982 lines
35 KiB
Python
982 lines
35 KiB
Python
"""
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Testing for the gradient boosting module (sklearn.ensemble.gradient_boosting).
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"""
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import numpy as np
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import warnings
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from sklearn import datasets
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from sklearn.base import clone
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.ensemble import GradientBoostingRegressor
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from sklearn.ensemble.gradient_boosting import ZeroEstimator
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from sklearn.metrics import mean_squared_error
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from sklearn.utils import check_random_state, tosequence
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from sklearn.utils.testing import assert_almost_equal, clean_warning_registry
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_greater
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from sklearn.utils.testing import assert_less
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_warns
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from sklearn.utils.validation import DataConversionWarning
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# toy sample
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
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y = [-1, -1, -1, 1, 1, 1]
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T = [[-1, -1], [2, 2], [3, 2]]
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true_result = [-1, 1, 1]
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rng = np.random.RandomState(0)
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# also load the boston dataset
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# and randomly permute it
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boston = datasets.load_boston()
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perm = rng.permutation(boston.target.size)
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boston.data = boston.data[perm]
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boston.target = boston.target[perm]
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# also load the iris dataset
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# and randomly permute it
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iris = datasets.load_iris()
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perm = rng.permutation(iris.target.size)
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iris.data = iris.data[perm]
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iris.target = iris.target[perm]
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def test_classification_toy():
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"""Check classification on a toy dataset."""
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for loss in ('deviance', 'exponential'):
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clf = GradientBoostingClassifier(loss=loss, n_estimators=10,
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random_state=1)
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assert_raises(ValueError, clf.predict, T)
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clf.fit(X, y)
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assert_array_equal(clf.predict(T), true_result)
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assert_equal(10, len(clf.estimators_))
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deviance_decrease = (clf.train_score_[:-1] - clf.train_score_[1:])
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assert np.any(deviance_decrease >= 0.0), \
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"Train deviance does not monotonically decrease."
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def test_parameter_checks():
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"""Check input parameter validation."""
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assert_raises(ValueError,
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GradientBoostingClassifier(n_estimators=0).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(n_estimators=-1).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(learning_rate=0.0).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(learning_rate=-1.0).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(loss='foobar').fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(min_samples_split=0.0).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(min_samples_split=-1.0).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(min_samples_leaf=0).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(min_samples_leaf=-1.).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(min_weight_fraction_leaf=-1.).fit,
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X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(min_weight_fraction_leaf=0.6).fit,
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X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(subsample=0.0).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(subsample=1.1).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(subsample=-0.1).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(max_depth=-0.1).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(max_depth=0).fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(init={}).fit, X, y)
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# test fit before feature importance
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assert_raises(ValueError,
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lambda: GradientBoostingClassifier().feature_importances_)
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# deviance requires ``n_classes >= 2``.
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assert_raises(ValueError,
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lambda X, y: GradientBoostingClassifier(
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loss='deviance').fit(X, y),
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X, [0, 0, 0, 0])
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def test_loss_function():
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assert_raises(ValueError,
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GradientBoostingClassifier(loss='ls').fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(loss='lad').fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(loss='quantile').fit, X, y)
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assert_raises(ValueError,
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GradientBoostingClassifier(loss='huber').fit, X, y)
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assert_raises(ValueError,
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GradientBoostingRegressor(loss='deviance').fit, X, y)
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assert_raises(ValueError,
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GradientBoostingRegressor(loss='exponential').fit, X, y)
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def test_classification_synthetic():
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"""Test GradientBoostingClassifier on synthetic dataset used by
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Hastie et al. in ESLII Example 12.7. """
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X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
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X_train, X_test = X[:2000], X[2000:]
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y_train, y_test = y[:2000], y[2000:]
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for loss in ('deviance', 'exponential'):
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gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=1,
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max_depth=1, loss=loss,
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learning_rate=1.0, random_state=0)
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gbrt.fit(X_train, y_train)
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error_rate = (1.0 - gbrt.score(X_test, y_test))
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assert error_rate < 0.09, \
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"GB(loss={}) failed with error {}".format(loss, error_rate)
