""" Testing for the gradient boosting module (sklearn.ensemble.gradient_boosting). """ import warnings import numpy as np from scipy.sparse import csr_matrix from scipy.sparse import csc_matrix from scipy.sparse import coo_matrix from scipy.special import expit import pytest from sklearn import datasets from sklearn.base import clone from sklearn.datasets import (make_classification, fetch_california_housing, make_regression) from sklearn.ensemble import GradientBoostingClassifier from sklearn.ensemble import GradientBoostingRegressor from sklearn.ensemble._gradient_boosting import predict_stages from sklearn.preprocessing import OneHotEncoder, scale from sklearn.svm import LinearSVC from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from sklearn.utils import check_random_state, tosequence from sklearn.utils._mocking import NoSampleWeightWrapper from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import skip_if_32bit from sklearn.exceptions import DataConversionWarning from sklearn.exceptions import NotFittedError from sklearn.dummy import DummyClassifier, DummyRegressor from sklearn.pipeline import make_pipeline from sklearn.linear_model import LinearRegression from sklearn.svm import NuSVR GRADIENT_BOOSTING_ESTIMATORS = [GradientBoostingClassifier, GradientBoostingRegressor] # toy sample X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]] y = [-1, -1, -1, 1, 1, 1] T = [[-1, -1], [2, 2], [3, 2]] true_result = [-1, 1, 1] # also make regression dataset X_reg, y_reg = make_regression( n_samples=500, n_features=10, n_informative=8, noise=10, random_state=7 ) y_reg = scale(y_reg) rng = np.random.RandomState(0) # also load the iris dataset # and randomly permute it iris = datasets.load_iris() perm = rng.permutation(iris.target.size) iris.data = iris.data[perm] iris.target = iris.target[perm] @pytest.mark.parametrize('loss', ('deviance', 'exponential')) def test_classification_toy(loss): # Check classification on a toy dataset. clf = GradientBoostingClassifier(loss=loss, n_estimators=10, random_state=1) assert_raises(ValueError, clf.predict, T) clf.fit(X, y) assert_array_equal(clf.predict(T), true_result) assert 10 == len(clf.estimators_) deviance_decrease = (clf.train_score_[:-1] - clf.train_score_[1:]) assert np.any(deviance_decrease >= 0.0) leaves = clf.apply(X) assert leaves.shape == (6, 10, 1) @pytest.mark.parametrize( "params, err_msg", [({"n_estimators": 0}, "n_estimators must be greater than 0"), ({"n_estimators": -1}, "n_estimators must be greater than 0"), ({"learning_rate": 0}, "learning_rate must be greater than 0"), ({"learning_rate": -1.0}, "learning_rate must be greater than 0"), ({"loss": "foobar"}, "Loss 'foobar' not supported"), ({"min_samples_split": 0.0}, "min_samples_split must be an integer"), ({"min_samples_split": -1.0}, "min_samples_split must be an integer"), ({"min_samples_split": 1.1}, "min_samples_split must be an integer"), ({"min_samples_leaf": 0}, "min_samples_leaf must be at least 1 or"), ({"min_samples_leaf": -1.0}, "min_samples_leaf must be at least 1 or"), ({"min_weight_fraction_leaf": -1.0}, "min_weight_fraction_leaf must in"), ({"min_weight_fraction_leaf": 0.6}, "min_weight_fraction_leaf must in"), ({"subsample": 0.0}, r"subsample must be in \(0,1\]"), ({"subsample": 1.1}, r"subsample must be in \(0,1\]"), ({"subsample": -0.1}, r"subsample must be in \(0,1\]"), ({"max_depth": -0.1}, "max_depth must be greater than zero"), ({"max_depth": 0}, "max_depth must be greater than zero"), ({"init": {}}, "The init parameter must be an estimator or 'zero'"), ({"max_features": "invalid"}, "Invalid value for max_features:"), ({"max_features": 0}, r"max_features must be in \(0, n_features\]"), ({"max_features": 100}, r"max_features must be in \(0, n_features\]"), ({"max_features": -0.1}, r"max_features must be in \(0, n_features\]"), ({"n_iter_no_change": "invalid"}, "n_iter_no_change should either be")] ) @pytest.mark.parametrize( "GradientBoosting, X, y", [(GradientBoostingRegressor, X_reg, y_reg), (GradientBoostingClassifier, iris.data, iris.target)] ) def test_gbdt_parameter_checks(GradientBoosting, X, y, params, err_msg): # Check input parameter validation for GradientBoosting with pytest.raises(ValueError, match=err_msg): GradientBoosting(**params).fit(X, y) @pytest.mark.parametrize( "params, err_msg", [({"loss": "huber", "alpha": 1.2}, r"alpha must be in \(0.0, 