198 lines
7.4 KiB
Python
198 lines
7.4 KiB
Python
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import itertools
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import re
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import time
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import warnings
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import joblib
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import numpy as np
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import pytest
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from numpy.testing import assert_array_equal
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from sklearn import config_context, get_config
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from sklearn.compose import make_column_transformer
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from sklearn.datasets import load_iris
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.model_selection import GridSearchCV
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from sklearn.utils.fixes import _IS_WASM
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from sklearn.utils.parallel import Parallel, delayed
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def get_working_memory():
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return get_config()["working_memory"]
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@pytest.mark.parametrize("n_jobs", [1, 2])
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@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
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def test_configuration_passes_through_to_joblib(n_jobs, backend):
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# Tests that the global global configuration is passed to joblib jobs
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with config_context(working_memory=123):
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results = Parallel(n_jobs=n_jobs, backend=backend)(
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delayed(get_working_memory)() for _ in range(2)
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)
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assert_array_equal(results, [123] * 2)
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def test_parallel_delayed_warnings():
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"""Informative warnings should be raised when mixing sklearn and joblib API"""
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# We should issue a warning when one wants to use sklearn.utils.fixes.Parallel
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# with joblib.delayed. The config will not be propagated to the workers.
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warn_msg = "`sklearn.utils.parallel.Parallel` needs to be used in conjunction"
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with pytest.warns(UserWarning, match=warn_msg) as records:
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Parallel()(joblib.delayed(time.sleep)(0) for _ in range(10))
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assert len(records) == 10
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# We should issue a warning if one wants to use sklearn.utils.fixes.delayed with
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# joblib.Parallel
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warn_msg = (
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"`sklearn.utils.parallel.delayed` should be used with "
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"`sklearn.utils.parallel.Parallel` to make it possible to propagate"
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)
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with pytest.warns(UserWarning, match=warn_msg) as records:
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joblib.Parallel()(delayed(time.sleep)(0) for _ in range(10))
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assert len(records) == 10
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@pytest.mark.parametrize("n_jobs", [1, 2])
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def test_dispatch_config_parallel(n_jobs):
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"""Check that we properly dispatch the configuration in parallel processing.
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Non-regression test for:
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https://github.com/scikit-learn/scikit-learn/issues/25239
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"""
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pd = pytest.importorskip("pandas")
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iris = load_iris(as_frame=True)
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class TransformerRequiredDataFrame(StandardScaler):
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def fit(self, X, y=None):
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assert isinstance(X, pd.DataFrame), "X should be a DataFrame"
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return super().fit(X, y)
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def transform(self, X, y=None):
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assert isinstance(X, pd.DataFrame), "X should be a DataFrame"
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return super().transform(X, y)
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dropper = make_column_transformer(
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("drop", [0]),
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remainder="passthrough",
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n_jobs=n_jobs,
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)
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param_grid = {"randomforestclassifier__max_depth": [1, 2, 3]}
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search_cv = GridSearchCV(
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make_pipeline(
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dropper,
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TransformerRequiredDataFrame(),
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RandomForestClassifier(n_estimators=5, n_jobs=n_jobs),
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),
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param_grid,
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cv=5,
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n_jobs=n_jobs,
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error_score="raise", # this search should not fail
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)
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# make sure that `fit` would fail in case we don't request dataframe
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with pytest.raises(AssertionError, match="X should be a DataFrame"):
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search_cv.fit(iris.data, iris.target)
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with config_context(transform_output="pandas"):
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# we expect each intermediate steps to output a DataFrame
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search_cv.fit(iris.data, iris.target)
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assert not np.isnan(search_cv.cv_results_["mean_test_score"]).any()
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def raise_warning():
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warnings.warn("Convergence warning", ConvergenceWarning)
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def _yield_n_jobs_backend_combinations():
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n_jobs_values = [1, 2]
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backend_values = ["loky", "threading", "multiprocessing"]
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for n_jobs, backend in itertools.product(n_jobs_values, backend_values):
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if n_jobs == 2 and backend == "loky":
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# XXX Mark thread-unsafe to avoid:
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# RuntimeError: The executor underlying Parallel has been shutdown.
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# See https://github.com/joblib/joblib/issues/1743 for more details.
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yield pytest.param(n_jobs, backend, marks=pytest.mark.thread_unsafe)
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else:
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yield n_jobs, backend
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@pytest.mark.parametrize("n_jobs, backend", _yield_n_jobs_backend_combinations())
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def test_filter_warning_propagates(n_jobs, backend):
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"""Check warning propagates to the job."""
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with warnings.catch_warnings():
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warnings.simplefilter("error", category=ConvergenceWarning)
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with pytest.raises(ConvergenceWarning):
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Parallel(n_jobs=n_jobs, backend=backend)(
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delayed(raise_warning)() for _ in range(2)
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)
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def get_warning_filters():
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# In free-threading Python >= 3.14, warnings filters are managed through a
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# ContextVar and warnings.filters is not modified inside a
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# warnings.catch_warnings context. You need to use warnings._get_filters().
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# For more details, see
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# https://docs.python.org/3.14/whatsnew/3.14.html#concurrent-safe-warnings-control
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filters_func = getattr(warnings, "_get_filters", None)
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return filters_func() if filters_func is not None else warnings.filters
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def test_check_warnings_threading():
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"""Check that warnings filters are set correctly in the threading backend."""
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with warnings.catch_warnings():
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warnings.simplefilter("error", category=ConvergenceWarning)
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main_warning_filters = get_warning_filters()
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assert ("error", None, ConvergenceWarning, None, 0) in main_warning_filters
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all_worker_warning_filters = Parallel(n_jobs=2, backend="threading")(
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delayed(get_warning_filters)() for _ in range(2)
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)
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def normalize_main_module(filters):
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# In Python 3.14 free-threaded, there is a small discrepancy main
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# warning filters have an entry with module = "__main__" whereas it
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# is a regex in the workers
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return [
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(
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action,
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message,
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type_,
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module
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if "__main__" not in str(module)
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or not isinstance(module, re.Pattern)
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else module.pattern,
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lineno,
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)
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for action, message, type_, module, lineno in main_warning_filters
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]
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for worker_warning_filter in all_worker_warning_filters:
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assert normalize_main_module(
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worker_warning_filter
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) == normalize_main_module(main_warning_filters)
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@pytest.mark.xfail(_IS_WASM, reason="Pyodide always use the sequential backend")
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def test_filter_warning_propagates_no_side_effect_with_loky_backend():
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with warnings.catch_warnings():
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warnings.simplefilter("error", category=ConvergenceWarning)
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Parallel(n_jobs=2, backend="loky")(delayed(time.sleep)(0) for _ in range(10))
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# Since loky workers are reused, make sure that inside the loky workers,
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# warnings filters have been reset to their original value. Using joblib
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# directly should not turn ConvergenceWarning into an error.
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joblib.Parallel(n_jobs=2, backend="loky")(
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joblib.delayed(warnings.warn)("Convergence warning", ConvergenceWarning)
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for _ in range(10)
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)
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