""" Testing for the tree module (sklearn.tree). """ import copy import pickle from functools import partial from itertools import product import struct import pytest import numpy as np from scipy.sparse import csc_matrix from scipy.sparse import csr_matrix from scipy.sparse import coo_matrix from sklearn.random_projection import sparse_random_matrix from sklearn.metrics import accuracy_score from sklearn.metrics import mean_squared_error from sklearn.utils.testing import assert_allclose from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_in from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_greater_equal from sklearn.utils.testing import assert_less from sklearn.utils.testing import assert_less_equal from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_warns from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import ignore_warnings from sklearn.utils.testing import assert_raise_message from sklearn.utils.validation import check_random_state from sklearn.exceptions import NotFittedError from sklearn.tree import DecisionTreeClassifier from sklearn.tree import DecisionTreeRegressor from sklearn.tree import ExtraTreeClassifier from sklearn.tree import ExtraTreeRegressor from sklearn import tree from sklearn.tree._tree import TREE_LEAF from sklearn.tree.tree import CRITERIA_CLF from sklearn.tree.tree import CRITERIA_REG from sklearn import datasets from sklearn.utils import compute_sample_weight CLF_CRITERIONS = ("gini", "entropy") REG_CRITERIONS = ("mse", "mae", "friedman_mse") CLF_TREES = { "DecisionTreeClassifier": DecisionTreeClassifier, "Presort-DecisionTreeClassifier": partial(DecisionTreeClassifier, presort=True), "ExtraTreeClassifier": ExtraTreeClassifier, } REG_TREES = { "DecisionTreeRegressor": DecisionTreeRegressor, "Presort-DecisionTreeRegressor": partial(DecisionTreeRegressor, presort=True), "ExtraTreeRegressor": ExtraTreeRegressor, } ALL_TREES = dict() ALL_TREES.update(CLF_TREES) ALL_TREES.update(REG_TREES) SPARSE_TREES = ["DecisionTreeClassifier", "DecisionTreeRegressor", "ExtraTreeClassifier", "ExtraTreeRegressor"] X_small = np.array([ [0, 0, 4, 0, 0, 0, 1, -14, 0, -4, 0, 0, 0, 0, ], [0, 0, 5, 3, 0, -4, 0, 0, 1, -5, 0.2, 0, 4, 1, ], [-1, -1, 0, 0, -4.5, 0, 0, 2.1, 1, 0, 0, -4.5, 0, 1, ], [-1, -1, 0, -1.2, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 1, ], [-1, -1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, ], [-1, -2, 0, 4, -3, 10, 4, 0, -3.2, 0, 4, 3, -4, 1, ], [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -3, 1, ], [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1, ], [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1, ], [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -1, 0, ], [2, 8, 5, 1, 0.5, -4, 10, 0, 1, -5, 3, 0, 2, 0, ], [2, 0, 1, 1, 1, -1, 1, 0, 0, -2, 3, 0, 1, 0, ], [2, 0, 1, 2, 3, -1, 10, 2, 0, -1, 1, 2, 2, 0, ], [1, 1, 0, 2, 2, -1, 1, 2, 0, -5, 1, 2, 3, 0, ], [3, 1, 0, 3, 0, -4, 10, 0, 1, -5, 3, 0, 3, 1, ], [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 0.5, 0, -3, 1, ], [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 1.5, 1, -1, -1, ], [2.11, 8, -6, -0.5, 0, 10, 0, 0, -3.2, 6, 0.5, 0, -1, -1, ], [2, 0, 5, 1, 0.5, -2, 10, 0, 1, -5, 3, 1, 0, -1, ], [2, 0, 1, 1, 1, -2, 1, 0, 0, -2, 0, 0, 0, 1, ], [2, 1, 1, 1, 2, -1, 10, 2, 0, -1, 0, 2, 1, 1, ], [1, 1, 0, 0, 1, -3, 1, 2, 0, -5, 1, 2, 1, 1, ], [3, 1, 0, 1, 0, -4, 1, 0, 1, -2, 0, 0, 1, 0, ]]) y_small = [1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0] y_small_reg = [1.0, 2.1, 1.2, 0.05, 10, 2.4, 3.1, 1.01, 0.01, 2.98, 3.1, 1.1, 0.0, 1.2, 2, 11, 0, 0, 4.5, 0.201, 1.06, 0.9, 0] # 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 load the iris dataset # and randomly permute it iris = datasets.load_iris() rng = np.random.RandomState(1) perm = rng.permutation(iris.target.size) iris.data = iris.data[perm] iris.target = iris.target[perm] # also load the boston dataset # and randomly permute it boston = datasets.load_boston() perm = rng.permutation(boston.target.size) boston.data = boston.data[perm] boston.target = boston.target[perm] digits = datasets.load_digits() perm = rng.permutation(digits.target.size) digits.data = digits.data[perm] digits.target = digits.target[perm] random_state = check_random_state(0) X_multilabel, y_multilabel = datasets.make_multilabel_classification( random_state=0, n_samples=30, n_features=10) # NB: despite their names X_sparse_* are numpy arrays (and not sparse matrices) X_sparse_pos = random_state.uniform(size=(20, 5)) X_sparse_pos[X_sparse_pos <= 0.8] = 0. y_random = random_state.randint(0, 4, size=(20, )) X_sparse_mix = sparse_random_matrix(20, 10, density=0.25, random_state=0).toarray() DATASETS = { "iris": {"X": iris.data, "y": iris.target}, "boston": {"X": boston.data, "y": boston.target}, "digits": {"X": digits.data, "y": digits.target}, "toy": {"X": X, "y": y}, "clf_small": {"X": X_small, "y": y_small}, "reg_small": {"X": X_small, "y": y_small_reg}, "multilabel": {"X": X_multilabel, "y": y_multilabel}, "sparse-pos": {"X": X_sparse_pos, "y": y_random}, "sparse-neg": {"X": - X_sparse_pos, "y": y_random}, "sparse-mix": {"X": X_sparse_mix, "y": y_random}, "zeros": {"X": np.zeros((20, 3)), "y": y_random} } for name in DATASETS: DATASETS[name]["X_sparse"] = csc_matrix(DATASETS[name]["X"]) def assert_tree_equal(d, s, message): assert_equal(s.node_count, d.node_count, "{0}: inequal number of node ({1} != {2})" "".format(message, s.node_count, d.node_count)) assert_array_equal(d.children_right, s.children_right, message + ": inequal children_right") assert_array_equal(d.children_left, s.children_left, message + ": inequal children_left") external = d.children_right == TREE_LEAF internal = np.logical_not(external) assert_array_equal(d.feature[internal], s.feature[internal], message + ": inequal features") assert_array_equal(d.threshold[internal], s.threshold[internal], message + ": inequal threshold") assert_array_equal(d.n_node_samples.sum(), s.n_node_samples.sum(), message + ": inequal sum(n_node_samples)") assert_array_equal(d.n_node_samples, s.n_node_samples, message + ": inequal n_node_samples") assert_almost_equal(d.impurity, s.impurity, err_msg=message + ": inequal impurity") assert_array_almost_equal(d.value[external], s.value[external], err_msg=message + ": inequal value") def test_classification_toy(): # Check classification on a toy dataset. for name, Tree in CLF_TREES.items(): clf = Tree(random_state=0) clf.fit(X, y) assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name)) clf = Tree(max_features=1, random_state=1) clf.fit(X, y) assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name)) def test_weighted_classification_toy(): # Check classification on a weighted toy dataset. for name, Tree in CLF_TREES.items(): clf = Tree(random_state=0) clf.fit(X, y, sample_weight=np.ones(len(X))) assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name)) clf.fit(X, y, sample_weight=np.full(len(X), 0.5)) assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name)) def test_regression_toy(): # Check regression on a toy dataset. for name, Tree in REG_TREES.items(): reg = Tree(random_state=1) reg.fit(X, y) assert_almost_equal(reg.predict(T), true_result, err_msg="Failed with {0}".format(name)) clf = Tree(max_features=1, random_state=1) clf.fit(X, y) assert_almost_equal(reg.predict(T), true_result, err_msg="Failed with {0}".format(name)) def test_xor(): # Check on a XOR problem y = np.zeros((10, 10)) y[:5, :5] = 1 y[5:, 5:] = 1 gridx, gridy = np.indices(y.shape) X = np.vstack([gridx.ravel(), gridy.ravel()]).T y = y.ravel() for name, Tree in CLF_TREES.items(): clf = Tree(random_state=0) clf.fit(X, y) assert_equal(clf.score(X, y), 1.0, "Failed with {0}".format(name)) clf = Tree(random_state=0, max_features=1) clf.fit(X, y) assert_equal(clf.score(X, y), 1.0, "Failed with {0}".format(name)) def test_iris(): # Check consistency on dataset iris. for (name, Tree), criterion in product(CLF_TREES.items(), CLF_CRITERIONS): clf = Tree(criterion=criterion, random_state=0) clf.fit(iris.data, iris.target) score = accuracy_score(clf.predict(iris.data), iris.target) assert_greater(score, 0.9, "Failed with {0}, criterion = {1} and score = {2}" "".format(name, criterion, score)) clf = Tree(criterion=criterion, max_features=2, random_state=0) clf.fit(iris.data, iris.target) score = accuracy_score(clf.predict(iris.data), iris.target) assert_greater(score, 0.5, "Failed with {0}, criterion = {1} and score = {2}" "".format(name, criterion, score)) def test_boston(): # Check consistency on dataset boston house prices. for (name, Tree), criterion in product(REG_TREES.items(), REG_CRITERIONS): reg = Tree(criterion=criterion, random_state=0) reg.fit(boston.data, boston.target) score = mean_squared_error(boston.target, reg.predict(boston.data)) assert_less(score, 1, "Failed with {0}, criterion = {1} and score = {2}" "".format(name, criterion, score)) # using fewer features reduces the learning ability of this tree, # but reduces training time. reg = Tree(criterion=criterion, max_features=6, random_state=0) reg.fit(boston.data, boston.target) score = mean_squared_error(boston.target, reg.predict(boston.data)) assert_less(score, 2, "Failed with {0}, criterion = {1} and score = {2}" "".format(name, criterion, score)) def test_probability(): # Predict probabilities using DecisionTreeClassifier. for name, Tree in CLF_TREES.items(): clf = Tree(max_depth=1, max_features=1, random_state=42) clf.fit(iris.data, iris.target) prob_predict = clf.predict_proba(iris.data) assert_array_almost_equal(np.sum(prob_predict, 1), np.ones(iris.data.shape[0]), err_msg="Failed with {0}".format(name)) assert_array_equal(np.argmax(prob_predict, 1), clf.predict(iris.data), err_msg="Failed with {0}".format(name)) assert_almost_equal(clf.predict_proba(iris.data), np.exp(clf.predict_log_proba(iris.data)), 8, err_msg="Failed with {0}".format(name)) def test_arrayrepr(): # Check the array representation. # Check resize X = np.arange(10000)[:, np.newaxis] y = np.arange(10000) for name, Tree in REG_TREES.items(): reg = Tree(max_depth=None, random_state=0) reg.fit(X, y) def test_pure_set(): # Check when y is pure. X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]] y = [1, 1, 1, 1, 1, 1] for name, TreeClassifier in CLF_TREES.items(): clf = TreeClassifier(random_state=0) clf.fit(X, y) assert_array_equal(clf.predict(X), y, err_msg="Failed with {0}".format(name)) for name, TreeRegressor in REG_TREES.items(): reg = TreeRegressor(random_state=0) reg.fit(X, y) assert_almost_equal(reg.predict(X), y, err_msg="Failed with {0}".format(name)) def test_numerical_stability(): # Check numerical stability. X = np.array([ [152.08097839, 140.40744019, 129.75102234, 159.90493774], [142.50700378, 135.81935120, 117.82884979, 162.75781250], [127.28772736, 140.40744019, 129.75102234, 159.90493774], [132.37025452, 143.71923828, 138.35694885, 157.84558105], [103.10237122, 143.71928406, 138.35696411, 157.84559631], [127.71276855, 143.71923828, 138.35694885, 157.84558105], [120.91514587, 140.40744019, 129.75102234, 159.90493774]]) y = np.array( [1., 0.70209277, 0.53896582, 0., 0.90914464, 0.48026916, 0.49622521]) with np.errstate(all="raise"): for name, Tree in REG_TREES.items(): reg = Tree(random_state=0) reg.fit(X, y) reg.fit(X, -y) reg.fit(-X, y) reg.fit(-X, -y) def test_importances(): # Check variable importances. X, y = datasets.make_classification(n_samples=5000, n_features=10, n_informative=3, n_redundant=0, n_repeated=0, shuffle=False, random_state=0) for name, Tree in CLF_TREES.items(): clf = Tree(random_state=0) clf.fit(X, y) importances = clf.feature_importances_ n_important = np.sum(importances > 0.1) assert_equal(importances.shape[0], 10, "Failed with {0}".format(name)) assert_equal(n_important, 3, "Failed with {0}".format(name)) # Check on iris that importances are the same for all builders clf = DecisionTreeClassifier(random_state=0) clf.fit(iris.data, iris.target) clf2 = DecisionTreeClassifier(random_state=0, max_leaf_nodes=len(iris.data)) clf2.fit(iris.data, iris.target) assert_array_equal(clf.feature_importances_, clf2.feature_importances_) def test_importances_raises(): # Check if variable importance before fit raises ValueError. clf = DecisionTreeClassifier() assert_raises(ValueError, getattr, clf, 