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gbrt = GradientBoostingClassifier(n_estimators=200, min_samples_split=1,
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max_depth=1,
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learning_rate=1.0, subsample=0.5,
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random_state=0)
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gbrt.fit(X_train, y_train)
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error_rate = (1.0 - gbrt.score(X_test, y_test))
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assert error_rate < 0.08, ("Stochastic GradientBoostingClassifier(loss={}) "
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"failed with error {}".format(loss, error_rate))
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def test_boston():
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"""Check consistency on dataset boston house prices with least squares
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and least absolute deviation. """
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for loss in ("ls", "lad", "huber"):
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for subsample in (1.0, 0.5):
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last_y_pred = None
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for i, sample_weight in enumerate((None, np.ones(len(boston.target)),
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2 * np.ones(len(boston.target)))):
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clf = GradientBoostingRegressor(n_estimators=100, loss=loss,
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max_depth=4, subsample=subsample,
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min_samples_split=1,
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random_state=1)
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assert_raises(ValueError, clf.predict, boston.data)
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clf.fit(boston.data, boston.target,
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sample_weight=sample_weight)
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y_pred = clf.predict(boston.data)
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mse = mean_squared_error(boston.target, y_pred)
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assert mse < 6.0, "Failed with loss %s and " \
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"mse = %.4f" % (loss, mse)
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if last_y_pred is not None:
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np.testing.assert_array_almost_equal(
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last_y_pred, y_pred,
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err_msg='pred_%d doesnt match last pred_%d for loss %r and subsample %r. '
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% (i, i - 1, loss, subsample))
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last_y_pred = y_pred
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def test_iris():
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"""Check consistency on dataset iris."""
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for subsample in (1.0, 0.5):
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for sample_weight in (None, np.ones(len(iris.target))):
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clf = GradientBoostingClassifier(n_estimators=100, loss='deviance',
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random_state=1, subsample=subsample)
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clf.fit(iris.data, iris.target, sample_weight=sample_weight)
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score = clf.score(iris.data, iris.target)
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assert score > 0.9, "Failed with subsample %.1f " \
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"and score = %f" % (subsample, score)
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def test_regression_synthetic():
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"""Test on synthetic regression datasets used in Leo Breiman,
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`Bagging Predictors?. Machine Learning 24(2): 123-140 (1996). """
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random_state = check_random_state(1)
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regression_params = {'n_estimators': 100, 'max_depth': 4,
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'min_samples_split': 1, 'learning_rate': 0.1,
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'loss': 'ls'}
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# Friedman1
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X, y = datasets.make_friedman1(n_samples=1200,
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random_state=random_state, noise=1.0)
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X_train, y_train = X[:200], y[:200]
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X_test, y_test = X[200:], y[200:]
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clf = GradientBoostingRegressor()
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clf.fit(X_train, y_train)
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mse = mean_squared_error(y_test, clf.predict(X_test))
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assert mse < 5.0, "Failed on Friedman1 with mse = %.4f" % mse
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# Friedman2
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X, y = datasets.make_friedman2(n_samples=1200, random_state=random_state)
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X_train, y_train = X[:200], y[:200]
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X_test, y_test = X[200:], y[200:]
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clf = GradientBoostingRegressor(**regression_params)
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clf.fit(X_train, y_train)
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mse = mean_squared_error(y_test, clf.predict(X_test))
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assert mse < 1700.0, "Failed on Friedman2 with mse = %.4f" % mse
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# Friedman3
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X, y = datasets.make_friedman3(n_samples=1200, random_state=random_state)
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X_train, y_train = X[:200], y[:200]
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X_test, y_test = X[200:], y[200:]
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clf = GradientBoostingRegressor(**regression_params)
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clf.fit(X_train, y_train)
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mse = mean_squared_error(y_test, clf.predict(X_test))
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assert mse < 0.015, "Failed on Friedman3 with mse = %.4f" % mse
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def test_feature_importances():
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X = np.array(boston.data, dtype=np.float32)
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y = np.array(boston.target, dtype=np.float32)
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clf = GradientBoostingRegressor(n_estimators=100, max_depth=5,
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min_samples_split=1, random_state=1)
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clf.fit(X, y)
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#feature_importances = clf.feature_importances_
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assert_true(hasattr(clf, 'feature_importances_'))
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X_new = clf.transform(X, threshold="mean")
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assert_less(X_new.shape[1], X.shape[1])
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feature_mask = clf.feature_importances_ > clf.feature_importances_.mean()
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assert_array_almost_equal(X_new, X[:, feature_mask])
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# true feature importance ranking
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# true_ranking = np.array([3, 1, 8, 2, 10, 9, 4, 11, 0, 6, 7, 5, 12])
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# assert_array_equal(true_ranking, feature_importances.argsort())
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def test_probability_log():
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"""Predict probabilities."""
