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