995 lines
39 KiB
Python
995 lines
39 KiB
Python
import numpy as np
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import pytest
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from numpy.testing import assert_allclose, assert_array_equal
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from sklearn.datasets import make_classification, make_regression
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from sklearn.datasets import make_low_rank_matrix
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from sklearn.preprocessing import KBinsDiscretizer, MinMaxScaler, OneHotEncoder
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from sklearn.model_selection import train_test_split, cross_val_score
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from sklearn.base import clone, BaseEstimator, TransformerMixin
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from sklearn.base import is_regressor
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from sklearn.pipeline import make_pipeline
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from sklearn.metrics import mean_poisson_deviance
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from sklearn.dummy import DummyRegressor
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from sklearn.exceptions import NotFittedError
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from sklearn.compose import make_column_transformer
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# To use this experimental feature, we need to explicitly ask for it:
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.ensemble._hist_gradient_boosting.loss import _LOSSES
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from sklearn.ensemble._hist_gradient_boosting.loss import LeastSquares
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from sklearn.ensemble._hist_gradient_boosting.loss import BinaryCrossEntropy
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from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower
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from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper
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from sklearn.utils import shuffle
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X_classification, y_classification = make_classification(random_state=0)
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X_regression, y_regression = make_regression(random_state=0)
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X_multi_classification, y_multi_classification = make_classification(
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n_classes=3, n_informative=3, random_state=0
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)
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def _make_dumb_dataset(n_samples):
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"""Make a dumb dataset to test early stopping."""
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rng = np.random.RandomState(42)
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X_dumb = rng.randn(n_samples, 1)
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y_dumb = (X_dumb[:, 0] > 0).astype('int64')
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return X_dumb, y_dumb
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@pytest.mark.parametrize('GradientBoosting, X, y', [
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(HistGradientBoostingClassifier, X_classification, y_classification),
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(HistGradientBoostingRegressor, X_regression, y_regression)
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])
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@pytest.mark.parametrize(
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'params, err_msg',
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[({'loss': 'blah'}, 'Loss blah is not supported for'),
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({'learning_rate': 0}, 'learning_rate=0 must be strictly positive'),
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({'learning_rate': -1}, 'learning_rate=-1 must be strictly positive'),
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({'max_iter': 0}, 'max_iter=0 must not be smaller than 1'),
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({'max_leaf_nodes': 0}, 'max_leaf_nodes=0 should not be smaller than 2'),
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({'max_leaf_nodes': 1}, 'max_leaf_nodes=1 should not be smaller than 2'),
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({'max_depth': 0}, 'max_depth=0 should not be smaller than 1'),
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({'min_samples_leaf': 0}, 'min_samples_leaf=0 should not be smaller'),
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({'l2_regularization': -1}, 'l2_regularization=-1 must be positive'),
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({'max_bins': 1}, 'max_bins=1 should be no smaller than 2 and no larger'),
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({'max_bins': 256}, 'max_bins=256 should be no smaller than 2 and no'),
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({'n_iter_no_change': -1}, 'n_iter_no_change=-1 must be positive'),
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({'validation_fraction': -1}, 'validation_fraction=-1 must be strictly'),
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({'validation_fraction': 0}, 'validation_fraction=0 must be strictly'),
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({'tol': -1}, 'tol=-1 must not be smaller than 0')]
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)
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def test_init_parameters_validation(GradientBoosting, X, y, params, err_msg):
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with pytest.raises(ValueError, match=err_msg):
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GradientBoosting(**params).fit(X, y)
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def test_invalid_classification_loss():
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binary_clf = HistGradientBoostingClassifier(loss="binary_crossentropy")
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err_msg = ("loss='binary_crossentropy' is not defined for multiclass "
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"classification with n_classes=3, use "
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"loss='categorical_crossentropy' instead")
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with pytest.raises(ValueError, match=err_msg):
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binary_clf.fit(np.zeros(shape=(3, 2)), np.arange(3))
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@pytest.mark.parametrize(
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'scoring, validation_fraction, early_stopping, n_iter_no_change, tol', [
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('neg_mean_squared_error', .1, True, 5, 1e-7), # use scorer
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('neg_mean_squared_error', None, True, 5, 1e-1), # use scorer on train
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(None, .1, True, 5, 1e-7), # same with default scorer
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(None, None, True, 5, 1e-1),
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('loss', .1, True, 5, 1e-7), # use loss
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('loss', None, True, 5, 1e-1), # use loss on training data
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(None, None, False, 5, None), # no early stopping
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])
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def test_early_stopping_regression(scoring, validation_fraction,
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early_stopping, n_iter_no_change, tol):
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max_iter = 200
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X, y = make_regression(n_samples=50, random_state=0)
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gb = HistGradientBoostingRegressor(
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verbose=1, # just for coverage
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min_samples_leaf=5, # easier to overfit fast
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scoring=scoring,
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tol=tol,
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early_stopping=early_stopping,
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validation_fraction=validation_fraction,
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max_iter=max_iter,
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n_iter_no_change=n_iter_no_change,
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random_state=0
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)
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gb.fit(X, y)
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if early_stopping:
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assert n_iter_no_change <= gb.n_iter_ < max_iter
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else:
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assert gb.n_iter_ == max_iter
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@pytest.mark.parametrize('data', (
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make_classification(n_samples=30, random_state=0),