1.0\)"), ({"loss": "quantile", "alpha": 1.2}, r"alpha must be in \(0.0, 1.0\)")] ) def test_gbdt_loss_alpha_error(params, err_msg): # check that an error is raised when alpha is not proper for quantile and # huber loss with pytest.raises(ValueError, match=err_msg): GradientBoostingRegressor(**params).fit(X_reg, y_reg) @pytest.mark.parametrize( "GradientBoosting, loss", [(GradientBoostingClassifier, "ls"), (GradientBoostingClassifier, "lad"), (GradientBoostingClassifier, "quantile"), (GradientBoostingClassifier, "huber"), (GradientBoostingRegressor, "deviance"), (GradientBoostingRegressor, "exponential")] ) def test_wrong_type_loss_function(GradientBoosting, loss): # check that we raise an error when not using the right type of loss # function with pytest.raises(ValueError): GradientBoosting(loss=loss).fit(X, y) @pytest.mark.parametrize('loss', ('deviance', 'exponential')) def test_classification_synthetic(loss): # Test GradientBoostingClassifier on synthetic dataset used by # Hastie et al. in ESLII Example 12.7. X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1) X_train, X_test = X[:2000], X[2000:] y_train, y_test = y[:2000], y[2000:] gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=2, max_depth=1, loss=loss, learning_rate=1.0, random_state=0) gbrt.fit(X_train, y_train) error_rate = (1.0 - gbrt.score(X_test, y_test)) assert error_rate < 0.09 gbrt = GradientBoostingClassifier(n_estimators=200, min_samples_split=2, max_depth=1, loss=loss, learning_rate=1.0, subsample=0.5, random_state=0) gbrt.fit(X_train, y_train) error_rate = (1.0 - gbrt.score(X_test, y_test)) assert error_rate < 0.08 @pytest.mark.parametrize('loss', ('ls', 'lad', 'huber')) @pytest.mark.parametrize('subsample', (1.0, 0.5)) def test_regression_dataset(loss, subsample): # Check consistency on regression dataset with least squares # and least absolute deviation. ones = np.ones(len(y_reg)) last_y_pred = None for sample_weight in [None, ones, 2 * ones]: reg = GradientBoostingRegressor(n_estimators=100, loss=loss, max_depth=4, subsample=subsample, min_samples_split=2, random_state=1) reg.fit(X_reg, y_reg, sample_weight=sample_weight) leaves = reg.apply(X_reg) assert leaves.shape == (500, 100) y_pred = reg.predict(X_reg) mse = mean_squared_error(y_reg, y_pred) assert mse < 0.04 if last_y_pred is not None: # FIXME: We temporarily bypass this test. This is due to the fact # that GBRT with and without `sample_weight` do not use the same # implementation of the median during the initialization with the # `DummyRegressor`. In the future, we should make sure that both # implementations should be the same. See PR #17377 for more. # assert_allclose(last_y_pred, y_pred) pass last_y_pred = y_pred @pytest.mark.parametrize('subsample', (1.0, 0.5)) @pytest.mark.parametrize('sample_weight', (None, 1)) def test_iris(subsample, sample_weight): if sample_weight == 1: sample_weight = np.ones(len(iris.target)) # Check consistency on dataset iris. clf = GradientBoostingClassifier(n_estimators=100, loss="deviance", random_state=1, subsample=subsample) clf.fit(iris.data, iris.target, sample_weight=sample_weight) score = clf.score(iris.data, iris.target) assert score > 0.9 leaves = clf.apply(iris.data) assert leaves.shape == (150, 100, 3) def test_regression_synthetic(): # Test on synthetic regression datasets used in Leo Breiman, # `Bagging Predictors?. Machine Learning 24(2): 123-140 (1996). random_state = check_random_state(1) regression_params = {'n_estimators': 100, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.1, 'loss': 'ls'} # Friedman1 X, y = datasets.make_friedman1(n_samples=1200, random_state=random_state, noise=1.0) X_train, y_train = X[:200], y[:200] X_test, y_test = X[200:], y[200:] clf = GradientBoostingRegressor() clf.fit(X_train, y_train) mse = mean_squared_error(y_test, clf.predict(X_test)) assert mse < 5.0 # Friedman2 X, y = datasets.make_friedman2(n_samples=1200, random_state=random_state) X_train, y_train = X[:200], y[:200] X_test, y_test = X[200:], y[200:] clf = GradientBoostingRegressor(**regression_params) clf.fit(X_train, y_train) mse = mean_squared_error(y_test, clf.predict(X_test)) assert mse < 1700.0 # Friedman3 X, y = datasets.make_friedman3(n_samples=1200, random_state=random_state) X_train, y_train = X[:200], y[:200] X_test, y_test = X[200:], y[200:] clf = GradientBoostingRegressor(**regression_params) clf.fit(X_train, y_train) mse = mean_squared_error(y_test, clf.predict(X_test)) assert mse < 0.015 @pytest.mark.parametrize( "GradientBoosting, X, y", [(GradientBoostingRegressor, X_reg, y_reg), (GradientBoostingClassifier, iris.data, iris.target)] ) def test_feature_importances(GradientBoosting, X, y): # smoke test to check that the gradient boosting expose an attribute # feature_importances_ gbdt = GradientBoosting() assert not hasattr(gbdt, "feature_importances_") gbdt.fit(X, y) assert hasattr(gbdt, 'feature_importances_') def test_probability_log(): # Predict probabilities. clf = GradientBoostingClassifier(n_estimators=100, random_state=1) assert_raises(ValueError, clf.predict_proba, T) clf.fit(X, y) assert_array_equal(clf.predict(T), true_result) # check if probabilities are in [0, 1]. y_proba = clf.predict_proba(T) assert np.all(y_proba >= 0.0) assert np.all(y_proba <= 1.0) # derive predictions from probabilities y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0) assert_array_equal(y_pred, true_result) def test_single_class_with_sample_weight(): sample_weight = [0, 0, 0, 1, 1, 1] clf = GradientBoostingClassifier(n_estimators=100, random_state=1) msg = ( "y contains 1 class after sample_weight trimmed classes with " "zero weights, while a minimum of 2 classes are required." ) with pytest.raises(ValueError, match=msg): clf.fit(X, y, sample_weight=sample_weight) def test_check_inputs_predict_stages(): # check that predict_stages through an error if the type of X is not # supported x, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) x_sparse_csc = csc_matrix(x) clf = GradientBoostingClassifier(n_estimators=100, random_state=1) clf.fit(x, y) score = np.zeros((y.shape)).reshape(-1, 1) assert_raise_message(ValueError, "When X is a sparse matrix, a CSR format is expected", predict_stages, clf.estimators_, x_sparse_csc, clf.learning_rate, score) x_fortran = np.asfortranarray(x) assert_raise_message(ValueError, "X should be C-ordered np.ndarray", predict_stages, clf.estimators_, x_fortran, clf.learning_rate, score) def test_max_feature_regression(): # Test to make sure random state is set properly. X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1) X_train, X_test = X[:2000], X[2000:] y_train, y_test = y[:2000], y[2000:] gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=5, max_depth=2, learning_rate=.1, max_features=2, random_state=1) gbrt.fit(X_train, y_train) deviance = gbrt.loss_(y_test, gbrt.decision_function(X_test)) assert deviance < 0.5, "GB failed with deviance %.4f" % deviance @pytest.mark.network def test_feature_importance_regression(): """Test that Gini importance is calculated correctly. This test follows the example from [1]_ (pg. 373). .. [1] Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. New York: Springer series in statistics. """ california = fetch_california_housing() X, y = california.data, california.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) reg = GradientBoostingRegressor(loss='huber', learning_rate=0.1, max_leaf_nodes=6, n_estimators=100, random_state=0) reg.fit(X_train, y_train) sorted_idx = np.argsort(reg.feature_importances_)[::-1] sorted_features = [california.feature_names[s] for s in sorted_idx] # The most important feature is the median income by far. assert sorted_features[0] == 'MedInc' # The three subsequent features are the following. Their relative ordering # might change a bit depending on the randomness of the trees and the # train / test split. assert set(sorted_features[1:4]) == {'Longitude', 'AveOccup', 'Latitude'} def test_max_feature_auto(): # Test if max features is set properly for floats and str. X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1) _, n_features = X.shape X_train = X[:2000] y_train = y[:2000] gbrt = GradientBoostingClassifier(n_estimators=1, max_features='auto') gbrt.fit(X_train, y_train) assert gbrt.max_features_ == int(np.sqrt(n_features)) gbrt = GradientBoostingRegressor(n_estimators=1, max_features='auto') gbrt.fit(X_train, y_train) assert gbrt.max_features_ == n_features gbrt = GradientBoostingRegressor(n_estimators=1, max_features=0.3) gbrt.fit(X_train, y_train) assert gbrt.max_features_ == int(n_features * 0.3) gbrt = GradientBoostingRegressor(n_estimators=1, max_features='sqrt') gbrt.fit(X_train, y_train) assert gbrt.max_features_ == int(np.sqrt(n_features)) gbrt = GradientBoostingRegressor(n_estimators=1, max_features='log2') gbrt.fit(X_train, y_train) assert gbrt.max_features_ == int(np.log2(n_features)) gbrt = GradientBoostingRegressor(n_estimators=1, max_features=0.01 / X.shape[1]) gbrt.fit(X_train, y_train) assert gbrt.max_features_ == 1 def test_staged_predict(): # Test whether staged decision function eventually gives # the same prediction. X, y = datasets.make_friedman1(n_samples=1200, random_state=1, noise=1.0) X_train, y_train = X[:200], y[:200] X_test = X[200:] clf = GradientBoostingRegressor() # test raise ValueError if not fitted assert_raises(ValueError, lambda X: np.fromiter( clf.staged_predict(X), dtype=np.float64), X_test) clf.fit(X_train, y_train) y_pred = clf.predict(X_test) # test if prediction for last stage equals ``predict`` for y in clf.staged_predict(X_test): assert y.shape == y_pred.shape assert_array_almost_equal(y_pred, y) def test_staged_predict_proba(): # Test whether staged predict proba eventually gives # the same prediction. X, y = datasets.make_hastie_10_2(n_samples=1200, random_state=1) X_train, y_train = X[:200], y[:200] X_test, y_test = X[200:], y[200:] clf = GradientBoostingClassifier(n_estimators=20) # test raise NotFittedError if not fitted assert_raises(NotFittedError, lambda X: np.fromiter( clf.staged_predict_proba(X), dtype=np.float64), X_test) clf.fit(X_train, y_train) # test if prediction for last stage equals ``predict`` for y_pred in clf.staged_predict(X_test): assert y_test.shape == y_pred.shape assert_array_equal(clf.predict(X_test), y_pred) # test if prediction for last stage equals ``predict_proba`` for staged_proba in clf.staged_predict_proba(X_test): assert y_test.shape[0] == staged_proba.shape[0] assert 2 == staged_proba.shape[1] assert_array_almost_equal(clf.predict_proba(X_test), staged_proba) @pytest.mark.parametrize('Estimator', GRADIENT_BOOSTING_ESTIMATORS) def test_staged_functions_defensive(Estimator): # test that staged_functions make defensive copies rng = np.random.RandomState(0) X = rng.uniform(size=(10, 3)) y = (4 * X[:, 0]).astype(int) + 1 # don't predict zeros estimator = Estimator() estimator.fit(X, y) for func in ['predict', 'decision_function', 'predict_proba']: staged_func = getattr(estimator, "staged_" + func, None) if staged_func is None: # regressor has no staged_predict_proba continue with warnings.catch_warnings(record=True): staged_result = list(staged_func(X)) staged_result[1][:] = 0 assert np.all(staged_result[0] != 0) 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 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 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(X_reg, y_reg) y_quantile = clf_quantile.predict(X_reg) clf_lad = GradientBoostingRegressor(n_estimators=100, loss='lad', max_depth=4, random_state=7) clf_lad.fit(X_reg, y_reg) y_lad = clf_lad.predict(X_reg) 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 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 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 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 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 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 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 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 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 io import 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 true_header == header n_lines = sum(1 for l in verbose_output.readlines()) # one for 1-10 and then 9 for 20-100 assert 10 + 9 == n_lines def test_more_verbose_output(): # Check verbose=2 does not cause error. from io import 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 true_header == header n_lines = sum(1 for l in verbose_output.readlines()) # 100 lines for n_estimators==100 assert 100 == n_lines @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start(Cls): # Test if warm start equals fit. X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) 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) if Cls is GradientBoostingRegressor: assert_array_almost_equal(est_ws.predict(X), est.predict(X)) else: # Random state is preserved and hence predict_proba must also be # same assert_array_equal(est_ws.predict(X), est.predict(X)) assert_array_almost_equal(est_ws.predict_proba(X), est.predict_proba(X)) @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_n_estimators(Cls): # Test if warm start