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clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
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assert_raises(ValueError, clf.predict_proba, T)
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clf.fit(X, y)
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assert_array_equal(clf.predict(T), true_result)
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# check if probabilities are in [0, 1].
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y_proba = clf.predict_proba(T)
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assert np.all(y_proba >= 0.0)
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assert np.all(y_proba <= 1.0)
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# derive predictions from probabilities
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y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
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assert_array_equal(y_pred, true_result)
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def test_check_inputs():
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"""Test input checks (shape and type of X and y)."""
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clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
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assert_raises(ValueError, clf.fit, X, y + [0, 1])
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from scipy import sparse
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X_sparse = sparse.csr_matrix(X)
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clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
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assert_raises(TypeError, clf.fit, X_sparse, y)
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clf = GradientBoostingClassifier().fit(X, y)
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assert_raises(TypeError, clf.predict, X_sparse)
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clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
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assert_raises(ValueError, clf.fit, X, y,
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sample_weight=([1] * len(y)) + [0, 1])
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def test_check_inputs_predict():
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"""X has wrong shape """
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clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
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clf.fit(X, y)
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x = np.array([1.0, 2.0])[:, np.newaxis]
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assert_raises(ValueError, clf.predict, x)
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x = np.array([])
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assert_raises(ValueError, clf.predict, x)
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x = np.array([1.0, 2.0, 3.0])[:, np.newaxis]
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assert_raises(ValueError, clf.predict, x)
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clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
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clf.fit(X, rng.rand(len(X)))
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x = np.array([1.0, 2.0])[:, np.newaxis]
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assert_raises(ValueError, clf.predict, x)
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x = np.array([])
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assert_raises(ValueError, clf.predict, x)
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x = np.array([1.0, 2.0, 3.0])[:, np.newaxis]
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assert_raises(ValueError, clf.predict, x)
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def test_check_max_features():
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"""test if max_features is valid. """
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clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
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max_features=0)
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assert_raises(ValueError, clf.fit, X, y)
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clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
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max_features=(len(X[0]) + 1))
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assert_raises(ValueError, clf.fit, X, y)
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def test_max_feature_regression():
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"""Test to make sure random state is set properly. """
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X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
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X_train, X_test = X[:2000], X[2000:]
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y_train, y_test = y[:2000], y[2000:]
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gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=5,
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max_depth=2, learning_rate=.1,
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max_features=2, random_state=1)
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gbrt.fit(X_train, y_train)
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deviance = gbrt.loss_(y_test, gbrt.decision_function(X_test))
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assert_true(deviance < 0.5, "GB failed with deviance %.4f" % deviance)
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def test_max_feature_auto():
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"""Test if max features is set properly for floats and str. """
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X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
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_, n_features = X.shape
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X_train = X[:2000]
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y_train = y[:2000]
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gbrt = GradientBoostingClassifier(n_estimators=1, max_features='auto')
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gbrt.fit(X_train, y_train)
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assert_equal(gbrt.max_features_, int(np.sqrt(n_features)))
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gbrt = GradientBoostingRegressor(n_estimators=1, max_features='auto')
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gbrt.fit(X_train, y_train)
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assert_equal(gbrt.max_features_, n_features)
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gbrt = GradientBoostingRegressor(n_estimators=1, max_features=0.3)
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gbrt.fit(X_train, y_train)
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assert_equal(gbrt.max_features_, int(n_features * 0.3))
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gbrt = GradientBoostingRegressor(n_estimators=1, max_features='sqrt')
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gbrt.fit(X_train, y_train)
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assert_equal(gbrt.max_features_, int(np.sqrt(n_features)))
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gbrt = GradientBoostingRegressor(n_estimators=1, max_features='log2')
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gbrt.fit(X_train, y_train)
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assert_equal(gbrt.max_features_, int(np.log2(n_features)))
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def test_staged_predict():
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"""Test whether staged decision function eventually gives
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the same prediction.
