config 100% | 161.2 KiB/s | 1.8 KiB | 00m00s [115/170] Installing rust-srpm-macros-0 100% | 0.0 B/s | 5.6 KiB | 00m00s [116/170] Installing qt6-srpm-macros-0: 100% | 0.0 B/s | 740.0 B | 00m00s [117/170] Installing qt5-srpm-macros-0: 100% | 0.0 B/s | 776.0 B | 00m00s [118/170] Installing perl-srpm-macros-0 100% | 0.0 B/s | 1.1 KiB | 00m00s [119/170] Installing package-notes-srpm 100% | 0.0 B/s | 2.0 KiB | 00m00s [120/170] Installing openblas-srpm-macr 100% | 0.0 B/s | 392.0 B | 00m00s [121/170] Installing ocaml-srpm-macros- 100% | 0.0 B/s | 2.2 KiB | 00m00s [122/170] Installing kernel-srpm-macros 100% | 0.0 B/s | 2.3 KiB | 00m00s [123/170] Installing gnat-srpm-macros-0 100% | 0.0 B/s | 1.3 KiB | 00m00s [124/170] Installing ghc-srpm-macros-0: 100% | 0.0 B/s | 1.0 KiB | 00m00s [125/170] Installing fpc-srpm-macros-0: 100% | 0.0 B/s | 420.0 B | 00m00s [126/170] Installing ansible-srpm-macro 100% | 35.4 MiB/s | 36.2 KiB | 00m00s [127/170] Installing coreutils-common-0 100% | 256.6 MiB/s | 11.3 MiB | 00m00s [128/170] Installing openssl-libs-1:3.5 100% | 329.2 MiB/s | 8.9 MiB | 00m00s [129/170] Installing coreutils-0:9.7-3. 100% | 108.9 MiB/s | 5.4 MiB | 00m00s [130/170] Installing ca-certificates-0: 100% | 1.2 MiB/s | 2.4 MiB | 00m02s [131/170] Installing libarchive-0:3.7.7 100% | 182.2 MiB/s | 932.6 KiB | 00m00s [132/170] Installing krb5-libs-0:1.21.3 100% | 85.2 MiB/s | 2.3 MiB | 00m00s >>> Running sysusers scriptlet: tpm2-tss-0:4.1.3-7.fc43.x86_64 >>> Finished sysusers scriptlet: tpm2-tss-0:4.1.3-7.fc43.x86_64 >>> Scriptlet output: >>> Creating group 'tss' with GID 59. >>> Creating user 'tss' (Account used for TPM access) with UID 59 and GID 59. >>> [133/170] Installing tpm2-tss-0:4.1.3-7 100% | 174.2 MiB/s | 1.6 MiB | 00m00s [134/170] Installing ima-evm-utils-libs 100% | 60.5 MiB/s | 62.0 KiB | 00m00s [135/170] Installing gnupg2-gpg-agent-0 100% | 21.3 MiB/s | 675.4 KiB | 00m00s [136/170] Installing libssh-0:0.11.1-4. 100% | 184.7 MiB/s | 567.5 KiB | 00m00s [137/170] Installing gzip-0:1.13-3.fc42 100% | 27.8 MiB/s | 398.4 KiB | 00m00s [138/170] Installing rpm-sequoia-0:1.8. 100% | 278.2 MiB/s | 2.5 MiB | 00m00s [139/170] Installing rpm-libs-0:5.99.90 100% | 227.4 MiB/s | 931.3 KiB | 00m00s [140/170] Installing libfsverity-0:1.6- 100% | 32.7 MiB/s | 33.5 KiB | 00m00s [141/170] Installing libevent-0:2.1.12- 100% | 221.4 MiB/s | 906.9 KiB | 00m00s [142/170] Installing openldap-0:2.6.9-5 100% | 160.9 MiB/s | 658.9 KiB | 00m00s [143/170] Installing libcurl-0:8.14.0-1 100% | 218.8 MiB/s | 896.4 KiB | 00m00s [144/170] Installing elfutils-debuginfo 100% | 6.5 MiB/s | 86.2 KiB | 00m00s [145/170] Installing binutils-0:2.44-3. 100% | 235.5 MiB/s | 25.9 MiB | 00m00s [146/170] Installing elfutils-0:0.193-2 100% | 132.8 MiB/s | 2.9 MiB | 00m00s [147/170] Installing gdb-minimal-0:16.3 100% | 250.0 MiB/s | 13.2 MiB | 00m00s [148/170] Installing debugedit-0:5.1-6. 100% | 14.7 MiB/s | 195.4 KiB | 00m00s [149/170] Installing curl-0:8.14.0-1.fc 100% | 14.0 MiB/s | 472.6 KiB | 00m00s [150/170] Installing rpm-0:5.99.90-5.fc 100% | 46.5 MiB/s | 2.5 MiB | 00m00s [151/170] Installing efi-srpm-macros-0: 100% | 40.2 MiB/s | 41.1 KiB | 00m00s [152/170] Installing lua-srpm-macros-0: 100% | 0.0 B/s | 1.9 KiB | 00m00s [153/170] Installing tree-sitter-srpm-m 100% | 8.4 MiB/s | 8.6 KiB | 00m00s [154/170] Installing zig-srpm-macros-0: 100% | 1.6 MiB/s | 1.7 KiB | 00m00s [155/170] Installing gnupg2-dirmngr-0:2 100% | 20.9 MiB/s | 621.1 KiB | 00m00s [156/170] Installing gnupg2-0:2.4.8-2.f 100% | 172.4 MiB/s | 6.6 MiB | 00m00s [157/170] Installing rpm-sign-libs-0:5. 100% | 39.6 MiB/s | 40.5 KiB | 00m00s [158/170] Installing rpm-build-libs-0:5 100% | 129.5 MiB/s | 265.2 KiB | 00m00s [159/170] Installing gpgverify-0:2.1-3. 100% | 9.2 MiB/s | 9.4 KiB | 00m00s [160/170] Installing rpm-build-0:5.99.9 100% | 18.9 MiB/s | 290.3 KiB | 00m00s [161/170] Installing pyproject-srpm-mac 100% | 2.4 MiB/s | 2.5 KiB | 00m00s [162/170] Installing redhat-rpm-config- 100% | 61.2 MiB/s | 188.0 KiB | 00m00s [163/170] Installing forge-srpm-macros- 100% | 39.3 MiB/s | 40.3 KiB | 00m00s [164/170] Installing fonts-srpm-macros- 100% | 55.7 MiB/s | 57.0 KiB | 00m00s [165/170] Installing go-srpm-macros-0:3 100% | 60.5 MiB/s | 62.0 KiB | 00m00s [166/170] Installing python-srpm-macros 100% | 25.9 MiB/s | 53.0 KiB | 00m00s [167/170] Installing util-linux-0:2.41- 100% | 64.7 MiB/s | 3.6 MiB | 00m00s [168/170] Installing which-0:2.23-1.fc4 100% | 6.4 MiB/s | 85.6 KiB | 00m00s [169/170] Installing shadow-utils-2:4.1 100% | 88.1 MiB/s | 4.1 MiB | 00m00s [170/170] Installing info-0:7.2-3.fc42. 100% | 136.4 KiB/s | 358.3 KiB | 00m03s Warning: skipped OpenPGP checks for 168 packages from repositories: copr_base, http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch Complete! Finish: installing minimal buildroot with dnf5 Start: creating root cache Finish: creating root cache Finish: chroot init INFO: Installed packages: INFO: add-determinism-0.6.0-1.fc43.x86_64 alternatives-1.33-1.fc43.x86_64 ansible-srpm-macros-1-17.1.fc42.noarch audit-libs-4.0.4-1.fc43.x86_64 bash-5.2.37-3.fc43.x86_64 binutils-2.44-3.fc43.x86_64 build-reproducibility-srpm-macros-0.6.0-1.fc43.noarch bzip2-1.0.8-20.fc42.x86_64 bzip2-libs-1.0.8-20.fc42.x86_64 ca-certificates-2024.2.69_v8.0.401-5.fc42.noarch coreutils-9.7-3.fc43.x86_64 coreutils-common-9.7-3.fc43.x86_64 cpio-2.15-2.fc41.x86_64 crypto-policies-20250402-2.git86c0178.fc43.noarch curl-8.14.0-1.fc43.x86_64 cyrus-sasl-lib-2.1.28-30.fc42.x86_64 debugedit-5.1-6.fc43.x86_64 diffutils-3.12-2.fc43.x86_64 dwz-0.15-9.fc42.x86_64 ed-1.21-2.fc42.x86_64 efi-srpm-macros-6-3.fc43.noarch elfutils-0.193-2.fc43.x86_64 elfutils-debuginfod-client-0.193-2.fc43.x86_64 elfutils-default-yama-scope-0.193-2.fc43.noarch elfutils-libelf-0.193-2.fc43.x86_64 elfutils-libs-0.193-2.fc43.x86_64 fedora-gpg-keys-43-0.2.noarch fedora-release-43-0.16.noarch fedora-release-common-43-0.16.noarch fedora-release-identity-basic-43-0.16.noarch fedora-repos-43-0.2.noarch fedora-repos-rawhide-43-0.2.noarch file-5.46-1.fc43.x86_64 file-libs-5.46-1.fc43.x86_64 filesystem-3.18-44.fc43.x86_64 filesystem-srpm-macros-3.18-44.fc43.noarch findutils-4.10.0-5.fc42.x86_64 fonts-srpm-macros-2.0.5-22.fc43.noarch forge-srpm-macros-0.4.0-2.fc42.noarch fpc-srpm-macros-1.3-14.fc42.noarch gawk-5.3.2-1.fc43.x86_64 gdb-minimal-16.3-1.fc43.x86_64 gdbm-libs-1.23-9.fc42.x86_64 ghc-srpm-macros-1.9.2-2.fc42.noarch glibc-2.41.9000-14.fc43.x86_64 glibc-common-2.41.9000-14.fc43.x86_64 glibc-gconv-extra-2.41.9000-14.fc43.x86_64 glibc-minimal-langpack-2.41.9000-14.fc43.x86_64 gmp-6.3.0-3.fc43.x86_64 gnat-srpm-macros-6-7.fc42.noarch gnupg2-2.4.8-2.fc43.x86_64 gnupg2-dirmngr-2.4.8-2.fc43.x86_64 gnupg2-gpg-agent-2.4.8-2.fc43.x86_64 gnupg2-gpgconf-2.4.8-2.fc43.x86_64 gnupg2-keyboxd-2.4.8-2.fc43.x86_64 gnupg2-verify-2.4.8-2.fc43.x86_64 gnutls-3.8.9-5.fc43.x86_64 go-srpm-macros-3.6.0-7.fc43.noarch gpgverify-2.1-3.fc43.noarch grep-3.12-1.fc43.x86_64 gzip-1.13-3.fc42.x86_64 ima-evm-utils-libs-1.6.2-5.fc43.x86_64 info-7.2-3.fc42.x86_64 jansson-2.14-2.fc42.x86_64 json-c-0.18-2.fc42.x86_64 kernel-srpm-macros-1.0-25.fc42.noarch keyutils-libs-1.6.3-5.fc42.x86_64 krb5-libs-1.21.3-5.fc42.x86_64 libacl-2.3.2-3.fc42.x86_64 libarchive-3.7.7-4.fc43.x86_64 libassuan-2.5.7-3.fc42.x86_64 libattr-2.5.2-5.fc42.x86_64 libblkid-2.41-2.fc43.x86_64 libbrotli-1.1.0-6.fc43.x86_64 libcap-2.76-1.fc43.x86_64 libcap-ng-0.8.5-4.fc43.x86_64 libcom_err-1.47.2-3.fc42.x86_64 libcurl-8.14.0-1.fc43.x86_64 libeconf-0.7.6-1.fc43.x86_64 libevent-2.1.12-15.fc42.x86_64 libfdisk-2.41-2.fc43.x86_64 libffi-3.4.8-1.fc43.x86_64 libfsverity-1.6-2.fc42.x86_64 libgcc-15.1.1-2.fc43.x86_64 libgcrypt-1.11.1-1.fc43.x86_64 libgomp-15.1.1-2.fc43.x86_64 libgpg-error-1.55-1.fc43.x86_64 libidn2-2.3.8-1.fc43.x86_64 libksba-1.6.7-3.fc42.x86_64 liblastlog2-2.41-2.fc43.x86_64 libmount-2.41-2.fc43.x86_64 libnghttp2-1.65.0-1.fc43.x86_64 libpkgconf-2.3.0-2.fc42.x86_64 libpsl-0.21.5-5.fc42.x86_64 libselinux-3.8-1.fc43.x86_64 libsemanage-3.8-1.fc43.x86_64 libsepol-3.8-1.fc42.x86_64 libsmartcols-2.41-2.fc43.x86_64 libssh-0.11.1-4.fc42.x86_64 libssh-config-0.11.1-4.fc42.noarch libstdc++-15.1.1-2.fc43.x86_64 libtasn1-4.20.0-1.fc43.x86_64 libtool-ltdl-2.5.4-4.fc42.x86_64 libunistring-1.1-9.fc42.x86_64 libusb1-1.0.28-2.fc43.x86_64 libuuid-2.41-2.fc43.x86_64 libverto-0.3.2-10.fc42.x86_64 libxcrypt-4.4.38-7.fc43.x86_64 libxml2-2.12.10-1.fc43.x86_64 libzstd-1.5.7-1.fc43.x86_64 lua-libs-5.4.7-3.fc43.x86_64 lua-srpm-macros-1-15.fc42.noarch lz4-libs-1.10.0-2.fc42.x86_64 mpfr-4.2.2-1.fc43.x86_64 ncurses-base-6.5-5.20250125.fc42.noarch ncurses-libs-6.5-5.20250125.fc42.x86_64 nettle-3.10.1-1.fc43.x86_64 npth-1.8-2.fc42.x86_64 ocaml-srpm-macros-10-4.fc42.noarch openblas-srpm-macros-2-19.fc42.noarch openldap-2.6.9-5.fc43.x86_64 openssl-libs-3.5.0-3.fc43.x86_64 p11-kit-0.25.5-8.fc43.x86_64 p11-kit-trust-0.25.5-8.fc43.x86_64 package-notes-srpm-macros-0.5-13.fc42.noarch pam-libs-1.7.0-4.fc42.x86_64 patch-2.8-1.fc43.x86_64 pcre2-10.45-1.fc43.x86_64 pcre2-syntax-10.45-1.fc43.noarch perl-srpm-macros-1-57.fc42.noarch pkgconf-2.3.0-2.fc42.x86_64 pkgconf-m4-2.3.0-2.fc42.noarch pkgconf-pkg-config-2.3.0-2.fc42.x86_64 popt-1.19-8.fc42.x86_64 publicsuffix-list-dafsa-20250116-1.fc42.noarch pyproject-srpm-macros-1.18.1-1.fc43.noarch python-srpm-macros-3.14-5.fc43.noarch qt5-srpm-macros-5.15.17-1.fc43.noarch qt6-srpm-macros-6.9.0-2.fc43.noarch readline-8.2-13.fc43.x86_64 redhat-rpm-config-343-5.fc43.noarch rpm-5.99.90-5.fc43.x86_64 rpm-build-5.99.90-5.fc43.x86_64 rpm-build-libs-5.99.90-5.fc43.x86_64 rpm-libs-5.99.90-5.fc43.x86_64 rpm-sequoia-1.8.0-1.fc43.x86_64 rpm-sign-libs-5.99.90-5.fc43.x86_64 rust-srpm-macros-26.3-4.fc42.noarch sed-4.9-4.fc42.x86_64 setup-2.15.0-25.fc43.noarch shadow-utils-4.17.4-1.fc43.x86_64 sqlite-libs-3.49.2-1.fc43.x86_64 systemd-libs-257.5-5.fc43.x86_64 systemd-standalone-sysusers-257.5-5.fc43.x86_64 tar-1.35-5.fc42.x86_64 tpm2-tss-4.1.3-7.fc43.x86_64 tree-sitter-srpm-macros-0.2.4-1.fc43.noarch unzip-6.0-66.fc42.x86_64 util-linux-2.41-2.fc43.x86_64 util-linux-core-2.41-2.fc43.x86_64 which-2.23-1.fc42.x86_64 xxhash-libs-0.8.3-2.fc42.x86_64 xz-5.8.1-1.fc43.x86_64 xz-libs-5.8.1-1.fc43.x86_64 zig-srpm-macros-1-4.fc42.noarch zip-3.0-43.fc42.x86_64 zlib-ng-compat-2.2.4-2.fc43.x86_64 zstd-1.5.7-1.fc43.x86_64 Start: buildsrpm Start: rpmbuild -bs Building target platforms: x86_64 Building for target x86_64 setting SOURCE_DATE_EPOCH=1737158400 Wrote: /builddir/build/SRPMS/python-imbalanced-learn-0.13.0-2.fc43.src.rpm Finish: rpmbuild -bs INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan INFO: /var/lib/mock/fedora-rawhide-x86_64-1748529486.082522/root/var/log/dnf5.log INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz /bin/tar: Removing leading `/' from member names Finish: buildsrpm INFO: Done(/var/lib/copr-rpmbuild/workspace/workdir-c9pfr21b/python-imbalanced-learn/python-imbalanced-learn.spec) Config(child) 0 minutes 35 seconds INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results INFO: Cleaning up build root ('cleanup_on_success=True') Start: clean chroot INFO: unmounting tmpfs. Finish: clean chroot INFO: Start(/var/lib/copr-rpmbuild/results/python-imbalanced-learn-0.13.0-2.fc43.src.rpm) Config(fedora-rawhide-x86_64) Start(bootstrap): chroot init INFO: mounting tmpfs at /var/lib/mock/fedora-rawhide-x86_64-bootstrap-1748529486.082522/root. INFO: reusing tmpfs at /var/lib/mock/fedora-rawhide-x86_64-bootstrap-1748529486.082522/root. INFO: calling preinit hooks INFO: enabled root cache INFO: enabled package manager cache Start(bootstrap): cleaning package manager metadata Finish(bootstrap): cleaning package manager metadata Finish(bootstrap): chroot init Start: chroot init INFO: mounting tmpfs at /var/lib/mock/fedora-rawhide-x86_64-1748529486.082522/root. INFO: calling preinit hooks INFO: enabled root cache Start: unpacking root cache Finish: unpacking root cache INFO: enabled package manager cache Start: cleaning package manager metadata Finish: cleaning package manager metadata INFO: enabled HW Info plugin INFO: Buildroot is handled by package management downloaded with a bootstrap image: rpm-5.99.90-5.fc43.x86_64 rpm-sequoia-1.8.0-1.fc43.x86_64 dnf5-5.2.13.1-2.fc43.x86_64 dnf5-plugins-5.2.13.1-2.fc43.x86_64 Finish: chroot init Start: build phase for python-imbalanced-learn-0.13.0-2.fc43.src.rpm Start: build setup for python-imbalanced-learn-0.13.0-2.fc43.src.rpm Building target platforms: x86_64 Building for target x86_64 setting SOURCE_DATE_EPOCH=1737158400 Wrote: /builddir/build/SRPMS/python-imbalanced-learn-0.13.0-2.fc43.src.rpm Updating and loading repositories: fedora 100% | 870.8 KiB/s | 28.7 KiB | 00m00s Copr repository 100% | 24.0 KiB/s | 1.5 KiB | 00m00s Additional repo http_kojipkgs_fedorapr 100% | 63.8 KiB/s | 3.8 KiB | 00m00s Copr repository 100% | 33.9 MiB/s | 5.9 MiB | 00m00s Repositories loaded. Package Arch Version Repository Size Installing: python3-devel x86_64 3.14.0~b2-1.fc43 copr_base 1.9 MiB python3-pytest noarch 8.3.5-3.fc43 copr_base 4.1 MiB python3-pytest-xdist noarch 3.7.0-1.fc43 copr_base 468.4 KiB Installing dependencies: expat x86_64 2.7.1-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 294.2 KiB mpdecimal x86_64 4.0.1-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 217.2 KiB pyproject-rpm-macros noarch 1.18.1-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 114.5 KiB python-pip-wheel noarch 25.1.1-3.fc43 copr_base 1.2 MiB python-rpm-macros noarch 3.14-5.fc43 copr_base 22.1 KiB python3 x86_64 3.14.0~b2-1.fc43 copr_base 28.9 KiB python3-execnet noarch 2.1.1-5.fc43 copr_base 970.2 KiB python3-iniconfig noarch 1.1.1-25.fc43 copr_base 21.0 KiB python3-libs x86_64 3.14.0~b2-1.fc43 copr_base 42.6 MiB python3-packaging noarch 25.0-1.fc43 copr_base 607.5 KiB python3-pluggy noarch 1.5.0-2.fc43 copr_base 213.2 KiB python3-rpm-generators noarch 14-12.fc42 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 81.7 KiB python3-rpm-macros noarch 3.14-5.fc43 copr_base 6.4 KiB tzdata noarch 2025b-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.6 MiB Transaction Summary: Installing: 17 packages Total size of inbound packages is 13 MiB. Need to download 13 MiB. After this operation, 54 MiB extra will be used (install 54 MiB, remove 0 B). [ 1/17] python3-pytest-xdist-0:3.7.0-1. 100% | 1.5 MiB/s | 107.8 KiB | 00m00s [ 2/17] python3-devel-0:3.14.0~b2-1.fc4 100% | 4.3 MiB/s | 385.9 KiB | 00m00s [ 3/17] python3-0:3.14.0~b2-1.fc43.x86_ 100% | 1.8 MiB/s | 27.0 KiB | 00m00s [ 4/17] python3-pytest-0:8.3.5-3.fc43.n 100% | 8.3 MiB/s | 775.2 KiB | 00m00s [ 5/17] python3-iniconfig-0:1.1.1-25.fc 100% | 1.9 MiB/s | 19.0 KiB | 00m00s [ 6/17] python3-packaging-0:25.0-1.fc43 100% | 4.9 MiB/s | 151.6 KiB | 00m00s [ 7/17] python3-pluggy-0:1.5.0-2.fc43.n 100% | 2.3 MiB/s | 56.7 KiB | 00m00s [ 8/17] python3-libs-0:3.14.0~b2-1.fc43 100% | 68.2 MiB/s | 9.4 MiB | 00m00s [ 9/17] python3-execnet-0:2.1.1-5.fc43. 100% | 2.4 MiB/s | 250.5 KiB | 00m00s [10/17] expat-0:2.7.1-1.fc43.x86_64 100% | 1.0 MiB/s | 115.9 KiB | 00m00s [11/17] mpdecimal-0:4.0.1-1.fc43.x86_64 100% | 5.3 MiB/s | 97.1 KiB | 00m00s [12/17] python-pip-wheel-0:25.1.1-3.fc4 100% | 38.9 MiB/s | 1.2 MiB | 00m00s [13/17] python-rpm-macros-0:3.14-5.fc43 100% | 1.2 MiB/s | 17.4 KiB | 00m00s [14/17] python3-rpm-macros-0:3.14-5.fc4 100% | 1.5 MiB/s | 12.1 KiB | 00m00s [15/17] pyproject-rpm-macros-0:1.18.1-1 100% | 2.6 MiB/s | 44.9 KiB | 00m00s [16/17] python3-rpm-generators-0:14-12. 100% | 530.6 KiB/s | 29.2 KiB | 00m00s [17/17] tzdata-0:2025b-1.fc43.noarch 100% | 4.7 MiB/s | 429.4 KiB | 00m00s -------------------------------------------------------------------------------- [17/17] Total 100% | 40.0 MiB/s | 13.1 MiB | 00m00s Running transaction [ 1/19] Verify package files 100% | 586.0 B/s | 17.0 B | 00m00s [ 2/19] Prepare transaction 100% | 377.0 B/s | 17.0 B | 00m00s [ 3/19] Installing python-rpm-macros-0: 100% | 22.3 MiB/s | 22.8 KiB | 00m00s [ 4/19] Installing python3-rpm-macros-0 100% | 0.0 B/s | 6.7 KiB | 00m00s [ 5/19] Installing pyproject-rpm-macros 100% | 28.4 MiB/s | 116.4 KiB | 00m00s [ 6/19] Installing tzdata-0:2025b-1.fc4 100% | 25.9 MiB/s | 1.9 MiB | 00m00s [ 7/19] Installing python-pip-wheel-0:2 100% | 415.0 MiB/s | 1.2 MiB | 00m00s [ 8/19] Installing mpdecimal-0:4.0.1-1. 100% | 106.8 MiB/s | 218.8 KiB | 00m00s [ 9/19] Installing expat-0:2.7.1-1.fc43 100% | 15.2 MiB/s | 296.3 KiB | 00m00s [10/19] Installing python3-libs-0:3.14. 100% | 209.7 MiB/s | 43.0 MiB | 00m00s [11/19] Installing python3-0:3.14.0~b2- 100% | 2.1 MiB/s | 30.6 KiB | 00m00s [12/19] Installing python3-packaging-0: 100% | 121.1 MiB/s | 620.0 KiB | 00m00s [13/19] Installing python3-rpm-generato 100% | 81.0 MiB/s | 82.9 KiB | 00m00s [14/19] Installing python3-iniconfig-0: 100% | 11.8 MiB/s | 24.2 KiB | 00m00s [15/19] Installing python3-pluggy-0:1.5 100% | 71.5 MiB/s | 219.7 KiB | 00m00s [16/19] Installing python3-pytest-0:8.3 100% | 125.3 MiB/s | 4.1 MiB | 00m00s [17/19] Installing python3-execnet-0:2. 100% | 107.6 MiB/s | 991.8 KiB | 00m00s [18/19] Installing python3-pytest-xdist 100% | 93.9 MiB/s | 480.8 KiB | 00m00s [19/19] Installing python3-devel-0:3.14 100% | 36.1 MiB/s | 2.0 MiB | 00m00s Warning: skipped OpenPGP checks for 17 packages from repositories: copr_base, http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch Complete! Finish: build setup for python-imbalanced-learn-0.13.0-2.fc43.src.rpm Start: rpmbuild python-imbalanced-learn-0.13.0-2.fc43.src.rpm Building target platforms: x86_64 Building for target x86_64 setting SOURCE_DATE_EPOCH=1737158400 Executing(%mkbuilddir): /bin/sh -e /var/tmp/rpm-tmp.rmfFQ4 Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.9rkqdE + umask 022 + cd /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + cd /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + rm -rf imbalanced-learn-0.13.0 + /usr/lib/rpm/rpmuncompress -x /builddir/build/SOURCES/imbalanced-learn-0.13.0.tar.gz + STATUS=0 + '[' 0 -ne 0 ']' + cd imbalanced-learn-0.13.0 + /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w . + rm -vrf doc/sphinxext/ removed 'doc/sphinxext/LICENSE.txt' removed 'doc/sphinxext/MANIFEST.in' removed 'doc/sphinxext/README.txt' removed 'doc/sphinxext/github_link.py' removed 'doc/sphinxext/sphinx_issues.py' removed directory 'doc/sphinxext/' + sed -i /sklearn-compat/d pyproject.toml + RPM_EC=0 ++ jobs -p + exit 0 Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.v0S0J7 + umask 022 + cd /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + cd imbalanced-learn-0.13.0 + echo pyproject-rpm-macros + echo python3-devel + echo 'python3dist(packaging)' + echo 'python3dist(pip) >= 19' + '[' -f pyproject.toml ']' + echo '(python3dist(tomli) if python3-devel < 3.11)' + rm -rfv '*.dist-info/' + '[' -f /usr/bin/python3 ']' + mkdir -p /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + echo -n + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + VALAFLAGS=-g + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 ' + LT_SYS_LIBRARY_PATH=/usr/lib64: + CC=gcc + CXX=g++ + TMPDIR=/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + RPM_TOXENV=py314 + FEDORA=43 + HOSTNAME=rpmbuild + /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/pyproject-wheeldir --output /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-buildrequires -x optional Handling setuptools>=71 from build-system.requires Requirement not satisfied: setuptools>=71 Handling setuptools_scm[toml]>=8 from build-system.requires Requirement not satisfied: setuptools_scm[toml]>=8 Exiting dependency generation pass: build backend + cat /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-buildrequires + rm -rfv '*.dist-info/' + RPM_EC=0 ++ jobs -p + exit 0 Wrote: /builddir/build/SRPMS/python-imbalanced-learn-0.13.0-2.fc43.buildreqs.nosrc.rpm INFO: Going to install missing dynamic buildrequires Updating and loading repositories: fedora 100% | 776.6 KiB/s | 28.7 KiB | 00m00s Copr repository 100% | 18.1 KiB/s | 1.5 KiB | 00m00s Additional repo http_kojipkgs_fedorapr 100% | 55.5 KiB/s | 3.8 KiB | 00m00s Repositories loaded. Package "pyproject-rpm-macros-1.18.1-1.fc43.noarch" is already installed. Package "python3-devel-3.14.0~b2-1.fc43.x86_64" is already installed. Package "python3-packaging-25.0-1.fc43.noarch" is already installed. Package "python3-pytest-8.3.5-3.fc43.noarch" is already installed. Package "python3-pytest-xdist-3.7.0-1.fc43.noarch" is already installed. Package Arch Version Repository Size Installing: python3-pip noarch 25.1.1-3.fc43 copr_base 12.5 MiB python3-setuptools noarch 78.1.1-5.fc43 copr_base 9.0 MiB python3-setuptools_scm noarch 8.3.1-2.fc43 copr_base 354.5 KiB python3-setuptools_scm+toml noarch 8.3.1-2.fc43 copr_base 9.8 KiB Transaction Summary: Installing: 4 packages Total size of inbound packages is 5 MiB. Need to download 5 MiB. After this operation, 22 MiB extra will be used (install 22 MiB, remove 0 B). [1/4] python3-setuptools_scm-0:8.3.1-2. 100% | 1.4 MiB/s | 106.7 KiB | 00m00s [2/4] python3-setuptools_scm+toml-0:8.3 100% | 44.8 KiB/s | 10.4 KiB | 00m00s [3/4] python3-setuptools-0:78.1.1-5.fc4 100% | 5.1 MiB/s | 1.9 MiB | 00m00s [4/4] python3-pip-0:25.1.1-3.fc43.noarc 100% | 6.4 MiB/s | 2.6 MiB | 00m00s -------------------------------------------------------------------------------- [4/4] Total 100% | 11.4 MiB/s | 4.6 MiB | 00m00s Running transaction [1/6] Verify package files 100% | 400.0 B/s | 4.0 B | 00m00s [2/6] Prepare transaction 100% | 133.0 B/s | 4.0 B | 00m00s [3/6] Installing python3-setuptools-0:7 100% | 128.1 MiB/s | 9.2 MiB | 00m00s [4/6] Installing python3-setuptools_scm 100% | 60.9 MiB/s | 374.4 KiB | 00m00s [5/6] Installing python3-setuptools_scm 100% | 60.5 KiB/s | 124.0 B | 00m00s [6/6] Installing python3-pip-0:25.1.1-3 100% | 101.9 MiB/s | 12.7 MiB | 00m00s Warning: skipped OpenPGP checks for 4 packages from repository: copr_base Complete! Building target platforms: x86_64 Building for target x86_64 setting SOURCE_DATE_EPOCH=1737158400 Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.yyPBK2 + umask 022 + cd /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + cd imbalanced-learn-0.13.0 + echo pyproject-rpm-macros + echo python3-devel + echo 'python3dist(packaging)' + echo 'python3dist(pip) >= 19' + '[' -f pyproject.toml ']' + echo '(python3dist(tomli) if python3-devel < 3.11)' + rm -rfv '*.dist-info/' + '[' -f /usr/bin/python3 ']' + mkdir -p /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + echo -n + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + VALAFLAGS=-g + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 ' + LT_SYS_LIBRARY_PATH=/usr/lib64: + CC=gcc + CXX=g++ + TMPDIR=/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + RPM_TOXENV=py314 + FEDORA=43 + HOSTNAME=rpmbuild + /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/pyproject-wheeldir --output /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-buildrequires -x optional Handling setuptools>=71 from build-system.requires Requirement satisfied: setuptools>=71 (installed: setuptools 78.1.1) Handling setuptools_scm[toml]>=8 from build-system.requires Requirement satisfied: setuptools_scm[toml]>=8 (installed: setuptools_scm 8.3.1) (extras are currently not checked) /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! dist._finalize_license_expression() /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running egg_info creating imbalanced_learn.egg-info writing imbalanced_learn.egg-info/PKG-INFO writing dependency_links to imbalanced_learn.egg-info/dependency_links.txt writing requirements to imbalanced_learn.egg-info/requires.txt writing top-level names to imbalanced_learn.egg-info/top_level.txt writing manifest file 'imbalanced_learn.egg-info/SOURCES.txt' reading manifest file 'imbalanced_learn.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' adding license file 'LICENSE' adding license file 'AUTHORS.rst' writing manifest file 'imbalanced_learn.egg-info/SOURCES.txt' /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! dist._finalize_license_expression() /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running dist_info writing imbalanced_learn.egg-info/PKG-INFO writing dependency_links to imbalanced_learn.egg-info/dependency_links.txt writing requirements to imbalanced_learn.egg-info/requires.txt writing top-level names to imbalanced_learn.egg-info/top_level.txt reading manifest file 'imbalanced_learn.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' adding license file 'LICENSE' adding license file 'AUTHORS.rst' writing manifest file 'imbalanced_learn.egg-info/SOURCES.txt' creating '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imbalanced_learn-0.13.0.dist-info' Handling numpy<3,>=1.24.3 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement not satisfied: numpy<3,>=1.24.3 Handling scipy<2,>=1.10.1 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement not satisfied: scipy<2,>=1.10.1 Handling scikit-learn<2,>=1.3.2 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement not satisfied: scikit-learn<2,>=1.3.2 Handling joblib<2,>=1.1.1 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement not satisfied: joblib<2,>=1.1.1 Handling threadpoolctl<4,>=2.0.0 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement not satisfied: threadpoolctl<4,>=2.0.0 Handling ipykernel; extra == "dev" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: ipykernel; extra == "dev" Handling ipython; extra == "dev" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: ipython; extra == "dev" Handling jupyterlab; extra == "dev" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: jupyterlab; extra == "dev" Handling pandas<3,>=1.5.3; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pandas<3,>=1.5.3; extra == "docs" Handling tensorflow<3,>=2.13.1; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: tensorflow<3,>=2.13.1; extra == "docs" Handling matplotlib<4,>=3.7.3; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: matplotlib<4,>=3.7.3; extra == "docs" Handling seaborn<1,>=0.12.2; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: seaborn<1,>=0.12.2; extra == "docs" Handling memory_profiler<1,>=0.61.0; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: memory_profiler<1,>=0.61.0; extra == "docs" Handling numpydoc<2,>=1.5.0; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: numpydoc<2,>=1.5.0; extra == "docs" Handling sphinx<9,>=8.0.2; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx<9,>=8.0.2; extra == "docs" Handling sphinx-gallery<1,>=0.13.0; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx-gallery<1,>=0.13.0; extra == "docs" Handling sphinxcontrib-bibtex<3,>=2.6.3; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinxcontrib-bibtex<3,>=2.6.3; extra == "docs" Handling sphinx-copybutton<1,>=0.5.2; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx-copybutton<1,>=0.5.2; extra == "docs" Handling pydata-sphinx-theme<1,>=0.15.4; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pydata-sphinx-theme<1,>=0.15.4; extra == "docs" Handling sphinx-design<1,>=0.6.1; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx-design<1,>=0.6.1; extra == "docs" Handling black==23.3.0; extra == "linters" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: black==23.3.0; extra == "linters" Handling ruff==0.4.8; extra == "linters" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: ruff==0.4.8; extra == "linters" Handling pre-commit; extra == "linters" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pre-commit; extra == "linters" Handling pandas<3,>=1.5.3; extra == "optional" from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement not satisfied: pandas<3,>=1.5.3; extra == "optional" Handling tensorflow<3,>=2.13.1; extra == "tensorflow" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: tensorflow<3,>=2.13.1; extra == "tensorflow" Handling keras<4,>=3.0.5; extra == "keras" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: keras<4,>=3.0.5; extra == "keras" Handling packaging<25,>=23.2; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: packaging<25,>=23.2; extra == "tests" Handling pytest<9,>=7.2.2; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pytest<9,>=7.2.2; extra == "tests" Handling pytest-cov<6,>=4.1.0; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pytest-cov<6,>=4.1.0; extra == "tests" Handling pytest-xdist<4,>=3.5.0; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pytest-xdist<4,>=3.5.0; extra == "tests" + cat /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-buildrequires + rm -rfv imbalanced_learn-0.13.0.dist-info/ removed 'imbalanced_learn-0.13.0.dist-info/top_level.txt' removed 'imbalanced_learn-0.13.0.dist-info/METADATA' removed 'imbalanced_learn-0.13.0.dist-info/licenses/LICENSE' removed 'imbalanced_learn-0.13.0.dist-info/licenses/AUTHORS.rst' removed directory 'imbalanced_learn-0.13.0.dist-info/licenses' removed directory 'imbalanced_learn-0.13.0.dist-info/' + RPM_EC=0 ++ jobs -p + exit 0 Wrote: /builddir/build/SRPMS/python-imbalanced-learn-0.13.0-2.fc43.buildreqs.nosrc.rpm INFO: Going to install missing dynamic buildrequires Updating and loading repositories: fedora 100% | 668.2 KiB/s | 28.7 KiB | 00m00s Copr repository 100% | 37.5 KiB/s | 1.5 KiB | 00m00s Additional repo http_kojipkgs_fedorapr 100% | 55.5 KiB/s | 3.8 KiB | 00m00s Copr repository 100% | 17.0 MiB/s | 5.9 MiB | 00m00s Repositories loaded. Package "pyproject-rpm-macros-1.18.1-1.fc43.noarch" is already installed. Package "python3-devel-3.14.0~b2-1.fc43.x86_64" is already installed. Package "python3-packaging-25.0-1.fc43.noarch" is already installed. Package "python3-pip-25.1.1-3.fc43.noarch" is already installed. Package "python3-pytest-8.3.5-3.fc43.noarch" is already installed. Package "python3-pytest-xdist-3.7.0-1.fc43.noarch" is already installed. Package "python3-setuptools-78.1.1-5.fc43.noarch" is already installed. Package "python3-setuptools_scm-8.3.1-2.fc43.noarch" is already installed. Package "python3-setuptools_scm+toml-8.3.1-2.fc43.noarch" is already installed. Package Arch Version Repository Size Installing: python3-joblib noarch 1.5.1-1.fc43 copr_base 2.3 MiB python3-numpy x86_64 1:2.2.6-1.fc43 copr_base 40.9 MiB python3-pandas x86_64 2.2.3-2.fc43~bootstrap.1 copr_base 43.5 MiB python3-scikit-learn x86_64 1.6.1-5.fc43 copr_base 56.9 MiB python3-scipy x86_64 1.14.1-3.fc43 copr_base 67.4 MiB python3-threadpoolctl noarch 3.5.0-5.fc43 copr_base 137.4 KiB Installing dependencies: flexiblas x86_64 3.4.5-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 50.4 KiB flexiblas-netlib x86_64 3.4.5-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 10.9 MiB flexiblas-openblas-openmp x86_64 3.4.5-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 39.2 KiB libgfortran x86_64 15.1.1-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 3.3 MiB libquadmath x86_64 15.1.1-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 317.9 KiB openblas x86_64 0.3.29-1.fc42 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 111.7 KiB openblas-openmp x86_64 0.3.29-1.fc42 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 43.7 MiB python3-charset-normalizer noarch 3.4.2-1.fc43 copr_base 354.0 KiB python3-cloudpickle noarch 3.1.1-3.fc43 copr_base 129.3 KiB python3-dateutil noarch 1:2.9.0.post0-1.fc43 copr_base 877.6 KiB python3-idna noarch 3.10-2.fc43 copr_base 730.6 KiB python3-numpy-f2py x86_64 1:2.2.6-1.fc43 copr_base 2.1 MiB python3-platformdirs noarch 4.2.2-4.fc43 copr_base 177.6 KiB python3-pooch noarch 1.8.2-5.fc43 copr_base 635.3 KiB python3-pytz noarch 2025.2-1.fc43 copr_base 224.1 KiB python3-requests noarch 2.32.3-12.fc43 copr_base 487.5 KiB python3-six noarch 1.17.0-2.fc43 copr_base 118.0 KiB python3-urllib3 noarch 2.4.0-2.fc43 copr_base 1.1 MiB Transaction Summary: Installing: 24 packages Total size of inbound packages is 56 MiB. Need to download 56 MiB. After this operation, 277 MiB extra will be used (install 277 MiB, remove 0 B). [ 1/24] python3-joblib-0:1.5.1-1.fc43.n 100% | 5.2 MiB/s | 544.5 KiB | 00m00s [ 2/24] python3-numpy-1:2.2.6-1.fc43.x8 100% | 32.3 MiB/s | 7.9 MiB | 00m00s [ 3/24] python3-pandas-0:2.2.3-2.fc43~b 100% | 21.4 MiB/s | 8.1 MiB | 00m00s [ 4/24] python3-threadpoolctl-0:3.5.0-5 100% | 617.6 KiB/s | 45.7 KiB | 00m00s [ 5/24] python3-scikit-learn-0:1.6.1-5. 100% | 29.8 MiB/s | 10.8 MiB | 00m00s [ 6/24] python3-cloudpickle-0:3.1.1-3.f 100% | 458.4 KiB/s | 48.6 KiB | 00m00s [ 7/24] python3-scipy-0:1.14.1-3.fc43.x 100% | 39.0 MiB/s | 17.1 MiB | 00m00s [ 8/24] python3-pytz-0:2025.2-1.fc43.no 100% | 489.4 KiB/s | 62.2 KiB | 00m00s [ 9/24] python3-dateutil-1:2.9.0.post0- 100% | 1.5 MiB/s | 334.2 KiB | 00m00s [10/24] python3-six-0:1.17.0-2.fc43.noa 100% | 4.6 MiB/s | 42.4 KiB | 00m00s [11/24] python3-pooch-0:1.8.2-5.fc43.no 100% | 6.9 MiB/s | 127.6 KiB | 00m00s [12/24] python3-numpy-f2py-1:2.2.6-1.fc 100% | 19.3 MiB/s | 455.3 KiB | 00m00s [13/24] python3-platformdirs-0:4.2.2-4. 100% | 2.9 MiB/s | 44.3 KiB | 00m00s [14/24] python3-requests-0:2.32.3-12.fc 100% | 7.4 MiB/s | 150.9 KiB | 00m00s [15/24] python3-charset-normalizer-0:3. 100% | 5.9 MiB/s | 109.2 KiB | 00m00s [16/24] python3-idna-0:3.10-2.fc43.noar 100% | 6.1 MiB/s | 119.2 KiB | 00m00s [17/24] python3-urllib3-0:2.4.0-2.fc43. 100% | 11.7 MiB/s | 276.7 KiB | 00m00s [18/24] flexiblas-0:3.4.5-1.fc43.x86_64 100% | 460.4 KiB/s | 26.2 KiB | 00m00s [19/24] flexiblas-openblas-openmp-0:3.4 100% | 958.5 KiB/s | 17.3 KiB | 00m00s [20/24] libgfortran-0:15.1.1-2.fc43.x86 100% | 6.9 MiB/s | 957.2 KiB | 00m00s [21/24] libquadmath-0:15.1.1-2.fc43.x86 100% | 4.6 MiB/s | 198.1 KiB | 00m00s [22/24] flexiblas-netlib-0:3.4.5-1.fc43 100% | 24.0 MiB/s | 3.4 MiB | 00m00s [23/24] openblas-0:0.3.29-1.fc42.x86_64 100% | 2.2 MiB/s | 42.3 KiB | 00m00s [24/24] openblas-openmp-0:0.3.29-1.fc42 100% | 62.5 MiB/s | 5.4 MiB | 00m00s -------------------------------------------------------------------------------- [24/24] Total 100% | 58.8 MiB/s | 56.3 MiB | 00m01s Running transaction [ 1/26] Verify package files 100% | 205.0 B/s | 24.0 B | 00m00s [ 2/26] Prepare transaction 100% | 266.0 B/s | 24.0 B | 00m00s [ 3/26] Installing libgfortran-0:15.1.1 100% | 238.8 MiB/s | 3.3 MiB | 00m00s [ 4/26] Installing python3-idna-0:3.10- 100% | 180.0 MiB/s | 737.1 KiB | 00m00s [ 5/26] Installing python3-urllib3-0:2. 100% | 122.0 MiB/s | 1.1 MiB | 00m00s [ 6/26] Installing openblas-0:0.3.29-1. 100% | 110.8 MiB/s | 113.5 KiB | 00m00s [ 7/26] Installing openblas-openmp-0:0. 100% | 450.7 MiB/s | 43.7 MiB | 00m00s [ 8/26] Installing libquadmath-0:15.1.1 100% | 155.8 MiB/s | 319.2 KiB | 00m00s [ 9/26] Installing flexiblas-netlib-0:3 100% | 232.7 MiB/s | 10.9 MiB | 00m00s [10/26] Installing flexiblas-0:3.4.5-1. 100% | 50.4 MiB/s | 51.6 KiB | 00m00s [11/26] Installing flexiblas-openblas-o 100% | 13.0 MiB/s | 40.1 KiB | 00m00s [12/26] Installing python3-numpy-1:2.2. 100% | 219.2 MiB/s | 41.2 MiB | 00m00s [13/26] Installing python3-numpy-f2py-1 100% | 61.1 MiB/s | 2.1 MiB | 00m00s [14/26] Installing python3-charset-norm 100% | 22.2 MiB/s | 364.1 KiB | 00m00s [15/26] Installing python3-requests-0:2 100% | 81.3 MiB/s | 499.8 KiB | 00m00s [16/26] Installing python3-platformdirs 100% | 89.8 MiB/s | 184.0 KiB | 00m00s [17/26] Installing python3-pooch-0:1.8. 100% | 63.5 MiB/s | 650.4 KiB | 00m00s [18/26] Installing python3-scipy-0:1.14 100% | 249.4 MiB/s | 67.8 MiB | 00m00s [19/26] Installing python3-six-0:1.17.0 100% | 58.7 MiB/s | 120.3 KiB | 00m00s [20/26] Installing python3-dateutil-1:2 100% | 124.3 MiB/s | 891.2 KiB | 00m00s [21/26] Installing python3-pytz-0:2025. 100% | 74.7 MiB/s | 229.4 KiB | 00m00s [22/26] Installing python3-cloudpickle- 100% | 65.0 MiB/s | 133.2 KiB | 00m00s [23/26] Installing python3-joblib-0:1.5 100% | 121.7 MiB/s | 2.3 MiB | 00m00s [24/26] Installing python3-threadpoolct 100% | 22.7 MiB/s | 139.6 KiB | 00m00s [25/26] Installing python3-scikit-learn 100% | 208.9 MiB/s | 57.5 MiB | 00m00s [26/26] Installing python3-pandas-0:2.2 100% | 181.6 MiB/s | 43.8 MiB | 00m00s Warning: skipped OpenPGP checks for 24 packages from repositories: copr_base, http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch Complete! Building target platforms: x86_64 Building for target x86_64 setting SOURCE_DATE_EPOCH=1737158400 Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.gGsTx6 + umask 022 + cd /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + cd imbalanced-learn-0.13.0 + echo pyproject-rpm-macros + echo python3-devel + echo 'python3dist(packaging)' + echo 'python3dist(pip) >= 19' + '[' -f pyproject.toml ']' + echo '(python3dist(tomli) if python3-devel < 3.11)' + rm -rfv '*.dist-info/' + '[' -f /usr/bin/python3 ']' + mkdir -p /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + echo -n + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + VALAFLAGS=-g + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 ' + LT_SYS_LIBRARY_PATH=/usr/lib64: + CC=gcc + CXX=g++ + TMPDIR=/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + RPM_TOXENV=py314 + FEDORA=43 + HOSTNAME=rpmbuild + /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/pyproject-wheeldir --output /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-buildrequires -x optional Handling setuptools>=71 from build-system.requires Requirement satisfied: setuptools>=71 (installed: setuptools 78.1.1) Handling setuptools_scm[toml]>=8 from build-system.requires Requirement satisfied: setuptools_scm[toml]>=8 (installed: setuptools_scm 8.3.1) (extras are currently not checked) /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! dist._finalize_license_expression() /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running egg_info writing imbalanced_learn.egg-info/PKG-INFO writing dependency_links to imbalanced_learn.egg-info/dependency_links.txt writing requirements to imbalanced_learn.egg-info/requires.txt writing top-level names to imbalanced_learn.egg-info/top_level.txt reading manifest file 'imbalanced_learn.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' adding license file 'LICENSE' adding license file 'AUTHORS.rst' writing manifest file 'imbalanced_learn.egg-info/SOURCES.txt' /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! dist._finalize_license_expression() /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running dist_info writing imbalanced_learn.egg-info/PKG-INFO writing dependency_links to imbalanced_learn.egg-info/dependency_links.txt writing requirements to imbalanced_learn.egg-info/requires.txt writing top-level names to imbalanced_learn.egg-info/top_level.txt reading manifest file 'imbalanced_learn.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' adding license file 'LICENSE' adding license file 'AUTHORS.rst' writing manifest file 'imbalanced_learn.egg-info/SOURCES.txt' creating '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imbalanced_learn-0.13.0.dist-info' Handling numpy<3,>=1.24.3 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: numpy<3,>=1.24.3 (installed: numpy 2.2.6) Handling scipy<2,>=1.10.1 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: scipy<2,>=1.10.1 (installed: scipy 1.14.1) Handling scikit-learn<2,>=1.3.2 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: scikit-learn<2,>=1.3.2 (installed: scikit-learn 1.6.1) Handling joblib<2,>=1.1.1 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: joblib<2,>=1.1.1 (installed: joblib 1.5.1) Handling threadpoolctl<4,>=2.0.0 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: threadpoolctl<4,>=2.0.0 (installed: threadpoolctl 3.5.0) Handling ipykernel; extra == "dev" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: ipykernel; extra == "dev" Handling ipython; extra == "dev" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: ipython; extra == "dev" Handling jupyterlab; extra == "dev" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: jupyterlab; extra == "dev" Handling pandas<3,>=1.5.3; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pandas<3,>=1.5.3; extra == "docs" Handling tensorflow<3,>=2.13.1; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: tensorflow<3,>=2.13.1; extra == "docs" Handling matplotlib<4,>=3.7.3; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: matplotlib<4,>=3.7.3; extra == "docs" Handling seaborn<1,>=0.12.2; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: seaborn<1,>=0.12.2; extra == "docs" Handling memory_profiler<1,>=0.61.0; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: memory_profiler<1,>=0.61.0; extra == "docs" Handling numpydoc<2,>=1.5.0; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: numpydoc<2,>=1.5.0; extra == "docs" Handling sphinx<9,>=8.0.2; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx<9,>=8.0.2; extra == "docs" Handling sphinx-gallery<1,>=0.13.0; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx-gallery<1,>=0.13.0; extra == "docs" Handling sphinxcontrib-bibtex<3,>=2.6.3; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinxcontrib-bibtex<3,>=2.6.3; extra == "docs" Handling sphinx-copybutton<1,>=0.5.2; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx-copybutton<1,>=0.5.2; extra == "docs" Handling pydata-sphinx-theme<1,>=0.15.4; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pydata-sphinx-theme<1,>=0.15.4; extra == "docs" Handling sphinx-design<1,>=0.6.1; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx-design<1,>=0.6.1; extra == "docs" Handling black==23.3.0; extra == "linters" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: black==23.3.0; extra == "linters" Handling ruff==0.4.8; extra == "linters" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: ruff==0.4.8; extra == "linters" Handling pre-commit; extra == "linters" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pre-commit; extra == "linters" Handling pandas<3,>=1.5.3; extra == "optional" from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: pandas<3,>=1.5.3; extra == "optional" (installed: pandas 2.2.3) Handling tensorflow<3,>=2.13.1; extra == "tensorflow" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: tensorflow<3,>=2.13.1; extra == "tensorflow" Handling keras<4,>=3.0.5; extra == "keras" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: keras<4,>=3.0.5; extra == "keras" Handling packaging<25,>=23.2; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: packaging<25,>=23.2; extra == "tests" Handling pytest<9,>=7.2.2; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pytest<9,>=7.2.2; extra == "tests" Handling pytest-cov<6,>=4.1.0; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pytest-cov<6,>=4.1.0; extra == "tests" Handling pytest-xdist<4,>=3.5.0; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pytest-xdist<4,>=3.5.0; extra == "tests" + cat /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-buildrequires + rm -rfv imbalanced_learn-0.13.0.dist-info/ removed 'imbalanced_learn-0.13.0.dist-info/top_level.txt' removed 'imbalanced_learn-0.13.0.dist-info/METADATA' removed 'imbalanced_learn-0.13.0.dist-info/licenses/LICENSE' removed 'imbalanced_learn-0.13.0.dist-info/licenses/AUTHORS.rst' removed directory 'imbalanced_learn-0.13.0.dist-info/licenses' removed directory 'imbalanced_learn-0.13.0.dist-info/' + RPM_EC=0 ++ jobs -p + exit 0 Wrote: /builddir/build/SRPMS/python-imbalanced-learn-0.13.0-2.fc43.buildreqs.nosrc.rpm INFO: Going to install missing dynamic buildrequires Updating and loading repositories: fedora 100% | 897.9 KiB/s | 28.7 KiB | 00m00s Copr repository 100% | 34.9 KiB/s | 1.5 KiB | 00m00s Additional repo http_kojipkgs_fedorapr 100% | 64.9 KiB/s | 3.8 KiB | 00m00s Repositories loaded. Package "pyproject-rpm-macros-1.18.1-1.fc43.noarch" is already installed. Package "python3-devel-3.14.0~b2-1.fc43.x86_64" is already installed. Package "python3-packaging-25.0-1.fc43.noarch" is already installed. Package "python3-pip-25.1.1-3.fc43.noarch" is already installed. Package "python3-pytest-8.3.5-3.fc43.noarch" is already installed. Package "python3-pytest-xdist-3.7.0-1.fc43.noarch" is already installed. Package "python3-setuptools-78.1.1-5.fc43.noarch" is already installed. Package "python3-setuptools_scm-8.3.1-2.fc43.noarch" is already installed. Package "python3-setuptools_scm+toml-8.3.1-2.fc43.noarch" is already installed. Nothing to do. Building target platforms: x86_64 Building for target x86_64 setting SOURCE_DATE_EPOCH=1737158400 Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.SVbkAa + umask 022 + cd /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + cd imbalanced-learn-0.13.0 + echo pyproject-rpm-macros + echo python3-devel + echo 'python3dist(packaging)' + echo 'python3dist(pip) >= 19' + '[' -f pyproject.toml ']' + echo '(python3dist(tomli) if python3-devel < 3.11)' + rm -rfv '*.dist-info/' + '[' -f /usr/bin/python3 ']' + mkdir -p /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + echo -n + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + VALAFLAGS=-g + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 ' + LT_SYS_LIBRARY_PATH=/usr/lib64: + CC=gcc + CXX=g++ + TMPDIR=/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + RPM_TOXENV=py314 + FEDORA=43 + HOSTNAME=rpmbuild + /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/pyproject-wheeldir --output /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-buildrequires -x optional Handling setuptools>=71 from build-system.requires Requirement satisfied: setuptools>=71 (installed: setuptools 78.1.1) Handling setuptools_scm[toml]>=8 from build-system.requires Requirement satisfied: setuptools_scm[toml]>=8 (installed: setuptools_scm 8.3.1) (extras are currently not checked) /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! dist._finalize_license_expression() /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running egg_info writing imbalanced_learn.egg-info/PKG-INFO writing dependency_links to imbalanced_learn.egg-info/dependency_links.txt writing requirements to imbalanced_learn.egg-info/requires.txt writing top-level names to imbalanced_learn.egg-info/top_level.txt reading manifest file 'imbalanced_learn.