221 lines
8.9 KiB
Python
221 lines
8.9 KiB
Python
from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn.datasets import make_classification, make_regression
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import numpy as np
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import pytest
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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.binning import _BinMapper
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from sklearn.ensemble._hist_gradient_boosting.utils import (
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get_equivalent_estimator)
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@pytest.mark.parametrize('seed', range(5))
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@pytest.mark.parametrize('min_samples_leaf', (1, 20))
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@pytest.mark.parametrize('n_samples, max_leaf_nodes', [
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(255, 4096),
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(1000, 8),
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])
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def test_same_predictions_regression(seed, min_samples_leaf, n_samples,
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max_leaf_nodes):
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# Make sure sklearn has the same predictions as lightgbm for easy targets.
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#
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# In particular when the size of the trees are bound and the number of
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# samples is large enough, the structure of the prediction trees found by
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# LightGBM and sklearn should be exactly identical.
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#
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# Notes:
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# - Several candidate splits may have equal gains when the number of
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# samples in a node is low (and because of float errors). Therefore the
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# predictions on the test set might differ if the structure of the tree
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# is not exactly the same. To avoid this issue we only compare the
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# predictions on the test set when the number of samples is large enough
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# and max_leaf_nodes is low enough.
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# - To ignore discrepancies caused by small differences the binning
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# strategy, data is pre-binned if n_samples > 255.
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# - We don't check the least_absolute_deviation loss here. This is because
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# LightGBM's computation of the median (used for the initial value of
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# raw_prediction) is a bit off (they'll e.g. return midpoints when there
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# is no need to.). Since these tests only run 1 iteration, the
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# discrepancy between the initial values leads to biggish differences in
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# the predictions. These differences are much smaller with more
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# iterations.
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pytest.importorskip("lightgbm")
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rng = np.random.RandomState(seed=seed)
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max_iter = 1
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max_bins = 255
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X, y = make_regression(n_samples=n_samples, n_features=5,
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n_informative=5, random_state=0)
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if n_samples > 255:
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# bin data and convert it to float32 so that the estimator doesn't
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# treat it as pre-binned
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X = _BinMapper(n_bins=max_bins + 1).fit_transform(X).astype(np.float32)
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
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est_sklearn = HistGradientBoostingRegressor(
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max_iter=max_iter,
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max_bins=max_bins,
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learning_rate=1,
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early_stopping=False,
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min_samples_leaf=min_samples_leaf,
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max_leaf_nodes=max_leaf_nodes)
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est_lightgbm = get_equivalent_estimator(est_sklearn, lib='lightgbm')
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est_lightgbm.fit(X_train, y_train)
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est_sklearn.fit(X_train, y_train)
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# We need X to be treated an numerical data, not pre-binned data.
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X_train, X_test = X_train.astype(np.float32), X_test.astype(np.float32)
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pred_lightgbm = est_lightgbm.predict(X_train)
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pred_sklearn = est_sklearn.predict(X_train)
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# less than 1% of the predictions are different up to the 3rd decimal
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assert np.mean(abs(pred_lightgbm - pred_sklearn) > 1e-3) < .011
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if max_leaf_nodes < 10 and n_samples >= 1000:
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pred_lightgbm = est_lightgbm.predict(X_test)
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pred_sklearn = est_sklearn.predict(X_test)
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# less than 1% of the predictions are different up to the 4th decimal
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assert np.mean(abs(pred_lightgbm - pred_sklearn) > 1e-4) < .01
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@pytest.mark.parametrize('seed', range(5))
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@pytest.mark.parametrize('min_samples_leaf', (1, 20))
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@pytest.mark.parametrize('n_samples, max_leaf_nodes', [
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(255, 4096),
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(1000, 8),
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])
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def test_same_predictions_classification(seed, min_samples_leaf, n_samples,
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max_leaf_nodes):
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# Same as test_same_predictions_regression but for classification
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pytest.importorskip("lightgbm")
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rng = np.random.RandomState(seed=seed)
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max_iter = 1
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max_bins = 255
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X, y = make_classification(n_samples=n_samples, n_classes=2, n_features=5,
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n_informative=5, n_redundant=0, random_state=0)
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if n_samples > 255:
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# bin data and convert it to float32 so that the estimator doesn't
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# treat it as pre-binned
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X = _BinMapper(n_bins=max_bins + 1).fit_transform(X).astype(np.float32)
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
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est_sklearn = HistGradientBoostingClassifier(
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loss='binary_crossentropy',
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max_iter=max_iter,
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max_bins=max_bins,
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learning_rate=1,
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early_stopping=False,
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min_samples_leaf=min_samples_leaf,
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max_leaf_nodes=max_leaf_nodes)
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est_lightgbm = get_equivalent_estimator(est_sklearn, lib='lightgbm')
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est_lightgbm.fit(X_train, y_train)
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est_sklearn.fit(X_train, y_train)
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# We need X to be treated an numerical data, not pre-binned data.