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make_classification(n_samples=30, n_classes=3, n_clusters_per_class=1,
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random_state=0)
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))
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@pytest.mark.parametrize(
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'scoring, validation_fraction, early_stopping, n_iter_no_change, tol', [
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('accuracy', .1, True, 5, 1e-7), # use scorer
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('accuracy', None, True, 5, 1e-1), # use scorer on training data
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(None, .1, True, 5, 1e-7), # same with default scorer
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(None, None, True, 5, 1e-1),
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('loss', .1, True, 5, 1e-7), # use loss
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('loss', None, True, 5, 1e-1), # use loss on training data
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(None, None, False, 5, None), # no early stopping
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])
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def test_early_stopping_classification(data, scoring, validation_fraction,
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early_stopping, n_iter_no_change, tol):
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max_iter = 50
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X, y = data
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gb = HistGradientBoostingClassifier(
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verbose=1, # just for coverage
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min_samples_leaf=5, # easier to overfit fast
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scoring=scoring,
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tol=tol,
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early_stopping=early_stopping,
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validation_fraction=validation_fraction,
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max_iter=max_iter,
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n_iter_no_change=n_iter_no_change,
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random_state=0
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)
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gb.fit(X, y)
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if early_stopping is True:
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assert n_iter_no_change <= gb.n_iter_ < max_iter
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else:
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assert gb.n_iter_ == max_iter
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@pytest.mark.parametrize('GradientBoosting, X, y', [
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(HistGradientBoostingClassifier, *_make_dumb_dataset(10000)),
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(HistGradientBoostingClassifier, *_make_dumb_dataset(10001)),
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(HistGradientBoostingRegressor, *_make_dumb_dataset(10000)),
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(HistGradientBoostingRegressor, *_make_dumb_dataset(10001))
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])
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def test_early_stopping_default(GradientBoosting, X, y):
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# Test that early stopping is enabled by default if and only if there
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# are more than 10000 samples
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gb = GradientBoosting(max_iter=10, n_iter_no_change=2, tol=1e-1)
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gb.fit(X, y)
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if X.shape[0] > 10000:
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assert gb.n_iter_ < gb.max_iter
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else:
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assert gb.n_iter_ == gb.max_iter
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@pytest.mark.parametrize(
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'scores, n_iter_no_change, tol, stopping',
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[
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([], 1, 0.001, False), # not enough iterations
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([1, 1, 1], 5, 0.001, False), # not enough iterations
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([1, 1, 1, 1, 1], 5, 0.001, False), # not enough iterations
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([1, 2, 3, 4, 5, 6], 5, 0.001, False), # significant improvement
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([1, 2, 3, 4, 5, 6], 5, 0., False), # significant improvement
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([1, 2, 3, 4, 5, 6], 5, 0.999, False), # significant improvement
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([1, 2, 3, 4, 5, 6], 5, 5 - 1e-5, False), # significant improvement
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([1] * 6, 5, 0., True), # no significant improvement
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([1] * 6, 5, 0.001, True), # no significant improvement
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([1] * 6, 5, 5, True), # no significant improvement
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]
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)
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def test_should_stop(scores, n_iter_no_change, tol, stopping):
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gbdt = HistGradientBoostingClassifier(
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n_iter_no_change=n_iter_no_change, tol=tol
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)
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assert gbdt._should_stop(scores) == stopping
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def test_least_absolute_deviation():
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# For coverage only.
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X, y = make_regression(n_samples=500, random_state=0)
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gbdt = HistGradientBoostingRegressor(loss='least_absolute_deviation',
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random_state=0)
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gbdt.fit(X, y)
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assert gbdt.score(X, y) > .9
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def test_least_absolute_deviation_sample_weight():
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# non regression test for issue #19400
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# make sure no error is thrown during fit of
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# HistGradientBoostingRegressor with least_absolute_deviation loss function
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# and passing sample_weight
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rng = np.random.RandomState(0)
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n_samples = 100
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X = rng.uniform(-1, 1, size=(n_samples, 2))
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y = rng.uniform(-1, 1, size=n_samples)
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sample_weight = rng.uniform(0, 1, size=n_samples)
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gbdt = HistGradientBoostingRegressor(loss='least_absolute_deviation')
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gbdt.fit(X, y, sample_weight=sample_weight)
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@pytest.mark.parametrize('y', [([1., -2., 0.]), ([0., 0., 0.])])
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def test_poisson_y_positive(y):
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# Test that ValueError is raised if either one y_i < 0 or sum(y_i) <= 0.
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err_msg = r"loss='poisson' requires non-negative y and sum\(y\) > 0."
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gbdt = HistGradientBoostingRegressor(loss='poisson', random_state=0)
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with pytest.raises(ValueError, match=err_msg):
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gbdt.fit(np.zeros(shape=(len(y), 1)), y)
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def test_poisson():
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# For Poisson distributed target, Poisson loss should give better results
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# than least squares measured in Poisson deviance as metric.
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rng = np.random.RandomState(42)
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n_train, n_test, n_features = 500, 100, 100
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X = make_low_rank_matrix(n_samples=n_train+n_test, n_features=n_features,
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random_state=rng)
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# We create a log-linear Poisson model and downscale coef as it will get
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# exponentiated.