equals fit - set n_estimators. X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) 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)) @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_max_depth(Cls): # Test if possible to fit trees of different depth in ensemble. X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) 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 @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_clear(Cls): # Test if fit clears state. X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) 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)) @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_zero_n_estimators(Cls): # Test if warm start with zero n_estimators raises error X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) 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) @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_smaller_n_estimators(Cls): # Test if warm start with smaller n_estimators raises error X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) 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) @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_equal_n_estimators(Cls): # Test if warm start with equal n_estimators does nothing X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) 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)) @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_oob_switch(Cls): # Test if oob can be turned on during warm start. X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) 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=bool)) @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_oob(Cls): # Test if warm start OOB equals fit. X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) 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]) @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_sparse(Cls): # Test that all sparse matrix types are supported X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) sparse_matrix_type = [csr_matrix, csc_matrix, coo_matrix] est_dense = Cls(n_estimators=100, max_depth=1, subsample=0.5, random_state=1, warm_start=True) est_dense.fit(X, y) est_dense.predict(X) est_dense.set_params(n_estimators=200) est_dense.fit(X, y) y_pred_dense = est_dense.predict(X) for sparse_constructor in sparse_matrix_type: X_sparse = sparse_constructor(X) est_sparse = Cls(n_estimators=100, max_depth=1, subsample=0.5, random_state=1, warm_start=True) est_sparse.fit(X_sparse, y) est_sparse.predict(X) est_sparse.set_params(n_estimators=200) est_sparse.fit(X_sparse, y) y_pred_sparse = est_sparse.predict(X) assert_array_almost_equal(est_dense.oob_improvement_[:100], est_sparse.oob_improvement_[:100]) assert_array_almost_equal(y_pred_dense, y_pred_sparse) @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_fortran(Cls): # Test that feeding a X in Fortran-ordered is giving the same results as # in C-ordered X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) est_c = Cls(n_estimators=1, random_state=1, warm_start=True) est_fortran = Cls(n_estimators=1, random_state=1, warm_start=True) est_c.fit(X, y) est_c.set_params(n_estimators=11) est_c.fit(X, y) X_fortran = np.asfortranarray(X) est_fortran.fit(X_fortran, y) est_fortran.set_params(n_estimators=11) est_fortran.fit(X_fortran, y) assert_array_almost_equal(est_c.predict(X), est_fortran.predict(X)) def early_stopping_monitor(i, est, locals): """Returns True on the 10th iteration. """ if i == 9: return True else: return False @pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS) def test_monitor_early_stopping(Cls): # Test if monitor return value works. X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) est = Cls(n_estimators=20, max_depth=1, random_state=1, subsample=0.5) est.fit(X, y, monitor=early_stopping_monitor) assert est.n_estimators == 20 # this is not altered assert est.estimators_.shape[0] == 10 assert est.train_score_.shape[0] == 10 assert est.oob_improvement_.shape[0] == 10 # try refit est.set_params(n_estimators=30) est.fit(X, y) assert est.n_estimators == 30 assert est.estimators_.shape[0] == 30 assert 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 est.n_estimators == 20 assert est.estimators_.shape[0] == 10 assert est.train_score_.shape[0] == 10 assert est.oob_improvement_.shape[0] == 10 # try refit est.set_params(n_estimators=30, warm_start=False) est.fit(X, y) assert est.n_estimators == 30 assert est.train_score_.shape[0] == 30 assert est.estimators_.shape[0] == 30 assert 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 tree.max_depth == k assert (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(X_reg, y_reg) tree = est.estimators_[-1, 0].tree_ assert (tree.children_left[tree.children_left == TREE_LEAF].shape[0] == k + 1) def test_zero_estimator_reg(): # Test if init='zero' works for regression. est = GradientBoostingRegressor(n_estimators=20, max_depth=1, random_state=1, init='zero') est.fit(X_reg, y_reg) y_pred = est.predict(X_reg) mse = mean_squared_error(y_reg, y_pred) assert_almost_equal(mse, 0.52, decimal=2) est = GradientBoostingRegressor(n_estimators=20, max_depth=1, random_state=1, init='foobar') assert_raises(ValueError, est.fit, X_reg, y_reg) def test_zero_estimator_clf(): # Test if init='zero' works for classification. X = iris.data y = np.array(iris.target) est = GradientBoostingClassifier(n_estimators=20, max_depth=1, random_state=1, init='zero') est.fit(X, y) assert est.score(X, y) > 0.96 # binary clf mask = y != 0 y[mask] = 1 y[~mask] = 0 est = GradientBoostingClassifier(n_estimators=20, max_depth=1, random_state=1, init='zero') est.fit(X, y) assert est.score(X, y) > 0.96 est = GradientBoostingClassifier(n_estimators=20, max_depth=1, random_state=1, init='foobar') assert_raises(ValueError, est.fit, X, y) @pytest.mark.parametrize('GBEstimator', GRADIENT_BOOSTING_ESTIMATORS) def test_max_leaf_nodes_max_depth(GBEstimator): # Test precedence of max_leaf_nodes over max_depth. X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) k = 4 est = GBEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y) tree = est.estimators_[0, 0].tree_ assert tree.max_depth == 1 est = GBEstimator(max_depth=1).fit(X, y) tree = est.estimators_[0, 0].tree_ assert tree.max_depth == 1 @pytest.mark.parametrize('GBEstimator', GRADIENT_BOOSTING_ESTIMATORS) def test_min_impurity_split(GBEstimator): # Test if min_impurity_split of base estimators is set # Regression test for #8006 X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) est = GBEstimator(min_impurity_split=0.1) est = assert_warns_message(FutureWarning, "min_impurity_decrease", est.fit, X, y) for tree in est.estimators_.flat: assert tree.min_impurity_split == 0.1 @pytest.mark.parametrize('GBEstimator', GRADIENT_BOOSTING_ESTIMATORS) def test_min_impurity_decrease(GBEstimator): X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) est = GBEstimator(min_impurity_decrease=0.1) est.fit(X, y) for tree in est.estimators_.flat: # Simply check if the parameter is passed on correctly. Tree tests # will suffice for the actual working of this param assert tree.min_impurity_decrease == 0.1 def test_warm_start_wo_nestimators_change(): # Test if warm_start does nothing if n_estimators is not changed. # Regression test for #3513. clf = GradientBoostingClassifier(n_estimators=10, warm_start=True) clf.fit([[0, 1], [2, 3]], [0, 1]) assert clf.estimators_.shape[0] == 10 clf.fit([[0, 1], [2, 3]], [0, 1]) assert clf.estimators_.shape[0] == 10 def test_probability_exponential(): # Predict probabilities. clf = GradientBoostingClassifier(loss='exponential', n_estimators=100, random_state=1) assert_raises(ValueError, clf.predict_proba, T) clf.fit(X, y) assert_array_equal(clf.predict(T), true_result) # check if probabilities are in [0, 1]. y_proba = clf.predict_proba(T) assert np.all(y_proba >= 0.0) assert np.all(y_proba <= 1.0) score = clf.decision_function(T).ravel() assert_array_almost_equal(y_proba[:, 1], expit(2 * score)) # derive predictions from probabilities y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0) assert_array_equal(y_pred, true_result) def test_non_uniform_weights_toy_edge_case_reg(): 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] for loss in ('huber', 'ls', 'lad', 'quantile'): gb = GradientBoostingRegressor(learning_rate=1.0, n_estimators=2, loss=loss) gb.fit(X, y, sample_weight=sample_weight) assert gb.predict([[1, 0]])[0] > 0.5 def test_non_uniform_weights_toy_edge_case_clf(): 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] for loss in ('deviance', 'exponential'): gb = GradientBoostingClassifier(n_estimators=5, loss=loss) gb.fit(X, y, sample_weight=sample_weight) assert_array_equal(gb.predict([[1, 0]]), [1]) @skip_if_32bit @pytest.mark.parametrize( 