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' adding license file 'LICENSE' adding license file 'AUTHORS.rst' writing manifest file 'imbalanced_learn.egg-info/SOURCES.txt' /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! dist._finalize_license_expression() /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running dist_info writing imbalanced_learn.egg-info/PKG-INFO writing dependency_links to imbalanced_learn.egg-info/dependency_links.txt writing requirements to imbalanced_learn.egg-info/requires.txt writing top-level names to imbalanced_learn.egg-info/top_level.txt reading manifest file 'imbalanced_learn.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' adding license file 'LICENSE' adding license file 'AUTHORS.rst' writing manifest file 'imbalanced_learn.egg-info/SOURCES.txt' creating '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imbalanced_learn-0.13.0.dist-info' Handling numpy<3,>=1.24.3 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: numpy<3,>=1.24.3 (installed: numpy 2.2.6) Handling scipy<2,>=1.10.1 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: scipy<2,>=1.10.1 (installed: scipy 1.14.1) Handling scikit-learn<2,>=1.3.2 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: scikit-learn<2,>=1.3.2 (installed: scikit-learn 1.6.1) Handling joblib<2,>=1.1.1 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: joblib<2,>=1.1.1 (installed: joblib 1.5.1) Handling threadpoolctl<4,>=2.0.0 from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: threadpoolctl<4,>=2.0.0 (installed: threadpoolctl 3.5.0) Handling ipykernel; extra == "dev" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: ipykernel; extra == "dev" Handling ipython; extra == "dev" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: ipython; extra == "dev" Handling jupyterlab; extra == "dev" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: jupyterlab; extra == "dev" Handling pandas<3,>=1.5.3; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pandas<3,>=1.5.3; extra == "docs" Handling tensorflow<3,>=2.13.1; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: tensorflow<3,>=2.13.1; extra == "docs" Handling matplotlib<4,>=3.7.3; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: matplotlib<4,>=3.7.3; extra == "docs" Handling seaborn<1,>=0.12.2; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: seaborn<1,>=0.12.2; extra == "docs" Handling memory_profiler<1,>=0.61.0; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: memory_profiler<1,>=0.61.0; extra == "docs" Handling numpydoc<2,>=1.5.0; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: numpydoc<2,>=1.5.0; extra == "docs" Handling sphinx<9,>=8.0.2; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx<9,>=8.0.2; extra == "docs" Handling sphinx-gallery<1,>=0.13.0; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx-gallery<1,>=0.13.0; extra == "docs" Handling sphinxcontrib-bibtex<3,>=2.6.3; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinxcontrib-bibtex<3,>=2.6.3; extra == "docs" Handling sphinx-copybutton<1,>=0.5.2; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx-copybutton<1,>=0.5.2; extra == "docs" Handling pydata-sphinx-theme<1,>=0.15.4; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pydata-sphinx-theme<1,>=0.15.4; extra == "docs" Handling sphinx-design<1,>=0.6.1; extra == "docs" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: sphinx-design<1,>=0.6.1; extra == "docs" Handling black==23.3.0; extra == "linters" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: black==23.3.0; extra == "linters" Handling ruff==0.4.8; extra == "linters" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: ruff==0.4.8; extra == "linters" Handling pre-commit; extra == "linters" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pre-commit; extra == "linters" Handling pandas<3,>=1.5.3; extra == "optional" from hook generated metadata: Requires-Dist (imbalanced-learn) Requirement satisfied: pandas<3,>=1.5.3; extra == "optional" (installed: pandas 2.2.3) Handling tensorflow<3,>=2.13.1; extra == "tensorflow" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: tensorflow<3,>=2.13.1; extra == "tensorflow" Handling keras<4,>=3.0.5; extra == "keras" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: keras<4,>=3.0.5; extra == "keras" Handling packaging<25,>=23.2; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: packaging<25,>=23.2; extra == "tests" Handling pytest<9,>=7.2.2; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pytest<9,>=7.2.2; extra == "tests" Handling pytest-cov<6,>=4.1.0; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pytest-cov<6,>=4.1.0; extra == "tests" Handling pytest-xdist<4,>=3.5.0; extra == "tests" from hook generated metadata: Requires-Dist (imbalanced-learn) Ignoring alien requirement: pytest-xdist<4,>=3.5.0; extra == "tests" + cat /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-buildrequires + rm -rfv imbalanced_learn-0.13.0.dist-info/ removed 'imbalanced_learn-0.13.0.dist-info/top_level.txt' removed 'imbalanced_learn-0.13.0.dist-info/METADATA' removed 'imbalanced_learn-0.13.0.dist-info/licenses/LICENSE' removed 'imbalanced_learn-0.13.0.dist-info/licenses/AUTHORS.rst' removed directory 'imbalanced_learn-0.13.0.dist-info/licenses' removed directory 'imbalanced_learn-0.13.0.dist-info/' + RPM_EC=0 ++ jobs -p + exit 0 Executing(%build): /bin/sh -e /var/tmp/rpm-tmp.mp8TlP + umask 022 + cd /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + export CFLAGS + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + export CXXFLAGS + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + export FFLAGS + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + export FCFLAGS + VALAFLAGS=-g + export VALAFLAGS + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + export RUSTFLAGS + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + export LDFLAGS + LT_SYS_LIBRARY_PATH=/usr/lib64: + export LT_SYS_LIBRARY_PATH + CC=gcc + export CC + CXX=g++ + export CXX + cd imbalanced-learn-0.13.0 + mkdir -p /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + VALAFLAGS=-g + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + LT_SYS_LIBRARY_PATH=/usr/lib64: + CC=gcc + CXX=g++ + TMPDIR=/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_wheel.py /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/pyproject-wheeldir Processing /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0 Preparing metadata (pyproject.toml): started Running command Preparing metadata (pyproject.toml) /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! dist._finalize_license_expression() /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running dist_info creating /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-modern-metadata-w7owux2i/imbalanced_learn.egg-info writing /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-modern-metadata-w7owux2i/imbalanced_learn.egg-info/PKG-INFO writing dependency_links to /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-modern-metadata-w7owux2i/imbalanced_learn.egg-info/dependency_links.txt writing requirements to /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-modern-metadata-w7owux2i/imbalanced_learn.egg-info/requires.txt writing top-level names to /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-modern-metadata-w7owux2i/imbalanced_learn.egg-info/top_level.txt writing manifest file '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-modern-metadata-w7owux2i/imbalanced_learn.egg-info/SOURCES.txt' reading manifest file '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-modern-metadata-w7owux2i/imbalanced_learn.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' adding license file 'LICENSE' adding license file 'AUTHORS.rst' writing manifest file '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-modern-metadata-w7owux2i/imbalanced_learn.egg-info/SOURCES.txt' creating '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-modern-metadata-w7owux2i/imbalanced_learn-0.13.0.dist-info' Preparing metadata (pyproject.toml): finished with status 'done' Building wheels for collected packages: imbalanced-learn Building wheel for imbalanced-learn (pyproject.toml): started Running command Building wheel for imbalanced-learn (pyproject.toml) /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! dist._finalize_license_expression() /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running bdist_wheel running build running build_py creating build/lib/imblearn copying imblearn/__init__.py -> build/lib/imblearn copying imblearn/_version.py -> build/lib/imblearn copying imblearn/base.py -> build/lib/imblearn copying imblearn/exceptions.py -> build/lib/imblearn copying imblearn/pipeline.py -> build/lib/imblearn running egg_info writing imbalanced_learn.egg-info/PKG-INFO writing dependency_links to imbalanced_learn.egg-info/dependency_links.txt writing requirements to imbalanced_learn.egg-info/requires.txt writing top-level names to imbalanced_learn.egg-info/top_level.txt reading manifest file 'imbalanced_learn.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' adding license file 'LICENSE' adding license file 'AUTHORS.rst' writing manifest file 'imbalanced_learn.egg-info/SOURCES.txt' copying imblearn/VERSION.txt -> build/lib/imblearn installing to build/bdist.linux-x86_64/wheel running install running install_lib creating build/bdist.linux-x86_64/wheel creating build/bdist.linux-x86_64/wheel/imblearn copying build/lib/imblearn/__init__.py -> build/bdist.linux-x86_64/wheel/./imblearn copying build/lib/imblearn/_version.py -> build/bdist.linux-x86_64/wheel/./imblearn copying build/lib/imblearn/base.py -> build/bdist.linux-x86_64/wheel/./imblearn copying build/lib/imblearn/exceptions.py -> build/bdist.linux-x86_64/wheel/./imblearn copying build/lib/imblearn/pipeline.py -> build/bdist.linux-x86_64/wheel/./imblearn copying build/lib/imblearn/VERSION.txt -> build/bdist.linux-x86_64/wheel/./imblearn running install_egg_info Copying imbalanced_learn.egg-info to build/bdist.linux-x86_64/wheel/./imbalanced_learn-0.13.0-py3.14.egg-info running install_scripts creating build/bdist.linux-x86_64/wheel/imbalanced_learn-0.13.0.dist-info/WHEEL creating '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir/pip-wheel-sx2fctm7/.tmp-h7ukbt1n/imbalanced_learn-0.13.0-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it adding 'imbalanced_learn-0.13.0.dist-info/licenses/AUTHORS.rst' adding 'imbalanced_learn-0.13.0.dist-info/licenses/LICENSE' adding 'imblearn/VERSION.txt' adding 'imblearn/__init__.py' adding 'imblearn/_version.py' adding 'imblearn/base.py' adding 'imblearn/exceptions.py' adding 'imblearn/pipeline.py' adding 'imbalanced_learn-0.13.0.dist-info/METADATA' adding 'imbalanced_learn-0.13.0.dist-info/WHEEL' adding 'imbalanced_learn-0.13.0.dist-info/top_level.txt' adding 'imbalanced_learn-0.13.0.dist-info/RECORD' removing build/bdist.linux-x86_64/wheel Building wheel for imbalanced-learn (pyproject.toml): finished with status 'done' Created wheel for imbalanced-learn: filename=imbalanced_learn-0.13.0-py3-none-any.whl size=23104 sha256=df0ae02ff93e5036dcc27f178088126292d6ace5bb6aab2210a8fee84f2188bf Stored in directory: /builddir/.cache/pip/wheels/0f/20/a9/52812b209503cc4c91ae9d223effa621344dcc5d04f72df5fb Successfully built imbalanced-learn + RPM_EC=0 ++ jobs -p + exit 0 Executing(%install): /bin/sh -e /var/tmp/rpm-tmp.3RXMEM + umask 022 + cd /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + '[' /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT '!=' / ']' + rm -rf /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT ++ dirname /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT + mkdir -p /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + mkdir /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + export CFLAGS + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + export CXXFLAGS + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + export FFLAGS + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + export FCFLAGS + VALAFLAGS=-g + export VALAFLAGS + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + export RUSTFLAGS + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + export LDFLAGS + LT_SYS_LIBRARY_PATH=/usr/lib64: + export LT_SYS_LIBRARY_PATH + CC=gcc + export CC + CXX=g++ + export CXX + cd imbalanced-learn-0.13.0 ++ ls /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/pyproject-wheeldir/imbalanced_learn-0.13.0-py3-none-any.whl ++ sed -E 's/([^-]+)-([^-]+)-.+\.whl/\1==\2/' ++ xargs basename --multiple + specifier=imbalanced_learn==0.13.0 + '[' -z imbalanced_learn==0.13.0 ']' + TMPDIR=/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir + /usr/bin/python3 -m pip install --root /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT --prefix /usr --no-deps --disable-pip-version-check --progress-bar off --verbose --ignore-installed --no-warn-script-location --no-index --no-cache-dir --find-links /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/pyproject-wheeldir imbalanced_learn==0.13.0 Using pip 25.1.1 from /usr/lib/python3.14/site-packages/pip (python 3.14) Looking in links: /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/pyproject-wheeldir Processing ./pyproject-wheeldir/imbalanced_learn-0.13.0-py3-none-any.whl Installing collected packages: imbalanced_learn Successfully installed imbalanced_learn-0.13.0 + '[' -d /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/bin ']' + rm -f /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-ghost-distinfo + site_dirs=() + '[' -d /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages ']' + site_dirs+=("/usr/lib/python3.14/site-packages") + '[' /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib64/python3.14/site-packages '!=' /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages ']' + '[' -d /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib64/python3.14/site-packages ']' + for site_dir in ${site_dirs[@]} + for distinfo in /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT$site_dir/*.dist-info + echo '%ghost /usr/lib/python3.14/site-packages/imbalanced_learn-0.13.0.dist-info' + sed -i s/pip/rpm/ /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imbalanced_learn-0.13.0.dist-info/INSTALLER + PYTHONPATH=/usr/lib/rpm/redhat + /usr/bin/python3 -B /usr/lib/rpm/redhat/pyproject_preprocess_record.py --buildroot /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT --record /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imbalanced_learn-0.13.0.dist-info/RECORD --output /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-record + rm -fv /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imbalanced_learn-0.13.0.dist-info/RECORD removed '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imbalanced_learn-0.13.0.dist-info/RECORD' + rm -fv /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imbalanced_learn-0.13.0.dist-info/REQUESTED removed '/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imbalanced_learn-0.13.0.dist-info/REQUESTED' ++ wc -l /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-ghost-distinfo ++ cut -f1 '-d ' + lines=1 + '[' 1 -ne 1 ']' + RPM_FILES_ESCAPE=4.19 + /usr/bin/python3 /usr/lib/rpm/redhat/pyproject_save_files.py --output-files /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-files --output-modules /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-modules --buildroot /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT --sitelib /usr/lib/python3.14/site-packages --sitearch /usr/lib64/python3.14/site-packages --python-version 3.14 --pyproject-record /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/python-imbalanced-learn-0.13.0-2.fc43.x86_64-pyproject-record --prefix /usr -l imblearn + /usr/lib/rpm/check-buildroot + /usr/lib/rpm/redhat/brp-ldconfig + /usr/lib/rpm/brp-compress + /usr/lib/rpm/brp-strip /usr/bin/strip + /usr/lib/rpm/brp-strip-comment-note /usr/bin/strip /usr/bin/objdump + /usr/lib/rpm/redhat/brp-strip-lto /usr/bin/strip + /usr/lib/rpm/brp-strip-static-archive /usr/bin/strip + /usr/lib/rpm/check-rpaths + /usr/lib/rpm/redhat/brp-mangle-shebangs + /usr/lib/rpm/brp-remove-la-files + /usr/lib/rpm/redhat/brp-python-rpm-in-distinfo + env /usr/lib/rpm/redhat/brp-python-bytecompile '' 1 0 -j2 Bytecompiling .py files below /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14 using python3.14 + /usr/lib/rpm/redhat/brp-python-hardlink + /usr/bin/add-determinism --brp -j2 /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imblearn/__pycache__/_version.cpython-314.pyc: rewriting with normalized contents /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imblearn/__pycache__/__init__.cpython-314.pyc: rewriting with normalized contents /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imblearn/__pycache__/exceptions.cpython-314.pyc: rewriting with normalized contents /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imblearn/__pycache__/base.cpython-314.pyc: rewriting with normalized contents /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages/imblearn/__pycache__/pipeline.cpython-314.pyc: rewriting with normalized contents Scanned 9 directories and 22 files, processed 5 inodes, 5 modified (0 replaced + 5 rewritten), 0 unsupported format, 0 errors Executing(%check): /bin/sh -e /var/tmp/rpm-tmp.LiqlcU + umask 022 + cd /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + export CFLAGS + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + export CXXFLAGS + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + export FFLAGS + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules ' + export FCFLAGS + VALAFLAGS=-g + export VALAFLAGS + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + export RUSTFLAGS + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + export LDFLAGS + LT_SYS_LIBRARY_PATH=/usr/lib64: + export LT_SYS_LIBRARY_PATH + CC=gcc + export CC + CXX=g++ + export CXX + cd imbalanced-learn-0.13.0 + k='not test_all_estimators' + k='not test_all_estimators and not test_classification_report_imbalanced_multiclass_with_unicode_label' + k='not test_all_estimators and not test_classification_report_imbalanced_multiclass_with_unicode_label and not test_rusboost' + k='not test_all_estimators and not test_classification_report_imbalanced_multiclass_with_unicode_label and not test_rusboost and not test_cluster_centroids_n_jobs' + k='not test_all_estimators and not test_classification_report_imbalanced_multiclass_with_unicode_label and not test_rusboost and not test_cluster_centroids_n_jobs and not test_fit_docstring' + k='not test_all_estimators and not test_classification_report_imbalanced_multiclass_with_unicode_label and not test_rusboost and not test_cluster_centroids_n_jobs and not test_fit_docstring and not keras' + k='not test_all_estimators and not test_classification_report_imbalanced_multiclass_with_unicode_label and not test_rusboost and not test_cluster_centroids_n_jobs and not test_fit_docstring and not keras and not test_function_sampler_validate' + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer ' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + PATH=/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/sbin + PYTHONPATH=/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib64/python3.14/site-packages:/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/BUILDROOT/usr/lib/python3.14/site-packages + PYTHONDONTWRITEBYTECODE=1 + PYTEST_ADDOPTS=' --ignore=/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/.pyproject-builddir' + PYTEST_XDIST_AUTO_NUM_WORKERS=2 + /usr/bin/pytest -v '-k not test_all_estimators and not test_classification_report_imbalanced_multiclass_with_unicode_label and not test_rusboost and not test_cluster_centroids_n_jobs and not test_fit_docstring and not keras and not test_function_sampler_validate' imblearn ============================= test session starts ============================== platform linux -- Python 3.14.0b2, pytest-8.3.5, pluggy-1.5.0 -- /usr/bin/python3 cachedir: .pytest_cache rootdir: /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0 configfile: pyproject.toml plugins: xdist-3.7.0 collecting ... collected 2619 items / 39 deselected / 4 skipped / 2580 selected imblearn/base.py::imblearn.base.FunctionSampler PASSED [ 0%] imblearn/combine/_smote_enn.py::imblearn.combine._smote_enn.SMOTEENN PASSED [ 0%] imblearn/combine/_smote_tomek.py::imblearn.combine._smote_tomek.SMOTETomek PASSED [ 0%] imblearn/combine/tests/test_smote_enn.py::test_sample_regular PASSED [ 0%] imblearn/combine/tests/test_smote_enn.py::test_sample_regular_pass_smote_enn PASSED [ 0%] imblearn/combine/tests/test_smote_enn.py::test_sample_regular_half PASSED [ 0%] imblearn/combine/tests/test_smote_enn.py::test_validate_estimator_init PASSED [ 0%] imblearn/combine/tests/test_smote_enn.py::test_validate_estimator_default PASSED [ 0%] imblearn/combine/tests/test_smote_enn.py::test_parallelisation PASSED [ 0%] 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imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params6-estimator1] FAILED [ 2%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params6-estimator2] FAILED [ 2%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params6-estimator3] FAILED [ 2%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params6-estimator4] FAILED [ 2%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params6-estimator5] FAILED [ 2%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params7-None] FAILED [ 2%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params7-estimator1] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params7-estimator2] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params7-estimator3] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params7-estimator4] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params7-estimator5] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params8-None] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params8-estimator1] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params8-estimator2] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params8-estimator3] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params8-estimator4] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params8-estimator5] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params9-None] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params9-estimator1] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params9-estimator2] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params9-estimator3] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params9-estimator4] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params9-estimator5] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params10-None] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params10-estimator1] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params10-estimator2] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params10-estimator3] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params10-estimator4] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params10-estimator5] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params11-None] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params11-estimator1] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params11-estimator2] FAILED [ 3%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params11-estimator3] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params11-estimator4] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params11-estimator5] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params12-None] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params12-estimator1] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params12-estimator2] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params12-estimator3] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params12-estimator4] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params12-estimator5] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params13-None] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params13-estimator1] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params13-estimator2] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params13-estimator3] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params13-estimator4] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params13-estimator5] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params14-None] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params14-estimator1] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params14-estimator2] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params14-estimator3] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params14-estimator4] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params14-estimator5] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params15-None] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params15-estimator1] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params15-estimator2] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params15-estimator3] FAILED [ 4%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params15-estimator4] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params15-estimator5] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params16-None] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params16-estimator1] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params16-estimator2] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params16-estimator3] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params16-estimator4] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params16-estimator5] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params17-None] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params17-estimator1] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params17-estimator2] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params17-estimator3] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params17-estimator4] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params17-estimator5] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params18-None] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params18-estimator1] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params18-estimator2] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params18-estimator3] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params18-estimator4] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params18-estimator5] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params19-None] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params19-estimator1] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params19-estimator2] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params19-estimator3] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params19-estimator4] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params19-estimator5] FAILED [ 5%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params20-None] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params20-estimator1] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params20-estimator2] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params20-estimator3] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params20-estimator4] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params20-estimator5] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params21-None] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params21-estimator1] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params21-estimator2] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params21-estimator3] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params21-estimator4] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params21-estimator5] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params22-None] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params22-estimator1] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params22-estimator2] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params22-estimator3] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params22-estimator4] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params22-estimator5] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params23-None] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params23-estimator1] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params23-estimator2] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params23-estimator3] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params23-estimator4] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier[params23-estimator5] FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_bootstrap_samples FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_bootstrap_features FAILED [ 6%] imblearn/ensemble/tests/test_bagging.py::test_probability FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_oob_score_classification FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_single_estimator FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_gridsearch FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_estimator PASSED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_bagging_with_pipeline FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_warm_start FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_warm_start_smaller_n_estimators FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_warm_start_equal_n_estimators FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_warm_start_equivalence FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_warm_start_with_oob_score_fails PASSED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_oob_score_removed_on_warm_start FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_oob_score_consistency FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_estimators_samples FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_max_samples_consistency FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier_samplers[None-15] FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier_samplers[sampler1-15] FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier_samplers[sampler2-15] FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier_samplers[sampler3-40] FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier_samplers[sampler4-40] FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier_with_function_sampler[True] FAILED [ 7%] imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier_with_function_sampler[False] FAILED [ 7%] imblearn/ensemble/tests/test_easy_ensemble.py::test_easy_ensemble_classifier[estimator0-10] PASSED [ 7%] imblearn/ensemble/tests/test_easy_ensemble.py::test_easy_ensemble_classifier[estimator0-20] PASSED [ 7%] imblearn/ensemble/tests/test_easy_ensemble.py::test_easy_ensemble_classifier[estimator1-10] PASSED [ 7%] imblearn/ensemble/tests/test_easy_ensemble.py::test_easy_ensemble_classifier[estimator1-20] PASSED [ 7%] imblearn/ensemble/tests/test_easy_ensemble.py::test_estimator PASSED [ 8%] imblearn/ensemble/tests/test_easy_ensemble.py::test_bagging_with_pipeline FAILED [ 8%] imblearn/ensemble/tests/test_easy_ensemble.py::test_warm_start FAILED [ 8%] imblearn/ensemble/tests/test_easy_ensemble.py::test_warm_start_smaller_n_estimators FAILED [ 8%] imblearn/ensemble/tests/test_easy_ensemble.py::test_warm_start_equal_n_estimators FAILED [ 8%] imblearn/ensemble/tests/test_easy_ensemble.py::test_warm_start_equivalence FAILED [ 8%] imblearn/ensemble/tests/test_easy_ensemble.py::test_easy_ensemble_classifier_single_estimator FAILED [ 8%] imblearn/ensemble/tests/test_easy_ensemble.py::test_easy_ensemble_classifier_grid_search FAILED [ 8%] imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest_error_warning_warm_start PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest_attributes FAILED [ 8%] imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest_sample_weight PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest_oob PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest_grid_search PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_little_tree_with_small_max_samples PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest_pruning PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest_oob_binomial[0.5] PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest_oob_binomial[0.1] PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_missing_values_is_resilient PASSED [ 8%] imblearn/ensemble/tests/test_forest.py::test_missing_value_is_predictive PASSED [ 8%] imblearn/metrics/_classification.py::imblearn.metrics._classification.classification_report_imbalanced PASSED [ 8%] imblearn/metrics/_classification.py::imblearn.metrics._classification.geometric_mean_score PASSED [ 8%] imblearn/metrics/_classification.py::imblearn.metrics._classification.macro_averaged_mean_absolute_error PASSED [ 8%] imblearn/metrics/_classification.py::imblearn.metrics._classification.make_index_balanced_accuracy PASSED [ 8%] imblearn/metrics/_classification.py::imblearn.metrics._classification.sensitivity_score PASSED [ 8%] imblearn/metrics/_classification.py::imblearn.metrics._classification.sensitivity_specificity_support PASSED [ 8%] imblearn/metrics/_classification.py::imblearn.metrics._classification.specificity_score PASSED [ 9%] imblearn/metrics/pairwise.py::imblearn.metrics.pairwise.ValueDifferenceMetric PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_score_binary PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_f_binary_single_class[y_pred0-1.0-0.0] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_f_binary_single_class[y_pred1-0.0-0.0] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_extra_labels[None-expected_specificty0] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_extra_labels[macro-0.9339999999999999] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_extra_labels[micro-0.9375] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_ignored_labels PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_error_multilabels PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_support_errors PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_sensitivity_specificity_unused_pos_label PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_support_binary PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_multiclass[y_true0-y_pred0-0.0-1.0] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_multiclass[y_true1-y_pred1-0.0-0.0] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_multiclass[y_true2-y_pred2-0.001-1.0] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_multiclass[y_true3-y_pred3-0.001-0.001] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_multiclass[y_true4-y_pred4-0.001-0.5] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_multiclass[y_true5-y_pred5-0.001-0.010000000000000002] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_multiclass[y_true6-y_pred6-0.001-1] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_multiclass[y_true7-y_pred7-0.001-0.6123724356957945] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_average[y_true0-y_pred0-macro-0.471] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_average[y_true1-y_pred1-micro-0.471] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_average[y_true2-y_pred2-weighted-0.471] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_average[y_true3-y_pred3-None-expected_gmean3] PASSED [ 9%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_sample_weight[y_true0-y_pred0-None-multiclass-0.707] PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_sample_weight[y_true1-y_pred1-sample_weight1-multiclass-0.707] PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_sample_weight[y_true2-y_pred2-sample_weight2-weighted-0.333] PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_score_prediction[multiclass-0.41] PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_score_prediction[None-expected_gmean1] PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_score_prediction[macro-0.68] PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_geometric_mean_score_prediction[weighted-0.65] PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_iba_geo_mean_binary PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_classification_report_imbalanced_multiclass PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_classification_report_imbalanced_multiclass_with_digits PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_classification_report_imbalanced_multiclass_with_string_label PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_classification_report_imbalanced_multiclass_with_long_string_label PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_iba_sklearn_metrics[accuracy_score-0.54756] PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_iba_sklearn_metrics[jaccard_score-0.33176] PASSED [ 10%] imblearn/metrics/tests/test_classification.py::test_iba_sklearn_metrics[precision_score-0.65025] PASSED [ 10%] 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imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_parameters_default_constructible] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_methods_sample_order_invariance] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_methods_subset_invariance] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_fit2d_1sample] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_fit2d_1feature] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_get_params_invariance] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_set_params] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_dict_unchanged] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_fit_idempotent] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_fit_check_is_fitted] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_n_features_in] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_fit1d] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_fit2d_predict1d] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ADASYN(random_state=42)-check_requires_y_none] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimator_cloneable0] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimator_cloneable1] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimator_tags_renamed] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_valid_tag_types] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimator_repr] PASSED [ 23%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_no_attributes_set_in_init] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_fit_score_takes_y] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimators_overwrite_params] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_dont_overwrite_parameters] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimators_fit_returns_self] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_readonly_memmap_input] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimators_unfitted] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_n_features_in_after_fitting] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_mixin_order] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_positive_only_tag_during_fit] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimators_dtypes] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_complex_data] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_dtype_object] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimators_empty_data_messages] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_pipeline_consistency] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimators_nan_inf] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimator_sparse_tag] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimator_sparse_array] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimator_sparse_matrix] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimators_pickle] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_estimators_pickle(readonly_memmap=True)] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_f_contiguous_array_estimator] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_parameters_default_constructible] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_methods_sample_order_invariance] PASSED [ 24%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_methods_subset_invariance] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_fit2d_1sample] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_fit2d_1feature] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_get_params_invariance] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_set_params] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_dict_unchanged] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_fit_idempotent] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_fit_check_is_fitted] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_n_features_in] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_fit1d] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_fit2d_predict1d] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[AllKNN()-check_requires_y_none] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_cloneable0] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_cloneable1] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_tags_renamed] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_valid_tag_types] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_repr] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_no_attributes_set_in_init] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit_score_takes_y] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_overwrite_params] FAILED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_dont_overwrite_parameters] FAILED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_fit_returns_self] FAILED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_readonly_memmap_input] FAILED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_unfitted] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_n_features_in_after_fitting] FAILED [ 25%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_mixin_order] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_positive_only_tag_during_fit] FAILED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_dtypes] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_complex_data] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_dtype_object] FAILED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_empty_data_messages] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_pipeline_consistency] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_sparse_tag] FAILED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_sparse_array] FAILED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_sparse_matrix] FAILED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_pickle] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_f_contiguous_array_estimator] FAILED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifier_data_not_an_array] FAILED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifiers_one_label] FAILED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifiers_one_label_sample_weights] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifiers_classes] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_partial_fit_n_features] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifiers_train] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifiers_train(readonly_memmap=True)] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifiers_regression_target] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_supervised_y_no_nan] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_supervised_y_2d] FAILED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_non_transformer_estimators_n_iter] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_decision_proba_consistency] PASSED [ 26%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_parameters_default_constructible] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_methods_sample_order_invariance] FAILED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_methods_subset_invariance] FAILED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit2d_1sample] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit2d_1feature] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_get_params_invariance] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_set_params] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_dict_unchanged] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit_idempotent] FAILED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit_check_is_fitted] FAILED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_n_features_in] FAILED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit1d] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit2d_predict1d] FAILED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_requires_y_none] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimator_cloneable0] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimator_cloneable1] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimator_tags_renamed] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_valid_tag_types] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimator_repr] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_no_attributes_set_in_init] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_fit_score_takes_y] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimators_overwrite_params] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_dont_overwrite_parameters] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimators_fit_returns_self] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_readonly_memmap_input] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimators_unfitted] PASSED [ 27%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_n_features_in_after_fitting] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_mixin_order] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimators_dtypes] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_sample_weights_pandas_series] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_sample_weights_not_an_array] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_sample_weights_list] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_sample_weights_shape] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_sample_weights_not_overwritten] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_sample_weight_equivalence_on_dense_data] XFAIL [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_sample_weight_equivalence_on_sparse_data] XFAIL [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_complex_data] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_dtype_object] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimators_empty_data_messages] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_pipeline_consistency] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimator_sparse_tag] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimator_sparse_array] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimator_sparse_matrix] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimators_pickle] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_classifier_data_not_an_array] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_classifiers_one_label] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_classifiers_one_label_sample_weights] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_classifiers_classes] PASSED [ 28%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_estimators_partial_fit_n_features] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_classifiers_train] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_classifiers_train(readonly_memmap=True)] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_classifiers_regression_target] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_supervised_y_no_nan] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_supervised_y_2d] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_class_weight_classifiers] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_non_transformer_estimators_n_iter] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_decision_proba_consistency] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_parameters_default_constructible] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_methods_sample_order_invariance] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_methods_subset_invariance] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_fit2d_1sample] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_fit2d_1feature] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_get_params_invariance] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_set_params] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_dict_unchanged] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_fit_idempotent] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_fit_check_is_fitted] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_n_features_in] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_fit1d] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_fit2d_predict1d] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BalancedRandomForestClassifier(random_state=42)-check_requires_y_none] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimator_cloneable0] PASSED [ 29%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimator_cloneable1] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimator_tags_renamed] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_valid_tag_types] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimator_repr] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_no_attributes_set_in_init] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_fit_score_takes_y] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimators_overwrite_params] PASSED 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imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_mixin_order] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimators_dtypes] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_complex_data] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_dtype_object] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimators_empty_data_messages] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_pipeline_consistency] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimators_nan_inf] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimator_sparse_tag] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimator_sparse_array] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimator_sparse_matrix] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimators_pickle] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 30%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_parameters_default_constructible] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_methods_sample_order_invariance] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_methods_subset_invariance] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_fit2d_1sample] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_fit2d_1feature] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_get_params_invariance] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_set_params] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_dict_unchanged] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_fit_idempotent] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_fit_check_is_fitted] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_n_features_in] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_fit1d] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_fit2d_predict1d] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[BorderlineSMOTE(random_state=42)-check_requires_y_none] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimator_cloneable0] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimator_cloneable1] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimator_tags_renamed] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_valid_tag_types] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimator_repr] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_no_attributes_set_in_init] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_fit_score_takes_y] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimators_overwrite_params] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_dont_overwrite_parameters] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimators_fit_returns_self] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_readonly_memmap_input] PASSED [ 31%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimators_unfitted] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_n_features_in_after_fitting] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_mixin_order] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimators_dtypes] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_complex_data] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_dtype_object] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimators_empty_data_messages] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_pipeline_consistency] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimators_nan_inf] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimator_sparse_tag] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimator_sparse_array] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimator_sparse_matrix] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimators_pickle] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_parameters_default_constructible] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_methods_sample_order_invariance] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_methods_subset_invariance] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_fit2d_1sample] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_fit2d_1feature] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_get_params_invariance] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_set_params] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_dict_unchanged] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_fit_idempotent] PASSED [ 32%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_fit_check_is_fitted] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_n_features_in] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_fit1d] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_fit2d_predict1d] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[ClusterCentroids(random_state=42)-check_requires_y_none] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimator_cloneable0] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimator_cloneable1] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimator_tags_renamed] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_valid_tag_types] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimator_repr] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_no_attributes_set_in_init] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_fit_score_takes_y] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimators_overwrite_params] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_dont_overwrite_parameters] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimators_fit_returns_self] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_readonly_memmap_input] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimators_unfitted] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_n_features_in_after_fitting] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_mixin_order] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimators_dtypes] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_complex_data] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_dtype_object] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimators_empty_data_messages] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_pipeline_consistency] PASSED [ 33%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimators_nan_inf] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimator_sparse_tag] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimator_sparse_array] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimator_sparse_matrix] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimators_pickle] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_parameters_default_constructible] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_methods_sample_order_invariance] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_methods_subset_invariance] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_fit2d_1sample] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_fit2d_1feature] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_get_params_invariance] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_set_params] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_dict_unchanged] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_fit_idempotent] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_fit_check_is_fitted] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_n_features_in] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_fit1d] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_fit2d_predict1d] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[CondensedNearestNeighbour(random_state=42)-check_requires_y_none] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_cloneable0] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_cloneable1] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_tags_renamed] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_valid_tag_types] PASSED [ 34%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_repr] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_no_attributes_set_in_init] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit_score_takes_y] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_overwrite_params] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_dont_overwrite_parameters] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_fit_returns_self] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_readonly_memmap_input] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_unfitted] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_n_features_in_after_fitting] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_mixin_order] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_positive_only_tag_during_fit] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_dtypes] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_complex_data] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_dtype_object] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_empty_data_messages] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_pipeline_consistency] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_nan_inf] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_sparse_tag] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_sparse_array] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_sparse_matrix] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_pickle] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_f_contiguous_array_estimator] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifier_data_not_an_array] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_one_label] FAILED [ 35%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_one_label_sample_weights] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_classes] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_partial_fit_n_features] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_train] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_train(readonly_memmap=True)] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_regression_target] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_supervised_y_no_nan] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_supervised_y_2d] FAILED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_non_transformer_estimators_n_iter] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_decision_proba_consistency] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_parameters_default_constructible] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_methods_sample_order_invariance] FAILED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_methods_subset_invariance] FAILED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit2d_1sample] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit2d_1feature] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_get_params_invariance] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_set_params] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_dict_unchanged] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit_idempotent] FAILED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit_check_is_fitted] FAILED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_n_features_in] FAILED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit1d] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit2d_predict1d] FAILED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_requires_y_none] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_cloneable0] PASSED [ 36%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_cloneable1] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_tags_renamed] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_valid_tag_types] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_repr] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_no_attributes_set_in_init] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit_score_takes_y] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_overwrite_params] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_dont_overwrite_parameters] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_fit_returns_self] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_readonly_memmap_input] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_unfitted] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_n_features_in_after_fitting] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_mixin_order] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_positive_only_tag_during_fit] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_dtypes] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_complex_data] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_dtype_object] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_empty_data_messages] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_pipeline_consistency] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_sparse_tag] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_sparse_array] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_sparse_matrix] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_pickle] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_f_contiguous_array_estimator] FAILED [ 37%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifier_data_not_an_array] FAILED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_one_label] FAILED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_one_label_sample_weights] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_classes] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_partial_fit_n_features] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_train] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_train(readonly_memmap=True)] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_regression_target] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_supervised_y_no_nan] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_supervised_y_2d] FAILED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_non_transformer_estimators_n_iter] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_decision_proba_consistency] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_parameters_default_constructible] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_methods_sample_order_invariance] FAILED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_methods_subset_invariance] FAILED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit2d_1sample] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit2d_1feature] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_get_params_invariance] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_set_params] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_dict_unchanged] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit_idempotent] FAILED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit_check_is_fitted] FAILED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_n_features_in] FAILED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit1d] PASSED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit2d_predict1d] FAILED [ 38%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_requires_y_none] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimator_cloneable0] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimator_cloneable1] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimator_tags_renamed] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_valid_tag_types] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimator_repr] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_no_attributes_set_in_init] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_fit_score_takes_y] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimators_overwrite_params] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_dont_overwrite_parameters] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimators_fit_returns_self] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_readonly_memmap_input] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimators_unfitted] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_n_features_in_after_fitting] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_mixin_order] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_positive_only_tag_during_fit] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimators_dtypes] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_complex_data] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_dtype_object] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimators_empty_data_messages] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_pipeline_consistency] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimators_nan_inf] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimator_sparse_tag] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimator_sparse_array] PASSED [ 39%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimator_sparse_matrix] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimators_pickle] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_estimators_pickle(readonly_memmap=True)] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_f_contiguous_array_estimator] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_parameters_default_constructible] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_methods_sample_order_invariance] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_methods_subset_invariance] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_fit2d_1sample] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_fit2d_1feature] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_get_params_invariance] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_set_params] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_dict_unchanged] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_fit_idempotent] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_fit_check_is_fitted] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_n_features_in] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_fit1d] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_fit2d_predict1d] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[EditedNearestNeighbours()-check_requires_y_none] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimator_cloneable0] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimator_cloneable1] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimator_tags_renamed] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_valid_tag_types] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimator_repr] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_no_attributes_set_in_init] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_fit_score_takes_y] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimators_overwrite_params] PASSED [ 40%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_dont_overwrite_parameters] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimators_fit_returns_self] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_readonly_memmap_input] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimators_unfitted] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_n_features_in_after_fitting] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_mixin_order] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_positive_only_tag_during_fit] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimators_dtypes] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_complex_data] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_dtype_object] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimators_empty_data_messages] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_pipeline_consistency] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimators_nan_inf] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimator_sparse_tag] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimator_sparse_array] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimator_sparse_matrix] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimators_pickle] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimators_pickle(readonly_memmap=True)] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_f_contiguous_array_estimator] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_parameters_default_constructible] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_methods_sample_order_invariance] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_methods_subset_invariance] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_fit2d_1sample] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_fit2d_1feature] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_get_params_invariance] PASSED [ 41%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_set_params] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_dict_unchanged] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_fit_idempotent] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_fit_check_is_fitted] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_n_features_in] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_fit1d] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_fit2d_predict1d] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_requires_y_none] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimator_cloneable0] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimator_cloneable1] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimator_tags_renamed] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_valid_tag_types] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimator_repr] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_no_attributes_set_in_init] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_fit_score_takes_y] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimators_overwrite_params] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_dont_overwrite_parameters] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimators_fit_returns_self] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_readonly_memmap_input] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimators_unfitted] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_n_features_in_after_fitting] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_mixin_order] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimators_dtypes] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_complex_data] PASSED [ 42%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_dtype_object] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimators_empty_data_messages] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_pipeline_consistency] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimators_nan_inf] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimator_sparse_tag] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimator_sparse_array] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimator_sparse_matrix] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimators_pickle] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_parameters_default_constructible] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_methods_sample_order_invariance] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_methods_subset_invariance] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_fit2d_1sample] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_fit2d_1feature] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_get_params_invariance] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_set_params] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_dict_unchanged] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_fit_idempotent] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_fit_check_is_fitted] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_n_features_in] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_fit1d] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_fit2d_predict1d] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[InstanceHardnessThreshold(random_state=42)-check_requires_y_none] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimator_cloneable0] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimator_cloneable1] PASSED [ 43%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimator_tags_renamed] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_valid_tag_types] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimator_repr] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_no_attributes_set_in_init] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_fit_score_takes_y] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimators_overwrite_params] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_dont_overwrite_parameters] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimators_fit_returns_self] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_readonly_memmap_input] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimators_unfitted] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_n_features_in_after_fitting] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_mixin_order] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_positive_only_tag_during_fit] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimators_dtypes] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_complex_data] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_dtype_object] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimators_empty_data_messages] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_pipeline_consistency] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimators_nan_inf] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimator_sparse_tag] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimator_sparse_array] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimator_sparse_matrix] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimators_pickle] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 44%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_f_contiguous_array_estimator] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_parameters_default_constructible] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_methods_sample_order_invariance] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_methods_subset_invariance] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_fit2d_1sample] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_fit2d_1feature] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_get_params_invariance] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_set_params] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_dict_unchanged] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_fit_idempotent] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_fit_check_is_fitted] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_n_features_in] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_fit1d] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_fit2d_predict1d] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[KMeansSMOTE(random_state=0)-check_requires_y_none] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimator_cloneable0] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimator_cloneable1] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimator_tags_renamed] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_valid_tag_types] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimator_repr] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_no_attributes_set_in_init] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_fit_score_takes_y] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimators_overwrite_params] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_dont_overwrite_parameters] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimators_fit_returns_self] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_readonly_memmap_input] PASSED [ 45%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimators_unfitted] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_n_features_in_after_fitting] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_mixin_order] 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imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimator_sparse_array] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimator_sparse_matrix] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimators_pickle] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_estimators_pickle(readonly_memmap=True)] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_f_contiguous_array_estimator] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_parameters_default_constructible] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_methods_sample_order_invariance] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_methods_subset_invariance] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_fit2d_1sample] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_fit2d_1feature] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_get_params_invariance] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_set_params] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_dict_unchanged] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_fit_idempotent] PASSED [ 46%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss()-check_fit_check_is_fitted] PASSED [ 47%] 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imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_estimator_repr] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_no_attributes_set_in_init] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_fit_score_takes_y] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_estimators_overwrite_params] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_dont_overwrite_parameters] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_estimators_fit_returns_self] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_readonly_memmap_input] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_estimators_unfitted] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_n_features_in_after_fitting] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_mixin_order] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_positive_only_tag_during_fit] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_estimators_dtypes] PASSED [ 47%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=2)-check_complex_data] PASSED [ 47%] 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imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_n_features_in_after_fitting] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_mixin_order] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_positive_only_tag_during_fit] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_estimators_dtypes] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_complex_data] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_dtype_object] PASSED [ 49%] 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imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_f_contiguous_array_estimator] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_parameters_default_constructible] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_methods_sample_order_invariance] PASSED [ 49%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_methods_subset_invariance] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_fit2d_1sample] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_fit2d_1feature] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_get_params_invariance] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_set_params] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_dict_unchanged] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_fit_idempotent] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_fit_check_is_fitted] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_n_features_in] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_fit1d] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_fit2d_predict1d] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NearMiss(version=3)-check_requires_y_none] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimator_cloneable0] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimator_cloneable1] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimator_tags_renamed] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_valid_tag_types] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimator_repr] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_no_attributes_set_in_init] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_fit_score_takes_y] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimators_overwrite_params] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_dont_overwrite_parameters] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimators_fit_returns_self] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_readonly_memmap_input] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimators_unfitted] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_n_features_in_after_fitting] PASSED [ 50%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_mixin_order] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_positive_only_tag_during_fit] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimators_dtypes] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_complex_data] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_dtype_object] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimators_empty_data_messages] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_pipeline_consistency] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimators_nan_inf] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimator_sparse_tag] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimator_sparse_array] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimator_sparse_matrix] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimators_pickle] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_estimators_pickle(readonly_memmap=True)] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_f_contiguous_array_estimator] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_parameters_default_constructible] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_methods_sample_order_invariance] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_methods_subset_invariance] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_fit2d_1sample] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_fit2d_1feature] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_get_params_invariance] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_set_params] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_dict_unchanged] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_fit_idempotent] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_fit_check_is_fitted] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_n_features_in] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_fit1d] PASSED [ 