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coef = rng.uniform(low=-2, high=2, size=n_features) / np.max(X, axis=0)
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y = rng.poisson(lam=np.exp(X @ coef))
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=n_test,
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random_state=rng)
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gbdt_pois = HistGradientBoostingRegressor(loss='poisson', random_state=rng)
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gbdt_ls = HistGradientBoostingRegressor(loss='least_squares',
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random_state=rng)
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gbdt_pois.fit(X_train, y_train)
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gbdt_ls.fit(X_train, y_train)
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dummy = DummyRegressor(strategy="mean").fit(X_train, y_train)
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for X, y in [(X_train, y_train), (X_test, y_test)]:
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metric_pois = mean_poisson_deviance(y, gbdt_pois.predict(X))
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# least_squares might produce non-positive predictions => clip
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metric_ls = mean_poisson_deviance(y, np.clip(gbdt_ls.predict(X), 1e-15,
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None))
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metric_dummy = mean_poisson_deviance(y, dummy.predict(X))
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assert metric_pois < metric_ls
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assert metric_pois < metric_dummy
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def test_binning_train_validation_are_separated():
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# Make sure training and validation data are binned separately.
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# See issue 13926
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rng = np.random.RandomState(0)
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validation_fraction = .2
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gb = HistGradientBoostingClassifier(
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early_stopping=True,
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validation_fraction=validation_fraction,
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random_state=rng
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)
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gb.fit(X_classification, y_classification)
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mapper_training_data = gb._bin_mapper
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# Note that since the data is small there is no subsampling and the
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# random_state doesn't matter
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mapper_whole_data = _BinMapper(random_state=0)
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mapper_whole_data.fit(X_classification)
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n_samples = X_classification.shape[0]
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assert np.all(mapper_training_data.n_bins_non_missing_ ==
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int((1 - validation_fraction) * n_samples))
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assert np.all(mapper_training_data.n_bins_non_missing_ !=
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mapper_whole_data.n_bins_non_missing_)
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def test_missing_values_trivial():
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# sanity check for missing values support. With only one feature and
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# y == isnan(X), the gbdt is supposed to reach perfect accuracy on the
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# training set.
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n_samples = 100
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n_features = 1
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rng = np.random.RandomState(0)
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X = rng.normal(size=(n_samples, n_features))
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mask = rng.binomial(1, .5, size=X.shape).astype(bool)
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X[mask] = np.nan
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y = mask.ravel()
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gb = HistGradientBoostingClassifier()
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gb.fit(X, y)
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assert gb.score(X, y) == pytest.approx(1)
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@pytest.mark.parametrize('problem', ('classification', 'regression'))
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@pytest.mark.parametrize(
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'missing_proportion, expected_min_score_classification, '
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'expected_min_score_regression', [
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(.1, .97, .89),
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(.2, .93, .81),
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(.5, .79, .52)])
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def test_missing_values_resilience(problem, missing_proportion,
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expected_min_score_classification,
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expected_min_score_regression):
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# Make sure the estimators can deal with missing values and still yield
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# decent predictions
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rng = np.random.RandomState(0)
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n_samples = 1000
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n_features = 2
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if problem == 'regression':
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X, y = make_regression(n_samples=n_samples, n_features=n_features,
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n_informative=n_features, random_state=rng)
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gb = HistGradientBoostingRegressor()
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expected_min_score = expected_min_score_regression
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else:
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X, y = make_classification(n_samples=n_samples, n_features=n_features,
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n_informative=n_features, n_redundant=0,
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n_repeated=0, random_state=rng)
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gb = HistGradientBoostingClassifier()
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expected_min_score = expected_min_score_classification
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mask = rng.binomial(1, missing_proportion, size=X.shape).astype(bool)
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X[mask] = np.nan
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gb.fit(X, y)
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assert gb.score(X, y) > expected_min_score
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@pytest.mark.parametrize('data', [
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make_classification(random_state=0, n_classes=2),
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make_classification(random_state=0, n_classes=3, n_informative=3)
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], ids=['binary_crossentropy', 'categorical_crossentropy'])
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def test_zero_division_hessians(data):
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# non regression test for issue #14018
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# make sure we avoid zero division errors when computing the leaves values.
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# If the learning rate is too high, the raw predictions are bad and will
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# saturate the softmax (or sigmoid in binary classif). This leads to
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# probabilities being exactly 0 or 1, gradients being constant, and
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# hessians being zero.
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X, y = data
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gb = HistGradientBoostingClassifier(learning_rate=100, max_iter=10)
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gb.fit(X, y)
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def test_small_trainset():
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# Make sure that the small trainset is stratified and has the expected
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# length (10k samples)
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n_samples = 20000
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original_distrib = {0: 0.1, 1: 0.2, 2: 0.3, 3: 0.4}
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rng = np.random.RandomState(42)
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X = rng.randn(n_samples).reshape(n_samples, 1)
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y = [[class_] * int(prop * n_samples) for (class_, prop)
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in original_distrib.items()]
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y = shuffle(np.concatenate(y))
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gb = HistGradientBoostingClassifier()
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# Compute the small training set
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X_small, y_small, _ = gb._get_small_trainset(X, y, seed=42,
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sample_weight_train=None)
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# Compute the class distribution in the small training set
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unique, counts = np.unique(y_small, return_counts=True)
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small_distrib = {class_: count / 10000 for (class_, count)
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in zip(unique, counts)}
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# Test that the small training set has the expected length
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assert X_small.shape[0] == 10000
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assert y_small.shape[0] == 10000
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# Test that the class distributions in the whole dataset and in the small
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# training set are identical
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assert small_distrib == pytest.approx(original_distrib)
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def test_missing_values_minmax_imputation():
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# Compare the buit-in missing value handling of Histogram GBC with an
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# a-priori missing value imputation strategy that should yield the same
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# results in terms of decision function.
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#
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# Each feature (containing NaNs) is replaced by 2 features:
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# - one where the nans are replaced by min(feature) - 1
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# - one where the nans are replaced by max(feature) + 1
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# A split where nans go to the left has an equivalent split in the
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# first (min) feature, and a split where nans go to the right has an
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# equivalent split in the second (max) feature.