'feature_importances_') def test_importances_gini_equal_mse(): # Check that gini is equivalent to mse for binary output variable X, y = datasets.make_classification(n_samples=2000, n_features=10, n_informative=3, n_redundant=0, n_repeated=0, shuffle=False, random_state=0) # The gini index and the mean square error (variance) might differ due # to numerical instability. Since those instabilities mainly occurs at # high tree depth, we restrict this maximal depth. clf = DecisionTreeClassifier(criterion="gini", max_depth=5, random_state=0).fit(X, y) reg = DecisionTreeRegressor(criterion="mse", max_depth=5, random_state=0).fit(X, y) assert_almost_equal(clf.feature_importances_, reg.feature_importances_) assert_array_equal(clf.tree_.feature, reg.tree_.feature) assert_array_equal(clf.tree_.children_left, reg.tree_.children_left) assert_array_equal(clf.tree_.children_right, reg.tree_.children_right) assert_array_equal(clf.tree_.n_node_samples, reg.tree_.n_node_samples) def test_max_features(): # Check max_features. for name, TreeRegressor in REG_TREES.items(): reg = TreeRegressor(max_features="auto") reg.fit(boston.data, boston.target) assert_equal(reg.max_features_, boston.data.shape[1]) for name, TreeClassifier in CLF_TREES.items(): clf = TreeClassifier(max_features="auto") clf.fit(iris.data, iris.target) assert_equal(clf.max_features_, 2) for name, TreeEstimator in ALL_TREES.items(): est = TreeEstimator(max_features="sqrt") est.fit(iris.data, iris.target) assert_equal(est.max_features_, int(np.sqrt(iris.data.shape[1]))) est = TreeEstimator(max_features="log2") est.fit(iris.data, iris.target) assert_equal(est.max_features_, int(np.log2(iris.data.shape[1]))) est = TreeEstimator(max_features=1) est.fit(iris.data, iris.target) assert_equal(est.max_features_, 1) est = TreeEstimator(max_features=3) est.fit(iris.data, iris.target) assert_equal(est.max_features_, 3) est = TreeEstimator(max_features=0.01) est.fit(iris.data, iris.target) assert_equal(est.max_features_, 1) est = TreeEstimator(max_features=0.5) est.fit(iris.data, iris.target) assert_equal(est.max_features_, int(0.5 * iris.data.shape[1])) est = TreeEstimator(max_features=1.0) est.fit(iris.data, iris.target) assert_equal(est.max_features_, iris.data.shape[1]) est = TreeEstimator(max_features=None) est.fit(iris.data, iris.target) assert_equal(est.max_features_, iris.data.shape[1]) # use values of max_features that are invalid est = TreeEstimator(max_features=10) assert_raises(ValueError, est.fit, X, y) est = TreeEstimator(max_features=-1) assert_raises(ValueError, est.fit, X, y) est = TreeEstimator(max_features=0.0) assert_raises(ValueError, est.fit, X, y) est = TreeEstimator(max_features=1.5) assert_raises(ValueError, est.fit, X, y) est = TreeEstimator(max_features="foobar") assert_raises(ValueError, est.fit, X, y) def test_error(): # Test that it gives proper exception on deficient input. for name, TreeEstimator in CLF_TREES.items(): # predict before fit est = TreeEstimator() assert_raises(NotFittedError, est.predict_proba, X) est.fit(X, y) X2 = [[-2, -1, 1]] # wrong feature shape for sample assert_raises(ValueError, est.predict_proba, X2) for name, TreeEstimator in ALL_TREES.items(): assert_raises(ValueError, TreeEstimator(min_samples_leaf=-1).fit, X, y) assert_raises(ValueError, TreeEstimator(min_samples_leaf=.6).fit, X, y) assert_raises(ValueError, TreeEstimator(min_samples_leaf=0.).fit, X, y) assert_raises(ValueError, TreeEstimator(min_samples_leaf=3.).fit, X, y) assert_raises(ValueError, TreeEstimator(min_weight_fraction_leaf=-1).fit, X, y) assert_raises(ValueError, TreeEstimator(min_weight_fraction_leaf=0.51).fit, X, y) assert_raises(ValueError, TreeEstimator(min_samples_split=-1).fit, X, y) assert_raises(ValueError, TreeEstimator(min_samples_split=0.0).fit, X, y) assert_raises(ValueError, TreeEstimator(min_samples_split=1.1).fit, X, y) assert_raises(ValueError, TreeEstimator(min_samples_split=2.5).fit, X, y) assert_raises(ValueError, TreeEstimator(max_depth=-1).fit, X, y) assert_raises(ValueError, TreeEstimator(max_features=42).fit, X, y) # min_impurity_split warning with ignore_warnings(category=DeprecationWarning): assert_raises(ValueError, TreeEstimator(min_impurity_split=-1.0).fit, X, y) assert_raises(ValueError, TreeEstimator(min_impurity_decrease=-1.0).fit, X, y) # Wrong dimensions est = TreeEstimator() y2 = y[:-1] assert_raises(ValueError, est.fit, X, y2) # Test with arrays that are non-contiguous. Xf = np.asfortranarray(X) est = TreeEstimator() est.fit(Xf, y) assert_almost_equal(est.predict(T), true_result) # predict before fitting est = TreeEstimator() assert_raises(NotFittedError, est.predict, T) # predict on vector with different dims est.fit(X, y) t = np.asarray(T) assert_raises(ValueError, est.predict, t[:, 1:]) # wrong sample shape Xt = np.array(X).T est = TreeEstimator() est.fit(np.dot(X, Xt), y) assert_raises(ValueError, est.predict, X) assert_raises(ValueError, est.apply, X) clf = TreeEstimator() clf.fit(X, y) assert_raises(ValueError, clf.predict, Xt) assert_raises(ValueError, clf.apply, Xt) # apply before fitting est = TreeEstimator() assert_raises(NotFittedError, est.apply, T) def test_min_samples_split(): """Test min_samples_split parameter""" X = np.asfortranarray(iris.data, dtype=tree._tree.DTYPE) y = iris.target # test both DepthFirstTreeBuilder and BestFirstTreeBuilder # by setting max_leaf_nodes for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()): TreeEstimator = ALL_TREES[name] # test for integer parameter est = TreeEstimator(min_samples_split=10, max_leaf_nodes=max_leaf_nodes, random_state=0) est.fit(X, y) # count samples on nodes, -1 means it is a leaf node_samples = est.tree_.n_node_samples[est.tree_.children_left != -1] assert_greater(np.min(node_samples), 9, "Failed with {0}".format(name)) # test for float parameter est = TreeEstimator(min_samples_split=0.2, max_leaf_nodes=max_leaf_nodes, random_state=0) est.fit(X, y) # count samples on nodes, -1 means it is a leaf node_samples = est.tree_.n_node_samples[est.tree_.children_left != -1] assert_greater(np.min(node_samples), 9, "Failed with {0}".format(name)) def