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[MNT] extend CI to newer python versions, deprecate python versions 3… · codejasleen/openml-python@bd8ae77 · GitHub

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[MNT] extend CI to newer python versions, deprecate python versions 3.8, 3.9 after EOL, marking further failing tests as xfail (openml#1579)
The CI runs only on python versions 3.8 and 3.9 both of which have already reached end of life. This PR updates the python versions, deprecating any logic that runs tests on python versions 3.8 and 3.9, or `scikit-learn` versions of that age. #### Metadata Reference Issue: openml#1544 Depends on openml#1584 fofr a fix, which should be merged first. #### Details * The test matrix is updated to python versions, 3.10-3.13. * Further failing tests are skipped using `mark.xfail` with `reason="failures_issue_1544" ` for all the remaining failed tests (after openml#1572) in issue: openml#1544
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‎.github/workflows/test.yml‎

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@@ -23,74 +23,97 @@ jobs:
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test:
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name: (${{ matrix.os }}, Py${{ matrix.python-version }}, sk${{ matrix.scikit-learn }}, sk-only:${{ matrix.sklearn-only }})
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.11"]
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scikit-learn: ["1.3.*", "1.4.*", "1.5.*"]
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python-version: ["3.10", "3.11", "3.12", "3.13"]
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scikit-learn: ["1.3.*", "1.4.*", "1.5.*", "1.6.*", "1.7.*"]
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os: [ubuntu-latest]
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sklearn-only: ["true"]
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fail-fast: false
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exclude:
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# incompatible version combinations
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- python-version: "3.13"
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scikit-learn: "1.3.*"
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- python-version: "3.13"
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scikit-learn: "1.4.*"
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include:
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# Full test run on Windows
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- os: windows-latest
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python-version: "3.12"
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scikit-learn: "1.5.*"
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sklearn-only: "false"
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# Coverage run
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- os: ubuntu-latest
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python-version: "3.12"
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scikit-learn: "1.5.*"
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sklearn-only: "false"
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code-cov: true
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steps:
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- uses: actions/checkout@v6
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with:
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fetch-depth: 2
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- name: Setup Python ${{ matrix.python-version }}
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if: matrix.os != 'windows-latest' # windows-latest only uses preinstalled Python (3.9.13)
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install test dependencies
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- name: Install test dependencies and scikit-learn
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run: |
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python -m pip install --upgrade pip
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pip install -e .[test]
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- name: Install scikit-learn ${{ matrix.scikit-learn }}
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run: |
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pip install scikit-learn==${{ matrix.scikit-learn }}
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- name: Install numpy for Python 3.8
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# Python 3.8 & scikit-learn<0.24 requires numpy<=1.23.5
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if: ${{ matrix.python-version == '3.8' && matrix.scikit-learn == '0.23.1' }}
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run: |
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pip install numpy==1.23.5
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- name: "Install NumPy 1.x and SciPy <1.11 for scikit-learn < 1.4"
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if: ${{ contains(fromJSON('["1.0.*", "1.1.*", "1.2.*", "1.3.*"]'), matrix.scikit-learn) }}
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run: |
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# scipy has a change to the 'mode' behavior which breaks scikit-learn < 1.4
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# numpy 2.0 has several breaking changes
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pip install "numpy<2.0" "scipy<1.11"
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- name: Install scipy ${{ matrix.scipy }}
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if: ${{ matrix.scipy }}
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run: |
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pip install scipy==${{ matrix.scipy }}
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pip install -e .[test] scikit-learn==${{ matrix.scikit-learn }}
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- name: Store repository status
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id: status-before
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if: matrix.os != 'windows-latest'
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run: |
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git_status=$(git status --porcelain -b)
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echo "BEFORE=$git_status" >> $GITHUB_ENV
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echo "Repository status before tests: $git_status"
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- name: Show installed dependencies
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run: python -m pip list
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- name: Run tests on Ubuntu Test
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if: matrix.os == 'ubuntu-latest'
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run: |
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if [ ${{ matrix.code-cov }} ]; then codecov='--cov=openml --long --cov-report=xml'; fi
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# Most of the time, running only the scikit-learn tests is sufficient
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if [ ${{ matrix.sklearn-only }} = 'true' ]; then marks='sklearn and not production'; else marks='not production'; fi
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echo pytest -n 4 --durations=20 --dist load -sv $codecov -o log_cli=true -m "$marks"
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if [ "${{ matrix.code-cov }}" = "true" ]; then
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codecov="--cov=openml --long --cov-report=xml"
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fi
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if [ "${{ matrix.sklearn-only }}" = "true" ]; then
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marks="sklearn and not production"
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else
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marks="not production"
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fi
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pytest -n 4 --durations=20 --dist load -sv $codecov -o log_cli=true -m "$marks"
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- name: Run tests on Ubuntu Production
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if: matrix.os == 'ubuntu-latest'
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run: |
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if [ ${{ matrix.code-cov }} ]; then codecov='--cov=openml --long --cov-report=xml'; fi
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# Most of the time, running only the scikit-learn tests is sufficient
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if [ ${{ matrix.sklearn-only }} = 'true' ]; then marks='sklearn and production'; else marks='production'; fi
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echo pytest -n 4 --durations=20 --dist load -sv $codecov -o log_cli=true -m "$marks"
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if [ "${{ matrix.code-cov }}" = "true" ]; then
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codecov="--cov=openml --long --cov-report=xml"
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fi
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if [ "${{ matrix.sklearn-only }}" = "true" ]; then
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marks="sklearn and production"
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else
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marks="production"
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fi
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pytest -n 4 --durations=20 --dist load -sv $codecov -o log_cli=true -m "$marks"
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- name: Run tests on Windows
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if: matrix.os == 'windows-latest'
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run: | # we need a separate step because of the bash-specific if-statement in the previous one.
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pytest -n 4 --durations=20 --dist load -sv --reruns 5 --reruns-delay 1
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- name: Check for files left behind by test
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if: matrix.os != 'windows-latest' && always()
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run: |
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echo "Not all generated files have been deleted!"
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exit 1
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fi
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- name: Upload coverage
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if: matrix.code-cov && always()
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uses: codecov/codecov-action@v4