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#
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# Assuming the data is such that there is never a tie to select the best
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# feature to split on during training, the learned decision trees should be
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# strictly equivalent (learn a sequence of splits that encode the same
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# decision function).
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#
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# The MinMaxImputer transformer is meant to be a toy implementation of the
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# "Missing In Attributes" (MIA) missing value handling for decision trees
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# https://www.sciencedirect.com/science/article/abs/pii/S0167865508000305
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# The implementation of MIA as an imputation transformer was suggested by
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# "Remark 3" in https://arxiv.org/abs/1902.06931
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class MinMaxImputer(TransformerMixin, BaseEstimator):
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def fit(self, X, y=None):
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mm = MinMaxScaler().fit(X)
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self.data_min_ = mm.data_min_
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self.data_max_ = mm.data_max_
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return self
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def transform(self, X):
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X_min, X_max = X.copy(), X.copy()
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for feature_idx in range(X.shape[1]):
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nan_mask = np.isnan(X[:, feature_idx])
|
|
X_min[nan_mask, feature_idx] = self.data_min_[feature_idx] - 1
|
|
X_max[nan_mask, feature_idx] = self.data_max_[feature_idx] + 1
|
|
|
|
return np.concatenate([X_min, X_max], axis=1)
|
|
|
|
def make_missing_value_data(n_samples=int(1e4), seed=0):
|
|
rng = np.random.RandomState(seed)
|
|
X, y = make_regression(n_samples=n_samples, n_features=4,
|
|
random_state=rng)
|
|
|
|
# Pre-bin the data to ensure a deterministic handling by the 2
|
|
# strategies and also make it easier to insert np.nan in a structured
|
|
# way:
|
|
X = KBinsDiscretizer(n_bins=42, encode="ordinal").fit_transform(X)
|
|
|
|
# First feature has missing values completely at random:
|
|
rnd_mask = rng.rand(X.shape[0]) > 0.9
|
|
X[rnd_mask, 0] = np.nan
|
|
|
|
# Second and third features have missing values for extreme values
|
|
# (censoring missingness):
|
|
low_mask = X[:, 1] == 0
|
|
X[low_mask, 1] = np.nan
|
|
|
|
high_mask = X[:, 2] == X[:, 2].max()
|
|
X[high_mask, 2] = np.nan
|
|
|
|
# Make the last feature nan pattern very informative:
|
|
y_max = np.percentile(y, 70)
|
|
y_max_mask = y >= y_max
|
|
y[y_max_mask] = y_max
|
|
X[y_max_mask, 3] = np.nan
|
|
|
|
# Check that there is at least one missing value in each feature:
|
|
for feature_idx in range(X.shape[1]):
|
|
assert any(np.isnan(X[:, feature_idx]))
|
|
|
|
# Let's use a test set to check that the learned decision function is
|
|
# the same as evaluated on unseen data. Otherwise it could just be the
|
|
# case that we find two independent ways to overfit the training set.
|
|
return train_test_split(X, y, random_state=rng)
|
|
|
|
# n_samples need to be large enough to minimize the likelihood of having
|
|
# several candidate splits with the same gain value in a given tree.
|
|
X_train, X_test, y_train, y_test = make_missing_value_data(
|
|
n_samples=int(1e4), seed=0)
|
|
|
|
# Use a small number of leaf nodes and iterations so as to keep
|
|
# under-fitting models to minimize the likelihood of ties when training the
|
|
# model.
|
|
gbm1 = HistGradientBoostingRegressor(max_iter=100,
|
|
max_leaf_nodes=5,
|
|
random_state=0)
|
|
gbm1.fit(X_train, y_train)
|
|
|
|
gbm2 = make_pipeline(MinMaxImputer(), clone(gbm1))
|
|
gbm2.fit(X_train, y_train)
|
|
|
|
# Check that the model reach the same score:
|
|
assert gbm1.score(X_train, y_train) == \
|
|
pytest.approx(gbm2.score(X_train, y_train))
|
|
|
|
assert gbm1.score(X_test, y_test) == \
|
|
pytest.approx(gbm2.score(X_test, y_test))
|
|
|
|
# Check the individual prediction match as a finer grained
|
|
# decision function check.
|
|
assert_allclose(gbm1.predict(X_train), gbm2.predict(X_train))
|
|
assert_allclose(gbm1.predict(X_test), gbm2.predict(X_test))
|
|
|
|
|
|
def test_infinite_values():
|
|
# Basic test for infinite values
|
|
|
|
X = np.array([-np.inf, 0, 1, np.inf]).reshape(-1, 1)
|
|
y = np.array([0, 0, 1, 1])
|
|
|
|
gbdt = HistGradientBoostingRegressor(min_samples_leaf=1)
|
|
gbdt.fit(X, y)
|
|
np.testing.assert_allclose(gbdt.predict(X), y, atol=1e-4)
|
|
|
|
|
|
def test_consistent_lengths():
|
|
X = np.array([-np.inf, 0, 1, np.inf]).reshape(-1, 1)
|
|
y = np.array([0, 0, 1, 1])
|
|
sample_weight = np.array([.1, .3, .1])
|
|
gbdt = HistGradientBoostingRegressor()
|
|
with pytest.raises(ValueError,
|
|
match=r"sample_weight.shape == \(3,\), expected"):
|
|
gbdt.fit(X, y, sample_weight)
|
|
|
|
with pytest.raises(ValueError,
|
|
match="Found input variables with inconsistent number"):
|
|
gbdt.fit(X, y[1:])
|
|
|
|
|
|
def test_infinite_values_missing_values():