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"""
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X, y = datasets.make_friedman1(n_samples=1200,
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random_state=1, noise=1.0)
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X_train, y_train = X[:200], y[:200]
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X_test = X[200:]
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clf = GradientBoostingRegressor()
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# test raise ValueError if not fitted
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assert_raises(ValueError, lambda X: np.fromiter(
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clf.staged_predict(X), dtype=np.float64), X_test)
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clf.fit(X_train, y_train)
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y_pred = clf.predict(X_test)
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# test if prediction for last stage equals ``predict``
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for y in clf.staged_predict(X_test):
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assert_equal(y.shape, y_pred.shape)
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assert_array_equal(y_pred, y)
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def test_staged_predict_proba():
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"""Test whether staged predict proba eventually gives
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the same prediction.
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"""
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X, y = datasets.make_hastie_10_2(n_samples=1200,
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random_state=1)
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X_train, y_train = X[:200], y[:200]
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X_test, y_test = X[200:], y[200:]
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clf = GradientBoostingClassifier(n_estimators=20)
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# test raise ValueError if not fitted
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assert_raises(ValueError, lambda X: np.fromiter(
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clf.staged_predict_proba(X), dtype=np.float64), X_test)
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clf.fit(X_train, y_train)
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# test if prediction for last stage equals ``predict``
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for y_pred in clf.staged_predict(X_test):
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assert_equal(y_test.shape, y_pred.shape)
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assert_array_equal(clf.predict(X_test), y_pred)
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# test if prediction for last stage equals ``predict_proba``
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for staged_proba in clf.staged_predict_proba(X_test):
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assert_equal(y_test.shape[0], staged_proba.shape[0])
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assert_equal(2, staged_proba.shape[1])
|
|
|
|
assert_array_equal(clf.predict_proba(X_test), staged_proba)
|
|
|
|
|
|
def test_serialization():
|
|
"""Check model serialization."""
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
clf.fit(X, y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert_equal(100, len(clf.estimators_))
|
|
|
|
try:
|
|
import cPickle as pickle
|
|
except ImportError:
|
|
import pickle
|
|
|
|
serialized_clf = pickle.dumps(clf, protocol=pickle.HIGHEST_PROTOCOL)
|
|
clf = None
|
|
clf = pickle.loads(serialized_clf)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert_equal(100, len(clf.estimators_))
|
|
|
|
|
|
def test_degenerate_targets():
|
|
"""Check if we can fit even though all targets are equal. """
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
# classifier should raise exception
|
|
assert_raises(ValueError, clf.fit, X, np.ones(len(X)))
|
|
|
|
clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
|
|
clf.fit(X, np.ones(len(X)))
|
|
clf.predict(rng.rand(2))
|
|
assert_array_equal(np.ones((1,), dtype=np.float64),
|
|
clf.predict(rng.rand(2)))
|
|
|
|
|
|
def test_quantile_loss():
|
|
"""Check if quantile loss with alpha=0.5 equals lad. """
|
|
clf_quantile = GradientBoostingRegressor(n_estimators=100, loss='quantile',
|
|
max_depth=4, alpha=0.5,
|
|
random_state=7)
|
|
|
|
clf_quantile.fit(boston.data, boston.target)
|
|
y_quantile = clf_quantile.predict(boston.data)
|
|
|
|
clf_lad = GradientBoostingRegressor(n_estimators=100, loss='lad',
|
|
max_depth=4, random_state=7)
|
|
|
|
clf_lad.fit(boston.data, boston.target)
|
|
y_lad = clf_lad.predict(boston.data)
|
|
assert_array_almost_equal(y_quantile, y_lad, decimal=4)
|
|
|
|
|
|