51%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_fit2d_predict1d] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[NeighbourhoodCleaningRule()-check_requires_y_none] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimator_cloneable0] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimator_cloneable1] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimator_tags_renamed] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_valid_tag_types] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimator_repr] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_no_attributes_set_in_init] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_fit_score_takes_y] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimators_overwrite_params] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_dont_overwrite_parameters] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimators_fit_returns_self] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_readonly_memmap_input] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimators_unfitted] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_n_features_in_after_fitting] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_mixin_order] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimators_dtypes] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_complex_data] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_dtype_object] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimators_empty_data_messages] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_pipeline_consistency] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimators_nan_inf] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimator_sparse_tag] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimator_sparse_array] PASSED [ 52%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimator_sparse_matrix] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimators_pickle] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_parameters_default_constructible] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_methods_sample_order_invariance] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_methods_subset_invariance] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_fit2d_1sample] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_fit2d_1feature] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_get_params_invariance] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_set_params] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_dict_unchanged] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_fit_idempotent] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_fit_check_is_fitted] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_n_features_in] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_fit1d] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_fit2d_predict1d] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[OneSidedSelection(random_state=42)-check_requires_y_none] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_cloneable0] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_cloneable1] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_tags_renamed] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_valid_tag_types] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_repr] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_no_attributes_set_in_init] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit_score_takes_y] PASSED [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_overwrite_params] XFAIL [ 53%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_dont_overwrite_parameters] XFAIL [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_fit_returns_self] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_readonly_memmap_input] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_unfitted] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_n_features_in_after_fitting] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_mixin_order] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_positive_only_tag_during_fit] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_dtypes] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_complex_data] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_dtype_object] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_empty_data_messages] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_pipeline_consistency] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_nan_inf] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_sparse_tag] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_sparse_array] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_sparse_matrix] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_pickle] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_pickle(readonly_memmap=True)] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_f_contiguous_array_estimator] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifier_data_not_an_array] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifiers_one_label] FAILED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifiers_one_label_sample_weights] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifiers_classes] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_partial_fit_n_features] PASSED [ 54%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifiers_train] XFAIL [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifiers_train(readonly_memmap=True)] XFAIL [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifiers_train(readonly_memmap=True,X_dtype=float32)] XFAIL [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifiers_regression_target] FAILED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_supervised_y_no_nan] FAILED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_supervised_y_2d] XFAIL [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_non_transformer_estimators_n_iter] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_decision_proba_consistency] FAILED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_parameters_default_constructible] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_methods_sample_order_invariance] FAILED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_methods_subset_invariance] FAILED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit2d_1sample] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit2d_1feature] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_get_params_invariance] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_set_params] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_dict_unchanged] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit_idempotent] FAILED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit_check_is_fitted] FAILED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_n_features_in] FAILED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit1d] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit2d_predict1d] FAILED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimator_cloneable0] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimator_cloneable1] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimator_tags_renamed] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_valid_tag_types] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimator_repr] PASSED [ 55%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_no_attributes_set_in_init] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_fit_score_takes_y] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimators_overwrite_params] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_dont_overwrite_parameters] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimators_fit_returns_self] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_readonly_memmap_input] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimators_unfitted] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_n_features_in_after_fitting] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_mixin_order] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_positive_only_tag_during_fit] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimators_dtypes] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_sample_weights_pandas_series] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_sample_weights_not_an_array] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_sample_weights_list] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_sample_weights_shape] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_sample_weights_not_overwritten] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_sample_weight_equivalence_on_dense_data] XFAIL [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_sample_weight_equivalence_on_sparse_data] XFAIL [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_complex_data] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_dtype_object] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimators_empty_data_messages] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_pipeline_consistency] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimators_nan_inf] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimator_sparse_tag] PASSED [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimator_sparse_array] XPASS [ 56%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimator_sparse_matrix] XPASS [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimators_pickle] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimators_pickle(readonly_memmap=True)] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_f_contiguous_array_estimator] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_classifier_data_not_an_array] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_classifiers_one_label] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_classifiers_one_label_sample_weights] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_classifiers_classes] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_estimators_partial_fit_n_features] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_classifiers_train] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_classifiers_train(readonly_memmap=True)] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_classifiers_regression_target] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_supervised_y_no_nan] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_supervised_y_2d] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_non_transformer_estimators_n_iter] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_decision_proba_consistency] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_parameters_default_constructible] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_methods_sample_order_invariance] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_methods_subset_invariance] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_fit2d_1sample] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_fit2d_1feature] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_get_params_invariance] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_set_params] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_dict_unchanged] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_fit_idempotent] PASSED [ 57%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_fit_check_is_fitted] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_n_features_in] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_fit1d] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_fit2d_predict1d] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_requires_y_none] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimator_cloneable0] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimator_cloneable1] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimator_tags_renamed] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_valid_tag_types] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimator_repr] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_no_attributes_set_in_init] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_fit_score_takes_y] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimators_overwrite_params] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_dont_overwrite_parameters] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimators_fit_returns_self] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_readonly_memmap_input] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimators_unfitted] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_n_features_in_after_fitting] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_mixin_order] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimators_dtypes] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_complex_data] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_dtype_object] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimators_empty_data_messages] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_pipeline_consistency] PASSED [ 58%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimator_sparse_tag] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimator_sparse_array] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimator_sparse_matrix] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimators_pickle] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_parameters_default_constructible] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_methods_sample_order_invariance] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_methods_subset_invariance] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_fit2d_1sample] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_fit2d_1feature] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_get_params_invariance] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_set_params] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_dict_unchanged] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_fit_idempotent] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_fit_check_is_fitted] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_n_features_in] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_fit1d] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_fit2d_predict1d] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomOverSampler(random_state=42)-check_requires_y_none] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimator_cloneable0] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimator_cloneable1] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimator_tags_renamed] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_valid_tag_types] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimator_repr] PASSED [ 59%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_no_attributes_set_in_init] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_fit_score_takes_y] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimators_overwrite_params] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_dont_overwrite_parameters] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimators_fit_returns_self] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_readonly_memmap_input] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimators_unfitted] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_n_features_in_after_fitting] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_mixin_order] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimators_dtypes] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_complex_data] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_dtype_object] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimators_empty_data_messages] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_pipeline_consistency] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimator_sparse_tag] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimator_sparse_array] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimator_sparse_matrix] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimators_pickle] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_parameters_default_constructible] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_methods_sample_order_invariance] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_methods_subset_invariance] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_fit2d_1sample] PASSED [ 60%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_fit2d_1feature] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_get_params_invariance] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_set_params] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_dict_unchanged] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_fit_idempotent] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_fit_check_is_fitted] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_n_features_in] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_fit1d] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_fit2d_predict1d] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RandomUnderSampler(random_state=42)-check_requires_y_none] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimator_cloneable0] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimator_cloneable1] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimator_tags_renamed] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_valid_tag_types] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimator_repr] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_no_attributes_set_in_init] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_fit_score_takes_y] PASSED 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imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_n_features_in_after_fitting] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_mixin_order] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_positive_only_tag_during_fit] PASSED [ 61%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimators_dtypes] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_complex_data] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_dtype_object] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimators_empty_data_messages] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_pipeline_consistency] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimators_nan_inf] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimator_sparse_tag] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimator_sparse_array] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimator_sparse_matrix] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimators_pickle] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_estimators_pickle(readonly_memmap=True)] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_f_contiguous_array_estimator] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_parameters_default_constructible] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_methods_sample_order_invariance] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_methods_subset_invariance] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_fit2d_1sample] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_fit2d_1feature] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_get_params_invariance] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_set_params] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_dict_unchanged] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_fit_idempotent] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_fit_check_is_fitted] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_n_features_in] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_fit1d] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_fit2d_predict1d] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RepeatedEditedNearestNeighbours()-check_requires_y_none] PASSED [ 62%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimator_cloneable0] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimator_cloneable1] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimator_tags_renamed] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_valid_tag_types] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimator_repr] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_no_attributes_set_in_init] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_fit_score_takes_y] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimators_overwrite_params] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_dont_overwrite_parameters] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimators_fit_returns_self] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_readonly_memmap_input] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimators_unfitted] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_n_features_in_after_fitting] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_mixin_order] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimators_dtypes] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_complex_data] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_dtype_object] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimators_empty_data_messages] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_pipeline_consistency] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimators_nan_inf] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimator_sparse_tag] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimator_sparse_array] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimator_sparse_matrix] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimators_pickle] PASSED [ 63%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_parameters_default_constructible] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_methods_sample_order_invariance] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_methods_subset_invariance] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_fit2d_1sample] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_fit2d_1feature] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_get_params_invariance] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_set_params] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_dict_unchanged] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_fit_idempotent] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_fit_check_is_fitted] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_n_features_in] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_fit1d] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_fit2d_predict1d] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTE(random_state=42)-check_requires_y_none] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimator_cloneable0] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimator_cloneable1] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimator_tags_renamed] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_valid_tag_types] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimator_repr] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_no_attributes_set_in_init] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_fit_score_takes_y] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimators_overwrite_params] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_dont_overwrite_parameters] PASSED [ 64%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimators_fit_returns_self] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_readonly_memmap_input] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimators_unfitted] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_n_features_in_after_fitting] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_mixin_order] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimators_dtypes] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_complex_data] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_dtype_object] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimators_empty_data_messages] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_pipeline_consistency] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimators_nan_inf] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimator_sparse_tag] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimator_sparse_array] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimator_sparse_matrix] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimators_pickle] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_parameters_default_constructible] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_methods_sample_order_invariance] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_methods_subset_invariance] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_fit2d_1sample] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_fit2d_1feature] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_get_params_invariance] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_set_params] PASSED [ 65%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_dict_unchanged] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_fit_idempotent] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_fit_check_is_fitted] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_n_features_in] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_fit1d] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_fit2d_predict1d] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEENN(random_state=42)-check_requires_y_none] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_cloneable0] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_cloneable1] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_tags_renamed] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_valid_tag_types] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_repr] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_no_attributes_set_in_init] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_fit_score_takes_y] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimators_overwrite_params] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_dont_overwrite_parameters] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimators_fit_returns_self] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_readonly_memmap_input] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimators_unfitted] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_n_features_in_after_fitting] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_mixin_order] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimators_dtypes] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_complex_data] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_dtype_object] PASSED [ 66%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimators_empty_data_messages] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_pipeline_consistency] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimators_nan_inf] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_sparse_tag] FAILED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_sparse_array] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_sparse_matrix] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimators_pickle] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_parameters_default_constructible] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_methods_sample_order_invariance] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_methods_subset_invariance] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_fit2d_1sample] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_fit2d_1feature] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_get_params_invariance] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_set_params] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_dict_unchanged] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_fit_idempotent] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_fit_check_is_fitted] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_n_features_in] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_fit1d] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_fit2d_predict1d] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_requires_y_none] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimator_cloneable0] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimator_cloneable1] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimator_tags_renamed] PASSED [ 67%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_valid_tag_types] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimator_repr] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_no_attributes_set_in_init] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_fit_score_takes_y] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimators_overwrite_params] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_dont_overwrite_parameters] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimators_fit_returns_self] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_readonly_memmap_input] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimators_unfitted] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_n_features_in_after_fitting] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_mixin_order] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimators_dtypes] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_complex_data] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_dtype_object] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimators_empty_data_messages] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_pipeline_consistency] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimators_nan_inf] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimator_sparse_tag] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimator_sparse_array] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimator_sparse_matrix] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimators_pickle] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_parameters_default_constructible] PASSED [ 68%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_methods_sample_order_invariance] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_methods_subset_invariance] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_fit2d_1sample] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_fit2d_1feature] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_get_params_invariance] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_set_params] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_dict_unchanged] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_fit_idempotent] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_fit_check_is_fitted] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_n_features_in] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_fit1d] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_fit2d_predict1d] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SMOTETomek(random_state=42)-check_requires_y_none] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimator_cloneable0] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimator_cloneable1] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimator_tags_renamed] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_valid_tag_types] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimator_repr] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_no_attributes_set_in_init] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_fit_score_takes_y] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimators_overwrite_params] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_dont_overwrite_parameters] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimators_fit_returns_self] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_readonly_memmap_input] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimators_unfitted] PASSED [ 69%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_n_features_in_after_fitting] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_mixin_order] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_positive_only_tag_during_fit] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimators_dtypes] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_complex_data] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_dtype_object] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimators_empty_data_messages] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_pipeline_consistency] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimators_nan_inf] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimator_sparse_tag] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimator_sparse_array] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimator_sparse_matrix] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimators_pickle] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_estimators_pickle(readonly_memmap=True)] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_f_contiguous_array_estimator] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_parameters_default_constructible] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_methods_sample_order_invariance] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_methods_subset_invariance] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_fit2d_1sample] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_fit2d_1feature] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_get_params_invariance] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_set_params] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_dict_unchanged] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_fit_idempotent] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_fit_check_is_fitted] PASSED [ 70%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_n_features_in] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_fit1d] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_fit2d_predict1d] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[SVMSMOTE(random_state=42)-check_requires_y_none] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimator_cloneable0] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimator_cloneable1] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimator_tags_renamed] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_valid_tag_types] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimator_repr] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_no_attributes_set_in_init] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_fit_score_takes_y] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimators_overwrite_params] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_dont_overwrite_parameters] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimators_fit_returns_self] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_readonly_memmap_input] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimators_unfitted] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_do_not_raise_errors_in_init_or_set_params] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_n_features_in_after_fitting] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_mixin_order] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_positive_only_tag_during_fit] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimators_dtypes] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_complex_data] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_dtype_object] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimators_empty_data_messages] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_pipeline_consistency] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimators_nan_inf] PASSED [ 71%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimator_sparse_tag] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimator_sparse_array] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimator_sparse_matrix] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimators_pickle] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_estimators_pickle(readonly_memmap=True)] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_f_contiguous_array_estimator] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_parameters_default_constructible] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_methods_sample_order_invariance] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_methods_subset_invariance] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_fit2d_1sample] PASSED [ 72%] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[TomekLinks()-check_fit2d_1feature] PASSED [ 72%] 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imblearn/tests/test_common.py::test_estimators_imblearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_with_encoded_labels] FAILED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifier_on_multilabel_or_multioutput_targets] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_with_encoded_labels] FAILED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_target_type] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_one_label] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_fit] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_fit_resample] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_sampling_strategy_fit_resample] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_sparse] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_pandas] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_pandas_sparse] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_list] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_multiclass_ova] PASSED [ 77%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_preserve_dtype] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_sample_indices] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_samplers_2d_target] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_sampler_get_feature_names_out] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[EditedNearestNeighbours()-check_sampler_get_feature_names_out_pandas] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_target_type] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_one_label] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_fit] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_fit_resample] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_sampling_strategy_fit_resample] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_sparse] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_pandas] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_pandas_sparse] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_list] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_multiclass_ova] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_preserve_dtype] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_sample_indices] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_samplers_2d_target] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_sampler_get_feature_names_out] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[FunctionSampler()-check_sampler_get_feature_names_out_pandas] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_target_type] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_one_label] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_fit] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_fit_resample] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_sampling_strategy_fit_resample] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_sparse] PASSED [ 78%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_pandas] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_pandas_sparse] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_list] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_multiclass_ova] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_preserve_dtype] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_sample_indices] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_samplers_2d_target] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_sampler_get_feature_names_out] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[InstanceHardnessThreshold(random_state=42)-check_sampler_get_feature_names_out_pandas] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_target_type] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_one_label] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_fit] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_fit_resample] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_sampling_strategy_fit_resample] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_sparse] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_pandas] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_pandas_sparse] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_list] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_multiclass_ova] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_preserve_dtype] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_sample_indices] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_samplers_2d_target] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_sampler_get_feature_names_out] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[KMeansSMOTE(random_state=0)-check_sampler_get_feature_names_out_pandas] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_target_type] PASSED [ 79%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_one_label] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_fit] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_fit_resample] XPASS [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_sampling_strategy_fit_resample] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_sparse] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_pandas] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_pandas_sparse] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_list] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_multiclass_ova] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_preserve_dtype] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_sample_indices] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_samplers_2d_target] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_sampler_get_feature_names_out] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss()-check_sampler_get_feature_names_out_pandas] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_target_type] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_one_label] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_fit] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_fit_resample] XPASS [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_sampling_strategy_fit_resample] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_sparse] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_pandas] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_pandas_sparse] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_list] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_multiclass_ova] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_preserve_dtype] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_sample_indices] PASSED [ 80%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_samplers_2d_target] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_sampler_get_feature_names_out] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=2)-check_sampler_get_feature_names_out_pandas] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_target_type] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_one_label] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_fit] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_fit_resample] XFAIL [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_sampling_strategy_fit_resample] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_sparse] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_pandas] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_pandas_sparse] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_list] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_multiclass_ova] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_preserve_dtype] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_sample_indices] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_samplers_2d_target] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_sampler_get_feature_names_out] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NearMiss(version=3)-check_sampler_get_feature_names_out_pandas] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_target_type] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_one_label] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_fit] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_fit_resample] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_sampling_strategy_fit_resample] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_sparse] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_pandas] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_pandas_sparse] PASSED [ 81%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_list] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_multiclass_ova] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_preserve_dtype] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_sample_indices] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_samplers_2d_target] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_sampler_get_feature_names_out] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[NeighbourhoodCleaningRule()-check_sampler_get_feature_names_out_pandas] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_target_type] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_one_label] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_fit] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_fit_resample] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_sampling_strategy_fit_resample] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_sparse] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_pandas] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_pandas_sparse] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_list] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_multiclass_ova] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_preserve_dtype] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_sample_indices] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_samplers_2d_target] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_sampler_get_feature_names_out] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[OneSidedSelection(random_state=42)-check_sampler_get_feature_names_out_pandas] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifier_on_multilabel_or_multioutput_targets] FAILED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0,sampling_strategy={'setosa':20,'virginica':20})),('logistic',LogisticRegression())])-check_classifiers_with_encoded_labels] FAILED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[RUSBoostClassifier()-check_classifier_on_multilabel_or_multioutput_targets] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[RUSBoostClassifier()-check_classifiers_with_encoded_labels] PASSED [ 82%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_target_type] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_one_label] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_fit] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_fit_resample] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_sampling_strategy_fit_resample] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_sparse] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_pandas] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_pandas_sparse] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_string] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_nan] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_list] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_multiclass_ova] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_preserve_dtype] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_sample_indices] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_samplers_2d_target] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_sampler_get_feature_names_out] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomOverSampler(random_state=42)-check_sampler_get_feature_names_out_pandas] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomUnderSampler(random_state=42)-check_target_type] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomUnderSampler(random_state=42)-check_samplers_one_label] PASSED [ 83%] imblearn/tests/test_common.py::test_estimators_imblearn[RandomUnderSampler(random_state=42)-check_samplers_fit] PASSED [ 83%] 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imblearn/tests/test_common.py::test_check_param_validation[NearMiss(version=3)] PASSED [ 88%] imblearn/tests/test_common.py::test_check_param_validation[NeighbourhoodCleaningRule()] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[OneSidedSelection(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[RUSBoostClassifier()] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[RandomOverSampler(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[RandomUnderSampler(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[RepeatedEditedNearestNeighbours()] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[SMOTE(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[SMOTEENN(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[SMOTEN(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[SMOTETomek(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[SVMSMOTE(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[TomekLinks()] PASSED [ 89%] imblearn/tests/test_common.py::test_check_param_validation[ValueDifferenceMetric()] PASSED [ 89%] imblearn/tests/test_common.py::test_strategy_as_ordered_dict[RandomOverSampler] PASSED [ 89%] imblearn/tests/test_common.py::test_strategy_as_ordered_dict[RandomUnderSampler] PASSED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[ADASYN(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[AllKNN()] PASSED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[BalancedBaggingClassifier(random_state=42)] FAILED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[BalancedRandomForestClassifier(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[BorderlineSMOTE(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[ClusterCentroids(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[CondensedNearestNeighbour(random_state=42)] PASSED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[EasyEnsembleClassifier(random_state=42)] FAILED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)] FAILED [ 89%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[EditedNearestNeighbours()] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[FunctionSampler()] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[InstanceHardnessThreshold(random_state=42)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[KMeansSMOTE(random_state=0)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[NearMiss()] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[NearMiss(version=2)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[NearMiss(version=3)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[NeighbourhoodCleaningRule()] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[OneSidedSelection(random_state=42)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])] FAILED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[RUSBoostClassifier()] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[RandomOverSampler(random_state=42)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[RandomUnderSampler(random_state=42)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[RepeatedEditedNearestNeighbours()] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[SMOTE(random_state=42)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[SMOTEENN(random_state=42)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[SMOTEN(random_state=42)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[SMOTETomek(random_state=42)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[SVMSMOTE(random_state=42)] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[TomekLinks()] PASSED [ 90%] imblearn/tests/test_common.py::test_pandas_column_name_consistency[ValueDifferenceMetric()] PASSED [ 90%] imblearn/tests/test_docstring_parameters.py::test_docstring_parameters SKIPPED [ 90%] imblearn/tests/test_docstring_parameters.py::test_tabs SKIPPED (coul...) [ 90%] imblearn/tests/test_exceptions.py::test_raise_isinstance_error PASSED [ 90%] imblearn/tests/test_pipeline.py::test_pipeline_init_tuple FAILED [ 90%] imblearn/tests/test_pipeline.py::test_pipeline_init PASSED [ 90%] imblearn/tests/test_pipeline.py::test_pipeline_methods_anova FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_fit_params FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_sample_weight_supported FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_sample_weight_unsupported FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_raise_set_params_error PASSED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_methods_pca_svm FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_methods_preprocessing_svm FAILED [ 91%] imblearn/tests/test_pipeline.py::test_fit_predict_on_pipeline FAILED [ 91%] imblearn/tests/test_pipeline.py::test_fit_predict_on_pipeline_without_fit_predict PASSED [ 91%] imblearn/tests/test_pipeline.py::test_fit_predict_with_intermediate_fit_params FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_transform FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_fit_transform FAILED [ 91%] imblearn/tests/test_pipeline.py::test_set_pipeline_steps PASSED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_correctly_adjusts_steps[None] FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_correctly_adjusts_steps[passthrough] FAILED [ 91%] imblearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[None] FAILED [ 91%] imblearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[passthrough] FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_ducktyping PASSED [ 91%] imblearn/tests/test_pipeline.py::test_make_pipeline PASSED [ 91%] imblearn/tests/test_pipeline.py::test_classes_property FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_memory_transformer FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_memory_sampler FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_methods_pca_rus_svm FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_methods_rus_pca_svm FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_sample FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_sample_transform FAILED [ 91%] imblearn/tests/test_pipeline.py::test_pipeline_none_classifier FAILED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_none_sampler_classifier FAILED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_sampler_none_classifier FAILED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_none_sampler_sample FAILED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_none_transformer FAILED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_methods_anova_rus FAILED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_with_step_that_implements_both_sample_and_transform PASSED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_with_step_that_it_is_pipeline PASSED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_fit_then_sample_with_sampler_last_estimator FAILED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_fit_then_sample_3_samplers_with_sampler_last_estimator FAILED [ 92%] imblearn/tests/test_pipeline.py::test_make_pipeline_memory PASSED [ 92%] imblearn/tests/test_pipeline.py::test_predict_with_predict_params FAILED [ 92%] imblearn/tests/test_pipeline.py::test_resampler_last_stage_passthrough FAILED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_score_samples_pca_lof_binary FAILED [ 92%] imblearn/tests/test_pipeline.py::test_score_samples_on_pipeline_without_score_samples FAILED [ 92%] imblearn/tests/test_pipeline.py::test_pipeline_param_error PASSED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est0-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$-fit] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est1-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$-fit_predict] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est2-\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$-fit] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est3-\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$-fit_predict] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est4-\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$-fit] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est5-\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$-fit_predict] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est6-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$-fit] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est7-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$-fit_transform] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est8-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$-fit] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est9-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$-fit_transform] FAILED [ 92%] imblearn/tests/test_pipeline.py::test_verbose[est10-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$-fit] FAILED [ 93%] imblearn/tests/test_pipeline.py::test_verbose[est11-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$-fit_transform] FAILED [ 93%] imblearn/tests/test_pipeline.py::test_verbose[est12-\\[FeatureUnion\\].*\\(step 1 of 2\\) Processing mult1.* total=.*\\n\\[FeatureUnion\\].*\\(step 2 of 2\\) Processing mult2.* total=.*\\n$-fit] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_verbose[est13-\\[FeatureUnion\\].*\\(step 1 of 2\\) Processing mult1.* total=.*\\n\\[FeatureUnion\\].*\\(step 2 of 2\\) Processing mult2.* total=.*\\n$-fit_transform] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_verbose[est14-\\[FeatureUnion\\].*\\(step 1 of 1\\) Processing mult2.* total=.*\\n$-fit] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_verbose[est15-\\[FeatureUnion\\].*\\(step 1 of 1\\) Processing mult2.* total=.*\\n$-fit_transform] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_score_samples_pca_lof_multiclass FAILED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_param_validation PASSED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_with_set_output FAILED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_warns_not_fitted[predict] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_warns_not_fitted[predict_proba] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_warns_not_fitted[predict_log_proba] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_warns_not_fitted[decision_function] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_warns_not_fitted[score] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_warns_not_fitted[score_samples] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_warns_not_fitted[transform] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_pipeline_warns_not_fitted[inverse_transform] PASSED [ 93%] imblearn/tests/test_pipeline.py::test_transform_input_explicit_value_check FAILED [ 93%] imblearn/tests/test_pipeline.py::test_transform_input_no_slep6 PASSED [ 93%] imblearn/tests/test_pipeline.py::test_transform_input_sklearn_version SKIPPED [ 93%] imblearn/tests/test_pipeline.py::test_metadata_routing_with_sampler FAILED [ 93%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.datasets.fetch_datasets] PASSED [ 93%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.datasets.make_imbalance] PASSED [ 93%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.metrics.classification_report_imbalanced] PASSED [ 93%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.metrics.geometric_mean_score] PASSED [ 93%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.metrics.macro_averaged_mean_absolute_error] PASSED [ 93%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.metrics.make_index_balanced_accuracy] PASSED [ 94%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.metrics.sensitivity_specificity_support] PASSED [ 94%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.metrics.sensitivity_score] PASSED [ 94%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.metrics.specificity_score] PASSED [ 94%] imblearn/tests/test_public_functions.py::test_function_param_validation[imblearn.pipeline.make_pipeline] PASSED [ 94%] imblearn/under_sampling/_prototype_generation/_cluster_centroids.py::imblearn.under_sampling._prototype_generation._cluster_centroids.ClusterCentroids PASSED [ 94%] imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py::test_fit_resample_check_voting[X0-soft] PASSED [ 94%] imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py::test_fit_resample_check_voting[X1-hard] PASSED [ 94%] imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py::test_fit_resample_auto PASSED [ 94%] imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py::test_fit_resample_half PASSED [ 94%] imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py::test_multiclass_fit_resample PASSED [ 94%] imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py::test_fit_resample_object PASSED [ 94%] imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py::test_fit_hard_voting PASSED [ 94%] imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py::test_cluster_centroids_hard_target_class PASSED [ 94%] imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py::test_cluster_centroids_custom_clusterer PASSED [ 94%] imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py::imblearn.under_sampling._prototype_selection._condensed_nearest_neighbour.CondensedNearestNeighbour SKIPPED [ 94%] imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py::imblearn.under_sampling._prototype_selection._edited_nearest_neighbours.AllKNN PASSED [ 94%] imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py::imblearn.under_sampling._prototype_selection._edited_nearest_neighbours.EditedNearestNeighbours PASSED [ 94%] imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py::imblearn.under_sampling._prototype_selection._edited_nearest_neighbours.RepeatedEditedNearestNeighbours PASSED [ 94%] imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py::imblearn.under_sampling._prototype_selection._instance_hardness_threshold.InstanceHardnessThreshold PASSED [ 94%] imblearn/under_sampling/_prototype_selection/_nearmiss.py::imblearn.under_sampling._prototype_selection._nearmiss.NearMiss PASSED [ 94%] imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py::imblearn.under_sampling._prototype_selection._neighbourhood_cleaning_rule.NeighbourhoodCleaningRule PASSED [ 94%] imblearn/under_sampling/_prototype_selection/_one_sided_selection.py::imblearn.under_sampling._prototype_selection._one_sided_selection.OneSidedSelection PASSED [ 94%] imblearn/under_sampling/_prototype_selection/_random_under_sampler.py::imblearn.under_sampling._prototype_selection._random_under_sampler.RandomUnderSampler PASSED [ 94%] imblearn/under_sampling/_prototype_selection/_tomek_links.py::imblearn.under_sampling._prototype_selection._tomek_links.TomekLinks PASSED [ 94%] imblearn/under_sampling/_prototype_selection/tests/test_allknn.py::test_allknn_fit_resample PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_allknn.py::test_all_knn_allow_minority PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_allknn.py::test_allknn_fit_resample_mode PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_allknn.py::test_allknn_fit_resample_with_nn_object PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_allknn.py::test_alknn_not_good_object PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py::test_cnn_init PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py::test_cnn_fit_resample PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py::test_cnn_fit_resample_with_object[1] PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py::test_cnn_fit_resample_with_object[n_neighbors1] PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py::test_condensed_nearest_neighbour_multiclass PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py::test_condensed_nearest_neighbors_deprecation PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_edited_nearest_neighbours.py::test_enn_init PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_edited_nearest_neighbours.py::test_enn_fit_resample PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_edited_nearest_neighbours.py::test_enn_fit_resample_mode PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_edited_nearest_neighbours.py::test_enn_fit_resample_with_nn_object PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_edited_nearest_neighbours.py::test_enn_check_kind_selection PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py::test_iht_init PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py::test_iht_fit_resample PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py::test_iht_fit_resample_half PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py::test_iht_fit_resample_class_obj PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py::test_iht_reproducibility PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py::test_iht_fit_resample_default_estimator PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py::test_iht_estimator_pipeline FAILED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_nearmiss.py::test_nm_fit_resample_auto PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_nearmiss.py::test_nm_fit_resample_float_sampling_strategy PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_nearmiss.py::test_nm_fit_resample_nn_obj PASSED [ 95%] imblearn/under_sampling/_prototype_selection/tests/test_neighbourhood_cleaning_rule.py::test_ncr_threshold_cleaning PASSED [ 96%] imblearn/under_sampling/_prototype_selection/tests/test_neighbourhood_cleaning_rule.py::test_ncr_n_neighbors PASSED [ 96%] imblearn/under_sampling/_prototype_selection/tests/test_neighbourhood_cleaning_rule.py::test_ncr_deprecate_kind_sel[all] PASSED [ 96%] imblearn/under_sampling/_prototype_selection/tests/test_neighbourhood_cleaning_rule.py::test_ncr_deprecate_kind_sel[mode] PASSED [ 96%] imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py::test_oss_init PASSED [ 96%] imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py::test_oss_fit_resample PASSED [ 96%] imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py::test_oss_with_object[1] PASSED [ 96%] imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py::test_oss_with_object[n_neighbors1] PASSED [ 96%] imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py::test_one_sided_selection_multiclass PASSED [ 96%] imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py::test_one_sided_selection_deprecation PASSED [ 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PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_check_sampling_strategy[0.5-over-sampling-expected_sampling_strategy19-target19] PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_check_sampling_strategy[0.5-under-sampling-expected_sampling_strategy20-target20] PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_sampling_strategy_callable_args PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_sampling_strategy_check_order[sampling_strategy0-under-sampling-expected_result0] PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_sampling_strategy_check_order[sampling_strategy1-over-sampling-expected_result1] PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_arrays_transformer_plain_list PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_arrays_transformer_numpy PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_arrays_transformer_pandas PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_deprecate_positional_args_warns_for_function PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_is_neighbors_object[estimator0-True] PASSED [ 99%] imblearn/utils/tests/test_validation.py::test_is_neighbors_object[estimator1-False] PASSED [100%] =================================== FAILURES =================================== ________ [doctest] imblearn.ensemble._bagging.BalancedBaggingClassifier ________ 257 >>> from imblearn.ensemble import BalancedBaggingClassifier 258 >>> X, y = make_classification(n_classes=2, class_sep=2, 259 ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, 260 ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) 261 >>> print('Original dataset shape %s' % Counter(y)) 262 Original dataset shape Counter({1: 900, 0: 100}) 263 >>> X_train, X_test, y_train, y_test = train_test_split(X, y, 264 ... random_state=0) 265 >>> bbc = BalancedBaggingClassifier(random_state=42) 266 >>> bbc.fit(X_train, y_train) UNEXPECTED EXCEPTION: DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") Traceback (most recent call last): File "/usr/lib64/python3.14/doctest.py", line 1395, in __run exec(compile(example.source, filename, "single", ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ compileflags, True), test.globs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "", line 1, in File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper return fit_method(estimator, *args, **kwargs) File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_bagging.py", line 337, in fit return super().fit(X, y) ~~~~~~~~~~~^^^^^^ File "/usr/lib64/python3.14/site-packages/sklearn/utils/validation.py", line 63, in inner_f return f(*args, **kwargs) File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper return fit_method(estimator, *args, **kwargs) File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 389, in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_bagging.py", line 352, in _fit return super()._fit(X, y, self.max_samples) ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 532, in _fit all_results = Parallel( n_jobs=n_jobs, verbose=self.verbose, **self._parallel_args() )( delayed(_parallel_build_estimators)( ...<10 lines>... for i in range(n_jobs) ) File "/usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py", line 77, in __call__ return super().__call__(iterable_with_config) ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.14/site-packages/joblib/parallel.py", line 1986, in __call__ return output if self.return_generator else list(output) ~~~~^^^^^^^^ File "/usr/lib/python3.14/site-packages/joblib/parallel.py", line 1914, in _get_sequential_output res = func(*args, **kwargs) File "/usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py", line 139, in __call__ return self.function(*args, **kwargs) ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 197, in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper return fit_method(estimator, *args, **kwargs) File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 518, in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 404, in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) File "/usr/lib/python3.14/site-packages/joblib/memory.py", line 1104, in cache if asyncio.iscoroutinefunction(func) ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ File "/usr/lib64/python3.14/asyncio/coroutines.py", line 23, in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ f"{warnings._DEPRECATED_MSG}; " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "use inspect.iscoroutinefunction() instead", ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ remove=(3,16)) ^^^^^^^^^^^^^^ File "/usr/lib64/python3.14/_py_warnings.py", line 830, in _deprecated _wm.warn(msg, DeprecationWarning, stacklevel=3) ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_bagging.py:266: UnexpectedException ______ [doctest] imblearn.ensemble._easy_ensemble.EasyEnsembleClassifier _______ 193 >>> from imblearn.ensemble import EasyEnsembleClassifier 194 >>> X, y = make_classification(n_classes=2, class_sep=2, 195 ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, 196 ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) 197 >>> print('Original dataset shape %s' % Counter(y)) 198 Original dataset shape Counter({1: 900, 0: 100}) 199 >>> X_train, X_test, y_train, y_test = train_test_split(X, y, 200 ... random_state=0) 201 >>> eec = EasyEnsembleClassifier(random_state=42) 202 >>> eec.fit(X_train, y_train) UNEXPECTED EXCEPTION: DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") Traceback (most recent call last): File "/usr/lib64/python3.14/doctest.py", line 1395, in __run exec(compile(example.source, filename, "single", ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ compileflags, True), test.globs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "", line 1, in File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper return fit_method(estimator, *args, **kwargs) File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_easy_ensemble.py", line 271, in fit return super().fit(X, y) ~~~~~~~~~~~^^^^^^ File "/usr/lib64/python3.14/site-packages/sklearn/utils/validation.py", line 63, in inner_f return f(*args, **kwargs) File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper return fit_method(estimator, *args, **kwargs) File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 389, in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_easy_ensemble.py", line 277, in _fit return super()._fit(X, y, self.max_samples) ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 532, in _fit all_results = Parallel( n_jobs=n_jobs, verbose=self.verbose, **self._parallel_args() )( delayed(_parallel_build_estimators)( ...<10 lines>... for i in range(n_jobs) ) File "/usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py", line 77, in __call__ return super().__call__(iterable_with_config) ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.14/site-packages/joblib/parallel.py", line 1986, in __call__ return output if self.return_generator else list(output) ~~~~^^^^^^^^ File "/usr/lib/python3.14/site-packages/joblib/parallel.py", line 1914, in _get_sequential_output res = func(*args, **kwargs) File "/usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py", line 139, in __call__ return self.function(*args, **kwargs) ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 197, in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper return fit_method(estimator, *args, **kwargs) File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 518, in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 404, in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) File "/usr/lib/python3.14/site-packages/joblib/memory.py", line 1104, in cache if asyncio.iscoroutinefunction(func) ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ File "/usr/lib64/python3.14/asyncio/coroutines.py", line 23, in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ f"{warnings._DEPRECATED_MSG}; " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "use inspect.iscoroutinefunction() instead", ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ remove=(3,16)) ^^^^^^^^^^^^^^ File "/usr/lib64/python3.14/_py_warnings.py", line 830, in _deprecated _wm.warn(msg, DeprecationWarning, stacklevel=3) ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_easy_ensemble.py:202: UnexpectedException ________________ test_balanced_bagging_classifier[params0-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params0-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params0-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params0-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params0-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params0-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_balanced_bagging_classifier[params1-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params1-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params1-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params1-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params1-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params1-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_balanced_bagging_classifier[params2-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params2-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params2-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params2-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params2-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params2-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_balanced_bagging_classifier[params3-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params3-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params3-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params3-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params3-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params3-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_balanced_bagging_classifier[params4-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params4-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params4-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params4-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params4-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params4-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_balanced_bagging_classifier[params5-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params5-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params5-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params5-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params5-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params5-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_balanced_bagging_classifier[params6-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params6-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params6-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params6-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params6-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params6-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_balanced_bagging_classifier[params7-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params7-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params7-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params7-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params7-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params7-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_balanced_bagging_classifier[params8-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params8-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params8-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params8-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params8-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params8-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_balanced_bagging_classifier[params9-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params9-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params9-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params9-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params9-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_balanced_bagging_classifier[params9-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params10-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params10-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params10-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params10-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params10-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params10-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params11-None] ________________ estimator = None params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params11-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params11-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params11-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params11-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params11-estimator5] _____________ estimator = SVC() params = {'bootstrap': True, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params12-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params12-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params12-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params12-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params12-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params12-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params13-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params13-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params13-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params13-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params13-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params13-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params14-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params14-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params14-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params14-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params14-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params14-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params15-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params15-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params15-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params15-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params15-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params15-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params16-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params16-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params16-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params16-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params16-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params16-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params17-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params17-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params17-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params17-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params17-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params17-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': True, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params18-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params18-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params18-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params18-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params18-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params18-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params19-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params19-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params19-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params19-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params19-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params19-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 1, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params20-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params20-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params20-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params20-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params20-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params20-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params21-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params21-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params21-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params21-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params21-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params21-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 2, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params22-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params22-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params22-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params22-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params22-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params22-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 0.5} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_balanced_bagging_classifier[params23-None] ________________ estimator = None params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params23-estimator1] _____________ estimator = DummyClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params23-estimator2] _____________ estimator = Perceptron() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params23-estimator3] _____________ estimator = DecisionTreeClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params23-estimator4] _____________ estimator = KNeighborsClassifier() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier[params23-estimator5] _____________ estimator = SVC() params = {'bootstrap': False, 'bootstrap_features': False, 'max_features': 4, 'max_samples': 1.0} @pytest.mark.parametrize( "estimator", [ None, DummyClassifier(strategy="prior"), Perceptron(max_iter=1000, tol=1e-3), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(gamma="scale"), ], ) @pytest.mark.parametrize( "params", ParameterGrid( { "max_samples": [0.5, 1.0], "max_features": [1, 2, 4], "bootstrap": [True, False], "bootstrap_features": [True, False], } ), ) def test_balanced_bagging_classifier(estimator, params): # Check classification for various parameter settings. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > bag = BalancedBaggingClassifier(estimator=estimator, random_state=0, **params).fit( X_train, y_train ) imblearn/ensemble/tests/test_bagging.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________________________ test_bootstrap_samples ____________________________ def test_bootstrap_samples(): # Test that bootstrapping samples generate non-perfect base estimators. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) estimator = DecisionTreeClassifier().fit(X_train, y_train) # without bootstrap, all trees are perfect on the training set # disable the resampling by passing an empty dictionary. ensemble = BalancedBaggingClassifier( estimator=DecisionTreeClassifier(), max_samples=1.0, bootstrap=False, n_estimators=10, sampling_strategy={}, random_state=0, > ).fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:98: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ___________________________ test_bootstrap_features ____________________________ def test_bootstrap_features(): # Test that bootstrapping features may generate duplicate features. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) ensemble = BalancedBaggingClassifier( estimator=DecisionTreeClassifier(), max_features=1.0, bootstrap_features=False, random_state=0, > ).fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:128: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________________________ test_probability _______________________________ def test_probability(): # Predict probabilities. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) with np.errstate(divide="ignore", invalid="ignore"): # Normal case ensemble = BalancedBaggingClassifier( estimator=DecisionTreeClassifier(), random_state=0 > ).fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:160: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________________ test_oob_score_classification _________________________ def test_oob_score_classification(): # Check that oob prediction is a good estimation of the generalization # error. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) for estimator in [DecisionTreeClassifier(), SVC(gamma="scale")]: clf = BalancedBaggingClassifier( estimator=estimator, n_estimators=100, bootstrap=True, oob_score=True, random_state=0, > ).fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:209: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________________________ test_single_estimator _____________________________ def test_single_estimator(): # Check singleton ensembles. X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf1 = BalancedBaggingClassifier( estimator=KNeighborsClassifier(), n_estimators=1, bootstrap=False, bootstrap_features=False, random_state=0, > ).fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:242: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________________________ test_gridsearch ________________________________ def test_gridsearch(): # Check that bagging ensembles can be grid-searched. # Transform iris into a binary classification task X, y = iris.data, iris.target.copy() y[y == 2] = 1 # Grid search with scoring based on decision_function parameters = {"n_estimators": (1, 2), "estimator__C": (1, 2)} GridSearchCV( BalancedBaggingClassifier(SVC(gamma="scale")), parameters, cv=3, scoring="roc_auc", > ).fit(X, y) imblearn/ensemble/tests/test_bagging.py:266: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:1024: in fit self._run_search(evaluate_candidates) /usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:1571: in _run_search evaluate_candidates(ParameterGrid(self.param_grid)) /usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:1001: in evaluate_candidates _warn_or_raise_about_fit_failures(out, self.error_score) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ results = [{'fit_error': 'Traceback (most recent call last):\n File "/usr/lib64/python3.14/site-packages/sklearn/model_selectio...scoroutinefunction() instead\n', 'fit_time': 0.0021936893463134766, 'n_test_samples': 50, 'score_time': 0.0, ...}, ...] error_score = nan def _warn_or_raise_about_fit_failures(results, error_score): fit_errors = [ result["fit_error"] for result in results if result["fit_error"] is not None ] if fit_errors: num_failed_fits = len(fit_errors) num_fits = len(results) fit_errors_counter = Counter(fit_errors) delimiter = "-" * 80 + "\n" fit_errors_summary = "\n".join( f"{delimiter}{n} fits failed with the following error:\n{error}" for error, n in fit_errors_counter.items() ) if num_failed_fits == num_fits: all_fits_failed_message = ( f"\nAll the {num_fits} fits failed.\n" "It is very likely that your model is misconfigured.\n" "You can try to debug the error by setting error_score='raise'.\n\n" f"Below are more details about the failures:\n{fit_errors_summary}" ) > raise ValueError(all_fits_failed_message) E ValueError: E All the 12 fits failed. E It is very likely that your model is misconfigured. E You can try to debug the error by setting error_score='raise'. E E Below are more details about the failures: E -------------------------------------------------------------------------------- E 12 fits failed with the following error: E Traceback (most recent call last): E File "/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py", line 866, in _fit_and_score E estimator.fit(X_train, y_train, **fit_params) E ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper E return fit_method(estimator, *args, **kwargs) E File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_bagging.py", line 337, in fit E return super().fit(X, y) E ~~~~~~~~~~~^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/utils/validation.py", line 63, in inner_f E return f(*args, **kwargs) E File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper E return fit_method(estimator, *args, **kwargs) E File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 389, in fit E return self._fit(X, y, max_samples=self.max_samples, **fit_params) E ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_bagging.py", line 352, in _fit E return super()._fit(X, y, self.max_samples) E ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 532, in _fit E all_results = Parallel( E n_jobs=n_jobs, verbose=self.verbose, **self._parallel_args() E )( E delayed(_parallel_build_estimators)( E ...<10 lines>... E for i in range(n_jobs) E ) E File "/usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py", line 77, in __call__ E return super().__call__(iterable_with_config) E ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^ E File "/usr/lib/python3.14/site-packages/joblib/parallel.py", line 1986, in __call__ E return output if self.return_generator else list(output) E ~~~~^^^^^^^^ E File "/usr/lib/python3.14/site-packages/joblib/parallel.py", line 1914, in _get_sequential_output E res = func(*args, **kwargs) E File "/usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py", line 139, in __call__ E return self.function(*args, **kwargs) E ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 197, in _parallel_build_estimators E estimator_fit(X_, y_, **fit_params_) E ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper E return fit_method(estimator, *args, **kwargs) E File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 518, in fit E Xt, yt = self._fit(X, y, routed_params, raw_params=params) E ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 404, in _fit E fit_transform_one_cached = memory.cache(_fit_transform_one) E File "/usr/lib/python3.14/site-packages/joblib/memory.py", line 1104, in cache E if asyncio.iscoroutinefunction(func) E ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ E File "/usr/lib64/python3.14/asyncio/coroutines.py", line 23, in iscoroutinefunction E warnings._deprecated("asyncio.iscoroutinefunction", E ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E f"{warnings._DEPRECATED_MSG}; " E ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E "use inspect.iscoroutinefunction() instead", E ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E remove=(3,16)) E ^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/_py_warnings.py", line 830, in _deprecated E _wm.warn(msg, DeprecationWarning, stacklevel=3) E ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:517: ValueError __________________________ test_bagging_with_pipeline __________________________ def test_bagging_with_pipeline(): X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) estimator = BalancedBaggingClassifier( make_pipeline(SelectKBest(k=1), DecisionTreeClassifier()), max_features=2, ) > estimator.fit(X, y).predict(X) imblearn/ensemble/tests/test_bagging.py:309: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________________________ test_warm_start ________________________________ random_state = 42 def test_warm_start(random_state=42): # Test if fitting incrementally with warm start gives a forest of the # right size and the same results as a normal fit. X, y = make_hastie_10_2(n_samples=20, random_state=1) clf_ws = None for n_estimators in [5, 10]: if clf_ws is None: clf_ws = BalancedBaggingClassifier( n_estimators=n_estimators, random_state=random_state, warm_start=True, ) else: clf_ws.set_params(n_estimators=n_estimators) > clf_ws.fit(X, y) imblearn/ensemble/tests/test_bagging.py:327: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________________ test_warm_start_smaller_n_estimators _____________________ def test_warm_start_smaller_n_estimators(): # Test if warm start'ed second fit with smaller n_estimators raises error. X, y = make_hastie_10_2(n_samples=20, random_state=1) clf = BalancedBaggingClassifier(n_estimators=5, warm_start=True) > clf.fit(X, y) imblearn/ensemble/tests/test_bagging.py:344: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ______________________ test_warm_start_equal_n_estimators ______________________ def test_warm_start_equal_n_estimators(): # Test that nothing happens when fitting without increasing n_estimators X, y = make_hastie_10_2(n_samples=20, random_state=1) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=43) clf = BalancedBaggingClassifier(n_estimators=5, warm_start=True, random_state=83) > clf.fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:356: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________________________ test_warm_start_equivalence __________________________ def test_warm_start_equivalence(): # warm started classifier with 5+5 estimators should be equivalent to # one classifier with 10 estimators X, y = make_hastie_10_2(n_samples=20, random_state=1) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=43) clf_ws = BalancedBaggingClassifier( n_estimators=5, warm_start=True, random_state=3141 ) > clf_ws.fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:377: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________________ test_oob_score_removed_on_warm_start _____________________ def test_oob_score_removed_on_warm_start(): X, y = make_hastie_10_2(n_samples=2000, random_state=1) clf = BalancedBaggingClassifier(n_estimators=50, oob_score=True) > clf.fit(X, y) imblearn/ensemble/tests/test_bagging.py:403: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning __________________________ test_oob_score_consistency __________________________ def test_oob_score_consistency(): # Make sure OOB scores are identical when random_state, estimator, and # training data are fixed and fitting is done twice X, y = make_hastie_10_2(n_samples=200, random_state=1) bagging = BalancedBaggingClassifier( KNeighborsClassifier(), max_samples=0.5, max_features=0.5, oob_score=True, random_state=1, ) > assert bagging.fit(X, y).oob_score_ == bagging.fit(X, y).oob_score_ imblearn/ensemble/tests/test_bagging.py:423: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ___________________________ test_estimators_samples ____________________________ def test_estimators_samples(): # Check that format of estimators_samples_ is correct and that results # generated at fit time can be identically reproduced at a later time # using data saved in object attributes. X, y = make_hastie_10_2(n_samples=200, random_state=1) # remap the y outside of the BalancedBaggingclassifier # _, y = np.unique(y, return_inverse=True) bagging = BalancedBaggingClassifier( LogisticRegression(), max_samples=0.5, max_features=0.5, random_state=1, bootstrap=False, ) > bagging.fit(X, y) imblearn/ensemble/tests/test_bagging.py:441: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________________________ test_max_samples_consistency _________________________ def test_max_samples_consistency(): # Make sure validated max_samples and original max_samples are identical # when valid integer max_samples supplied by user max_samples = 100 X, y = make_hastie_10_2(n_samples=2 * max_samples, random_state=1) bagging = BalancedBaggingClassifier( KNeighborsClassifier(), max_samples=max_samples, max_features=0.5, random_state=1, ) > bagging.fit(X, y) imblearn/ensemble/tests/test_bagging.py:480: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ______________ test_balanced_bagging_classifier_samplers[None-15] ______________ sampler = None, n_samples_bootstrap = 15 @pytest.mark.filterwarnings("ignore:Number of distinct clusters") @pytest.mark.parametrize( "sampler, n_samples_bootstrap", [ (None, 15), (RandomUnderSampler(), 15), # under-sampling with sample_indices_ ( ClusterCentroids(estimator=KMeans(n_init=1)), 15, ), # under-sampling without sample_indices_ (RandomOverSampler(), 40), # over-sampling with sample_indices_ (SMOTE(), 40), # over-sampling without sample_indices_ ], ) def test_balanced_bagging_classifier_samplers(sampler, n_samples_bootstrap): # check that we can pass any kind of sampler to a bagging classifier X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = BalancedBaggingClassifier( estimator=CountDecisionTreeClassifier(), n_estimators=2, sampler=sampler, random_state=0, ) > clf.fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:522: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier_samplers[sampler1-15] ____________ sampler = RandomUnderSampler(), n_samples_bootstrap = 15 @pytest.mark.filterwarnings("ignore:Number of distinct clusters") @pytest.mark.parametrize( "sampler, n_samples_bootstrap", [ (None, 15), (RandomUnderSampler(), 15), # under-sampling with sample_indices_ ( ClusterCentroids(estimator=KMeans(n_init=1)), 15, ), # under-sampling without sample_indices_ (RandomOverSampler(), 40), # over-sampling with sample_indices_ (SMOTE(), 40), # over-sampling without sample_indices_ ], ) def test_balanced_bagging_classifier_samplers(sampler, n_samples_bootstrap): # check that we can pass any kind of sampler to a bagging classifier X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = BalancedBaggingClassifier( estimator=CountDecisionTreeClassifier(), n_estimators=2, sampler=sampler, random_state=0, ) > clf.fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:522: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier_samplers[sampler2-15] ____________ sampler = ClusterCentroids(estimator=KMeans(n_init=1)), n_samples_bootstrap = 15 @pytest.mark.filterwarnings("ignore:Number of distinct clusters") @pytest.mark.parametrize( "sampler, n_samples_bootstrap", [ (None, 15), (RandomUnderSampler(), 15), # under-sampling with sample_indices_ ( ClusterCentroids(estimator=KMeans(n_init=1)), 15, ), # under-sampling without sample_indices_ (RandomOverSampler(), 40), # over-sampling with sample_indices_ (SMOTE(), 40), # over-sampling without sample_indices_ ], ) def test_balanced_bagging_classifier_samplers(sampler, n_samples_bootstrap): # check that we can pass any kind of sampler to a bagging classifier X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = BalancedBaggingClassifier( estimator=CountDecisionTreeClassifier(), n_estimators=2, sampler=sampler, random_state=0, ) > clf.fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:522: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier_samplers[sampler3-40] ____________ sampler = RandomOverSampler(), n_samples_bootstrap = 40 @pytest.mark.filterwarnings("ignore:Number of distinct clusters") @pytest.mark.parametrize( "sampler, n_samples_bootstrap", [ (None, 15), (RandomUnderSampler(), 15), # under-sampling with sample_indices_ ( ClusterCentroids(estimator=KMeans(n_init=1)), 15, ), # under-sampling without sample_indices_ (RandomOverSampler(), 40), # over-sampling with sample_indices_ (SMOTE(), 40), # over-sampling without sample_indices_ ], ) def test_balanced_bagging_classifier_samplers(sampler, n_samples_bootstrap): # check that we can pass any kind of sampler to a bagging classifier X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = BalancedBaggingClassifier( estimator=CountDecisionTreeClassifier(), n_estimators=2, sampler=sampler, random_state=0, ) > clf.fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:522: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________ test_balanced_bagging_classifier_samplers[sampler4-40] ____________ sampler = SMOTE(), n_samples_bootstrap = 40 @pytest.mark.filterwarnings("ignore:Number of distinct clusters") @pytest.mark.parametrize( "sampler, n_samples_bootstrap", [ (None, 15), (RandomUnderSampler(), 15), # under-sampling with sample_indices_ ( ClusterCentroids(estimator=KMeans(n_init=1)), 15, ), # under-sampling without sample_indices_ (RandomOverSampler(), 40), # over-sampling with sample_indices_ (SMOTE(), 40), # over-sampling without sample_indices_ ], ) def test_balanced_bagging_classifier_samplers(sampler, n_samples_bootstrap): # check that we can pass any kind of sampler to a bagging classifier X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = BalancedBaggingClassifier( estimator=CountDecisionTreeClassifier(), n_estimators=2, sampler=sampler, random_state=0, ) > clf.fit(X_train, y_train) imblearn/ensemble/tests/test_bagging.py:522: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________ test_balanced_bagging_classifier_with_function_sampler[True] _________ replace = True @pytest.mark.parametrize("replace", [True, False]) def test_balanced_bagging_classifier_with_function_sampler(replace): # check that we can provide a FunctionSampler in BalancedBaggingClassifier X, y = make_classification( n_samples=1_000, n_features=10, n_classes=2, weights=[0.3, 0.7], random_state=0, ) def roughly_balanced_bagging(X, y, replace=False): """Implementation of Roughly Balanced Bagging for binary problem.""" # find the minority and majority classes class_counts = Counter(y) majority_class = max(class_counts, key=class_counts.get) minority_class = min(class_counts, key=class_counts.get) # compute the number of sample to draw from the majority class using # a negative binomial distribution n_minority_class = class_counts[minority_class] n_majority_resampled = np.random.negative_binomial(n=n_minority_class, p=0.5) # draw randomly with or without replacement majority_indices = np.random.choice( np.flatnonzero(y == majority_class), size=n_majority_resampled, replace=replace, ) minority_indices = np.random.choice( np.flatnonzero(y == minority_class), size=n_minority_class, replace=replace, ) indices = np.hstack([majority_indices, minority_indices]) return X[indices], y[indices] # Roughly Balanced Bagging rbb = BalancedBaggingClassifier( estimator=CountDecisionTreeClassifier(random_state=0), n_estimators=2, sampler=FunctionSampler( func=roughly_balanced_bagging, kw_args={"replace": replace} ), random_state=0, ) > rbb.fit(X, y) imblearn/ensemble/tests/test_bagging.py:579: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________ test_balanced_bagging_classifier_with_function_sampler[False] _________ replace = False @pytest.mark.parametrize("replace", [True, False]) def test_balanced_bagging_classifier_with_function_sampler(replace): # check that we can provide a FunctionSampler in BalancedBaggingClassifier X, y = make_classification( n_samples=1_000, n_features=10, n_classes=2, weights=[0.3, 0.7], random_state=0, ) def roughly_balanced_bagging(X, y, replace=False): """Implementation of Roughly Balanced Bagging for binary problem.""" # find the minority and majority classes class_counts = Counter(y) majority_class = max(class_counts, key=class_counts.get) minority_class = min(class_counts, key=class_counts.get) # compute the number of sample to draw from the majority class using # a negative binomial distribution n_minority_class = class_counts[minority_class] n_majority_resampled = np.random.negative_binomial(n=n_minority_class, p=0.5) # draw randomly with or without replacement majority_indices = np.random.choice( np.flatnonzero(y == majority_class), size=n_majority_resampled, replace=replace, ) minority_indices = np.random.choice( np.flatnonzero(y == minority_class), size=n_minority_class, replace=replace, ) indices = np.hstack([majority_indices, minority_indices]) return X[indices], y[indices] # Roughly Balanced Bagging rbb = BalancedBaggingClassifier( estimator=CountDecisionTreeClassifier(random_state=0), n_estimators=2, sampler=FunctionSampler( func=roughly_balanced_bagging, kw_args={"replace": replace} ), random_state=0, ) > rbb.fit(X, y) imblearn/ensemble/tests/test_bagging.py:579: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning __________________________ test_bagging_with_pipeline __________________________ def test_bagging_with_pipeline(): X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) estimator = EasyEnsembleClassifier( n_estimators=2, estimator=make_pipeline(SelectKBest(k=1), GradientBoostingClassifier()), ) > estimator.fit(X, y).predict(X) imblearn/ensemble/tests/test_easy_ensemble.py:109: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________________________ test_warm_start ________________________________ random_state = 42 def test_warm_start(random_state=42): # Test if fitting incrementally with warm start gives a forest of the # right size and the same results as a normal fit. X, y = make_hastie_10_2(n_samples=20, random_state=1) clf_ws = None for n_estimators in [5, 10]: if clf_ws is None: clf_ws = EasyEnsembleClassifier( n_estimators=n_estimators, random_state=random_state, warm_start=True, ) else: clf_ws.set_params(n_estimators=n_estimators) > clf_ws.fit(X, y) imblearn/ensemble/tests/test_easy_ensemble.py:127: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________________ test_warm_start_smaller_n_estimators _____________________ def test_warm_start_smaller_n_estimators(): # Test if warm start'ed second fit with smaller n_estimators raises error. X, y = make_hastie_10_2(n_samples=20, random_state=1) clf = EasyEnsembleClassifier(n_estimators=5, warm_start=True) > clf.fit(X, y) imblearn/ensemble/tests/test_easy_ensemble.py:144: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ______________________ test_warm_start_equal_n_estimators ______________________ def test_warm_start_equal_n_estimators(): # Test that nothing happens when fitting without increasing n_estimators X, y = make_hastie_10_2(n_samples=20, random_state=1) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=43) clf = EasyEnsembleClassifier(n_estimators=5, warm_start=True, random_state=83) > clf.fit(X_train, y_train) imblearn/ensemble/tests/test_easy_ensemble.py:156: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________________________ test_warm_start_equivalence __________________________ def test_warm_start_equivalence(): # warm started classifier with 5+5 estimators should be equivalent to # one classifier with 10 estimators X, y = make_hastie_10_2(n_samples=20, random_state=1) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=43) clf_ws = EasyEnsembleClassifier(n_estimators=5, warm_start=True, random_state=3141) > clf_ws.fit(X_train, y_train) imblearn/ensemble/tests/test_easy_ensemble.py:175: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_easy_ensemble_classifier_single_estimator ________________ def test_easy_ensemble_classifier_single_estimator(): X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) > clf1 = EasyEnsembleClassifier(n_estimators=1, random_state=0).fit(X_train, y_train) imblearn/ensemble/tests/test_easy_ensemble.py:196: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning __________________ test_easy_ensemble_classifier_grid_search ___________________ def test_easy_ensemble_classifier_grid_search(): X, y = make_imbalance( iris.data, iris.target, sampling_strategy={0: 20, 1: 25, 2: 50}, random_state=0, ) parameters = { "n_estimators": [1, 2], "estimator__n_estimators": [3, 4], } grid_search = GridSearchCV( EasyEnsembleClassifier(estimator=GradientBoostingClassifier()), parameters, cv=5, ) > grid_search.fit(X, y) imblearn/ensemble/tests/test_easy_ensemble.py:222: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:1024: in fit self._run_search(evaluate_candidates) /usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:1571: in _run_search evaluate_candidates(ParameterGrid(self.param_grid)) /usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:1001: in evaluate_candidates _warn_or_raise_about_fit_failures(out, self.error_score) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ results = [{'fit_error': 'Traceback (most recent call last):\n File "/usr/lib64/python3.14/site-packages/sklearn/model_selectio...iscoroutinefunction() instead\n', 'fit_time': 0.002209901809692383, 'n_test_samples': 19, 'score_time': 0.0, ...}, ...] error_score = nan def _warn_or_raise_about_fit_failures(results, error_score): fit_errors = [ result["fit_error"] for result in results if result["fit_error"] is not None ] if fit_errors: num_failed_fits = len(fit_errors) num_fits = len(results) fit_errors_counter = Counter(fit_errors) delimiter = "-" * 80 + "\n" fit_errors_summary = "\n".join( f"{delimiter}{n} fits failed with the following error:\n{error}" for error, n in fit_errors_counter.items() ) if num_failed_fits == num_fits: all_fits_failed_message = ( f"\nAll the {num_fits} fits failed.\n" "It is very likely that your model is misconfigured.\n" "You can try to debug the error by setting error_score='raise'.\n\n" f"Below are more details about the failures:\n{fit_errors_summary}" ) > raise ValueError(all_fits_failed_message) E ValueError: E All the 20 fits failed. E It is very likely that your model is misconfigured. E You can try to debug the error by setting error_score='raise'. E E Below are more details about the failures: E -------------------------------------------------------------------------------- E 20 fits failed with the following error: E Traceback (most recent call last): E File "/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py", line 866, in _fit_and_score E estimator.fit(X_train, y_train, **fit_params) E ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper E return fit_method(estimator, *args, **kwargs) E File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_easy_ensemble.py", line 271, in fit E return super().fit(X, y) E ~~~~~~~~~~~^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/utils/validation.py", line 63, in inner_f E return f(*args, **kwargs) E File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper E return fit_method(estimator, *args, **kwargs) E File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 389, in fit E return self._fit(X, y, max_samples=self.max_samples, **fit_params) E ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/ensemble/_easy_ensemble.py", line 277, in _fit E return super()._fit(X, y, self.max_samples) E ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 532, in _fit E all_results = Parallel( E n_jobs=n_jobs, verbose=self.verbose, **self._parallel_args() E )( E delayed(_parallel_build_estimators)( E ...<10 lines>... E for i in range(n_jobs) E ) E File "/usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py", line 77, in __call__ E return super().__call__(iterable_with_config) E ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^ E File "/usr/lib/python3.14/site-packages/joblib/parallel.py", line 1986, in __call__ E return output if self.return_generator else list(output) E ~~~~^^^^^^^^ E File "/usr/lib/python3.14/site-packages/joblib/parallel.py", line 1914, in _get_sequential_output E res = func(*args, **kwargs) E File "/usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py", line 139, in __call__ E return self.function(*args, **kwargs) E ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py", line 197, in _parallel_build_estimators E estimator_fit(X_, y_, **fit_params_) E ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper E return fit_method(estimator, *args, **kwargs) E File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 518, in fit E Xt, yt = self._fit(X, y, routed_params, raw_params=params) E ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 404, in _fit E fit_transform_one_cached = memory.cache(_fit_transform_one) E File "/usr/lib/python3.14/site-packages/joblib/memory.py", line 1104, in cache E if asyncio.iscoroutinefunction(func) E ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ E File "/usr/lib64/python3.14/asyncio/coroutines.py", line 23, in iscoroutinefunction E warnings._deprecated("asyncio.iscoroutinefunction", E ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E f"{warnings._DEPRECATED_MSG}; " E ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E "use inspect.iscoroutinefunction() instead", E ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E remove=(3,16)) E ^^^^^^^^^^^^^^ E File "/usr/lib64/python3.14/_py_warnings.py", line 830, in _deprecated E _wm.warn(msg, DeprecationWarning, stacklevel=3) E ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:517: ValueError ____________________ test_balanced_random_forest_attributes ____________________ imbalanced_dataset = (array([[-1.17193225, 0.92171218], [-0.83031743, 1.19351593], [-2.16240487, -2.36749441], ..., ..., -0.9010522 ], [ 1.17004811, -0.15316994], [-0.88490082, 0.87375556]]), array([2, 2, 2, ..., 2, 1, 2])) def test_balanced_random_forest_attributes(imbalanced_dataset): X, y = imbalanced_dataset n_estimators = 10 brf = BalancedRandomForestClassifier( n_estimators=n_estimators, random_state=0, sampling_strategy="all", replacement=True, bootstrap=False, ) brf.fit(X, y) for idx in range(n_estimators): X_res, y_res = brf.samplers_[idx].fit_resample(X, y) X_res_2, y_res_2 = ( brf.pipelines_[idx].named_steps["randomundersampler"].fit_resample(X, y) ) assert_allclose(X_res, X_res_2) assert_array_equal(y_res, y_res_2) y_pred = brf.estimators_[idx].fit(X_res, y_res).predict(X) > y_pred_2 = brf.pipelines_[idx].fit(X, y).predict(X) imblearn/ensemble/tests/test_forest.py:83: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________________ test_smotenc_categorical_features_auto ____________________ def test_smotenc_categorical_features_auto(): """Check that we can automatically detect categorical features based on pandas dataframe. """ pd = pytest.importorskip("pandas") X = pd.DataFrame( { "A": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "B": ["a", "b"] * 5, "C": ["a", "b", "c"] * 3 + ["a"], } ) X = pd.concat([X] * 10, ignore_index=True) > X["B"] = X["B"].astype("category") imblearn/over_sampling/_smote/tests/test_smote_nc.py:353: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = A B C 0 1 a a 1 2 b b 2 3 a c 3 4 b a 4 5 a b .. .. .. .. 95 6 b c 96 7 a a 97 8 b b 98 9 a c 99 10 b a [100 rows x 3 columns] key = 'B' value = 0 a 1 b 2 a 3 b 4 a .. 95 b 96 a 97 b 98 a 99 b Name: B, Length: 100, dtype: category Categories (2, object): ['a', 'b'] def __setitem__(self, key, value) -> None: if not PYPY and using_copy_on_write(): if sys.getrefcount(self) <= 3: warnings.warn( _chained_assignment_msg, ChainedAssignmentError, stacklevel=2 ) elif not PYPY and not using_copy_on_write(): if sys.getrefcount(self) <= 3 and ( warn_copy_on_write() or ( not warn_copy_on_write() and any(b.refs.has_reference() for b in self._mgr.blocks) # type: ignore[union-attr] ) ): > warnings.warn( _chained_assignment_warning_msg, FutureWarning, stacklevel=2 ) E FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! E You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. E A typical example is when you are setting values in a column of a DataFrame, like: E E df["col"][row_indexer] = value E E Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. E E See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy /usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning _____________________ [doctest] imblearn.pipeline.Pipeline _____________________ 215 ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, 216 ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) 217 >>> print(f'Original dataset shape {Counter(y)}') 218 Original dataset shape Counter({1: 900, 0: 100}) 219 >>> pca = PCA() 220 >>> smt = SMOTE(random_state=42) 221 >>> knn = KNN() 222 >>> pipeline = Pipeline([('smt', smt), ('pca', pca), ('knn', knn)]) 223 >>> X_train, X_test, y_train, y_test = tts(X, y, random_state=42) 224 >>> pipeline.fit(X_train, y_train) UNEXPECTED EXCEPTION: DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") Traceback (most recent call last): File "/usr/lib64/python3.14/doctest.py", line 1395, in __run exec(compile(example.source, filename, "single", ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ compileflags, True), test.globs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "", line 1, in File "/usr/lib64/python3.14/site-packages/sklearn/base.py", line 1389, in wrapper return fit_method(estimator, *args, **kwargs) File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 518, in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py", line 404, in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) File "/usr/lib/python3.14/site-packages/joblib/memory.py", line 1104, in cache if asyncio.iscoroutinefunction(func) ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ File "/usr/lib64/python3.14/asyncio/coroutines.py", line 23, in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ f"{warnings._DEPRECATED_MSG}; " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "use inspect.iscoroutinefunction() instead", ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ remove=(3,16)) ^^^^^^^^^^^^^^ File "/usr/lib64/python3.14/_py_warnings.py", line 830, in _deprecated _wm.warn(msg, DeprecationWarning, stacklevel=3) ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/pipeline.py:224: UnexpectedException _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_overwrite_params] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3616: in check_estimators_overwrite_params estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_dont_overwrite_parameters] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1734: in check_dont_overwrite_parameters estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimators_fit_returns_self] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3208: in check_estimators_fit_returns_self assert estimator.fit(X, y) is estimator /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_readonly_memmap_input] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3227: in check_readonly_memmap_input assert estimator.fit(X, y) is estimator /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_n_features_in_after_fitting] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4453: in check_n_features_in_after_fitting estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_positive_only_tag_during_fit] _ name = 'BalancedBaggingClassifier' estimator_orig = BalancedBaggingClassifier(n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_positive_only_tag_during_fit(name, estimator_orig): """Test that the estimator correctly sets the tags.input_tags.positive_only If the tag is False, the estimator should accept negative input regardless of the tags.input_tags.pairwise flag. """ estimator = clone(estimator_orig) tags = get_tags(estimator) X, y = load_iris(return_X_y=True) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 0) X = _enforce_estimator_tags_X(estimator, X) X -= X.mean() if tags.input_tags.positive_only: with raises(ValueError, match="Negative values in data"): estimator.fit(X, y) else: # This should pass try: > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4016: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'BalancedBaggingClassifier' estimator_orig = BalancedBaggingClassifier(n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_positive_only_tag_during_fit(name, estimator_orig): """Test that the estimator correctly sets the tags.input_tags.positive_only If the tag is False, the estimator should accept negative input regardless of the tags.input_tags.pairwise flag. """ estimator = clone(estimator_orig) tags = get_tags(estimator) X, y = load_iris(return_X_y=True) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 0) X = _enforce_estimator_tags_X(estimator, X) X -= X.mean() if tags.input_tags.positive_only: with raises(ValueError, match="Negative values in data"): estimator.fit(X, y) else: # This should pass try: estimator.fit(X, y) except Exception as e: err_msg = ( f"Estimator {repr(name)} raised {e.__class__.__name__} unexpectedly." " This happens when passing negative input values as X." " If negative values are not supported for this estimator instance," " then the tags.input_tags.positive_only tag needs to be set to True." ) > raise AssertionError(err_msg) from e E AssertionError: Estimator 'BalancedBaggingClassifier' raised DeprecationWarning unexpectedly. This happens when passing negative input values as X. If negative values are not supported for this estimator instance, then the tags.input_tags.positive_only tag needs to be set to True. /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4024: AssertionError _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_dtype_object] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1643: in check_dtype_object estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_sparse_tag] _ name = 'BalancedBaggingClassifier' estimator_orig = BalancedBaggingClassifier(n_estimators=5, random_state=42) def check_estimator_sparse_tag(name, estimator_orig): """Check that estimator tag related with accepting sparse data is properly set.""" if SPARSE_ARRAY_PRESENT: sparse_container = sparse.csr_array else: sparse_container = sparse.csr_matrix estimator = clone(estimator_orig) rng = np.random.RandomState(0) n_samples = 15 if name == "SpectralCoclustering" else 40 X = rng.uniform(size=(n_samples, 3)) X[X < 0.6] = 0 y = rng.randint(0, 3, size=n_samples) X = _enforce_estimator_tags_X(estimator, X) y = _enforce_estimator_tags_y(estimator, y) X = sparse_container(X) tags = get_tags(estimator) if tags.input_tags.sparse: try: > estimator.fit(X, y) # should pass /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1255: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'BalancedBaggingClassifier' estimator_orig = BalancedBaggingClassifier(n_estimators=5, random_state=42) def check_estimator_sparse_tag(name, estimator_orig): """Check that estimator tag related with accepting sparse data is properly set.""" if SPARSE_ARRAY_PRESENT: sparse_container = sparse.csr_array else: sparse_container = sparse.csr_matrix estimator = clone(estimator_orig) rng = np.random.RandomState(0) n_samples = 15 if name == "SpectralCoclustering" else 40 X = rng.uniform(size=(n_samples, 3)) X[X < 0.6] = 0 y = rng.randint(0, 3, size=n_samples) X = _enforce_estimator_tags_X(estimator, X) y = _enforce_estimator_tags_y(estimator, y) X = sparse_container(X) tags = get_tags(estimator) if tags.input_tags.sparse: try: estimator.fit(X, y) # should pass except Exception as e: err_msg = ( f"Estimator {name} raised an exception. " f"The tag self.input_tags.sparse={tags.input_tags.sparse} " "might not be consistent with the estimator's ability to " "handle sparse data (i.e. controlled by the parameter `accept_sparse`" " in `validate_data` or `check_array` functions)." ) > raise AssertionError(err_msg) from e E AssertionError: Estimator BalancedBaggingClassifier raised an exception. The tag self.input_tags.sparse=True might not be consistent with the estimator's ability to handle sparse data (i.e. controlled by the parameter `accept_sparse` in `validate_data` or `check_array` functions). /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1264: AssertionError _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_sparse_array] _ name = 'BalancedBaggingClassifier' estimator_orig = BalancedBaggingClassifier(n_estimators=5, random_state=42) sparse_type = def _check_estimator_sparse_container(name, estimator_orig, sparse_type): rng = np.random.RandomState(0) X = rng.uniform(size=(40, 3)) X[X < 0.6] = 0 X = _enforce_estimator_tags_X(estimator_orig, X) y = (4 * rng.uniform(size=X.shape[0])).astype(np.int32) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) tags = get_tags(estimator_orig) for matrix_format, X in _generate_sparse_data(sparse_type(X)): # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) if name in ["Scaler", "StandardScaler"]: estimator.set_params(with_mean=False) # fit and predict if "64" in matrix_format: err_msg = ( f"Estimator {name} doesn't seem to support {matrix_format} " "matrix, and is not failing gracefully, e.g. by using " "check_array(X, accept_large_sparse=False)." ) else: err_msg = ( f"Estimator {name} doesn't seem to fail gracefully on sparse " "data: error message should state explicitly that sparse " "input is not supported if this is not the case, e.g. by using " "check_array(X, accept_sparse=False)." ) with raises( (TypeError, ValueError), match=["sparse", "Sparse"], may_pass=True, err_msg=err_msg, ): with ignore_warnings(category=FutureWarning): > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1329: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1351: in check_estimator_sparse_array _check_estimator_sparse_container(name, estimator_orig, sparse.csr_array) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1322: in _check_estimator_sparse_container with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator BalancedBaggingClassifier doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case, e.g. by using check_array(X, accept_sparse=False). /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_estimator_sparse_matrix] _ name = 'BalancedBaggingClassifier' estimator_orig = BalancedBaggingClassifier(n_estimators=5, random_state=42) sparse_type = def _check_estimator_sparse_container(name, estimator_orig, sparse_type): rng = np.random.RandomState(0) X = rng.uniform(size=(40, 3)) X[X < 0.6] = 0 X = _enforce_estimator_tags_X(estimator_orig, X) y = (4 * rng.uniform(size=X.shape[0])).astype(np.int32) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) tags = get_tags(estimator_orig) for matrix_format, X in _generate_sparse_data(sparse_type(X)): # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) if name in ["Scaler", "StandardScaler"]: estimator.set_params(with_mean=False) # fit and predict if "64" in matrix_format: err_msg = ( f"Estimator {name} doesn't seem to support {matrix_format} " "matrix, and is not failing gracefully, e.g. by using " "check_array(X, accept_large_sparse=False)." ) else: err_msg = ( f"Estimator {name} doesn't seem to fail gracefully on sparse " "data: error message should state explicitly that sparse " "input is not supported if this is not the case, e.g. by using " "check_array(X, accept_sparse=False)." ) with raises( (TypeError, ValueError), match=["sparse", "Sparse"], may_pass=True, err_msg=err_msg, ): with ignore_warnings(category=FutureWarning): > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1329: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1346: in check_estimator_sparse_matrix _check_estimator_sparse_container(name, estimator_orig, sparse.csr_matrix) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1322: in _check_estimator_sparse_container with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator BalancedBaggingClassifier doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case, e.g. by using check_array(X, accept_sparse=False). /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_f_contiguous_array_estimator] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1367: in check_f_contiguous_array_estimator estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifier_data_not_an_array] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3726: in check_classifier_data_not_an_array check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type) /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3778: in check_estimators_data_not_an_array estimator_1.fit(X_, y_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_classifiers_one_label] _ name = 'BalancedBaggingClassifier' classifier_orig = BalancedBaggingClassifier(n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_classifiers_one_label(name, classifier_orig): error_string_fit = "Classifier can't train when only one class is present." error_string_predict = "Classifier can't predict when only one class is present." classifier = clone(classifier_orig) rnd = np.random.RandomState(0) X_train = rnd.uniform(size=(10, 3)) X_test = rnd.uniform(size=(10, 3)) X_train, X_test = _enforce_estimator_tags_X(classifier, X_train, X_test=X_test) y = np.ones(10) # catch deprecation warnings with ignore_warnings(category=FutureWarning): with raises( ValueError, match="class", may_pass=True, err_msg=error_string_fit ) as cm: > classifier.fit(X_train, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2656: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2653: in check_classifiers_one_label with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Classifier can't train when only one class is present. /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_supervised_y_2d] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3271: in check_supervised_y_2d estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_methods_sample_order_invariance] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1870: in check_methods_sample_order_invariance estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_methods_subset_invariance] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1830: in check_methods_subset_invariance estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit_idempotent] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4298: in check_fit_idempotent estimator.fit(X_train, y_train) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit_check_is_fitted] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4358: in check_fit_check_is_fitted estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_n_features_in] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4388: in check_n_features_in estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[BalancedBaggingClassifier(random_state=42)-check_fit2d_predict1d] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1787: in check_fit2d_predict1d estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_overwrite_params] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3616: in check_estimators_overwrite_params estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_dont_overwrite_parameters] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1734: in check_dont_overwrite_parameters estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_fit_returns_self] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3208: in check_estimators_fit_returns_self assert estimator.fit(X, y) is estimator /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_readonly_memmap_input] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3227: in check_readonly_memmap_input assert estimator.fit(X, y) is estimator /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_n_features_in_after_fitting] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4453: in check_n_features_in_after_fitting estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_positive_only_tag_during_fit] _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_positive_only_tag_during_fit(name, estimator_orig): """Test that the estimator correctly sets the tags.input_tags.positive_only If the tag is False, the estimator should accept negative input regardless of the tags.input_tags.pairwise flag. """ estimator = clone(estimator_orig) tags = get_tags(estimator) X, y = load_iris(return_X_y=True) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 0) X = _enforce_estimator_tags_X(estimator, X) X -= X.mean() if tags.input_tags.positive_only: with raises(ValueError, match="Negative values in data"): estimator.fit(X, y) else: # This should pass try: > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4016: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_positive_only_tag_during_fit(name, estimator_orig): """Test that the estimator correctly sets the tags.input_tags.positive_only If the tag is False, the estimator should accept negative input regardless of the tags.input_tags.pairwise flag. """ estimator = clone(estimator_orig) tags = get_tags(estimator) X, y = load_iris(return_X_y=True) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 0) X = _enforce_estimator_tags_X(estimator, X) X -= X.mean() if tags.input_tags.positive_only: with raises(ValueError, match="Negative values in data"): estimator.fit(X, y) else: # This should pass try: estimator.fit(X, y) except Exception as e: err_msg = ( f"Estimator {repr(name)} raised {e.