|
|
# High level test making sure that inf and nan values are properly handled
|
|
# when both are present. This is similar to
|
|
# test_split_on_nan_with_infinite_values() in test_grower.py, though we
|
|
# cannot check the predictions for binned values here.
|
|
|
|
X = np.asarray([-np.inf, 0, 1, np.inf, np.nan]).reshape(-1, 1)
|
|
y_isnan = np.isnan(X.ravel())
|
|
y_isinf = X.ravel() == np.inf
|
|
|
|
stump_clf = HistGradientBoostingClassifier(min_samples_leaf=1, max_iter=1,
|
|
learning_rate=1, max_depth=2)
|
|
|
|
assert stump_clf.fit(X, y_isinf).score(X, y_isinf) == 1
|
|
assert stump_clf.fit(X, y_isnan).score(X, y_isnan) == 1
|
|
|
|
|
|
def test_crossentropy_binary_problem():
|
|
# categorical_crossentropy should only be used if there are more than two
|
|
# classes present. PR #14869
|
|
X = [[1], [0]]
|
|
y = [0, 1]
|
|
gbrt = HistGradientBoostingClassifier(loss='categorical_crossentropy')
|
|
with pytest.raises(ValueError,
|
|
match="'categorical_crossentropy' is not suitable for"):
|
|
gbrt.fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize("scoring", [None, 'loss'])
|
|
def test_string_target_early_stopping(scoring):
|
|
# Regression tests for #14709 where the targets need to be encoded before
|
|
# to compute the score
|
|
rng = np.random.RandomState(42)
|
|
X = rng.randn(100, 10)
|
|
y = np.array(['x'] * 50 + ['y'] * 50, dtype=object)
|
|
gbrt = HistGradientBoostingClassifier(n_iter_no_change=10, scoring=scoring)
|
|
gbrt.fit(X, y)
|
|
|
|
|
|
def test_zero_sample_weights_regression():
|
|
# Make sure setting a SW to zero amounts to ignoring the corresponding
|
|
# sample
|
|
|
|
X = [[1, 0],
|
|
[1, 0],
|
|
[1, 0],
|
|
[0, 1]]
|
|
y = [0, 0, 1, 0]
|
|
# ignore the first 2 training samples by setting their weight to 0
|
|
sample_weight = [0, 0, 1, 1]
|
|
gb = HistGradientBoostingRegressor(min_samples_leaf=1)
|
|
gb.fit(X, y, sample_weight=sample_weight)
|
|
assert gb.predict([[1, 0]])[0] > 0.5
|
|
|
|
|
|
def test_zero_sample_weights_classification():
|
|
# Make sure setting a SW to zero amounts to ignoring the corresponding
|
|
# sample
|
|
|
|
X = [[1, 0],
|
|
[1, 0],
|
|
[1, 0],
|
|
[0, 1]]
|
|
y = [0, 0, 1, 0]
|
|
# ignore the first 2 training samples by setting their weight to 0
|
|
sample_weight = [0, 0, 1, 1]
|
|
gb = HistGradientBoostingClassifier(loss='binary_crossentropy',
|
|
min_samples_leaf=1)
|
|
gb.fit(X, y, sample_weight=sample_weight)
|
|
assert_array_equal(gb.predict([[1, 0]]), [1])
|
|
|
|
X = [[1, 0],
|
|
[1, 0],
|
|
[1, 0],
|
|
[0, 1],
|
|
[1, 1]]
|
|
y = [0, 0, 1, 0, 2]
|
|
# ignore the first 2 training samples by setting their weight to 0
|
|
sample_weight = [0, 0, 1, 1, 1]
|
|
gb = HistGradientBoostingClassifier(loss='categorical_crossentropy',
|
|
min_samples_leaf=1)
|
|
gb.fit(X, y, sample_weight=sample_weight)
|
|
assert_array_equal(gb.predict([[1, 0]]), [1])
|
|
|
|
|
|
@pytest.mark.parametrize('problem', (
|
|
'regression',
|
|
'binary_classification',
|
|
'multiclass_classification'
|
|
))
|
|
@pytest.mark.parametrize('duplication', ('half', 'all'))
|
|
def test_sample_weight_effect(problem, duplication):
|
|
# High level test to make sure that duplicating a sample is equivalent to
|
|
# giving it weight of 2.
|
|
|
|
# fails for n_samples > 255 because binning does not take sample weights
|
|
# into account. Keeping n_samples <= 255 makes
|
|
# sure only unique values are used so SW have no effect on binning.
|
|
n_samples = 255
|
|
n_features = 2
|
|
if problem == 'regression':
|
|
X, y = make_regression(n_samples=n_samples, n_features=n_features,
|
|
n_informative=n_features, random_state=0)
|
|
Klass = HistGradientBoostingRegressor
|
|
else:
|
|
n_classes = 2 if problem == 'binary_classification' else 3
|
|
X, y = make_classification(n_samples=n_samples, n_features=n_features,
|
|
n_informative=n_features, n_redundant=0,
|
|
n_clusters_per_class=1,
|
|
n_classes=n_classes, random_state=0)
