102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
|
import time
|
||
|
|
|
||
|
|
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.model_selection import GridSearchCV
|
||
|
|
from sklearn.pipeline import make_pipeline
|
||
|
|
from sklearn.preprocessing import StandardScaler
|
||
|
|
|
||
|
|
from sklearn.utils.parallel import delayed, Parallel
|
||
|
|
|
||
|
|
|
||
|
|
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()
|