def test_symbol_labels():
|
|
"""Test with non-integer class labels. """
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
symbol_y = tosequence(map(str, y))
|
|
|
|
clf.fit(X, symbol_y)
|
|
assert_array_equal(clf.predict(T), tosequence(map(str, true_result)))
|
|
assert_equal(100, len(clf.estimators_))
|
|
|
|
|
|
def test_float_class_labels():
|
|
"""Test with float class labels. """
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
float_y = np.asarray(y, dtype=np.float32)
|
|
|
|
clf.fit(X, float_y)
|
|
assert_array_equal(clf.predict(T),
|
|
np.asarray(true_result, dtype=np.float32))
|
|
assert_equal(100, len(clf.estimators_))
|
|
|
|
|
|
def test_shape_y():
|
|
"""Test with float class labels. """
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
y_ = np.asarray(y, dtype=np.int32)
|
|
y_ = y_[:, np.newaxis]
|
|
|
|
# This will raise a DataConversionWarning that we want to
|
|
# "always" raise, elsewhere the warnings gets ignored in the
|
|
# later tests, and the tests that check for this warning fail
|
|
assert_warns(DataConversionWarning, clf.fit, X, y_)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert_equal(100, len(clf.estimators_))
|
|
|
|
|
|
def test_mem_layout():
|
|
"""Test with different memory layouts of X and y"""
|
|
X_ = np.asfortranarray(X)
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(X_, y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert_equal(100, len(clf.estimators_))
|
|
|
|
X_ = np.ascontiguousarray(X)
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(X_, y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert_equal(100, len(clf.estimators_))
|
|
|
|
y_ = np.asarray(y, dtype=np.int32)
|
|
y_ = np.ascontiguousarray(y_)
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(X, y_)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert_equal(100, len(clf.estimators_))
|
|
|
|
y_ = np.asarray(y, dtype=np.int32)
|
|
y_ = np.asfortranarray(y_)
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(X, y_)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert_equal(100, len(clf.estimators_))
|
|
|
|
|
|
def test_oob_improvement():
|
|
"""Test if oob improvement has correct shape and regression test. """
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
|
|
subsample=0.5)
|
|
clf.fit(X, y)
|
|
assert clf.oob_improvement_.shape[0] == 100
|
|
# hard-coded regression test - change if modification in OOB computation
|
|
assert_array_almost_equal(clf.oob_improvement_[:5],
|
|
np.array([0.19, 0.15, 0.12, -0.12, -0.11]),
|
|
decimal=2)
|
|
|
|
|
|
def test_oob_improvement_raise():
|
|
"""Test if oob improvement has correct shape. """
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
|
|
subsample=1.0)
|
|
clf.fit(X, y)
|
|
assert_raises(AttributeError, lambda: clf.oob_improvement_)
|
|
|
|
|
|
def test_oob_multilcass_iris():
|
|
"""Check OOB improvement on multi-class dataset."""
|
|
clf = GradientBoostingClassifier(n_estimators=100, loss='deviance',
|
|
random_state=1, subsample=0.5)
|
|
clf.fit(iris.data, iris.target)
|
|
score = clf.score(iris.data, iris.target)
|
|
assert score > 0.9, "Failed with subsample %.1f " \
|
|
"and score = %f" % (0.5, score)
|
|
|
|
assert clf.oob_improvement_.shape[0] == clf.n_estimators
|
|
# hard-coded regression test - change if modification in OOB computation
|
|
# FIXME: the following snippet does not yield the same results on 32 bits
|
|
# assert_array_almost_equal(clf.oob_improvement_[:5],
|
|
# np.array([12.68, 10.45, 8.18, 6.43, 5.13]),
|
|
# decimal=2)
|
|
|
|
|
|
def test_verbose_output():
|
|
"""Check verbose=1 does not cause error. """
|
|
from sklearn.externals.six.moves import cStringIO as StringIO
|
|
import sys
|
|
old_stdout = sys.stdout
|
|
sys.stdout = StringIO()
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
|
|
verbose=1, subsample=0.8)
|
|
clf.fit(X, y)
|
|
verbose_output = sys.stdout
|
|
sys.stdout = old_stdout
|
|
|
|
# check output
|
|
verbose_output.seek(0)
|
|
header = verbose_output.readline().rstrip()
|
|
# with OOB
|
|
true_header = ' '.join(['%10s'] + ['%16s'] * 3) % (
|
|
'Iter', 'Train Loss', 'OOB Improve', 'Remaining Time')
|
|
assert_equal(true_header, header)
|
|
|
|
n_lines = sum(1 for l in verbose_output.readlines())
|
|
# one for 1-10 and then 9 for 20-100
|
|
assert_equal(10 + 9, n_lines)
|
|
|
|
|
|
def test_more_verbose_output():
|
|
"""Check verbose=2 does not cause error. """
|
|
from sklearn.externals.six.moves import cStringIO as StringIO
|
|
import sys
|
|
old_stdout = sys.stdout
|
|
sys.stdout = StringIO()
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
|
|
verbose=2)
|
|
clf.fit(X, y)
|
|
verbose_output = sys.stdout
|
|
sys.stdout = old_stdout
|
|
|
|
# check output
|
|
verbose_output.seek(0)
|
|
header = verbose_output.readline().rstrip()
|
|
# no OOB
|
|
true_header = ' '.join(['%10s'] + ['%16s'] * 2) % (
|
|
'Iter', 'Train Loss', 'Remaining Time')
|
|
assert_equal(true_header, header)
|
|
|
|
n_lines = sum(1 for l in verbose_output.readlines())
