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# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import re
import numpy as np
import pytest
from numpy.testing import assert_array_equal
from sklearn import config_context
from sklearn.base import (
BaseEstimator,
clone,
is_classifier,
is_clusterer,
is_outlier_detector,
is_regressor,
)
from sklearn.cluster import KMeans
from sklearn.compose import make_column_transformer
from sklearn.datasets import make_classification, make_regression
from sklearn.exceptions import NotFittedError, UnsetMetadataPassedError
from sklearn.frozen import FrozenEstimator
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.neighbors import LocalOutlierFactor
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler, StandardScaler
from sklearn.utils._testing import set_random_state
from sklearn.utils.validation import check_is_fitted
@pytest.fixture
def regression_dataset():
return make_regression()
@pytest.fixture
def classification_dataset():
return make_classification()
@pytest.mark.parametrize(
"estimator, dataset",
[
(LinearRegression(), "regression_dataset"),
(LogisticRegression(), "classification_dataset"),
(make_pipeline(StandardScaler(), LinearRegression()), "regression_dataset"),
(
make_pipeline(StandardScaler(), LogisticRegression()),
"classification_dataset",
),
(StandardScaler(), "regression_dataset"),
(KMeans(), "regression_dataset"),
(LocalOutlierFactor(), "regression_dataset"),
(
make_column_transformer(
(StandardScaler(), [0]),
(RobustScaler(), [1]),
),
"regression_dataset",
),
],
)
@pytest.mark.parametrize(
"method",
["predict", "predict_proba", "predict_log_proba", "decision_function", "transform"],
)
def test_frozen_methods(estimator, dataset, request, method):
"""Test that frozen.fit doesn't do anything, and that all other methods are
exposed by the frozen estimator and return the same values as the estimator.
"""
X, y = request.getfixturevalue(dataset)
set_random_state(estimator)
estimator.fit(X, y)
frozen = FrozenEstimator(estimator)
# this should be no-op
frozen.fit([[1]], [1])
if hasattr(estimator, method):
assert_array_equal(getattr(estimator, method)(X), getattr(frozen, method)(X))
assert is_classifier(estimator) == is_classifier(frozen)
assert is_regressor(estimator) == is_regressor(frozen)
assert is_clusterer(estimator) == is_clusterer(frozen)
assert is_outlier_detector(estimator) == is_outlier_detector(frozen)
@config_context(enable_metadata_routing=True)
def test_frozen_metadata_routing(regression_dataset):
"""Test that metadata routing works with frozen estimators."""
class ConsumesMetadata(BaseEstimator):
def __init__(self, on_fit=None, on_predict=None):
self.on_fit = on_fit
self.on_predict = on_predict
def fit(self, X, y, metadata=None):
if self.on_fit:
assert metadata is not None
self.fitted_ = True
return self
def predict(self, X, metadata=None):
if self.on_predict:
assert metadata is not None
return np.ones(len(X))
X, y = regression_dataset
pipeline = make_pipeline(
ConsumesMetadata(on_fit=True, on_predict=True)
.set_fit_request(metadata=True)
.set_predict_request(metadata=True)
)
pipeline.fit(X, y, metadata="test")
frozen = FrozenEstimator(pipeline)
pipeline.predict(X, metadata="test")
frozen.predict(X, metadata="test")
frozen["consumesmetadata"].set_predict_request(metadata=False)
with pytest.raises(
TypeError,
match=re.escape(
"Pipeline.predict got unexpected argument(s) {'metadata'}, which are not "
"routed to any object."
),
):
frozen.predict(X, metadata="test")
frozen["consumesmetadata"].set_predict_request(metadata=None)
with pytest.raises(UnsetMetadataPassedError):
frozen.predict(X, metadata="test")
def test_composite_fit(classification_dataset):
"""Test that calling fit_transform and fit_predict doesn't call fit."""
class Estimator(BaseEstimator):
def fit(self, X, y):
try:
self._fit_counter += 1
except AttributeError:
self._fit_counter = 1
return self
def fit_transform(self, X, y=None):
# only here to test that it doesn't get called
... # pragma: no cover
def fit_predict(self, X, y=None):
# only here to test that it doesn't get called
... # pragma: no cover
X, y = classification_dataset
est = Estimator().fit(X, y)
frozen = FrozenEstimator(est)
with pytest.raises(AttributeError):
frozen.fit_predict(X, y)
with pytest.raises(AttributeError):
frozen.fit_transform(X, y)
assert frozen._fit_counter == 1
def test_clone_frozen(regression_dataset):
"""Test that cloning a frozen estimator keeps the frozen state."""
X, y = regression_dataset
estimator = LinearRegression().fit(X, y)
frozen = FrozenEstimator(estimator)
cloned = clone(frozen)
assert cloned.estimator is estimator
def test_check_is_fitted(regression_dataset):
"""Test that check_is_fitted works on frozen estimators."""
X, y = regression_dataset
estimator = LinearRegression()
frozen = FrozenEstimator(estimator)
with pytest.raises(NotFittedError):
check_is_fitted(frozen)
estimator = LinearRegression().fit(X, y)
frozen = FrozenEstimator(estimator)
check_is_fitted(frozen)
def test_frozen_tags():
"""Test that frozen estimators have the same tags as the original estimator
except for the skip_test tag."""
class Estimator(BaseEstimator):
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.input_tags.categorical = True
return tags
estimator = Estimator()
frozen = FrozenEstimator(estimator)
frozen_tags = frozen.__sklearn_tags__()
estimator_tags = estimator.__sklearn_tags__()
assert frozen_tags._skip_test is True
assert estimator_tags._skip_test is False
assert estimator_tags.input_tags.categorical is True
assert frozen_tags.input_tags.categorical is True
def test_frozen_params():
"""Test that FrozenEstimator only exposes the estimator parameter."""
est = LogisticRegression()
frozen = FrozenEstimator(est)
with pytest.raises(ValueError, match="You cannot set parameters of the inner"):
frozen.set_params(estimator__C=1)
assert frozen.get_params() == {"estimator": est}
other_est = LocalOutlierFactor()
frozen.set_params(estimator=other_est)
assert frozen.get_params() == {"estimator": other_est}
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