__class__.__name__} unexpectedly." " This happens when passing negative input values as X." " If negative values are not supported for this estimator instance," " then the tags.input_tags.positive_only tag needs to be set to True." ) > raise AssertionError(err_msg) from e E AssertionError: Estimator 'EasyEnsembleClassifier' raised DeprecationWarning unexpectedly. This happens when passing negative input values as X. If negative values are not supported for this estimator instance, then the tags.input_tags.positive_only tag needs to be set to True. /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4024: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_dtype_object] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1643: in check_dtype_object estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimators_nan_inf] _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_estimators_nan_inf(name, estimator_orig): # Checks that Estimator X's do not contain NaN or inf. rnd = np.random.RandomState(0) X_train_finite = _enforce_estimator_tags_X( estimator_orig, rnd.uniform(size=(10, 3)) ) X_train_nan = rnd.uniform(size=(10, 3)) X_train_nan[0, 0] = np.nan X_train_inf = rnd.uniform(size=(10, 3)) X_train_inf[0, 0] = np.inf y = np.ones(10) y[:5] = 0 y = _enforce_estimator_tags_y(estimator_orig, y) error_string_fit = f"Estimator {name} doesn't check for NaN and inf in fit." error_string_predict = f"Estimator {name} doesn't check for NaN and inf in predict." error_string_transform = ( f"Estimator {name} doesn't check for NaN and inf in transform." ) for X_train in [X_train_nan, X_train_inf]: # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) set_random_state(estimator, 1) # try to fit with raises(ValueError, match=["inf", "NaN"], err_msg=error_string_fit): > estimator.fit(X_train, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2356: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2355: in check_estimators_nan_inf with raises(ValueError, match=["inf", "NaN"], err_msg=error_string_fit): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator EasyEnsembleClassifier doesn't check for NaN and inf in fit. /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_sparse_tag] _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(n_estimators=5, random_state=42) def check_estimator_sparse_tag(name, estimator_orig): """Check that estimator tag related with accepting sparse data is properly set.""" if SPARSE_ARRAY_PRESENT: sparse_container = sparse.csr_array else: sparse_container = sparse.csr_matrix estimator = clone(estimator_orig) rng = np.random.RandomState(0) n_samples = 15 if name == "SpectralCoclustering" else 40 X = rng.uniform(size=(n_samples, 3)) X[X < 0.6] = 0 y = rng.randint(0, 3, size=n_samples) X = _enforce_estimator_tags_X(estimator, X) y = _enforce_estimator_tags_y(estimator, y) X = sparse_container(X) tags = get_tags(estimator) if tags.input_tags.sparse: try: > estimator.fit(X, y) # should pass /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1255: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(n_estimators=5, random_state=42) def check_estimator_sparse_tag(name, estimator_orig): """Check that estimator tag related with accepting sparse data is properly set.""" if SPARSE_ARRAY_PRESENT: sparse_container = sparse.csr_array else: sparse_container = sparse.csr_matrix estimator = clone(estimator_orig) rng = np.random.RandomState(0) n_samples = 15 if name == "SpectralCoclustering" else 40 X = rng.uniform(size=(n_samples, 3)) X[X < 0.6] = 0 y = rng.randint(0, 3, size=n_samples) X = _enforce_estimator_tags_X(estimator, X) y = _enforce_estimator_tags_y(estimator, y) X = sparse_container(X) tags = get_tags(estimator) if tags.input_tags.sparse: try: estimator.fit(X, y) # should pass except Exception as e: err_msg = ( f"Estimator {name} raised an exception. " f"The tag self.input_tags.sparse={tags.input_tags.sparse} " "might not be consistent with the estimator's ability to " "handle sparse data (i.e. controlled by the parameter `accept_sparse`" " in `validate_data` or `check_array` functions)." ) > raise AssertionError(err_msg) from e E AssertionError: Estimator EasyEnsembleClassifier raised an exception. The tag self.input_tags.sparse=True might not be consistent with the estimator's ability to handle sparse data (i.e. controlled by the parameter `accept_sparse` in `validate_data` or `check_array` functions). /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1264: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_sparse_array] _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(n_estimators=5, random_state=42) sparse_type = def _check_estimator_sparse_container(name, estimator_orig, sparse_type): rng = np.random.RandomState(0) X = rng.uniform(size=(40, 3)) X[X < 0.6] = 0 X = _enforce_estimator_tags_X(estimator_orig, X) y = (4 * rng.uniform(size=X.shape[0])).astype(np.int32) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) tags = get_tags(estimator_orig) for matrix_format, X in _generate_sparse_data(sparse_type(X)): # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) if name in ["Scaler", "StandardScaler"]: estimator.set_params(with_mean=False) # fit and predict if "64" in matrix_format: err_msg = ( f"Estimator {name} doesn't seem to support {matrix_format} " "matrix, and is not failing gracefully, e.g. by using " "check_array(X, accept_large_sparse=False)." ) else: err_msg = ( f"Estimator {name} doesn't seem to fail gracefully on sparse " "data: error message should state explicitly that sparse " "input is not supported if this is not the case, e.g. by using " "check_array(X, accept_sparse=False)." ) with raises( (TypeError, ValueError), match=["sparse", "Sparse"], may_pass=True, err_msg=err_msg, ): with ignore_warnings(category=FutureWarning): > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1329: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1351: in check_estimator_sparse_array _check_estimator_sparse_container(name, estimator_orig, sparse.csr_array) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1322: in _check_estimator_sparse_container with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator EasyEnsembleClassifier doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case, e.g. by using check_array(X, accept_sparse=False). /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_estimator_sparse_matrix] _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(n_estimators=5, random_state=42) sparse_type = def _check_estimator_sparse_container(name, estimator_orig, sparse_type): rng = np.random.RandomState(0) X = rng.uniform(size=(40, 3)) X[X < 0.6] = 0 X = _enforce_estimator_tags_X(estimator_orig, X) y = (4 * rng.uniform(size=X.shape[0])).astype(np.int32) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) tags = get_tags(estimator_orig) for matrix_format, X in _generate_sparse_data(sparse_type(X)): # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) if name in ["Scaler", "StandardScaler"]: estimator.set_params(with_mean=False) # fit and predict if "64" in matrix_format: err_msg = ( f"Estimator {name} doesn't seem to support {matrix_format} " "matrix, and is not failing gracefully, e.g. by using " "check_array(X, accept_large_sparse=False)." ) else: err_msg = ( f"Estimator {name} doesn't seem to fail gracefully on sparse " "data: error message should state explicitly that sparse " "input is not supported if this is not the case, e.g. by using " "check_array(X, accept_sparse=False)." ) with raises( (TypeError, ValueError), match=["sparse", "Sparse"], may_pass=True, err_msg=err_msg, ): with ignore_warnings(category=FutureWarning): > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1329: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1346: in check_estimator_sparse_matrix _check_estimator_sparse_container(name, estimator_orig, sparse.csr_matrix) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1322: in _check_estimator_sparse_container with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator EasyEnsembleClassifier doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case, e.g. by using check_array(X, accept_sparse=False). /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_f_contiguous_array_estimator] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1367: in check_f_contiguous_array_estimator estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifier_data_not_an_array] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3726: in check_classifier_data_not_an_array check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type) /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3778: in check_estimators_data_not_an_array estimator_1.fit(X_, y_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_one_label] _ name = 'EasyEnsembleClassifier' classifier_orig = EasyEnsembleClassifier(n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_classifiers_one_label(name, classifier_orig): error_string_fit = "Classifier can't train when only one class is present." error_string_predict = "Classifier can't predict when only one class is present." classifier = clone(classifier_orig) rnd = np.random.RandomState(0) X_train = rnd.uniform(size=(10, 3)) X_test = rnd.uniform(size=(10, 3)) X_train, X_test = _enforce_estimator_tags_X(classifier, X_train, X_test=X_test) y = np.ones(10) # catch deprecation warnings with ignore_warnings(category=FutureWarning): with raises( ValueError, match="class", may_pass=True, err_msg=error_string_fit ) as cm: > classifier.fit(X_train, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2656: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2653: in check_classifiers_one_label with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Classifier can't train when only one class is present. /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_supervised_y_2d] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3271: in check_supervised_y_2d estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_methods_sample_order_invariance] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1870: in check_methods_sample_order_invariance estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_methods_subset_invariance] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1830: in check_methods_subset_invariance estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit_idempotent] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4298: in check_fit_idempotent estimator.fit(X_train, y_train) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit_check_is_fitted] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4358: in check_fit_check_is_fitted estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_n_features_in] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4388: in check_n_features_in estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(random_state=42)-check_fit2d_predict1d] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1787: in check_fit2d_predict1d estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_overwrite_params] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3616: in check_estimators_overwrite_params estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_dont_overwrite_parameters] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1734: in check_dont_overwrite_parameters estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimators_fit_returns_self] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3208: in check_estimators_fit_returns_self assert estimator.fit(X, y) is estimator /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_readonly_memmap_input] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3227: in check_readonly_memmap_input assert estimator.fit(X, y) is estimator /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_n_features_in_after_fitting] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4453: in check_n_features_in_after_fitting estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_positive_only_tag_during_fit] _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_positive_only_tag_during_fit(name, estimator_orig): """Test that the estimator correctly sets the tags.input_tags.positive_only If the tag is False, the estimator should accept negative input regardless of the tags.input_tags.pairwise flag. """ estimator = clone(estimator_orig) tags = get_tags(estimator) X, y = load_iris(return_X_y=True) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 0) X = _enforce_estimator_tags_X(estimator, X) X -= X.mean() if tags.input_tags.positive_only: with raises(ValueError, match="Negative values in data"): estimator.fit(X, y) else: # This should pass try: > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4016: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_positive_only_tag_during_fit(name, estimator_orig): """Test that the estimator correctly sets the tags.input_tags.positive_only If the tag is False, the estimator should accept negative input regardless of the tags.input_tags.pairwise flag. """ estimator = clone(estimator_orig) tags = get_tags(estimator) X, y = load_iris(return_X_y=True) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 0) X = _enforce_estimator_tags_X(estimator, X) X -= X.mean() if tags.input_tags.positive_only: with raises(ValueError, match="Negative values in data"): estimator.fit(X, y) else: # This should pass try: estimator.fit(X, y) except Exception as e: err_msg = ( f"Estimator {repr(name)} raised {e.__class__.__name__} unexpectedly." " This happens when passing negative input values as X." " If negative values are not supported for this estimator instance," " then the tags.input_tags.positive_only tag needs to be set to True." ) > raise AssertionError(err_msg) from e E AssertionError: Estimator 'EasyEnsembleClassifier' raised DeprecationWarning unexpectedly. This happens when passing negative input values as X. If negative values are not supported for this estimator instance, then the tags.input_tags.positive_only tag needs to be set to True. /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4024: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_dtype_object] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1643: in check_dtype_object estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_sparse_tag] _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) def check_estimator_sparse_tag(name, estimator_orig): """Check that estimator tag related with accepting sparse data is properly set.""" if SPARSE_ARRAY_PRESENT: sparse_container = sparse.csr_array else: sparse_container = sparse.csr_matrix estimator = clone(estimator_orig) rng = np.random.RandomState(0) n_samples = 15 if name == "SpectralCoclustering" else 40 X = rng.uniform(size=(n_samples, 3)) X[X < 0.6] = 0 y = rng.randint(0, 3, size=n_samples) X = _enforce_estimator_tags_X(estimator, X) y = _enforce_estimator_tags_y(estimator, y) X = sparse_container(X) tags = get_tags(estimator) if tags.input_tags.sparse: try: > estimator.fit(X, y) # should pass /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1255: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) def check_estimator_sparse_tag(name, estimator_orig): """Check that estimator tag related with accepting sparse data is properly set.""" if SPARSE_ARRAY_PRESENT: sparse_container = sparse.csr_array else: sparse_container = sparse.csr_matrix estimator = clone(estimator_orig) rng = np.random.RandomState(0) n_samples = 15 if name == "SpectralCoclustering" else 40 X = rng.uniform(size=(n_samples, 3)) X[X < 0.6] = 0 y = rng.randint(0, 3, size=n_samples) X = _enforce_estimator_tags_X(estimator, X) y = _enforce_estimator_tags_y(estimator, y) X = sparse_container(X) tags = get_tags(estimator) if tags.input_tags.sparse: try: estimator.fit(X, y) # should pass except Exception as e: err_msg = ( f"Estimator {name} raised an exception. " f"The tag self.input_tags.sparse={tags.input_tags.sparse} " "might not be consistent with the estimator's ability to " "handle sparse data (i.e. controlled by the parameter `accept_sparse`" " in `validate_data` or `check_array` functions)." ) > raise AssertionError(err_msg) from e E AssertionError: Estimator EasyEnsembleClassifier raised an exception. The tag self.input_tags.sparse=True might not be consistent with the estimator's ability to handle sparse data (i.e. controlled by the parameter `accept_sparse` in `validate_data` or `check_array` functions). /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1264: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_sparse_array] _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) sparse_type = def _check_estimator_sparse_container(name, estimator_orig, sparse_type): rng = np.random.RandomState(0) X = rng.uniform(size=(40, 3)) X[X < 0.6] = 0 X = _enforce_estimator_tags_X(estimator_orig, X) y = (4 * rng.uniform(size=X.shape[0])).astype(np.int32) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) tags = get_tags(estimator_orig) for matrix_format, X in _generate_sparse_data(sparse_type(X)): # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) if name in ["Scaler", "StandardScaler"]: estimator.set_params(with_mean=False) # fit and predict if "64" in matrix_format: err_msg = ( f"Estimator {name} doesn't seem to support {matrix_format} " "matrix, and is not failing gracefully, e.g. by using " "check_array(X, accept_large_sparse=False)." ) else: err_msg = ( f"Estimator {name} doesn't seem to fail gracefully on sparse " "data: error message should state explicitly that sparse " "input is not supported if this is not the case, e.g. by using " "check_array(X, accept_sparse=False)." ) with raises( (TypeError, ValueError), match=["sparse", "Sparse"], may_pass=True, err_msg=err_msg, ): with ignore_warnings(category=FutureWarning): > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1329: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1351: in check_estimator_sparse_array _check_estimator_sparse_container(name, estimator_orig, sparse.csr_array) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1322: in _check_estimator_sparse_container with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator EasyEnsembleClassifier doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case, e.g. by using check_array(X, accept_sparse=False). /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_estimator_sparse_matrix] _ name = 'EasyEnsembleClassifier' estimator_orig = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) sparse_type = def _check_estimator_sparse_container(name, estimator_orig, sparse_type): rng = np.random.RandomState(0) X = rng.uniform(size=(40, 3)) X[X < 0.6] = 0 X = _enforce_estimator_tags_X(estimator_orig, X) y = (4 * rng.uniform(size=X.shape[0])).astype(np.int32) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) tags = get_tags(estimator_orig) for matrix_format, X in _generate_sparse_data(sparse_type(X)): # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) if name in ["Scaler", "StandardScaler"]: estimator.set_params(with_mean=False) # fit and predict if "64" in matrix_format: err_msg = ( f"Estimator {name} doesn't seem to support {matrix_format} " "matrix, and is not failing gracefully, e.g. by using " "check_array(X, accept_large_sparse=False)." ) else: err_msg = ( f"Estimator {name} doesn't seem to fail gracefully on sparse " "data: error message should state explicitly that sparse " "input is not supported if this is not the case, e.g. by using " "check_array(X, accept_sparse=False)." ) with raises( (TypeError, ValueError), match=["sparse", "Sparse"], may_pass=True, err_msg=err_msg, ): with ignore_warnings(category=FutureWarning): > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1329: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1346: in check_estimator_sparse_matrix _check_estimator_sparse_container(name, estimator_orig, sparse.csr_matrix) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1322: in _check_estimator_sparse_container with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator EasyEnsembleClassifier doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case, e.g. by using check_array(X, accept_sparse=False). /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_f_contiguous_array_estimator] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1367: in check_f_contiguous_array_estimator estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifier_data_not_an_array] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3726: in check_classifier_data_not_an_array check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type) /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3778: in check_estimators_data_not_an_array estimator_1.fit(X_, y_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_one_label] _ name = 'EasyEnsembleClassifier' classifier_orig = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) @ignore_warnings(category=FutureWarning) def check_classifiers_one_label(name, classifier_orig): error_string_fit = "Classifier can't train when only one class is present." error_string_predict = "Classifier can't predict when only one class is present." classifier = clone(classifier_orig) rnd = np.random.RandomState(0) X_train = rnd.uniform(size=(10, 3)) X_test = rnd.uniform(size=(10, 3)) X_train, X_test = _enforce_estimator_tags_X(classifier, X_train, X_test=X_test) y = np.ones(10) # catch deprecation warnings with ignore_warnings(category=FutureWarning): with raises( ValueError, match="class", may_pass=True, err_msg=error_string_fit ) as cm: > classifier.fit(X_train, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2656: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2653: in check_classifiers_one_label with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Classifier can't train when only one class is present. /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_supervised_y_2d] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3271: in check_supervised_y_2d estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_methods_sample_order_invariance] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1870: in check_methods_sample_order_invariance estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_methods_subset_invariance] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1830: in check_methods_subset_invariance estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit_idempotent] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4298: in check_fit_idempotent estimator.fit(X_train, y_train) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit_check_is_fitted] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4358: in check_fit_check_is_fitted estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_n_features_in] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4388: in check_n_features_in estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_fit2d_predict1d] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1787: in check_fit2d_predict1d estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_fit_returns_self] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3208: in check_estimators_fit_returns_self assert estimator.fit(X, y) is estimator /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_readonly_memmap_input] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3227: in check_readonly_memmap_input assert estimator.fit(X, y) is estimator /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_n_features_in_after_fitting] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4453: in check_n_features_in_after_fitting estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_positive_only_tag_during_fit] _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) @ignore_warnings(category=FutureWarning) def check_positive_only_tag_during_fit(name, estimator_orig): """Test that the estimator correctly sets the tags.input_tags.positive_only If the tag is False, the estimator should accept negative input regardless of the tags.input_tags.pairwise flag. """ estimator = clone(estimator_orig) tags = get_tags(estimator) X, y = load_iris(return_X_y=True) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 0) X = _enforce_estimator_tags_X(estimator, X) X -= X.mean() if tags.input_tags.positive_only: with raises(ValueError, match="Negative values in data"): estimator.fit(X, y) else: # This should pass try: > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4016: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) @ignore_warnings(category=FutureWarning) def check_positive_only_tag_during_fit(name, estimator_orig): """Test that the estimator correctly sets the tags.input_tags.positive_only If the tag is False, the estimator should accept negative input regardless of the tags.input_tags.pairwise flag. """ estimator = clone(estimator_orig) tags = get_tags(estimator) X, y = load_iris(return_X_y=True) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 0) X = _enforce_estimator_tags_X(estimator, X) X -= X.mean() if tags.input_tags.positive_only: with raises(ValueError, match="Negative values in data"): estimator.fit(X, y) else: # This should pass try: estimator.fit(X, y) except Exception as e: err_msg = ( f"Estimator {repr(name)} raised {e.__class__.__name__} unexpectedly." " This happens when passing negative input values as X." " If negative values are not supported for this estimator instance," " then the tags.input_tags.positive_only tag needs to be set to True." ) > raise AssertionError(err_msg) from e E AssertionError: Estimator 'Pipeline' raised DeprecationWarning unexpectedly. This happens when passing negative input values as X. If negative values are not supported for this estimator instance, then the tags.input_tags.positive_only tag needs to be set to True. /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4024: AssertionError _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_complex_data] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1686: in check_complex_data estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_dtype_object] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1643: in check_dtype_object estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_empty_data_messages] _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) @ignore_warnings(category=FutureWarning) def check_estimators_empty_data_messages(name, estimator_orig): e = clone(estimator_orig) set_random_state(e, 1) X_zero_samples = np.empty(0).reshape(0, 3) # The precise message can change depending on whether X or y is # validated first. Let us test the type of exception only: err_msg = ( f"The estimator {name} does not raise a ValueError when an " "empty data is used to train. Perhaps use check_array in train." ) with raises(ValueError, err_msg=err_msg): > e.fit(X_zero_samples, []) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2319: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2318: in check_estimators_empty_data_messages with raises(ValueError, err_msg=err_msg): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: The estimator Pipeline does not raise a ValueError when an empty data is used to train. Perhaps use check_array in train. /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimators_nan_inf] _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) @ignore_warnings(category=FutureWarning) def check_estimators_nan_inf(name, estimator_orig): # Checks that Estimator X's do not contain NaN or inf. rnd = np.random.RandomState(0) X_train_finite = _enforce_estimator_tags_X( estimator_orig, rnd.uniform(size=(10, 3)) ) X_train_nan = rnd.uniform(size=(10, 3)) X_train_nan[0, 0] = np.nan X_train_inf = rnd.uniform(size=(10, 3)) X_train_inf[0, 0] = np.inf y = np.ones(10) y[:5] = 0 y = _enforce_estimator_tags_y(estimator_orig, y) error_string_fit = f"Estimator {name} doesn't check for NaN and inf in fit." error_string_predict = f"Estimator {name} doesn't check for NaN and inf in predict." error_string_transform = ( f"Estimator {name} doesn't check for NaN and inf in transform." ) for X_train in [X_train_nan, X_train_inf]: # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) set_random_state(estimator, 1) # try to fit with raises(ValueError, match=["inf", "NaN"], err_msg=error_string_fit): > estimator.fit(X_train, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2356: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2355: in check_estimators_nan_inf with raises(ValueError, match=["inf", "NaN"], err_msg=error_string_fit): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator Pipeline doesn't check for NaN and inf in fit. /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_sparse_tag] _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) def check_estimator_sparse_tag(name, estimator_orig): """Check that estimator tag related with accepting sparse data is properly set.""" if SPARSE_ARRAY_PRESENT: sparse_container = sparse.csr_array else: sparse_container = sparse.csr_matrix estimator = clone(estimator_orig) rng = np.random.RandomState(0) n_samples = 15 if name == "SpectralCoclustering" else 40 X = rng.uniform(size=(n_samples, 3)) X[X < 0.6] = 0 y = rng.randint(0, 3, size=n_samples) X = _enforce_estimator_tags_X(estimator, X) y = _enforce_estimator_tags_y(estimator, y) X = sparse_container(X) tags = get_tags(estimator) if tags.input_tags.sparse: try: > estimator.fit(X, y) # should pass /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1255: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) def check_estimator_sparse_tag(name, estimator_orig): """Check that estimator tag related with accepting sparse data is properly set.""" if SPARSE_ARRAY_PRESENT: sparse_container = sparse.csr_array else: sparse_container = sparse.csr_matrix estimator = clone(estimator_orig) rng = np.random.RandomState(0) n_samples = 15 if name == "SpectralCoclustering" else 40 X = rng.uniform(size=(n_samples, 3)) X[X < 0.6] = 0 y = rng.randint(0, 3, size=n_samples) X = _enforce_estimator_tags_X(estimator, X) y = _enforce_estimator_tags_y(estimator, y) X = sparse_container(X) tags = get_tags(estimator) if tags.input_tags.sparse: try: estimator.fit(X, y) # should pass except Exception as e: err_msg = ( f"Estimator {name} raised an exception. " f"The tag self.input_tags.sparse={tags.input_tags.sparse} " "might not be consistent with the estimator's ability to " "handle sparse data (i.e. controlled by the parameter `accept_sparse`" " in `validate_data` or `check_array` functions)." ) > raise AssertionError(err_msg) from e E AssertionError: Estimator Pipeline raised an exception. The tag self.input_tags.sparse=True might not be consistent with the estimator's ability to handle sparse data (i.e. controlled by the parameter `accept_sparse` in `validate_data` or `check_array` functions). /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1264: AssertionError _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_sparse_array] _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) sparse_type = def _check_estimator_sparse_container(name, estimator_orig, sparse_type): rng = np.random.RandomState(0) X = rng.uniform(size=(40, 3)) X[X < 0.6] = 0 X = _enforce_estimator_tags_X(estimator_orig, X) y = (4 * rng.uniform(size=X.shape[0])).astype(np.int32) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) tags = get_tags(estimator_orig) for matrix_format, X in _generate_sparse_data(sparse_type(X)): # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) if name in ["Scaler", "StandardScaler"]: estimator.set_params(with_mean=False) # fit and predict if "64" in matrix_format: err_msg = ( f"Estimator {name} doesn't seem to support {matrix_format} " "matrix, and is not failing gracefully, e.g. by using " "check_array(X, accept_large_sparse=False)." ) else: err_msg = ( f"Estimator {name} doesn't seem to fail gracefully on sparse " "data: error message should state explicitly that sparse " "input is not supported if this is not the case, e.g. by using " "check_array(X, accept_sparse=False)." ) with raises( (TypeError, ValueError), match=["sparse", "Sparse"], may_pass=True, err_msg=err_msg, ): with ignore_warnings(category=FutureWarning): > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1329: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1351: in check_estimator_sparse_array _check_estimator_sparse_container(name, estimator_orig, sparse.csr_array) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1322: in _check_estimator_sparse_container with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator Pipeline doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case, e.g. by using check_array(X, accept_sparse=False). /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_estimator_sparse_matrix] _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) sparse_type = def _check_estimator_sparse_container(name, estimator_orig, sparse_type): rng = np.random.RandomState(0) X = rng.uniform(size=(40, 3)) X[X < 0.6] = 0 X = _enforce_estimator_tags_X(estimator_orig, X) y = (4 * rng.uniform(size=X.shape[0])).astype(np.int32) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) tags = get_tags(estimator_orig) for matrix_format, X in _generate_sparse_data(sparse_type(X)): # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) if name in ["Scaler", "StandardScaler"]: estimator.set_params(with_mean=False) # fit and predict if "64" in matrix_format: err_msg = ( f"Estimator {name} doesn't seem to support {matrix_format} " "matrix, and is not failing gracefully, e.g. by using " "check_array(X, accept_large_sparse=False)." ) else: err_msg = ( f"Estimator {name} doesn't seem to fail gracefully on sparse " "data: error message should state explicitly that sparse " "input is not supported if this is not the case, e.g. by using " "check_array(X, accept_sparse=False)." ) with raises( (TypeError, ValueError), match=["sparse", "Sparse"], may_pass=True, err_msg=err_msg, ): with ignore_warnings(category=FutureWarning): > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1329: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1346: in check_estimator_sparse_matrix _check_estimator_sparse_container(name, estimator_orig, sparse.csr_matrix) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1322: in _check_estimator_sparse_container with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator Pipeline doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case, e.g. by using check_array(X, accept_sparse=False). /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_f_contiguous_array_estimator] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1367: in check_f_contiguous_array_estimator estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifier_data_not_an_array] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3726: in check_classifier_data_not_an_array check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type) /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:3778: in check_estimators_data_not_an_array estimator_1.fit(X_, y_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifiers_one_label] _ name = 'Pipeline' classifier_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) @ignore_warnings(category=FutureWarning) def check_classifiers_one_label(name, classifier_orig): error_string_fit = "Classifier can't train when only one class is present." error_string_predict = "Classifier can't predict when only one class is present." classifier = clone(classifier_orig) rnd = np.random.RandomState(0) X_train = rnd.uniform(size=(10, 3)) X_test = rnd.uniform(size=(10, 3)) X_train, X_test = _enforce_estimator_tags_X(classifier, X_train, X_test=X_test) y = np.ones(10) # catch deprecation warnings with ignore_warnings(category=FutureWarning): with raises( ValueError, match="class", may_pass=True, err_msg=error_string_fit ) as cm: > classifier.fit(X_train, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2656: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:2653: in check_classifiers_one_label with raises( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Classifier can't train when only one class is present. /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifiers_regression_target] _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) @ignore_warnings(category=FutureWarning) def check_classifiers_regression_target(name, estimator_orig): # Check if classifier throws an exception when fed regression targets X, y = _regression_dataset() X = _enforce_estimator_tags_X(estimator_orig, X) e = clone(estimator_orig) err_msg = ( "When a classifier is passed a continuous target, it should raise a ValueError" " with a message containing 'Unknown label type: ' or a message indicating that" " a continuous target is passed and the message should include the word" " 'continuous'" ) msg = "Unknown label type: |continuous" if not get_tags(e).no_validation: with raises(ValueError, match=msg, err_msg=err_msg): > e.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4167: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4166: in check_classifiers_regression_target with raises(ValueError, match=msg, err_msg=err_msg): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: When a classifier is passed a continuous target, it should raise a ValueError with a message containing 'Unknown label type: ' or a message indicating that a continuous target is passed and the message should include the word 'continuous' /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_supervised_y_no_nan] _ name = 'Pipeline' estimator_orig = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) @ignore_warnings(category=FutureWarning) def check_supervised_y_no_nan(name, estimator_orig): # Checks that the Estimator targets are not NaN. estimator = clone(estimator_orig) rng = np.random.RandomState(888) X = rng.standard_normal(size=(10, 5)) for value in [np.nan, np.inf]: y = np.full(10, value) y = _enforce_estimator_tags_y(estimator, y) module_name = estimator.