|
|
Klass = HistGradientBoostingClassifier
|
|
|
|
# This test can't pass if min_samples_leaf > 1 because that would force 2
|
|
# samples to be in the same node in est_sw, while these samples would be
|
|
# free to be separate in est_dup: est_dup would just group together the
|
|
# duplicated samples.
|
|
est = Klass(min_samples_leaf=1)
|
|
|
|
# Create dataset with duplicate and corresponding sample weights
|
|
if duplication == 'half':
|
|
lim = n_samples // 2
|
|
else:
|
|
lim = n_samples
|
|
X_dup = np.r_[X, X[:lim]]
|
|
y_dup = np.r_[y, y[:lim]]
|
|
sample_weight = np.ones(shape=(n_samples))
|
|
sample_weight[:lim] = 2
|
|
|
|
est_sw = clone(est).fit(X, y, sample_weight=sample_weight)
|
|
est_dup = clone(est).fit(X_dup, y_dup)
|
|
|
|
# checking raw_predict is stricter than just predict for classification
|
|
assert np.allclose(est_sw._raw_predict(X_dup),
|
|
est_dup._raw_predict(X_dup))
|
|
|
|
|
|
@pytest.mark.parametrize('loss_name', ('least_squares',
|
|
'least_absolute_deviation'))
|
|
def test_sum_hessians_are_sample_weight(loss_name):
|
|
# For losses with constant hessians, the sum_hessians field of the
|
|
# histograms must be equal to the sum of the sample weight of samples at
|
|
# the corresponding bin.
|
|
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 1000
|
|
n_features = 2
|
|
X, y = make_regression(n_samples=n_samples, n_features=n_features,
|
|
random_state=rng)
|
|
bin_mapper = _BinMapper()
|
|
X_binned = bin_mapper.fit_transform(X)
|
|
|
|
sample_weight = rng.normal(size=n_samples)
|
|
|
|
loss = _LOSSES[loss_name](sample_weight=sample_weight)
|
|
gradients, hessians = loss.init_gradients_and_hessians(
|
|
n_samples=n_samples, prediction_dim=1, sample_weight=sample_weight)
|
|
raw_predictions = rng.normal(size=(1, n_samples))
|
|
loss.update_gradients_and_hessians(gradients, hessians, y,
|
|
raw_predictions, sample_weight)
|
|
|
|
# build sum_sample_weight which contains the sum of the sample weights at
|
|
# each bin (for each feature). This must be equal to the sum_hessians
|
|
# field of the corresponding histogram
|
|
sum_sw = np.zeros(shape=(n_features, bin_mapper.n_bins))
|
|
for feature_idx in range(n_features):
|
|
for sample_idx in range(n_samples):
|
|
sum_sw[feature_idx, X_binned[sample_idx, feature_idx]] += (
|
|
sample_weight[sample_idx])
|
|
|
|
# Build histogram
|
|
grower = TreeGrower(X_binned, gradients[0], hessians[0],
|
|
n_bins=bin_mapper.n_bins)
|
|
histograms = grower.histogram_builder.compute_histograms_brute(
|
|
grower.root.sample_indices)
|
|
|
|
for feature_idx in range(n_features):
|
|
for bin_idx in range(bin_mapper.n_bins):
|
|
assert histograms[feature_idx, bin_idx]['sum_hessians'] == (
|
|
pytest.approx(sum_sw[feature_idx, bin_idx], rel=1e-5))
|
|
|
|
|
|
def test_max_depth_max_leaf_nodes():
|
|
# Non regression test for
|
|
# https://github.com/scikit-learn/scikit-learn/issues/16179
|
|
# there was a bug when the max_depth and the max_leaf_nodes criteria were
|
|
# met at the same time, which would lead to max_leaf_nodes not being
|
|
# respected.
|
|
X, y = make_classification(random_state=0)
|
|
est = HistGradientBoostingClassifier(max_depth=2, max_leaf_nodes=3,
|
|
max_iter=1).fit(X, y)
|
|
tree = est._predictors[0][0]
|
|
assert tree.get_max_depth() == 2
|
|
assert tree.get_n_leaf_nodes() == 3 # would be 4 prior to bug fix
|
|
|
|
|
|
def test_early_stopping_on_test_set_with_warm_start():
|
|
# Non regression test for #16661 where second fit fails with
|
|
# warm_start=True, early_stopping is on, and no validation set
|
|
X, y = make_classification(random_state=0)
|
|
gb = HistGradientBoostingClassifier(
|
|
max_iter=1, scoring='loss', warm_start=True, early_stopping=True,
|
|
n_iter_no_change=1, validation_fraction=None)
|
|
|
|
gb.fit(X, y)
|
|
# does not raise on second call
|
|
gb.set_params(max_iter=2)
|
|
gb.fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize('Est', (HistGradientBoostingClassifier,
|
|
HistGradientBoostingRegressor))
|
|
def test_single_node_trees(Est):