test_min_samples_leaf(): # Test if leaves contain more than leaf_count training examples X = np.asfortranarray(iris.data, dtype=tree._tree.DTYPE) y = iris.target # test both DepthFirstTreeBuilder and BestFirstTreeBuilder # by setting max_leaf_nodes for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()): TreeEstimator = ALL_TREES[name] # test integer parameter est = TreeEstimator(min_samples_leaf=5, max_leaf_nodes=max_leaf_nodes, random_state=0) est.fit(X, y) out = est.tree_.apply(X) node_counts = np.bincount(out) # drop inner nodes leaf_count = node_counts[node_counts != 0] assert_greater(np.min(leaf_count), 4, "Failed with {0}".format(name)) # test float parameter est = TreeEstimator(min_samples_leaf=0.1, max_leaf_nodes=max_leaf_nodes, random_state=0) est.fit(X, y) out = est.tree_.apply(X) node_counts = np.bincount(out) # drop inner nodes leaf_count = node_counts[node_counts != 0] assert_greater(np.min(leaf_count), 4, "Failed with {0}".format(name)) def check_min_weight_fraction_leaf(name, datasets, sparse=False): """Test if leaves contain at least min_weight_fraction_leaf of the training set""" if sparse: X = DATASETS[datasets]["X_sparse"].astype(np.float32) else: X = DATASETS[datasets]["X"].astype(np.float32) y = DATASETS[datasets]["y"] weights = rng.rand(X.shape[0]) total_weight = np.sum(weights) TreeEstimator = ALL_TREES[name] # test both DepthFirstTreeBuilder and BestFirstTreeBuilder # by setting max_leaf_nodes for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 6)): est = TreeEstimator(min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, random_state=0) est.fit(X, y, sample_weight=weights) if sparse: out = est.tree_.apply(X.tocsr()) else: out = est.tree_.apply(X) node_weights = np.bincount(out, weights=weights) # drop inner nodes leaf_weights = node_weights[node_weights != 0] assert_greater_equal( np.min(leaf_weights), total_weight * est.min_weight_fraction_leaf, "Failed with {0} " "min_weight_fraction_leaf={1}".format( name, est.min_weight_fraction_leaf)) # test case with no weights passed in total_weight = X.shape[0] for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 6)): est = TreeEstimator(min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, random_state=0) est.fit(X, y) if sparse: out = est.tree_.apply(X.tocsr()) else: out = est.tree_.apply(X) node_weights = np.bincount(out) # drop inner nodes leaf_weights = node_weights[node_weights != 0] assert_greater_equal( np.min(leaf_weights), total_weight * est.min_weight_fraction_leaf, "Failed with {0} " "min_weight_fraction_leaf={1}".format( name, est.min_weight_fraction_leaf)) @pytest.mark.parametrize("name", ALL_TREES) def test_min_weight_fraction_leaf_on_dense_input(name): check_min_weight_fraction_leaf(name, "iris") @pytest.mark.parametrize("name", SPARSE_TREES) def test_min_weight_fraction_leaf_on_sparse_input(name): check_min_weight_fraction_leaf(name, "multilabel", True) def check_min_weight_fraction_leaf_with_min_samples_leaf(name, datasets, sparse=False): """Test the interaction between min_weight_fraction_leaf and min_samples_leaf when sample_weights is not provided in fit.""" if sparse: X = DATASETS[datasets]["X_sparse"].astype(np.float32) else: X = DATASETS[datasets]["X"].astype(np.float32) y = DATASETS[datasets]["y"] total_weight = X.shape[0] TreeEstimator = ALL_TREES[name] for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 3)): # test integer min_samples_leaf est = TreeEstimator(min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, min_samples_leaf=5, random_state=0) est.fit(X, y) if sparse: out = est.tree_.apply(X.tocsr()) else: out = est.tree_.apply(X) node_weights = np.bincount(out) # drop inner nodes leaf_weights = node_weights[node_weights != 0] assert_greater_equal( np.min(leaf_weights), max((total_weight * est.min_weight_fraction_leaf), 5), "Failed with {0} " "min_weight_fraction_leaf={1}, " "min_samples_leaf={2}".format(name, est.min_weight_fraction_leaf, est.min_samples_leaf)) for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 3)): # test float min_samples_leaf est = TreeEstimator(min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, min_samples_leaf=.1, random_state=0) est.fit(X, y) if sparse: out = est.tree_.apply(X.tocsr()) else: out = est.tree_.apply(X) node_weights = np.bincount(out) # drop inner nodes leaf_weights = node_weights[node_weights != 0] assert_greater_equal( np.min(leaf_weights), max((total_weight * est.min_weight_fraction_leaf), (total_weight * est.min_samples_leaf)), "Failed with {0} " "min_weight_fraction_leaf={1}, " "min_samples_leaf={2}".format(name, est.min_weight_fraction_leaf, est.min_samples_leaf)) @pytest.mark.parametrize("name", ALL_TREES) def test_min_weight_fraction_leaf_with_min_samples_leaf_on_dense_input(name): check_min_weight_fraction_leaf_with_min_samples_leaf(name, "iris") @pytest.mark.parametrize("name", SPARSE_TREES) def test_min_weight_fraction_leaf_with_min_samples_leaf_on_sparse_input(name): check_min_weight_fraction_leaf_with_min_samples_leaf( name, "multilabel", True) def test_min_impurity_split(): # test if min_impurity_split creates leaves with impurity # [0, min_impurity_split) when min_samples_leaf = 1 and # min_samples_split = 2. X = np.asfortranarray(iris.data, dtype=tree._tree.DTYPE) y = iris.target # test both DepthFirstTreeBuilder and BestFirstTreeBuilder # by setting max_leaf_nodes for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()): TreeEstimator = ALL_TREES[name] min_impurity_split = .5 # verify leaf nodes without min_impurity_split less than # impurity 1e-7 est = TreeEstimator(max_leaf_nodes=max_leaf_nodes, random_state=0) assert_true(est.min_impurity_split is None, "Failed, min_impurity_split = {0} > 1e-7".format( est.min_impurity_split)) try: assert_warns(DeprecationWarning, est.fit, X, y) except AssertionError: pass for node in range(est.tree_.node_count): if (est.tree_.children_left[node] == TREE_LEAF or est.tree_.children_right[node] == TREE_LEAF): assert_equal(est.tree_.impurity[node], 0., "Failed with {0} " "min_impurity_split={1}".format( est.tree_.impurity[node], est.min_impurity_split)) # verify leaf nodes have impurity [0,min_impurity_split] when using # min_impurity_split est = TreeEstimator(max_leaf_nodes=max_leaf_nodes, min_impurity_split=min_impurity_split, random_state=0) assert_warns_message(DeprecationWarning, "Use the min_impurity_decrease", est.fit, X, y) for node in range(est.tree_.node_count): if (est.tree_.children_left[node] == TREE_LEAF or est.tree_.children_right[node] == TREE_LEAF): assert_greater_equal(est.tree_.impurity[node], 0, "Failed with {0}, " "min_impurity_split={1}".format( est.tree_.impurity[node], est.min_impurity_split)) assert_less_equal(est.tree_.impurity[node], min_impurity_split, "Failed with {0}, " "min_impurity_split={1}".format( est.tree_.impurity[node], est.min_impurity_split)) def test_min_impurity_decrease(): # test if min_impurity_decrease ensure that a split is made only if # if the impurity decrease is atleast that value X, y = datasets.make_classification(n_samples=10000, random_state=42) # test both DepthFirstTreeBuilder and BestFirstTreeBuilder # by setting max_leaf_nodes for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()): TreeEstimator = ALL_TREES[name] # Check default value of min_impurity_decrease, 1e-7 est1 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, random_state=0) # Check with explicit value of 0.05 est2 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.05, random_state=0) # Check with a much lower value of 0.0001 est3 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.0001, random_state=0) # Check with a much lower value of 0.1 est4 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.1, random_state=0) for est, expected_decrease in ((est1, 1e-7), (est2, 0.05), (est3, 0.0001), (est4, 0.1)): assert_less_equal(est.min_impurity_decrease, expected_decrease, "Failed, min_impurity_decrease = {0} > {1}" .format(est.min_impurity_decrease, expected_decrease)) est.fit(X, y) for node in range(est.tree_.node_count): # If current node is a not leaf node, check if the split was # justified w.r.t the min_impurity_decrease if est.tree_.children_left[node] != TREE_LEAF: imp_parent = est.tree_.impurity[node] wtd_n_node = est.tree_.weighted_n_node_samples[node] left = est.tree_.children_left[node] wtd_n_left = est.tree_.weighted_n_node_samples[left] imp_left = est.tree_.impurity[left] wtd_imp_left = wtd_n_left * imp_left right = est.tree_.children_right[node] wtd_n_right = est.tree_.weighted_n_node_samples[right] imp_right = est.tree_.impurity[right] wtd_imp_right = wtd_n_right * imp_right wtd_avg_left_right_imp = wtd_imp_right + wtd_imp_left wtd_avg_left_right_imp /= wtd_n_node fractional_node_weight = ( est.tree_.weighted_n_node_samples[node] / X.shape[0]) actual_decrease = fractional_node_weight * ( imp_parent - wtd_avg_left_right_imp) assert_greater_equal(actual_decrease, expected_decrease, "Failed with {0} " "expected min_impurity_decrease={1}" .format(actual_decrease, expected_decrease)) for name, TreeEstimator in ALL_TREES.items(): if "Classifier" in name: X, y = iris.data, iris.target else: X, y = boston.data, boston.target est = TreeEstimator(random_state=0) est.fit(X, y) score = est.score(X, y) fitted_attribute = dict() for attribute in ["max_depth", "node_count", "capacity"]: fitted_attribute[attribute] = getattr(est.tree_, attribute) serialized_object = pickle.dumps(est) est2 = pickle.loads(serialized_object) assert_equal(type(est2), est.__class__) score2 = est2.score(X, y) assert_equal(score, score2, "Failed to generate same score after pickling " "with {0}".format(name)) for attribute in fitted_attribute: assert_equal(getattr(est2.tree_, attribute), fitted_attribute[attribute], "Failed to generate same attribute {0} after " "pickling with {1}".format(attribute, name)) def test_multioutput(): # Check estimators on multi-output problems. X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [-2, 1], [-1, 1], [-1, 2], [2, -1], [1, -1], [1, -2]] y = [[-1, 0], [-1, 0], [-1, 0], [1, 1], [1, 1], [1, 1], [-1, 2], [-1, 2], [-1, 2], [1, 3], [1, 3], [1, 3]] T = [[-1, -1], [1, 1], [-1, 1], [1, -1]] y_true = [[-1, 0], [1, 1], [-1, 2], [1, 3]] # toy classification problem for name, TreeClassifier in CLF_TREES.items(): clf = TreeClassifier(random_state=0) y_hat = clf.fit(X, y).predict(T) assert_array_equal(y_hat, y_true) assert_equal(y_hat.shape, (4, 2)) proba = clf.predict_proba(T) assert_equal(len(proba), 2) assert_equal(proba[0].shape, (4, 2)) assert_equal(proba[1].shape, (4, 4)) log_proba = clf.predict_log_proba(T) assert_equal(len(log_proba), 2) assert_equal(log_proba[0].shape, (4, 2)) assert_equal(log_proba[1].shape, (4, 4)) # toy regression problem for name, TreeRegressor in REG_TREES.items(): reg = TreeRegressor(random_state=0) y_hat = reg.fit(X, y).predict(T) assert_almost_equal(y_hat, y_true) assert_equal(y_hat.shape, (4, 2)) def test_classes_shape(): # Test that n_classes_ and classes_ have proper shape. for name, TreeClassifier in CLF_TREES.items(): # Classification, single output clf = TreeClassifier(random_state=0) clf.fit(X, y) assert_equal(clf.n_classes_, 2) assert_array_equal(clf.classes_, [-1, 1]) # Classification, multi-output _y = np.vstack((y, np.array(y) * 2)).T clf = TreeClassifier(random_state=0) clf.fit(X, _y) assert_equal(len(clf.n_classes_), 2) assert_equal(len(clf.classes_), 2) assert_array_equal(clf.n_classes_, [2, 2]) assert_array_equal(clf.classes_, [[-1, 1], [-2, 2]]) def test_unbalanced_iris(): # Check class rebalancing. unbalanced_X = iris.data[:125] unbalanced_y = iris.target[:125] sample_weight = compute_sample_weight("balanced", unbalanced_y) for name, TreeClassifier in CLF_TREES.items(): clf = TreeClassifier(random_state=0) clf.fit(unbalanced_X, unbalanced_y, sample_weight=sample_weight) assert_almost_equal(clf.predict(unbalanced_X), unbalanced_y) def test_memory_layout(): # Check that it works no matter the memory layout for (name, TreeEstimator), dtype in product(ALL_TREES.items(), [np.float64, np.float32]): est = TreeEstimator(random_state=0) # Nothing X = np.asarray(iris.data, dtype=dtype) y = iris.target assert_array_equal(est.fit(X, y).predict(X), y) # C-order X = np.asarray(iris.data, order="C", dtype=dtype) y = iris.target