__module__ if module_name.startswith("sklearn.") and not ( "test_" in module_name or module_name.endswith("_testing") ): # In scikit-learn we want the error message to mention the input # name and be specific about the kind of unexpected value. if np.isinf(value): match = ( r"Input (y|Y) contains infinity or a value too large for" r" dtype\('float64'\)." ) else: match = r"Input (y|Y) contains NaN." else: # Do not impose a particular error message to third-party libraries. match = None err_msg = ( f"Estimator {name} should have raised error on fitting array y with inf" " value." ) with raises(ValueError, match=match, err_msg=err_msg): > estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1038: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning The above exception was the direct cause of the following exception: estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1037: in check_supervised_y_no_nan with raises(ValueError, match=match, err_msg=err_msg): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = exc_type = exc_value = DeprecationWarning("'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead") _ = def __exit__(self, exc_type, exc_value, _): # see # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000 if exc_type is None: # No exception was raised in the block if self.may_pass: return True # CM is happy else: err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}" raise AssertionError(err_msg) if not any( issubclass(exc_type, expected_type) for expected_type in self.expected_exc_types ): if self.err_msg is not None: > raise AssertionError(self.err_msg) from exc_value E AssertionError: Estimator Pipeline should have raised error on fitting array y with inf value. /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:1104: AssertionError _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_decision_proba_consistency] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4190: in check_decision_proba_consistency estimator.fit(X_train, y_train) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_methods_sample_order_invariance] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1870: in check_methods_sample_order_invariance estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_methods_subset_invariance] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1830: in check_methods_subset_invariance estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit_idempotent] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4298: in check_fit_idempotent estimator.fit(X_train, y_train) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit_check_is_fitted] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4358: in check_fit_check_is_fitted estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_n_features_in] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:4388: in check_n_features_in estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_fit2d_predict1d] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/utils/_testing.py:147: in wrapper return fn(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1787: in check_fit2d_predict1d estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_sparse_tag] _ estimator = SMOTEN(random_state=42) check = functools.partial(, 'SMOTEN') request = > @parametrize_with_checks_sklearn( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:46: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'SMOTEN', estimator_orig = SMOTEN(random_state=42) def check_estimator_sparse_tag(name, estimator_orig): """Check that estimator tag related with accepting sparse data is properly set.""" if SPARSE_ARRAY_PRESENT: sparse_container = sparse.csr_array else: sparse_container = sparse.csr_matrix estimator = clone(estimator_orig) rng = np.random.RandomState(0) n_samples = 15 if name == "SpectralCoclustering" else 40 X = rng.uniform(size=(n_samples, 3)) X[X < 0.6] = 0 y = rng.randint(0, 3, size=n_samples) X = _enforce_estimator_tags_X(estimator, X) y = _enforce_estimator_tags_y(estimator, y) X = sparse_container(X) tags = get_tags(estimator) if tags.input_tags.sparse: try: estimator.fit(X, y) # should pass except Exception as e: err_msg = ( f"Estimator {name} raised an exception. " f"The tag self.input_tags.sparse={tags.input_tags.sparse} " "might not be consistent with the estimator's ability to " "handle sparse data (i.e. controlled by the parameter `accept_sparse`" " in `validate_data` or `check_array` functions)." ) raise AssertionError(err_msg) from e else: err_msg = ( f"Estimator {name} raised an exception. " "The estimator failed when fitted on sparse data in accordance " f"with its tag self.input_tags.sparse={tags.input_tags.sparse} " "but didn't raise the appropriate error: error message should " "state explicitly that sparse input is not supported if this is " "not the case, e.g. by using check_array(X, accept_sparse=False)." ) try: estimator.fit(X, y) # should fail with appropriate error except (ValueError, TypeError) as e: if re.search("[Ss]parse", str(e)): # Got the right error type and mentioning sparse issue return raise AssertionError(err_msg) from e except Exception as e: raise AssertionError(err_msg) from e > raise AssertionError( f"Estimator {name} didn't fail when fitted on sparse data " "but should have according to its tag " f"self.input_tags.sparse={tags.input_tags.sparse}. " f"The tag is inconsistent and must be fixed." ) E AssertionError: Estimator SMOTEN didn't fail when fitted on sparse data but should have according to its tag self.input_tags.sparse=False. The tag is inconsistent and must be fixed. /usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:1283: AssertionError _ test_estimators_imblearn[BalancedBaggingClassifier(random_state=42)-check_classifiers_with_encoded_labels] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'BalancedBaggingClassifier') request = > @parametrize_with_checks( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_imblearn(estimator, check, request): # Common tests for estimator instances with ignore_warnings( category=( FutureWarning, ConvergenceWarning, UserWarning, FutureWarning, ) ): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:63: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/utils/estimator_checks.py:645: in check_classifiers_with_encoded_labels classifier.fit(df, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_imblearn[EasyEnsembleClassifier(random_state=42)-check_classifiers_with_encoded_labels] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_imblearn(estimator, check, request): # Common tests for estimator instances with ignore_warnings( category=( FutureWarning, ConvergenceWarning, UserWarning, FutureWarning, ) ): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:63: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/utils/estimator_checks.py:645: in check_classifiers_with_encoded_labels classifier.fit(df, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_imblearn[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)-check_classifiers_with_encoded_labels] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) check = functools.partial(, 'EasyEnsembleClassifier') request = > @parametrize_with_checks( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_imblearn(estimator, check, request): # Common tests for estimator instances with ignore_warnings( category=( FutureWarning, ConvergenceWarning, UserWarning, FutureWarning, ) ): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:63: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/utils/estimator_checks.py:645: in check_classifiers_with_encoded_labels classifier.fit(df, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_imblearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])-check_classifier_on_multilabel_or_multioutput_targets] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_imblearn(estimator, check, request): # Common tests for estimator instances with ignore_warnings( category=( FutureWarning, ConvergenceWarning, UserWarning, FutureWarning, ) ): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:63: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/utils/estimator_checks.py:620: in check_classifier_on_multilabel_or_multioutput_targets estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_estimators_imblearn[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0,sampling_strategy={'setosa':20,'virginica':20})),('logistic',LogisticRegression())])-check_classifiers_with_encoded_labels] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0, sa... 'virginica': 20})), ('logistic', LogisticRegression())]) check = functools.partial(, 'Pipeline') request = > @parametrize_with_checks( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators_imblearn(estimator, check, request): # Common tests for estimator instances with ignore_warnings( category=( FutureWarning, ConvergenceWarning, UserWarning, FutureWarning, ) ): _set_checking_parameters(estimator) > check(estimator) imblearn/tests/test_common.py:63: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/utils/estimator_checks.py:645: in check_classifiers_with_encoded_labels classifier.fit(df, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_pandas_column_name_consistency[BalancedBaggingClassifier(random_state=42)] _ estimator = BalancedBaggingClassifier(n_estimators=5, random_state=42) @pytest.mark.parametrize( "estimator", _tested_estimators(), ids=_get_check_estimator_ids ) def test_pandas_column_name_consistency(estimator): _set_checking_parameters(estimator) with ignore_warnings(category=(FutureWarning)): with warnings.catch_warnings(record=True) as record: > check_dataframe_column_names_consistency( estimator.__class__.__name__, estimator ) imblearn/tests/test_common.py:98: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/utils/estimator_checks.py:768: in check_dataframe_column_names_consistency estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_bagging.py:337: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_bagging.py:352: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_pandas_column_name_consistency[EasyEnsembleClassifier(random_state=42)] _ estimator = EasyEnsembleClassifier(n_estimators=5, random_state=42) @pytest.mark.parametrize( "estimator", _tested_estimators(), ids=_get_check_estimator_ids ) def test_pandas_column_name_consistency(estimator): _set_checking_parameters(estimator) with ignore_warnings(category=(FutureWarning)): with warnings.catch_warnings(record=True) as record: > check_dataframe_column_names_consistency( estimator.__class__.__name__, estimator ) imblearn/tests/test_common.py:98: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/utils/estimator_checks.py:768: in check_dataframe_column_names_consistency estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_pandas_column_name_consistency[EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42),random_state=42)] _ estimator = EasyEnsembleClassifier(estimator=DecisionTreeClassifier(random_state=42), n_estimators=5, random_state=42) @pytest.mark.parametrize( "estimator", _tested_estimators(), ids=_get_check_estimator_ids ) def test_pandas_column_name_consistency(estimator): _set_checking_parameters(estimator) with ignore_warnings(category=(FutureWarning)): with warnings.catch_warnings(record=True) as record: > check_dataframe_column_names_consistency( estimator.__class__.__name__, estimator ) imblearn/tests/test_common.py:98: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/utils/estimator_checks.py:768: in check_dataframe_column_names_consistency estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/ensemble/_easy_ensemble.py:271: in fit return super().fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:63: in inner_f return f(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:389: in fit return self._fit(X, y, max_samples=self.max_samples, **fit_params) imblearn/ensemble/_easy_ensemble.py:277: in _fit return super()._fit(X, y, self.max_samples) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:532: in _fit all_results = Parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:197: in _parallel_build_estimators estimator_fit(X_, y_, **fit_params_) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_pandas_column_name_consistency[Pipeline(steps=[('sampler',RandomUnderSampler(random_state=0)),('logistic',LogisticRegression())])] _ estimator = Pipeline(steps=[('sampler', RandomUnderSampler(random_state=0)), ('logistic', LogisticRegression())]) @pytest.mark.parametrize( "estimator", _tested_estimators(), ids=_get_check_estimator_ids ) def test_pandas_column_name_consistency(estimator): _set_checking_parameters(estimator) with ignore_warnings(category=(FutureWarning)): with warnings.catch_warnings(record=True) as record: > check_dataframe_column_names_consistency( estimator.__class__.__name__, estimator ) imblearn/tests/test_common.py:98: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/utils/estimator_checks.py:768: in check_dataframe_column_names_consistency estimator.fit(X, y) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ___________________________ test_pipeline_init_tuple ___________________________ def test_pipeline_init_tuple(): # Pipeline accepts steps as tuple X = np.array([[1, 2]]) pipe = Pipeline((("transf", Transf()), ("clf", FitParamT()))) > pipe.fit(X, y=None) imblearn/tests/test_pipeline.py:204: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________________________ test_pipeline_methods_anova __________________________ def test_pipeline_methods_anova(): # Test the various methods of the pipeline (anova). iris = load_iris() X = iris.data y = iris.target # Test with Anova + LogisticRegression clf = LogisticRegression() filter1 = SelectKBest(f_classif, k=2) pipe = Pipeline([("anova", filter1), ("logistic", clf)]) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:290: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ___________________________ test_pipeline_fit_params ___________________________ def test_pipeline_fit_params(): # Test that the pipeline can take fit parameters pipe = Pipeline([("transf", Transf()), ("clf", FitParamT())]) > pipe.fit(X=None, y=None, clf__should_succeed=True) imblearn/tests/test_pipeline.py:300: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________________ test_pipeline_sample_weight_supported _____________________ def test_pipeline_sample_weight_supported(): # Pipeline should pass sample_weight X = np.array([[1, 2]]) pipe = Pipeline([("transf", Transf()), ("clf", FitParamT())]) > pipe.fit(X, y=None) imblearn/tests/test_pipeline.py:315: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ___________________ test_pipeline_sample_weight_unsupported ____________________ def test_pipeline_sample_weight_unsupported(): # When sample_weight is None it shouldn't be passed X = np.array([[1, 2]]) pipe = Pipeline([("transf", Transf()), ("clf", Mult())]) > pipe.fit(X, y=None) imblearn/tests/test_pipeline.py:326: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________________ test_pipeline_methods_pca_svm _________________________ def test_pipeline_methods_pca_svm(): # Test the various methods of the pipeline (pca + svm). iris = load_iris() X = iris.data y = iris.target # Test with PCA + SVC clf = SVC(gamma="scale", probability=True, random_state=0) pca = PCA(svd_solver="full", n_components="mle", whiten=True) pipe = Pipeline([("pca", pca), ("svc", clf)]) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:353: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ___________________ test_pipeline_methods_preprocessing_svm ____________________ def test_pipeline_methods_preprocessing_svm(): # Test the various methods of the pipeline (preprocessing + svm). iris = load_iris() X = iris.data y = iris.target n_samples = X.shape[0] n_classes = len(np.unique(y)) scaler = StandardScaler() pca = PCA(n_components=2, svd_solver="randomized", whiten=True) clf = SVC( gamma="scale", probability=True, random_state=0, decision_function_shape="ovr", ) for preprocessing in [scaler, pca]: pipe = Pipeline([("preprocess", preprocessing), ("svc", clf)]) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:378: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________________________ test_fit_predict_on_pipeline _________________________ def test_fit_predict_on_pipeline(): # test that the fit_predict method is implemented on a pipeline # test that the fit_predict on pipeline yields same results as applying # transform and clustering steps separately iris = load_iris() scaler = StandardScaler() km = KMeans(random_state=0, n_init=10) # As pipeline doesn't clone estimators on construction, # it must have its own estimators scaler_for_pipeline = StandardScaler() km_for_pipeline = KMeans(random_state=0, n_init=10) # first compute the transform and clustering step separately scaled = scaler.fit_transform(iris.data) separate_pred = km.fit_predict(scaled) # use a pipeline to do the transform and clustering in one step pipe = Pipeline([("scaler", scaler_for_pipeline), ("Kmeans", km_for_pipeline)]) > pipeline_pred = pipe.fit_predict(iris.data) imblearn/tests/test_pipeline.py:414: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:792: in fit_predict Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_fit_predict_with_intermediate_fit_params _________________ def test_fit_predict_with_intermediate_fit_params(): # tests that Pipeline passes fit_params to intermediate steps # when fit_predict is invoked pipe = Pipeline([("transf", TransfFitParams()), ("clf", FitParamT())]) > pipe.fit_predict( X=None, y=None, transf__should_get_this=True, clf__should_succeed=True ) imblearn/tests/test_pipeline.py:434: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:792: in fit_predict Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ___________________________ test_pipeline_transform ____________________________ def test_pipeline_transform(): # Test whether pipeline works with a transformer at the end. # Also test pipeline.transform and pipeline.inverse_transform iris = load_iris() X = iris.data pca = PCA(n_components=2, svd_solver="full") pipeline = Pipeline([("pca", pca)]) # test transform and fit_transform: > X_trans = pipeline.fit(X).transform(X) imblearn/tests/test_pipeline.py:451: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________________________ test_pipeline_fit_transform __________________________ def test_pipeline_fit_transform(): # Test whether pipeline works with a transformer missing fit_transform iris = load_iris() X = iris.data y = iris.target transf = Transf() pipeline = Pipeline([("mock", transf)]) # test fit_transform: > X_trans = pipeline.fit_transform(X, y) imblearn/tests/test_pipeline.py:471: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:585: in fit_transform Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________________ test_pipeline_correctly_adjusts_steps[None] __________________ passthrough = None @pytest.mark.parametrize("passthrough", [None, "passthrough"]) def test_pipeline_correctly_adjusts_steps(passthrough): X = np.array([[1]]) y = np.array([1]) mult2 = Mult(mult=2) mult3 = Mult(mult=3) mult5 = Mult(mult=5) pipeline = Pipeline( [("m2", mult2), ("bad", passthrough), ("m3", mult3), ("m5", mult5)] ) > pipeline.fit(X, y) imblearn/tests/test_pipeline.py:514: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ______________ test_pipeline_correctly_adjusts_steps[passthrough] ______________ passthrough = 'passthrough' @pytest.mark.parametrize("passthrough", [None, "passthrough"]) def test_pipeline_correctly_adjusts_steps(passthrough): X = np.array([[1]]) y = np.array([1]) mult2 = Mult(mult=2) mult3 = Mult(mult=3) mult5 = Mult(mult=5) pipeline = Pipeline( [("m2", mult2), ("bad", passthrough), ("m3", mult3), ("m5", mult5)] ) > pipeline.fit(X, y) imblearn/tests/test_pipeline.py:514: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ___________________ test_set_pipeline_step_passthrough[None] ___________________ passthrough = None @pytest.mark.parametrize("passthrough", [None, "passthrough"]) def test_set_pipeline_step_passthrough(passthrough): # Test setting Pipeline steps to None X = np.array([[1]]) y = np.array([1]) mult2 = Mult(mult=2) mult3 = Mult(mult=3) mult5 = Mult(mult=5) def make(): return Pipeline([("m2", mult2), ("m3", mult3), ("last", mult5)]) pipeline = make() exp = 2 * 3 * 5 > assert_array_equal([[exp]], pipeline.fit_transform(X, y)) imblearn/tests/test_pipeline.py:535: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:585: in fit_transform Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________ test_set_pipeline_step_passthrough[passthrough] ________________ passthrough = 'passthrough' @pytest.mark.parametrize("passthrough", [None, "passthrough"]) def test_set_pipeline_step_passthrough(passthrough): # Test setting Pipeline steps to None X = np.array([[1]]) y = np.array([1]) mult2 = Mult(mult=2) mult3 = Mult(mult=3) mult5 = Mult(mult=5) def make(): return Pipeline([("m2", mult2), ("m3", mult3), ("last", mult5)]) pipeline = make() exp = 2 * 3 * 5 > assert_array_equal([[exp]], pipeline.fit_transform(X, y)) imblearn/tests/test_pipeline.py:535: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:585: in fit_transform Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________________________ test_classes_property _____________________________ def test_classes_property(): iris = load_iris() X = iris.data y = iris.target reg = make_pipeline(SelectKBest(k=1), LinearRegression()) > reg.fit(X, y) imblearn/tests/test_pipeline.py:649: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________________ test_pipeline_memory_transformer _______________________ def test_pipeline_memory_transformer(): iris = load_iris() X = iris.data y = iris.target cachedir = mkdtemp() try: memory = Memory(cachedir, verbose=10) # Test with Transformer + SVC clf = SVC(gamma="scale", probability=True, random_state=0) transf = DummyTransf() pipe = Pipeline([("transf", clone(transf)), ("svc", clf)]) cached_pipe = Pipeline([("transf", transf), ("svc", clf)], memory=memory) # Memoize the transformer at the first fit > cached_pipe.fit(X, y) imblearn/tests/test_pipeline.py:677: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1114: in cache cls = AsyncMemorizedFunc if asyncio.iscoroutinefunction(func) else MemorizedFunc /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________________________ test_pipeline_memory_sampler _________________________ def test_pipeline_memory_sampler(): X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) cachedir = mkdtemp() try: memory = Memory(cachedir, verbose=10) # Test with Transformer + SVC clf = SVC(gamma="scale", probability=True, random_state=0) transf = DummySampler() pipe = Pipeline([("transf", clone(transf)), ("svc", clf)]) cached_pipe = Pipeline([("transf", transf), ("svc", clf)], memory=memory) # Memoize the transformer at the first fit > cached_pipe.fit(X, y) imblearn/tests/test_pipeline.py:752: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1114: in cache cls = AsyncMemorizedFunc if asyncio.iscoroutinefunction(func) else MemorizedFunc /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ______________________ test_pipeline_methods_pca_rus_svm _______________________ def test_pipeline_methods_pca_rus_svm(): # Test the various methods of the pipeline (pca + svm). X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) # Test with PCA + SVC clf = SVC(gamma="scale", probability=True, random_state=0) pca = PCA() rus = RandomUnderSampler(random_state=0) pipe = Pipeline([("pca", pca), ("rus", rus), ("svc", clf)]) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:824: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ______________________ test_pipeline_methods_rus_pca_svm _______________________ def test_pipeline_methods_rus_pca_svm(): # Test the various methods of the pipeline (pca + svm). X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) # Test with PCA + SVC clf = SVC(gamma="scale", probability=True, random_state=0) pca = PCA() rus = RandomUnderSampler(random_state=0) pipe = Pipeline([("rus", rus), ("pca", pca), ("svc", clf)]) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:851: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________________________ test_pipeline_sample _____________________________ def test_pipeline_sample(): # Test whether pipeline works with a sampler at the end. # Also test pipeline.sampler X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) rus = RandomUnderSampler(random_state=0) pipeline = Pipeline([("rus", rus)]) # test transform and fit_transform: > X_trans, y_trans = pipeline.fit_resample(X, y) imblearn/tests/test_pipeline.py:878: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:726: in fit_resample Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________________ test_pipeline_sample_transform ________________________ def test_pipeline_sample_transform(): # Test whether pipeline works with a sampler at the end. # Also test pipeline.sampler X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) rus = RandomUnderSampler(random_state=0) pca = PCA() pca2 = PCA() pipeline = Pipeline([("pca", pca), ("rus", rus), ("pca2", pca2)]) > pipeline.fit(X, y).transform(X) imblearn/tests/test_pipeline.py:918: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________________ test_pipeline_none_classifier _________________________ def test_pipeline_none_classifier(): # Test pipeline using None as preprocessing step and a classifier X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) clf = LogisticRegression(solver="lbfgs", random_state=0) pipe = make_pipeline(None, clf) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:937: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________________ test_pipeline_none_sampler_classifier _____________________ def test_pipeline_none_sampler_classifier(): # Test pipeline using None, RUS and a classifier X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) clf = LogisticRegression(solver="lbfgs", random_state=0) rus = RandomUnderSampler(random_state=0) pipe = make_pipeline(None, rus, clf) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:961: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________________ test_pipeline_sampler_none_classifier _____________________ def test_pipeline_sampler_none_classifier(): # Test pipeline using RUS, None and a classifier X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) clf = LogisticRegression(solver="lbfgs", random_state=0) rus = RandomUnderSampler(random_state=0) pipe = make_pipeline(rus, None, clf) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:985: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ______________________ test_pipeline_none_sampler_sample _______________________ def test_pipeline_none_sampler_sample(): # Test pipeline using None step and a sampler X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) rus = RandomUnderSampler(random_state=0) pipe = make_pipeline(None, rus) > pipe.fit_resample(X, y) imblearn/tests/test_pipeline.py:1009: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:726: in fit_resample Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________________ test_pipeline_none_transformer ________________________ def test_pipeline_none_transformer(): # Test pipeline using None and a transformer that implements transform and # inverse_transform X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) pca = PCA(whiten=True) pipe = make_pipeline(None, pca) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:1030: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________________ test_pipeline_methods_anova_rus ________________________ def test_pipeline_methods_anova_rus(): # Test the various methods of the pipeline (anova). X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=5000, random_state=0, ) # Test with RandomUnderSampling + Anova + LogisticRegression clf = LogisticRegression(solver="lbfgs") rus = RandomUnderSampler(random_state=0) filter1 = SelectKBest(f_classif, k=2) pipe = Pipeline([("rus", rus), ("anova", filter1), ("logistic", clf)]) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:1055: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning __________ test_pipeline_fit_then_sample_with_sampler_last_estimator ___________ def test_pipeline_fit_then_sample_with_sampler_last_estimator(): X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=50000, random_state=0, ) rus = RandomUnderSampler(random_state=42) enn = ENN() pipeline = make_pipeline(rus, enn) > X_fit_resample_resampled, y_fit_resample_resampled = pipeline.fit_resample(X, y) imblearn/tests/test_pipeline.py:1124: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:726: in fit_resample Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____ test_pipeline_fit_then_sample_3_samplers_with_sampler_last_estimator _____ def test_pipeline_fit_then_sample_3_samplers_with_sampler_last_estimator(): X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=50000, random_state=0, ) rus = RandomUnderSampler(random_state=42) enn = ENN() pipeline = make_pipeline(rus, enn, rus) > X_fit_resample_resampled, y_fit_resample_resampled = pipeline.fit_resample(X, y) imblearn/tests/test_pipeline.py:1149: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:726: in fit_resample Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _______________________ test_predict_with_predict_params _______________________ def test_predict_with_predict_params(): # tests that Pipeline passes predict_params to the final estimator # when predict is invoked pipe = Pipeline([("transf", Transf()), ("clf", DummyEstimatorParams())]) > pipe.fit(None, None) imblearn/tests/test_pipeline.py:1173: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ____________________ test_resampler_last_stage_passthrough _____________________ def test_resampler_last_stage_passthrough(): X, y = make_classification( n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=50000, random_state=0, ) rus = RandomUnderSampler(random_state=42) pipe = make_pipeline(rus, None) > pipe.fit_resample(X, y) imblearn/tests/test_pipeline.py:1194: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:726: in fit_resample Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning __________________ test_pipeline_score_samples_pca_lof_binary __________________ def test_pipeline_score_samples_pca_lof_binary(): X, y = make_classification( n_classes=2, class_sep=2, weights=[0.3, 0.7], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=500, random_state=0, ) # Test that the score_samples method is implemented on a pipeline. # Test that the score_samples method on pipeline yields same results as # applying transform and score_samples steps separately. rus = RandomUnderSampler(random_state=42) pca = PCA(svd_solver="full", n_components="mle", whiten=True) lof = LocalOutlierFactor(novelty=True) pipe = Pipeline([("rus", rus), ("pca", pca), ("lof", lof)]) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:1217: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _____________ test_score_samples_on_pipeline_without_score_samples _____________ def test_score_samples_on_pipeline_without_score_samples(): X = np.array([[1], [2]]) y = np.array([1, 2]) # Test that a pipeline does not have score_samples method when the final # step of the pipeline does not have score_samples defined. pipe = make_pipeline(LogisticRegression()) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:1232: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est0-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$-fit] _ est = Pipeline(steps=[('transf', ), ('clf', FitParamT())]) method = 'fit' pattern = '\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcfbda12b0> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est1-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$-fit_predict] _ est = Pipeline(steps=[('transf', ), ('clf', FitParamT())]) method = 'fit_predict' pattern = '\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcf3136c10> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:792: in fit_predict Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est2-\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$-fit] _ est = Pipeline(steps=[('transf', ), ('noop', None), ('clf', FitParamT())]) method = 'fit' pattern = '\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcf3137610> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est3-\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$-fit_predict] _ est = Pipeline(steps=[('transf', ), ('noop', None), ('clf', FitParamT())]) method = 'fit_predict' pattern = '\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcfc1e6650> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:792: in fit_predict Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est4-\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$-fit] _ est = Pipeline(steps=[('transf', ), ('noop', 'passthrough'), ('clf', FitParamT())]) method = 'fit' pattern = '\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcfc1e50f0> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est5-\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$-fit_predict] _ est = Pipeline(steps=[('transf', ), ('noop', 'passthrough'), ('clf', FitParamT())]) method = 'fit_predict' pattern = '\\[Pipeline\\].*\\(step 1 of 3\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 3\\) Processing noop.* total=.*\\n\\[Pipeline\\].*\\(step 3 of 3\\) Processing clf.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcf3059eb0> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:792: in fit_predict Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est6-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$-fit] _ est = Pipeline(steps=[('transf', ), ('clf', None)]) method = 'fit' pattern = '\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcfc069f20> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est7-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$-fit_transform] _ est = Pipeline(steps=[('transf', ), ('clf', None)]) method = 'fit_transform' pattern = '\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing clf.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcfc06b570> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:585: in fit_transform Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est8-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$-fit] _ est = Pipeline(steps=[('transf', None), ('mult', Mult())]), method = 'fit' pattern = '\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcf2be9c50> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est9-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$-fit_transform] _ est = Pipeline(steps=[('transf', None), ('mult', Mult())]) method = 'fit_transform' pattern = '\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcf2be9e50> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:585: in fit_transform Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est10-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$-fit] _ est = Pipeline(steps=[('transf', 'passthrough'), ('mult', Mult())]) method = 'fit' pattern = '\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcf12e07d0> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _ test_verbose[est11-\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$-fit_transform] _ est = Pipeline(steps=[('transf', 'passthrough'), ('mult', Mult())]) method = 'fit_transform' pattern = '\\[Pipeline\\].*\\(step 1 of 2\\) Processing transf.* total=.*\\n\\[Pipeline\\].*\\(step 2 of 2\\) Processing mult.* total=.*\\n$' capsys = <_pytest.capture.CaptureFixture object at 0x7fdcf12e1d60> @pytest.mark.parametrize("est, pattern, method", parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) > func(X, y) imblearn/tests/test_pipeline.py:1320: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:585: in fit_transform Xt, yt = self._fit(X, y, routed_params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________ test_pipeline_score_samples_pca_lof_multiclass ________________ def test_pipeline_score_samples_pca_lof_multiclass(): X, y = load_iris(return_X_y=True) sampling_strategy = {0: 50, 1: 30, 2: 20} X, y = make_imbalance(X, y, sampling_strategy=sampling_strategy) # Test that the score_samples method is implemented on a pipeline. # Test that the score_samples method on pipeline yields same results as # applying transform and score_samples steps separately. rus = RandomUnderSampler() pca = PCA(svd_solver="full", n_components="mle", whiten=True) lof = LocalOutlierFactor(novelty=True) pipe = Pipeline([("rus", rus), ("pca", pca), ("lof", lof)]) > pipe.fit(X, y) imblearn/tests/test_pipeline.py:1339: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ________________________ test_pipeline_with_set_output _________________________ def test_pipeline_with_set_output(): pd = pytest.importorskip("pandas") X, y = load_iris(return_X_y=True, as_frame=True) pipeline = make_pipeline( StandardScaler(), RandomUnderSampler(), LogisticRegression() ).set_output(transform="default") > pipeline.fit(X, y) imblearn/tests/test_pipeline.py:1360: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning __________________ test_transform_input_explicit_value_check ___________________ @pytest.mark.skipif( sklearn_version < parse_version("1.4"), reason="scikit-learn < 1.4 does not support transform_input", ) @config_context(enable_metadata_routing=True) def test_transform_input_explicit_value_check(): """Test that the right transformed values are passed to `fit`.""" class Transformer(TransformerMixin, BaseEstimator): def fit(self, X, y): self.fitted_ = True return self def transform(self, X): return X + 1 class Estimator(ClassifierMixin, BaseEstimator): def fit(self, X, y, X_val=None, y_val=None): assert_array_equal(X, np.array([[1, 2]])) assert_array_equal(y, np.array([0, 1])) assert_array_equal(X_val, np.array([[2, 3]])) assert_array_equal(y_val, np.array([0, 1])) return self X = np.array([[0, 1]]) y = np.array([0, 1]) X_val = np.array([[1, 2]]) y_val = np.array([0, 1]) pipe = Pipeline( [ ("transformer", Transformer()), ("estimator", Estimator().set_fit_request(X_val=True, y_val=True)), ], transform_input=["X_val"], ) > pipe.fit(X, y, X_val=X_val, y_val=y_val) imblearn/tests/test_pipeline.py:1468: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning ______________________ test_metadata_routing_with_sampler ______________________ def test_metadata_routing_with_sampler(): """Check that we can use a sampler with metadata routing.""" X, y = make_classification() cost_matrix = np.random.rand(X.shape[0], 2, 2) class CostSensitiveSampler(BaseSampler): def fit_resample(self, X, y, cost_matrix=None): return self._fit_resample(X, y, cost_matrix=cost_matrix) def _fit_resample(self, X, y, cost_matrix=None): self.cost_matrix_ = cost_matrix return X, y with config_context(enable_metadata_routing=True): sampler = CostSensitiveSampler().set_fit_resample_request(cost_matrix=True) pipeline = Pipeline([("sampler", sampler), ("model", LogisticRegression())]) > pipeline.fit(X, y, cost_matrix=cost_matrix) imblearn/tests/test_pipeline.py:1517: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/pipeline.py:518: in fit Xt, yt = self._fit(X, y, routed_params, raw_params=params) imblearn/pipeline.py:404: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning _________________________ test_iht_estimator_pipeline __________________________ def test_iht_estimator_pipeline(): """Check that we can pass a pipeline containing a classifier. Checking if we have a classifier should not be based on inheriting from `ClassifierMixin`. Non-regression test for: https://github.com/scikit-learn-contrib/imbalanced-learn/pull/1049 """ model = make_pipeline(GradientBoostingClassifier(random_state=RND_SEED)) iht = InstanceHardnessThreshold(estimator=model, random_state=RND_SEED) > X_resampled, y_resampled = iht.fit_resample(X, Y) imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py:110: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ imblearn/base.py:202: in fit_resample return super().fit_resample(X, y, **params) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) imblearn/base.py:105: in fit_resample output = self._fit_resample(X, y, **params) imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py:166: in _fit_resample probabilities = cross_val_predict( /usr/lib64/python3.14/site-packages/sklearn/utils/_param_validation.py:189: in wrapper return func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:1247: in cross_val_predict predictions = parallel( /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:77: in __call__ return super().__call__(iterable_with_config) /usr/lib/python3.14/site-packages/joblib/parallel.py:1986: in __call__ return output if self.return_generator else list(output) /usr/lib/python3.14/site-packages/joblib/parallel.py:1914: in _get_sequential_output res = func(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/utils/parallel.py:139: in __call__ return self.function(*args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:1332: in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) /usr/lib64/python3.14/site-packages/sklearn/base.py:1389: in wrapper return fit_method(estimator, *args, **kwargs) /usr/lib64/python3.14/site-packages/sklearn/pipeline.py:654: in fit Xt = self._fit(X, y, routed_params, raw_params=params) /usr/lib64/python3.14/site-packages/sklearn/pipeline.py:566: in _fit fit_transform_one_cached = memory.cache(_fit_transform_one) /usr/lib/python3.14/site-packages/joblib/memory.py:1104: in cache if asyncio.iscoroutinefunction(func) /usr/lib64/python3.14/asyncio/coroutines.py:23: in iscoroutinefunction warnings._deprecated("asyncio.iscoroutinefunction", _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'asyncio.iscoroutinefunction' message = '{name!r} is deprecated and slated for removal in Python {remove}; use inspect.iscoroutinefunction() instead' def _deprecated(name, message=_DEPRECATED_MSG, *, remove, _version=sys.version_info): """Warn that *name* is deprecated or should be removed. RuntimeError is raised if *remove* specifies a major/minor tuple older than the current Python version or the same version but past the alpha. The *message* argument is formatted with *name* and *remove* as a Python version tuple (e.g. (3, 11)). """ remove_formatted = f"{remove[0]}.{remove[1]}" if (_version[:2] > remove) or (_version[:2] == remove and _version[3] != "alpha"): msg = f"{name!r} was slated for removal after Python {remove_formatted} alpha" raise RuntimeError(msg) else: msg = message.format(name=name, remove=remove_formatted) > _wm.warn(msg, DeprecationWarning, stacklevel=3) E DeprecationWarning: 'asyncio.iscoroutinefunction' is deprecated and slated for removal in Python 3.16; use inspect.iscoroutinefunction() instead /usr/lib64/python3.14/_py_warnings.py:830: DeprecationWarning =============================== warnings summary =============================== imblearn/utils/_test_common/instance_generator.py:117 imblearn/utils/_test_common/instance_generator.py:117 imblearn/utils/_test_common/instance_generator.py:117 imblearn/utils/_test_common/instance_generator.py:117 imblearn/utils/_test_common/instance_generator.py:117 /builddir/build/BUILD/python-imbalanced-learn-0.13.0-build/imbalanced-learn-0.13.0/imblearn/utils/_test_common/instance_generator.py:117: SkipTestWarning: Can't instantiate estimator SMOTENC warnings.warn(msg, SkipTestWarning) imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimator_sparse_array] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[FunctionSampler()-check_estimator_sparse_matrix] /usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:1014: UserWarning: Can't check dok sparse matrix for nan or inf. array = _ensure_sparse_format( imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_supervised_y_no_nan] imblearn/tests/test_common.py::test_estimators_compatibility_sklearn[RUSBoostClassifier()-check_supervised_y_no_nan] /usr/lib64/python3.14/site-packages/sklearn/utils/_array_api.py:399: RuntimeWarning: invalid value encountered in cast return x.astype(dtype, copy=copy, casting=casting) -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html =========================== short test summary info ============================ SKIPPED [3] imblearn/keras/tests/test_generator.py:8: could not import 'keras': No module named 'keras' SKIPPED [2] imblearn/tensorflow/tests/test_generator.py:12: could not import 'tensorflow': No module named 'tensorflow' SKIPPED [2] ../../../../../usr/lib/python3.14/site-packages/_pytest/unittest.py:385: Zenodo dataset can not be loaded. SKIPPED [1] imblearn/tests/test_docstring_parameters.py:69: numpydoc is required to test the docstrings SKIPPED [1] imblearn/tests/test_pipeline.py:1480: scikit-learn >= 1.4 supports transform_input SKIPPED [1] ../../../../../usr/lib/python3.14/site-packages/_pytest/doctest.py:458: all tests skipped by +SKIP option = 323 failed, 2236 passed, 10 skipped, 39 deselected, 11 xfailed, 4 xpassed, 9 warnings in 122.02s (0:02:02) = error: Bad exit status from /var/tmp/rpm-tmp.LiqlcU (%check) RPM build errors: Bad exit status from /var/tmp/rpm-tmp.LiqlcU (%check) Finish: rpmbuild python-imbalanced-learn-0.13.0-2.fc43.src.rpm Finish: build phase for python-imbalanced-learn-0.13.0-2.fc43.src.rpm INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan INFO: /var/lib/mock/fedora-rawhide-x86_64-1748529486.082522/root/var/log/dnf5.log INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz /bin/tar: Removing leading `/' from member names ERROR: Exception(/var/lib/copr-rpmbuild/results/python-imbalanced-learn-0.13.0-2.fc43.src.rpm) Config(fedora-rawhide-x86_64) 2 minutes 24 seconds INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results INFO: Cleaning up build root ('cleanup_on_failure=True') Start: clean chroot INFO: unmounting tmpfs. Finish: clean chroot ERROR: Command failed: # /usr/bin/systemd-nspawn -q -M a19e5796194f4fc3973fdfc49f21476a -D /var/lib/mock/fedora-rawhide-x86_64-1748529486.082522/root -a -u mockbuild --capability=cap_ipc_lock --rlimit=RLIMIT_NOFILE=10240 --capability=cap_ipc_lock --bind=/tmp/mock-resolv.23jfn37z:/etc/resolv.conf --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/fuse --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin '--setenv=PROMPT_COMMAND=printf "\033]0;\007"' '--setenv=PS1= \s-\v\$ ' --setenv=LANG=C.UTF-8 --resolv-conf=off bash --login -c '/usr/bin/rpmbuild -ba --noprep --target x86_64 /builddir/build/originals/python-imbalanced-learn.spec' Copr build error: Build failed