|
|
# Make sure it's still possible to build single-node trees. In that case
|
|
# the value of the root is set to 0. That's a correct value: if the tree is
|
|
# single-node that's because min_gain_to_split is not respected right from
|
|
# the root, so we don't want the tree to have any impact on the
|
|
# predictions.
|
|
|
|
X, y = make_classification(random_state=0)
|
|
y[:] = 1 # constant target will lead to a single root node
|
|
|
|
est = Est(max_iter=20)
|
|
est.fit(X, y)
|
|
|
|
assert all(len(predictor[0].nodes) == 1 for predictor in est._predictors)
|
|
assert all(predictor[0].nodes[0]['value'] == 0
|
|
for predictor in est._predictors)
|
|
# Still gives correct predictions thanks to the baseline prediction
|
|
assert_allclose(est.predict(X), y)
|
|
|
|
|
|
@pytest.mark.parametrize('Est, loss, X, y', [
|
|
(
|
|
HistGradientBoostingClassifier,
|
|
BinaryCrossEntropy(sample_weight=None),
|
|
X_classification,
|
|
y_classification
|
|
),
|
|
(
|
|
HistGradientBoostingRegressor,
|
|
LeastSquares(sample_weight=None),
|
|
X_regression,
|
|
y_regression
|
|
)
|
|
])
|
|
def test_custom_loss(Est, loss, X, y):
|
|
est = Est(loss=loss, max_iter=20)
|
|
est.fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize('HistGradientBoosting, X, y', [
|
|
(HistGradientBoostingClassifier, X_classification, y_classification),
|
|
(HistGradientBoostingRegressor, X_regression, y_regression),
|
|
(HistGradientBoostingClassifier,
|
|
X_multi_classification, y_multi_classification),
|
|
])
|
|
def test_staged_predict(HistGradientBoosting, X, y):
|
|
|
|
# Test whether staged predictor eventually gives
|
|
# the same prediction.
|
|
X_train, X_test, y_train, y_test = train_test_split(
|
|
X, y,
|
|
test_size=0.5,
|
|
random_state=0
|
|
)
|
|
gb = HistGradientBoosting(max_iter=10)
|
|
|
|
# test raise NotFittedError if not fitted
|
|
with pytest.raises(NotFittedError):
|
|
next(gb.staged_predict(X_test))
|
|
|
|
gb.fit(X_train, y_train)
|
|
|
|
# test if the staged predictions of each iteration
|
|
# are equal to the corresponding predictions of the same estimator
|
|
# trained from scratch.
|
|
# this also test limit case when max_iter = 1
|
|
method_names = (
|
|
['predict'] if is_regressor(gb)
|
|
else ['predict', 'predict_proba', 'decision_function']
|
|
)
|
|
for method_name in method_names:
|
|
|
|
staged_method = getattr(gb, 'staged_' + method_name)
|
|
staged_predictions = list(staged_method(X_test))
|
|
assert len(staged_predictions) == gb.n_iter_
|
|
for n_iter, staged_predictions in enumerate(staged_method(X_test), 1):
|
|
aux = HistGradientBoosting(max_iter=n_iter)
|
|
aux.fit(X_train, y_train)
|
|
pred_aux = getattr(aux, method_name)(X_test)
|
|
|
|
assert_allclose(staged_predictions, pred_aux)
|
|
assert staged_predictions.shape == pred_aux.shape
|
|
|
|
|
|
@pytest.mark.parametrize("insert_missing", [False, True])
|
|
@pytest.mark.parametrize("Est", (HistGradientBoostingRegressor,
|
|
HistGradientBoostingClassifier))
|
|
@pytest.mark.parametrize("bool_categorical_parameter", [True, False])
|
|
def test_unknown_categories_nan(insert_missing, Est,
|
|
bool_categorical_parameter):
|
|
# Make sure no error is raised at predict if a category wasn't seen during
|
|
# fit. We also make sure they're treated as nans.
|
|
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 1000
|
|
f1 = rng.rand(n_samples)
|
|
f2 = rng.randint(4, size=n_samples)
|
|
X = np.c_[f1, f2]
|
|
y = np.zeros(shape=n_samples)
|
|
y[X[:, 1] % 2 == 0] = 1
|
|
|
|
if bool_categorical_parameter:
|
|
categorical_features = [False, True]
|
|
else:
|
|
categorical_features = [1]
|
|
|
|
if insert_missing:
|
|
mask = rng.binomial(1, 0.01, size=X.shape).astype(bool)
|
|
assert mask.sum() > 0
|
|
X[mask] = np.nan
|
|
|
|
est = Est(max_iter=20, categorical_features=categorical_features).fit(X, y)
|
|
assert_array_equal(est.is_categorical_, [False, True])
|
|
|
|
# Make sure no error is raised on unknown categories and nans
|
|
# unknown categories will be treated as nans
|
|
X_test = np.zeros((10, X.shape[1]), dtype=float)
|
|
X_test[:5, 1] = 30
|
|
X_test[5:, 1] = np.nan
|
|
assert len(np.unique(est.predict(X_test))) == 1
|
|
|
|
|
|
def test_categorical_encoding_strategies():