assert_array_equal(est.fit(X, y).predict(X), y) # F-order X = np.asarray(iris.data, order="F", dtype=dtype) y = iris.target assert_array_equal(est.fit(X, y).predict(X), y) # Contiguous X = np.ascontiguousarray(iris.data, dtype=dtype) y = iris.target assert_array_equal(est.fit(X, y).predict(X), y) if not est.presort: # csr matrix X = csr_matrix(iris.data, dtype=dtype) y = iris.target assert_array_equal(est.fit(X, y).predict(X), y) # csc_matrix X = csc_matrix(iris.data, dtype=dtype) y = iris.target assert_array_equal(est.fit(X, y).predict(X), y) # Strided X = np.asarray(iris.data[::3], dtype=dtype) y = iris.target[::3] assert_array_equal(est.fit(X, y).predict(X), y) def test_sample_weight(): # Check sample weighting. # Test that zero-weighted samples are not taken into account X = np.arange(100)[:, np.newaxis] y = np.ones(100) y[:50] = 0.0 sample_weight = np.ones(100) sample_weight[y == 0] = 0.0 clf = DecisionTreeClassifier(random_state=0) clf.fit(X, y, sample_weight=sample_weight) assert_array_equal(clf.predict(X), np.ones(100)) # Test that low weighted samples are not taken into account at low depth X = np.arange(200)[:, np.newaxis] y = np.zeros(200) y[50:100] = 1 y[100:200] = 2 X[100:200, 0] = 200 sample_weight = np.ones(200) sample_weight[y == 2] = .51 # Samples of class '2' are still weightier clf = DecisionTreeClassifier(max_depth=1, random_state=0) clf.fit(X, y, sample_weight=sample_weight) assert_equal(clf.tree_.threshold[0], 149.5) sample_weight[y == 2] = .5 # Samples of class '2' are no longer weightier clf = DecisionTreeClassifier(max_depth=1, random_state=0) clf.fit(X, y, sample_weight=sample_weight) assert_equal(clf.tree_.threshold[0], 49.5) # Threshold should have moved # Test that sample weighting is the same as having duplicates X = iris.data y = iris.target duplicates = rng.randint(0, X.shape[0], 100) clf = DecisionTreeClassifier(random_state=1) clf.fit(X[duplicates], y[duplicates]) sample_weight = np.bincount(duplicates, minlength=X.shape[0]) clf2 = DecisionTreeClassifier(random_state=1) clf2.fit(X, y, sample_weight=sample_weight) internal = clf.tree_.children_left != tree._tree.TREE_LEAF assert_array_almost_equal(clf.tree_.threshold[internal], clf2.tree_.threshold[internal]) def test_sample_weight_invalid(): # Check sample weighting raises errors. X = np.arange(100)[:, np.newaxis] y = np.ones(100) y[:50] = 0.0 clf = DecisionTreeClassifier(random_state=0) sample_weight = np.random.rand(100, 1) assert_raises(ValueError, clf.fit, X, y, sample_weight=sample_weight) sample_weight = np.array(0) assert_raises(ValueError, clf.fit, X, y, sample_weight=sample_weight) sample_weight = np.ones(101) assert_raises(ValueError, clf.fit, X, y, sample_weight=sample_weight) sample_weight = np.ones(99) assert_raises(ValueError, clf.fit, X, y, sample_weight=sample_weight) def check_class_weights(name): """Check class_weights resemble sample_weights behavior.""" TreeClassifier = CLF_TREES[name] # Iris is balanced, so no effect expected for using 'balanced' weights clf1 = TreeClassifier(random_state=0) clf1.fit(iris.data, iris.target) clf2 = TreeClassifier(class_weight='balanced', random_state=0) clf2.fit(iris.data, iris.target) assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_) # Make a multi-output problem with three copies of Iris iris_multi = np.vstack((iris.target, iris.target, iris.target)).T # Create user-defined weights that should balance over the outputs clf3 = TreeClassifier(class_weight=[{0: 2., 1: 2., 2: 1.}, {0: 2., 1: 1., 2: 2.}, {0: 1., 1: 2., 2: 2.}], random_state=0) clf3.fit(iris.data, iris_multi) assert_almost_equal(clf2.feature_importances_, clf3.feature_importances_) # Check against multi-output "auto" which should also have no effect clf4 = TreeClassifier(class_weight='balanced', random_state=0) clf4.fit(iris.data, iris_multi) assert_almost_equal(clf3.feature_importances_, clf4.feature_importances_) # Inflate importance of class 1, check against user-defined weights sample_weight = np.ones(iris.target.shape) sample_weight[iris.target == 1] *= 100 class_weight = {0: 1., 1: 100., 2: 1.} clf1 = TreeClassifier(random_state=0) clf1.fit(iris.data, iris.target, sample_weight) clf2 = TreeClassifier(class_weight=class_weight, random_state=0) clf2.fit(iris.data, iris.target) assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_) # Check that sample_weight and class_weight are multiplicative clf1 = TreeClassifier(random_state=0) clf1.fit(iris.data, iris.target, sample_weight ** 2) clf2 = TreeClassifier(class_weight=class_weight, random_state=0) clf2.fit(iris.data, iris.target, sample_weight) assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_) @pytest.mark.parametrize("name", CLF_TREES) def test_class_weights(name): check_class_weights(name) def check_class_weight_errors(name): # Test if class_weight raises errors and warnings when expected. TreeClassifier = CLF_TREES[name] _y = np.vstack((y, np.array(y) * 2)).T # Invalid preset string clf = TreeClassifier(class_weight='the larch', random_state=0) assert_raises(ValueError, clf.fit, X, y) assert_raises(ValueError, clf.fit, X, _y) # Not a list or preset for multi-output clf = TreeClassifier(class_weight=1, random_state=0) assert_raises(ValueError, clf.fit, X, _y) # Incorrect length list for multi-output clf = TreeClassifier(class_weight=[{-1: 0.5, 1: 1.