|
|
# 100 lines for n_estimators==100
|
|
assert_equal(100, n_lines)
|
|
|
|
|
|
def test_warm_start():
|
|
"""Test if warm start equals fit. """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=200, max_depth=1)
|
|
est.fit(X, y)
|
|
|
|
est_ws = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est_ws.fit(X, y)
|
|
est_ws.set_params(n_estimators=200)
|
|
est_ws.fit(X, y)
|
|
|
|
assert_array_almost_equal(est_ws.predict(X), est.predict(X))
|
|
|
|
|
|
def test_warm_start_n_estimators():
|
|
"""Test if warm start equals fit - set n_estimators. """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=300, max_depth=1)
|
|
est.fit(X, y)
|
|
|
|
est_ws = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est_ws.fit(X, y)
|
|
est_ws.set_params(n_estimators=300)
|
|
est_ws.fit(X, y)
|
|
|
|
assert_array_almost_equal(est_ws.predict(X), est.predict(X))
|
|
|
|
|
|
def test_warm_start_max_depth():
|
|
"""Test if possible to fit trees of different depth in ensemble. """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est.fit(X, y)
|
|
est.set_params(n_estimators=110, max_depth=2)
|
|
est.fit(X, y)
|
|
|
|
# last 10 trees have different depth
|
|
assert est.estimators_[0, 0].max_depth == 1
|
|
for i in range(1, 11):
|
|
assert est.estimators_[-i, 0].max_depth == 2
|
|
|
|
|
|
def test_warm_start_clear():
|
|
"""Test if fit clears state. """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=100, max_depth=1)
|
|
est.fit(X, y)
|
|
|
|
est_2 = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est_2.fit(X, y) # inits state
|
|
est_2.set_params(warm_start=False)
|
|
est_2.fit(X, y) # clears old state and equals est
|
|
|
|
assert_array_almost_equal(est_2.predict(X), est.predict(X))
|
|
|
|
|
|
def test_warm_start_zero_n_estimators():
|
|
"""Test if warm start with zero n_estimators raises error """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est.fit(X, y)
|
|
est.set_params(n_estimators=0)
|
|
assert_raises(ValueError, est.fit, X, y)
|
|
|
|
|
|
def test_warm_start_smaller_n_estimators():
|
|
"""Test if warm start with smaller n_estimators raises error """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est.fit(X, y)
|
|
est.set_params(n_estimators=99)
|
|
assert_raises(ValueError, est.fit, X, y)
|
|
|
|
|
|
def test_warm_start_equal_n_estimators():
|
|
"""Test if warm start with equal n_estimators does nothing """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=100, max_depth=1)
|
|
est.fit(X, y)
|
|
|
|
est2 = clone(est)
|
|
est2.set_params(n_estimators=est.n_estimators, warm_start=True)
|
|
est2.fit(X, y)
|
|
|
|
assert_array_almost_equal(est2.predict(X), est.predict(X))
|
|
|
|
|
|
def test_warm_start_oob_switch():
|
|
"""Test if oob can be turned on during warm start. """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est.fit(X, y)
|
|
est.set_params(n_estimators=110, subsample=0.5)
|
|
est.fit(X, y)
|
|
|
|
assert_array_equal(est.oob_improvement_[:100], np.zeros(100))
|
|
# the last 10 are not zeros
|
|
assert_array_equal(est.oob_improvement_[-10:] == 0.0,
|
|
np.zeros(10, dtype=np.bool))
|
|
|
|
|
|
def test_warm_start_oob():
|
|
"""Test if warm start OOB equals fit. """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=200, max_depth=1, subsample=0.5,
|
|
random_state=1)
|
|
est.fit(X, y)
|
|
|
|
est_ws = Cls(n_estimators=100, max_depth=1, subsample=0.5,
|
|
random_state=1, warm_start=True)
|
|
est_ws.fit(X, y)
|
|
est_ws.set_params(n_estimators=200)
|
|
est_ws.fit(X, y)
|
|
|
|
assert_array_almost_equal(est_ws.oob_improvement_[:100],
|
|
est.oob_improvement_[:100])
|
|
|
|
|
|
def early_stopping_monitor(i, est, locals):
|
|
"""Returns True on the 10th iteration. """
|
|
if i == 9:
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
def test_monitor_early_stopping():
|
|
"""Test if monitor return value works. """
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
|
|
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
|
|
est = Cls(n_estimators=20, max_depth=1, random_state=1, subsample=0.5)
|
|
est.fit(X, y, monitor=early_stopping_monitor)
|
|
assert_equal(est.n_estimators, 20) # this is not altered
|
|
assert_equal(est.estimators_.shape[0], 10)
|
|
assert_equal(est.train_score_.shape[0], 10)
|
|
assert_equal(est.oob_improvement_.shape[0], 10)
|
|
|
|
# try refit
|
|
est.set_params(n_estimators=30)
|
|
est.fit(X, y)
|
|
assert_equal(est.n_estimators, 30)
|
|
assert_equal(est.estimators_.shape[0], 30)
|
|
assert_equal(est.train_score_.shape[0], 30)
|
|
|
|
est = Cls(n_estimators=20, max_depth=1, random_state=1, subsample=0.5,
|
|
warm_start=True)
|
|
est.fit(X, y, monitor=early_stopping_monitor)
|
|
assert_equal(est.n_estimators, 20)
|
|
assert_equal(est.estimators_.shape[0], 10)
|
|
assert_equal(est.train_score_.shape[0], 10)
|
|
assert_equal(est.oob_improvement_.shape[0], 10)
|
|
|
|
# try refit
|
|
est.set_params(n_estimators=30, warm_start=False)
|
|
est.fit(X, y)
|
|
assert_equal(est.n_estimators, 30)
|
|