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X_train, X_test = X_train.astype(np.float32), X_test.astype(np.float32)
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pred_lightgbm = est_lightgbm.predict(X_train)
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pred_sklearn = est_sklearn.predict(X_train)
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assert np.mean(pred_sklearn == pred_lightgbm) > .89
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acc_lightgbm = accuracy_score(y_train, pred_lightgbm)
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acc_sklearn = accuracy_score(y_train, pred_sklearn)
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np.testing.assert_almost_equal(acc_lightgbm, acc_sklearn)
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if max_leaf_nodes < 10 and n_samples >= 1000:
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pred_lightgbm = est_lightgbm.predict(X_test)
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pred_sklearn = est_sklearn.predict(X_test)
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assert np.mean(pred_sklearn == pred_lightgbm) > .89
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acc_lightgbm = accuracy_score(y_test, pred_lightgbm)
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acc_sklearn = accuracy_score(y_test, pred_sklearn)
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np.testing.assert_almost_equal(acc_lightgbm, acc_sklearn, decimal=2)
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@pytest.mark.parametrize('seed', range(5))
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@pytest.mark.parametrize('min_samples_leaf', (1, 20))
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@pytest.mark.parametrize('n_samples, max_leaf_nodes', [
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(255, 4096),
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(10000, 8),
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])
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def test_same_predictions_multiclass_classification(
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seed, min_samples_leaf, n_samples, max_leaf_nodes):
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# Same as test_same_predictions_regression but for classification
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pytest.importorskip("lightgbm")
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rng = np.random.RandomState(seed=seed)
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max_iter = 1
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max_bins = 255
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lr = 1
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X, y = make_classification(n_samples=n_samples, n_classes=3, n_features=5,
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n_informative=5, n_redundant=0,
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n_clusters_per_class=1, random_state=0)
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if n_samples > 255:
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# bin data and convert it to float32 so that the estimator doesn't
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# treat it as pre-binned
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X = _BinMapper(n_bins=max_bins + 1).fit_transform(X).astype(np.float32)
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
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est_sklearn = HistGradientBoostingClassifier(
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loss='categorical_crossentropy',
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max_iter=max_iter,
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max_bins=max_bins,
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learning_rate=lr,
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early_stopping=False,
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min_samples_leaf=min_samples_leaf,
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max_leaf_nodes=max_leaf_nodes)
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est_lightgbm = get_equivalent_estimator(est_sklearn, lib='lightgbm')
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est_lightgbm.fit(X_train, y_train)
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est_sklearn.fit(X_train, y_train)
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# We need X to be treated an numerical data, not pre-binned data.
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X_train, X_test = X_train.astype(np.float32), X_test.astype(np.float32)
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pred_lightgbm = est_lightgbm.predict(X_train)
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pred_sklearn = est_sklearn.predict(X_train)
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assert np.mean(pred_sklearn == pred_lightgbm) > .89
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proba_lightgbm = est_lightgbm.predict_proba(X_train)
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proba_sklearn = est_sklearn.predict_proba(X_train)
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# assert more than 75% of the predicted probabilities are the same up to
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# the second decimal
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assert np.mean(np.abs(proba_lightgbm - proba_sklearn) < 1e-2) > .75
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acc_lightgbm = accuracy_score(y_train, pred_lightgbm)
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acc_sklearn = accuracy_score(y_train, pred_sklearn)
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np.testing.assert_almost_equal(acc_lightgbm, acc_sklearn, decimal=2)
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if max_leaf_nodes < 10 and n_samples >= 1000:
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pred_lightgbm = est_lightgbm.predict(X_test)
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pred_sklearn = est_sklearn.predict(X_test)
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assert np.mean(pred_sklearn == pred_lightgbm) > .89
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proba_lightgbm = est_lightgbm.predict_proba(X_train)
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proba_sklearn = est_sklearn.predict_proba(X_train)
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# assert more than 75% of the predicted probabilities are the same up
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# to the second decimal
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assert np.mean(np.abs(proba_lightgbm - proba_sklearn) < 1e-2) > .75
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acc_lightgbm = accuracy_score(y_test, pred_lightgbm)
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acc_sklearn = accuracy_score(y_test, pred_sklearn)
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np.testing.assert_almost_equal(acc_lightgbm, acc_sklearn, decimal=2)
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