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