assert_equal(est.train_score_.shape[0], 30)
|
|
assert_equal(est.estimators_.shape[0], 30)
|
|
assert_equal(est.oob_improvement_.shape[0], 30)
|
|
|
|
|
|
def test_complete_classification():
|
|
"""Test greedy trees with max_depth + 1 leafs. """
|
|
from sklearn.tree._tree import TREE_LEAF
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
k = 4
|
|
|
|
est = GradientBoostingClassifier(n_estimators=20, max_depth=None,
|
|
random_state=1, max_leaf_nodes=k+1)
|
|
est.fit(X, y)
|
|
|
|
tree = est.estimators_[0, 0].tree_
|
|
assert_equal(tree.max_depth, k)
|
|
assert_equal(tree.children_left[tree.children_left == TREE_LEAF].shape[0],
|
|
k + 1)
|
|
|
|
|
|
def test_complete_regression():
|
|
"""Test greedy trees with max_depth + 1 leafs. """
|
|
from sklearn.tree._tree import TREE_LEAF
|
|
k = 4
|
|
|
|
est = GradientBoostingRegressor(n_estimators=20, max_depth=None,
|
|
random_state=1, max_leaf_nodes=k+1)
|
|
est.fit(boston.data, boston.target)
|
|
|
|
tree = est.estimators_[-1, 0].tree_
|
|
assert_equal(tree.children_left[tree.children_left == TREE_LEAF].shape[0],
|
|
k + 1)
|
|
|
|
|
|
def test_zero_estimator_reg():
|
|
"""Test if ZeroEstimator works for regression. """
|
|
est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
|
|
random_state=1, init=ZeroEstimator())
|
|
est.fit(boston.data, boston.target)
|
|
y_pred = est.predict(boston.data)
|
|
mse = mean_squared_error(boston.target, y_pred)
|
|
assert_almost_equal(mse, 33.0, decimal=0)
|
|
|
|
est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
|
|
random_state=1, init='zero')
|
|
est.fit(boston.data, boston.target)
|
|
y_pred = est.predict(boston.data)
|
|
mse = mean_squared_error(boston.target, y_pred)
|
|
assert_almost_equal(mse, 33.0, decimal=0)
|
|
|
|
est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
|
|
random_state=1, init='foobar')
|
|
assert_raises(ValueError, est.fit, boston.data, boston.target)
|
|
|
|
|
|
def test_zero_estimator_clf():
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"""Test if ZeroEstimator works for classification. """
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X = iris.data
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y = np.array(iris.target)
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est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
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random_state=1, init=ZeroEstimator())
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est.fit(X, y)
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assert est.score(X, y) > 0.96
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est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
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random_state=1, init='zero')
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est.fit(X, y)
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assert est.score(X, y) > 0.96
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# binary clf
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mask = y != 0
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y[mask] = 1
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y[~mask] = 0
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est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
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random_state=1, init='zero')
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est.fit(X, y)
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assert est.score(X, y) > 0.96
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est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
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random_state=1, init='foobar')
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assert_raises(ValueError, est.fit, X, y)
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def test_max_leaf_nodes_max_depth():
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"""Test preceedence of max_leaf_nodes over max_depth. """
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X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
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all_estimators = [GradientBoostingRegressor,
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GradientBoostingClassifier]
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k = 4
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for GBEstimator in all_estimators:
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est = GBEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y)
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tree = est.estimators_[0, 0].tree_
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assert_greater(tree.max_depth, 1)
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est = GBEstimator(max_depth=1).fit(X, y)
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tree = est.estimators_[0, 0].tree_
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assert_equal(tree.max_depth, 1)