‎pyproject.toml‎

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"Operating System :: Unix",
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"Operating System :: MacOS",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.8",
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"Programming Language :: Python :: 3.9",
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: 3.11",
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"Programming Language :: Python :: 3.12",
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"Programming Language :: Python :: 3.13",
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"Programming Language :: Python :: 3.14",
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]
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license = { file = "LICENSE" }
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‎tests/test_runs/test_run_functions.py‎

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sentinel=sentinel,
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)
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@pytest.mark.skip(reason="failures_issue_1544")
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@pytest.mark.sklearn()
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def test_run_and_upload_logistic_regression(self):
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lr = LogisticRegression(solver="lbfgs", max_iter=1000)
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n_test_obs = self.TEST_SERVER_TASK_SIMPLE["n_test_obs"]
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self._run_and_upload_classification(lr, task_id, n_missing_vals, n_test_obs, "62501")
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@pytest.mark.skip(reason="failures_issue_1544")
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@pytest.mark.sklearn()
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def test_run_and_upload_linear_regression(self):
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lr = LinearRegression()
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n_test_obs = self.TEST_SERVER_TASK_REGRESSION["n_test_obs"]
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self._run_and_upload_regression(lr, task_id, n_missing_vals, n_test_obs, "62501")
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@pytest.mark.skip(reason="failures_issue_1544")
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@pytest.mark.sklearn()
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def test_run_and_upload_pipeline_dummy_pipeline(self):
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pipeline1 = Pipeline(
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n_test_obs = self.TEST_SERVER_TASK_SIMPLE["n_test_obs"]
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self._run_and_upload_classification(pipeline1, task_id, n_missing_vals, n_test_obs, "62501")
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@pytest.mark.skip(reason="failures_issue_1544")
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@pytest.mark.sklearn()
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@unittest.skipIf(
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Version(sklearn.__version__) < Version("0.20"),
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sentinel=sentinel,
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)
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@pytest.mark.skip(reason="failures_issue_1544")
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@pytest.mark.sklearn()
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@unittest.skip("https://github.com/openml/OpenML/issues/1180")
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@unittest.skipIf(
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call_count += 1
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assert call_count == 3
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@pytest.mark.skip(reason="failures_issue_1544")
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@pytest.mark.sklearn()
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def test_run_and_upload_gridsearch(self):
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estimator_name = (
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trace = openml.runs.get_run_trace(run.run_id)
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assert len(trace.trace_iterations) == 5
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@pytest.mark.skip(reason="failures_issue_1544")
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@pytest.mark.sklearn()
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def test_run_and_upload_maskedarrays(self):
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# This testcase is important for 2 reasons:

‎tests/test_tasks/test_learning_curve_task.py‎

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from __future__ import annotations
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import pandas as pd
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import pytest
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from openml.tasks import TaskType, get_task
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‎tests/test_tasks/test_regression_task.py‎

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import ast
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import pandas as pd
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import pytest
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import openml
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from openml.exceptions import OpenMLServerException

‎tests/test_tasks/test_supervised_task.py‎

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import pandas as pd
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from openml.tasks import get_task
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import pytest
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from .test_task import OpenMLTaskTest
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‎tests/test_tasks/test_task_functions.py‎

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)
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@mock.patch("openml.tasks.functions.get_dataset")
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@pytest.mark.xfail(reason="failures_issue_1544")
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def test_removal_upon_download_failure(self, get_dataset):
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class WeirdException(Exception):
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pass

‎tests/test_tasks/test_task_methods.py‎

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import openml
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from openml.testing import TestBase
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import pytest
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# Common methods between tasks

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