'EstimatorClass', (GradientBoostingClassifier, GradientBoostingRegressor) ) @pytest.mark.parametrize('sparse_matrix', (csr_matrix, csc_matrix, coo_matrix)) def test_sparse_input(EstimatorClass, sparse_matrix): y, X = datasets.make_multilabel_classification(random_state=0, n_samples=50, n_features=1, n_classes=20) y = y[:, 0] X_sparse = sparse_matrix(X) dense = EstimatorClass(n_estimators=10, random_state=0, max_depth=2, min_impurity_decrease=1e-7).fit(X, y) sparse = EstimatorClass(n_estimators=10, random_state=0, max_depth=2, min_impurity_decrease=1e-7).fit(X_sparse, y) assert_array_almost_equal(sparse.apply(X), dense.apply(X)) assert_array_almost_equal(sparse.predict(X), dense.predict(X)) assert_array_almost_equal(sparse.feature_importances_, dense.feature_importances_) assert_array_almost_equal(sparse.predict(X_sparse), dense.predict(X)) assert_array_almost_equal(dense.predict(X_sparse), sparse.predict(X)) if issubclass(EstimatorClass, GradientBoostingClassifier): assert_array_almost_equal(sparse.predict_proba(X), dense.predict_proba(X)) assert_array_almost_equal(sparse.predict_log_proba(X), dense.predict_log_proba(X)) assert_array_almost_equal(sparse.decision_function(X_sparse), sparse.decision_function(X)) assert_array_almost_equal(dense.decision_function(X_sparse), sparse.decision_function(X)) for res_sparse, res in zip(sparse.staged_decision_function(X_sparse), sparse.staged_decision_function(X)): assert_array_almost_equal(res_sparse, res) def test_gradient_boosting_early_stopping(): X, y = make_classification(n_samples=1000, random_state=0) gbc = GradientBoostingClassifier(n_estimators=1000, n_iter_no_change=10, learning_rate=0.1, max_depth=3, random_state=42) gbr = GradientBoostingRegressor(n_estimators=1000, n_iter_no_change=10, learning_rate=0.1, max_depth=3, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) # Check if early_stopping works as expected for est, tol, early_stop_n_estimators in ((gbc, 1e-1, 28), (gbr, 1e-1, 13), (gbc, 1e-3, 70), (gbr, 1e-3, 28)): est.set_params(tol=tol) est.fit(X_train, y_train) assert est.n_estimators_ == early_stop_n_estimators assert est.score(X_test, y_test) > 0.7 # Without early stopping gbc = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42) gbc.fit(X, y) gbr = GradientBoostingRegressor(n_estimators=200, learning_rate=0.1, max_depth=3, random_state=42) gbr.fit(X, y) assert gbc.n_estimators_ == 100 assert gbr.n_estimators_ == 200 def test_gradient_boosting_validation_fraction(): X, y = make_classification(n_samples=1000, random_state=0) gbc = GradientBoostingClassifier(n_estimators=100, n_iter_no_change=10, validation_fraction=0.1, learning_rate=0.1, max_depth=3, random_state=42) gbc2 = clone(gbc).set_params(validation_fraction=0.3) gbc3 = clone(gbc).set_params(n_iter_no_change=20) gbr = GradientBoostingRegressor(n_estimators=100, n_iter_no_change=10, learning_rate=0.1, max_depth=3, validation_fraction=0.1, random_state=42) gbr2 = clone(gbr).set_params(validation_fraction=0.3) gbr3 = clone(gbr).set_params(n_iter_no_change=20) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) # Check if validation_fraction has an effect gbc.fit(X_train, y_train) gbc2.fit(X_train, y_train) assert gbc.n_estimators_ != gbc2.n_estimators_ gbr.fit(X_train, y_train) gbr2.fit(X_train, y_train) assert gbr.n_estimators_ != gbr2.n_estimators_ # Check if n_estimators_ increase monotonically with n_iter_no_change # Set validation gbc3.fit(X_train, y_train) gbr3.fit(X_train, y_train) assert gbr.n_estimators_ < gbr3.n_estimators_ assert gbc.n_estimators_ < gbc3.n_estimators_ def test_early_stopping_stratified(): # Make sure data splitting for early stopping is stratified X = [[1, 2], [2, 3], [3, 4], [4, 5]] y = [0, 0, 0, 1] gbc = GradientBoostingClassifier(n_iter_no_change=5) with pytest.raises( ValueError, match='The least populated class in y has only 1 member'): gbc.fit(X, y) def _make_multiclass(): return make_classification(n_classes=3, n_clusters_per_class=1) @pytest.mark.parametrize( "gb, dataset_maker, init_estimator", [(GradientBoostingClassifier, make_classification, DummyClassifier), (GradientBoostingClassifier, _make_multiclass, DummyClassifier), (GradientBoostingRegressor, make_regression, DummyRegressor)], ids=["binary classification", "multiclass classification", "regression"]) def test_gradient_boosting_with_init(gb, dataset_maker, init_estimator): # Check that GradientBoostingRegressor works when init is a sklearn # estimator. # Check that an error is raised if trying to fit with sample weight but # initial estimator does not support sample weight X, y = dataset_maker() sample_weight = np.random.RandomState(42).rand(100) # init supports sample weights init_est = init_estimator() gb(init=init_est).fit(X, y, sample_weight=sample_weight) # init does not support sample weights init_est = NoSampleWeightWrapper(init_estimator()) gb(init=init_est).fit(X, y) # ok no sample weights with pytest.raises(ValueError, match="estimator.