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def test_warm_start_wo_nestimators_change():
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"""Test if warm_start does nothing if n_estimators is not changed.
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Regression test for #3513.
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"""
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clf = GradientBoostingClassifier(n_estimators=10, warm_start=True)
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clf.fit([[0, 1], [2, 3]], [0, 1])
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assert clf.estimators_.shape[0] == 10
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clf.fit([[0, 1], [2, 3]], [0, 1])
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assert clf.estimators_.shape[0] == 10
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|
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def test_probability_exponential():
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"""Predict probabilities."""
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clf = GradientBoostingClassifier(loss='exponential',
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n_estimators=100, random_state=1)
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assert_raises(ValueError, clf.predict_proba, T)
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clf.fit(X, y)
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assert_array_equal(clf.predict(T), true_result)
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# check if probabilities are in [0, 1].
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y_proba = clf.predict_proba(T)
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assert np.all(y_proba >= 0.0)
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assert np.all(y_proba <= 1.0)
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score = clf.decision_function(T).ravel()
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assert_array_equal(y_proba[:, 1],
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1.0 / (1.0 + np.exp(-2 * score)))
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# derive predictions from probabilities
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y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
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assert_array_equal(y_pred, true_result)
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def test_non_uniform_weights_toy_edge_case_reg():
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X = [[1, 0],
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[1, 0],
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[1, 0],
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[0, 1],
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]
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y = [0, 0, 1, 0]
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# ignore the first 2 training samples by setting their weight to 0
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sample_weight = [0, 0, 1, 1]
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for loss in ('ls', 'huber', 'lad', 'quantile'):
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gb = GradientBoostingRegressor(n_estimators=5)
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gb.fit(X, y, sample_weight=sample_weight)
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assert_true(gb.predict([[1, 0]])[0] > 0.5)
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|
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def test_non_uniform_weights_toy_edge_case_clf():
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X = [[1, 0],
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[1, 0],
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[1, 0],
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|
[0, 1],
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]
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y = [0, 0, 1, 0]
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# ignore the first 2 training samples by setting their weight to 0
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sample_weight = [0, 0, 1, 1]
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for loss in ('deviance', 'exponential'):
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gb = GradientBoostingClassifier(n_estimators=5)
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gb.fit(X, y, sample_weight=sample_weight)
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assert_array_equal(gb.predict([[1, 0]]), [1])
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|
|
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if __name__ == "__main__":
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import nose
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|
nose.runmodule()
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