|
|
# Check native categorical handling vs different encoding strategies. We
|
|
# make sure that native encoding needs only 1 split to achieve a perfect
|
|
# prediction on a simple dataset. In contrast, OneHotEncoded data needs
|
|
# more depth / splits, and treating categories as ordered (just using
|
|
# OrdinalEncoder) requires even more depth.
|
|
|
|
# dataset with one random continuous feature, and one categorical feature
|
|
# with values in [0, 5], e.g. from an OrdinalEncoder.
|
|
# class == 1 iff categorical value in {0, 2, 4}
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 10_000
|
|
f1 = rng.rand(n_samples)
|
|
f2 = rng.randint(6, size=n_samples)
|
|
X = np.c_[f1, f2]
|
|
y = np.zeros(shape=n_samples)
|
|
y[X[:, 1] % 2 == 0] = 1
|
|
|
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# make sure dataset is balanced so that the baseline_prediction doesn't
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# influence predictions too much with max_iter = 1
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assert 0.49 < y.mean() < 0.51
|
|
|
|
clf_cat = HistGradientBoostingClassifier(
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max_iter=1, max_depth=1, categorical_features=[False, True])
|
|
|
|
# Using native categorical encoding, we get perfect predictions with just
|
|
# one split
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assert cross_val_score(clf_cat, X, y).mean() == 1
|
|
|
|
# quick sanity check for the bitset: 0, 2, 4 = 2**0 + 2**2 + 2**4 = 21
|
|
expected_left_bitset = [21, 0, 0, 0, 0, 0, 0, 0]
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left_bitset = clf_cat.fit(X, y)._predictors[0][0].raw_left_cat_bitsets[0]
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assert_array_equal(left_bitset, expected_left_bitset)
|
|
|
|
# Treating categories as ordered, we need more depth / more splits to get
|
|
# the same predictions
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|
clf_no_cat = HistGradientBoostingClassifier(max_iter=1, max_depth=4,
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|
categorical_features=None)
|
|
assert cross_val_score(clf_no_cat, X, y).mean() < .9
|
|
|
|
clf_no_cat.set_params(max_depth=5)
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|
assert cross_val_score(clf_no_cat, X, y).mean() == 1
|
|
|
|
# Using OHEd data, we need less splits than with pure OEd data, but we
|
|
# still need more splits than with the native categorical splits
|
|
ct = make_column_transformer((OneHotEncoder(sparse=False), [1]),
|
|
remainder='passthrough')
|
|
X_ohe = ct.fit_transform(X)
|
|
clf_no_cat.set_params(max_depth=2)
|
|
assert cross_val_score(clf_no_cat, X_ohe, y).mean() < .9
|
|
|
|
clf_no_cat.set_params(max_depth=3)
|
|
assert cross_val_score(clf_no_cat, X_ohe, y).mean() == 1
|
|
|
|
|
|
@pytest.mark.parametrize('Est', (HistGradientBoostingClassifier,
|
|
HistGradientBoostingRegressor))
|
|
@pytest.mark.parametrize("categorical_features, monotonic_cst, expected_msg", [
|
|
(["hello", "world"], None,
|
|
("categorical_features must be an array-like of bools or array-like of "
|
|
"ints.")),
|
|
([0, -1], None,
|
|
(r"categorical_features set as integer indices must be in "
|
|
r"\[0, n_features - 1\]")),
|
|
([True, True, False, False, True], None,
|
|
r"categorical_features set as a boolean mask must have shape "
|
|
r"\(n_features,\)"),
|
|
([True, True, False, False], [0, -1, 0, 1],
|
|
"Categorical features cannot have monotonic constraints"),
|
|
])
|
|
def test_categorical_spec_errors(Est, categorical_features, monotonic_cst,
|
|
expected_msg):
|
|
# Test errors when categories are specified incorrectly
|
|
n_samples = 100
|
|
X, y = make_classification(random_state=0, n_features=4,
|
|
n_samples=n_samples)
|
|
rng = np.random.RandomState(0)
|
|
X[:, 0] = rng.randint(0, 10, size=n_samples)
|
|
X[:, 1] = rng.randint(0, 10, size=n_samples)
|
|
est = Est(categorical_features=categorical_features,
|
|
monotonic_cst=monotonic_cst)
|
|
|
|
with pytest.raises(ValueError, match=expected_msg):
|
|
est.fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize('Est', (HistGradientBoostingClassifier,
|
|
HistGradientBoostingRegressor))
|
|
@pytest.mark.parametrize('categorical_features', ([False, False], []))
|
|
@pytest.mark.parametrize('as_array', (True, False))
|
|
def test_categorical_spec_no_categories(Est, categorical_features, as_array):
|
|
# Make sure we can properly detect that no categorical features are present
|
|
# even if the categorical_features parameter is not None
|
|
X = np.arange(10).reshape(5, 2)
|
|
y = np.arange(5)
|
|
if as_array:
|
|
categorical_features = np.asarray(categorical_features)
|
|
est = Est(categorical_features=categorical_features).fit(X, y)
|
|
assert est.is_categorical_ is None
|
|
|
|
|
|
@pytest.mark.parametrize('Est', (HistGradientBoostingClassifier,
|
|
HistGradientBoostingRegressor))
|
|
def test_categorical_bad_encoding_errors(Est):
|
|
# Test errors when categories are encoded incorrectly
|
|
|
|
gb = Est(categorical_features=[True], max_bins=2)
|
|
|
|
X = np.array([[0, 1, 2]]).T
|
|
y = np.arange(3)
|
|
msg = ("Categorical feature at index 0 is expected to have a "
|
|
"cardinality <= 2")
|
|
with pytest.raises(ValueError, match=msg):
|
|
gb.fit(X, y)
|
|
|
|
X = np.array([[0, 2]]).T
|
|
y = np.arange(2)
|
|
msg = ("Categorical feature at index 0 is expected to be encoded with "
|
|
"values < 2")
|
|
with pytest.raises(ValueError, match=msg):
|
|
gb.fit(X, y)
|
|
|
|
# nans are ignored in the counts
|
|
X = np.array([[0, 1, np.nan]]).T
|
|
y = np.arange(3)
|
|
gb.fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize('Est', (HistGradientBoostingClassifier,
|
|
HistGradientBoostingRegressor))
|
|
def test_uint8_predict(Est):
|
|
# Non regression test for
|
|
# https://github.com/scikit-learn/scikit-learn/issues/18408
|
|
# Make sure X can be of dtype uint8 (i.e. X_BINNED_DTYPE) in predict. It
|
|
# will be converted to X_DTYPE.
|
|
|
|
rng = np.random.RandomState(0)
|
|
|
|
X = rng.randint(0, 100, size=(10, 2)).astype(np.uint8)
|
|
y = rng.randint(0, 2, size=10).astype(np.uint8)
|
|
est = Est()
|
|
est.fit(X, y)
|
|
est.predict(X)
|