*does not support sample weights"): gb(init=init_est).fit(X, y, sample_weight=sample_weight) def test_gradient_boosting_with_init_pipeline(): # Check that the init estimator can be a pipeline (see issue #13466) X, y = make_regression(random_state=0) init = make_pipeline(LinearRegression()) gb = GradientBoostingRegressor(init=init) gb.fit(X, y) # pipeline without sample_weight works fine with pytest.raises( ValueError, match='The initial estimator Pipeline does not support sample ' 'weights'): gb.fit(X, y, sample_weight=np.ones(X.shape[0])) # Passing sample_weight to a pipeline raises a ValueError. This test makes # sure we make the distinction between ValueError raised by a pipeline that # was passed sample_weight, and a ValueError raised by a regular estimator # whose input checking failed. with pytest.raises( ValueError, match='nu <= 0 or nu > 1'): # Note that NuSVR properly supports sample_weight init = NuSVR(gamma='auto', nu=1.5) gb = GradientBoostingRegressor(init=init) gb.fit(X, y, sample_weight=np.ones(X.shape[0])) @pytest.mark.parametrize('estimator, missing_method', [ (GradientBoostingClassifier(init=LinearSVC()), 'predict_proba'), (GradientBoostingRegressor(init=OneHotEncoder()), 'predict') ]) def test_gradient_boosting_init_wrong_methods(estimator, missing_method): # Make sure error is raised if init estimators don't have the required # methods (fit, predict, predict_proba) message = ("The init parameter must be a valid estimator and support " "both fit and " + missing_method) with pytest.raises(ValueError, match=message): estimator.fit(X, y) def test_early_stopping_n_classes(): # when doing early stopping (_, , y_train, _ = train_test_split(X, y)) # there might be classes in y that are missing in y_train. As the init # estimator will be trained on y_train, we need to raise an error if this # happens. X = [[1]] * 10 y = [0, 0] + [1] * 8 # only 2 negative class over 10 samples gb = GradientBoostingClassifier(n_iter_no_change=5, random_state=0, validation_fraction=8) with pytest.raises( ValueError, match='The training data after the early stopping split'): gb.fit(X, y) # No error if we let training data be big enough gb = GradientBoostingClassifier(n_iter_no_change=5, random_state=0, validation_fraction=4) def test_gbr_degenerate_feature_importances(): # growing an ensemble of single node trees. See #13620 X = np.zeros((10, 10)) y = np.ones((10,)) gbr = GradientBoostingRegressor().fit(X, y) assert_array_equal(gbr.feature_importances_, np.zeros(10, dtype=np.float64)) # TODO: Remove in 1.1 when `n_classes_` is deprecated def test_gbr_deprecated_attr(): # check that accessing n_classes_ in GradientBoostingRegressor raises # a deprecation warning X = np.zeros((10, 10)) y = np.ones((10,)) gbr = GradientBoostingRegressor().fit(X, y) msg = "Attribute n_classes_ was deprecated" with pytest.warns(FutureWarning, match=msg): gbr.n_classes_ # TODO: Remove in 1.1 when `n_classes_` is deprecated @pytest.mark.filterwarnings("ignore:Attribute n_classes_ was deprecated") def test_attr_error_raised_if_not_fitted(): # check that accessing n_classes_ in not fitted GradientBoostingRegressor # raises an AttributeError gbr = GradientBoostingRegressor() # test raise AttributeError if not fitted msg = ( f"{GradientBoostingRegressor.__name__} object has no n_classes_ " f"attribute." ) with pytest.raises(AttributeError, match=msg): gbr.n_classes_ # TODO: Update in 1.1 to check for the error raised @pytest.mark.parametrize('estimator', [ GradientBoostingClassifier(criterion='mae'), GradientBoostingRegressor(criterion='mae') ]) def test_criterion_mae_deprecation(estimator): # checks whether a deprecation warning is issues when criterion='mae' # is used. msg = ("criterion='mae' was deprecated in version 0.24 and " "will be removed in version 1.1") with pytest.warns(FutureWarning, match=msg): estimator.fit(X, y)