}], random_state=0) assert_raises(ValueError, clf.fit, X, _y) @pytest.mark.parametrize("name", CLF_TREES) def test_class_weight_errors(name): check_class_weight_errors(name) def test_max_leaf_nodes(): # 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 for name, TreeEstimator in ALL_TREES.items(): est = TreeEstimator(max_depth=None, max_leaf_nodes=k + 1).fit(X, y) tree = est.tree_ assert_equal((tree.children_left == TREE_LEAF).sum(), k + 1) # max_leaf_nodes in (0, 1) should raise ValueError est = TreeEstimator(max_depth=None, max_leaf_nodes=0) assert_raises(ValueError, est.fit, X, y) est = TreeEstimator(max_depth=None, max_leaf_nodes=1) assert_raises(ValueError, est.fit, X, y) est = TreeEstimator(max_depth=None, max_leaf_nodes=0.1) assert_raises(ValueError, est.fit, X, y) def test_max_leaf_nodes_max_depth(): # Test precedence of max_leaf_nodes over max_depth. X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1) k = 4 for name, TreeEstimator in ALL_TREES.items(): est = TreeEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y) tree = est.tree_ assert_greater(tree.max_depth, 1) def test_arrays_persist(): # Ensure property arrays' memory stays alive when tree disappears # non-regression for #2726 for attr in ['n_classes', 'value', 'children_left', 'children_right', 'threshold', 'impurity', 'feature', 'n_node_samples']: value = getattr(DecisionTreeClassifier().fit([[0], [1]], [0, 1]).tree_, attr) # if pointing to freed memory, contents may be arbitrary assert_true(-3 <= value.flat[0] < 3, 'Array points to arbitrary memory') def test_only_constant_features(): random_state = check_random_state(0) X = np.zeros((10, 20)) y = random_state.randint(0, 2, (10, )) for name, TreeEstimator in ALL_TREES.items(): est = TreeEstimator(random_state=0) est.fit(X, y) assert_equal(est.tree_.max_depth, 0) def test_behaviour_constant_feature_after_splits(): X = np.transpose(np.vstack(([[0, 0, 0, 0, 0, 1, 2, 4, 5, 6, 7]], np.zeros((4, 11))))) y = [0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3] for name, TreeEstimator in ALL_TREES.items(): # do not check extra random trees if "ExtraTree" not in name: est = TreeEstimator(random_state=0, max_features=1) est.fit(X, y) assert_equal(est.tree_.max_depth, 2) assert_equal(est.tree_.node_count, 5) def test_with_only_one_non_constant_features(): X = np.hstack([np.array([[1.], [1.], [0.], [0.]]), np.zeros((4, 1000))]) y = np.array([0., 1., 0., 1.0]) for name, TreeEstimator in CLF_TREES.items(): est = TreeEstimator(random_state=0, max_features=1) est.fit(X, y) assert_equal(est.tree_.max_depth, 1) assert_array_equal(est.predict_proba(X), np.full((4, 2), 0.5)) for name, TreeEstimator in REG_TREES.items(): est = TreeEstimator(random_state=0, max_features=1) est.fit(X, y) assert_equal(est.tree_.max_depth, 1) assert_array_equal(est.predict(X), np.full((4, ), 0.5)) def test_big_input(): # Test if the warning for too large inputs is appropriate. X = np.repeat(10 ** 40., 4).astype(np.float64).reshape(-1, 1) clf = DecisionTreeClassifier() try: clf.fit(X, [0, 1, 0, 1]) except ValueError as e: assert_in("float32", str(e)) def test_realloc(): from sklearn.tree._utils import _realloc_test assert_raises(MemoryError, _realloc_test) def test_huge_allocations(): n_bits = 8 * struct.calcsize("P") X = np.random.randn(10, 2) y = np.random.randint(0, 2, 10) # Sanity check: we cannot request more memory than the size of the address # space. Currently raises OverflowError. huge = 2 ** (n_bits + 1) clf = DecisionTreeClassifier(splitter='best', max_leaf_nodes=huge) assert_raises(Exception, clf.fit, X, y) # Non-regression test: MemoryError used to be dropped by Cython # because of missing "except *". huge = 2 ** (n_bits - 1) - 1 clf = DecisionTreeClassifier(splitter='best', max_leaf_nodes=huge) assert_raises(MemoryError, clf.fit, X, y) def check_sparse_input(tree, dataset, max_depth=None): TreeEstimator = ALL_TREES[tree] X = DATASETS[dataset]["X"] X_sparse = DATASETS[dataset]["X_sparse"] y = DATASETS[dataset]["y"] # Gain testing time if dataset in ["digits", "boston"]: n_samples = X.shape[0] // 5 X = X[:n_samples] X_sparse = X_sparse[:n_samples] y = y[:n_samples] for sparse_format in (csr_matrix, csc_matrix, coo_matrix): X_sparse = sparse_format(X_sparse) # Check the default (depth first search) d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y) s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y) assert_tree_equal(d.tree_, s.tree_, "{0} with dense and sparse format gave different " "trees".format(tree)) y_pred = d.predict(X) if tree in CLF_TREES: y_proba = d.predict_proba(X) y_log_proba = d.predict_log_proba(X) for sparse_matrix in (csr_matrix, csc_matrix, coo_matrix): X_sparse_test = sparse_matrix(X_sparse, dtype=np.float32) assert_array_almost_equal(s.predict(X_sparse_test), y_pred) if tree in CLF_TREES: assert_array_almost_equal(s.predict_proba(X_sparse_test), y_proba) assert_array_almost_equal(s.predict_log_proba(X_sparse_test), y_log_proba) @pytest.mark.parametrize("tree_type", SPARSE_TREES) @pytest.mark.parametrize( "dataset", ("clf_small", "toy", "digits", "multilabel", "sparse-pos", "sparse-neg", "sparse-mix", "zeros") ) def test_sparse_input(tree_type, dataset): max_depth = 3 if dataset == "digits" else None check_sparse_input(tree_type, dataset, max_depth) @pytest.mark.parametrize("tree_type", set(SPARSE_TREES).intersection(REG_TREES)) @pytest.mark.parametrize("dataset", ["boston", "reg_small"]) def test_sparse_input_reg_trees(tree_type, dataset): # Due to numerical instability of MSE and too strict test, we limit the # maximal depth check_sparse_input(tree_type, dataset, 2) def check_sparse_parameters(tree, dataset): TreeEstimator = ALL_TREES[tree] X = DATASETS[dataset]["X"] X_sparse = DATASETS[dataset]["X_sparse"] y = DATASETS[dataset]["y"] # Check max_features d = TreeEstimator(random_state=0, max_features=1, max_depth=2).fit(X, y) s = TreeEstimator(random_state=0, max_features=1, max_depth=2).fit(X_sparse, y) assert_tree_equal(d.tree_, s.tree_, "{0} with dense and sparse format gave different " "trees".format(tree)) assert_array_almost_equal(s.predict(X), d.predict(X)) # Check min_samples_split d = TreeEstimator(random_state=0, max_features=1, min_samples_split=10).fit(X, y) s = TreeEstimator(random_state=0, max_features=1, min_samples_split=10).fit(X_sparse, y) assert_tree_equal(d.tree_, s.tree_, "{0} with dense and sparse format gave different " "trees".format(tree)) assert_array_almost_equal(s.predict(X), d.predict(X)) # Check min_samples_leaf d = TreeEstimator(random_state=0, min_samples_leaf=X_sparse.shape[0] // 2).fit(X, y) s = TreeEstimator(random_state=0, min_samples_leaf=X_sparse.shape[0] // 2).fit(X_sparse, y) assert_tree_equal(d.tree_, s.tree_, "{0} with dense and sparse format gave different " "trees".format(tree)) assert_array_almost_equal(s.predict(X), d.predict(X)) # Check best-first search d = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X, y) s = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X_sparse, y) assert_tree_equal(d.tree_, s.tree_, "{0} with dense and sparse format gave different " "trees".format(tree)) assert_array_almost_equal(s.predict(X), d.predict(X)) def check_sparse_criterion(tree, dataset): TreeEstimator = ALL_TREES[tree] X = DATASETS[dataset]["X"] X_sparse = DATASETS[dataset]["X_sparse"] y = DATASETS[dataset]["y"] # Check various criterion CRITERIONS = REG_CRITERIONS if tree in REG_TREES else CLF_CRITERIONS for criterion in CRITERIONS: d = TreeEstimator(random_state=0, max_depth=3, criterion=criterion).fit(X, y) s = TreeEstimator(random_state=0, max_depth=3, criterion=criterion).fit(X_sparse, y) assert_tree_equal(d.tree_, s.tree_, "{0} with dense and sparse format gave different " "trees".format(tree)) assert_array_almost_equal(s.predict(X), d.predict(X)) @pytest.mark.parametrize("tree_type", SPARSE_TREES) @pytest.mark.parametrize("dataset", ["sparse-pos", "sparse-neg", "sparse-mix", "zeros"]) @pytest.mark.parametrize("check", [check_sparse_parameters, check_sparse_criterion]) def test_sparse(tree_type, dataset, check): check(tree_type, dataset) def check_explicit_sparse_zeros(tree, max_depth=3, n_features=10): TreeEstimator = ALL_TREES[tree] # n_samples set n_feature to ease construction of a simultaneous # construction of a csr and csc matrix n_samples = n_features samples = np.arange(n_samples) # Generate X, y random_state = check_random_state(0) indices = [] data = [] offset = 0 indptr = [offset] for i in range(n_features): n_nonzero_i = random_state.binomial(n_samples, 0.5) indices_i = random_state.permutation(samples)[:n_nonzero_i] indices.append(indices_i) data_i = random_state.binomial(3, 0.5, size=(n_nonzero_i, )) - 1 data.append(data_i) offset += n_nonzero_i indptr.append(offset) indices = np.concatenate(indices) data = np.array(np.concatenate(data), dtype=np.float32) X_sparse = csc_matrix((data, indices, indptr), shape=(n_samples, n_features)) X = X_sparse.toarray() X_sparse_test = csr_matrix((data, indices, indptr), shape=(n_samples, n_features)) X_test = X_sparse_test.toarray() y = random_state.randint(0, 3, size=(n_samples, )) # Ensure that X_sparse_test owns its data, indices and indptr array X_sparse_test = X_sparse_test.copy() # Ensure that we have explicit zeros assert_greater((X_sparse.data == 0.).sum(), 0) assert_greater((X_sparse_test.data == 0.).sum(), 0) # Perform the comparison d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y) s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y) assert_tree_equal(d.tree_, s.tree_, "{0} with dense and sparse format gave different " "trees".format(tree)) Xs = (X_test, X_sparse_test) for X1, X2 in product(Xs, Xs): assert_array_almost_equal(s.tree_.apply(X1), d.tree_.apply(X2)) assert_array_almost_equal(s.apply(X1), d.apply(X2)) assert_array_almost_equal(s.apply(X1), s.tree_.apply(X1)) assert_array_almost_equal(s.tree_.decision_path(X1).toarray(), d.tree_.decision_path(X2).toarray()) assert_array_almost_equal(s.decision_path(X1).toarray(), d.decision_path(X2).toarray()) assert_array_almost_equal(s.decision_path(X1).toarray(), s.tree_.decision_path(X1).toarray()) assert_array_almost_equal(s.predict(X1), d.predict(X2)) if tree in CLF_TREES: assert_array_almost_equal(s.predict_proba(X1), d.predict_proba(X2)) @pytest.mark.parametrize("tree_type", SPARSE_TREES) def test_explicit_sparse_zeros(tree_type): check_explicit_sparse_zeros(tree_type) @ignore_warnings def check_raise_error_on_1d_input(name): TreeEstimator = ALL_TREES[name] X = iris.data[:, 0].ravel() X_2d = iris.data[:, 0].reshape((-1, 1)) y = iris.target assert_raises(ValueError, TreeEstimator(random_state=0).fit, X, y) est = TreeEstimator(random_state=0) est.fit(X_2d, y) assert_raises(ValueError, est.predict, [X]) @pytest.mark.parametrize("name", ALL_TREES) def test_1d_input(name): with ignore_warnings(): check_raise_error_on_1d_input(name) def _check_min_weight_leaf_split_level(TreeEstimator, X, y, sample_weight): est = TreeEstimator(random_state=0) est.fit(X, y, sample_weight=sample_weight) assert_equal(est.tree_.max_depth, 1) est = TreeEstimator(random_state=0, min_weight_fraction_leaf=0.4) est.fit(X, y, sample_weight=sample_weight) assert_equal(est.tree_.max_depth, 0) def check_min_weight_leaf_split_level(name): TreeEstimator = ALL_TREES[name] X = np.array([[0], [0], [0], [0], [1]]) y = [0, 0, 0, 0, 1] sample_weight = [0.2, 0.2, 0.2, 0.2, 0.2] _check_min_weight_leaf_split_level(TreeEstimator, X, y, sample_weight) if not TreeEstimator().presort: _check_min_weight_leaf_split_level(TreeEstimator, csc_matrix(X), y, sample_weight) @pytest.mark.parametrize("name", ALL_TREES) def test_min_weight_leaf_split_level(name): check_min_weight_leaf_split_level(name) def check_public_apply(name): X_small32 = X_small.astype(tree._tree.DTYPE) est = ALL_TREES[name]() est.fit(X_small, y_small) assert_array_equal(est.apply(X_small), est.tree_.apply(X_small32)) def check_public_apply_sparse(name): X_small32 = csr_matrix(X_small.astype(tree._tree.DTYPE)) est = ALL_TREES[name]() est.fit(X_small, y_small) assert_array_equal(est.apply(X_small), est.tree_.apply(X_small32)) @pytest.mark.parametrize("name", ALL_TREES) def test_public_apply_all_trees(name): check_public_apply(name) @pytest.mark.parametrize("name", SPARSE_TREES) def test_public_apply_sparse_trees(name): check_public_apply_sparse(name) def check_presort_sparse(est, X, y): assert_raises(ValueError, est.fit, X, y) def test_presort_sparse(): ests = (DecisionTreeClassifier(presort=True), DecisionTreeRegressor(presort=True)) sparse_matrices = (csr_matrix, csc_matrix, coo_matrix) y, X = datasets.make_multilabel_classification(random_state=0, n_samples=50, n_features=1, n_classes=20) y = y[:, 0] for est, sparse_matrix in product(ests, sparse_matrices): check_presort_sparse(est, sparse_matrix(X), y) @pytest.mark.parametrize('cls', (DecisionTreeRegressor, DecisionTreeClassifier)) def test_invalid_presort(cls): allowed_presort = ('auto', True, False) invalid_presort = 'invalid' msg = ("'presort' should be in {}. " "Got {!r} instead.".format(allowed_presort, invalid_presort)) est = cls(presort=invalid_presort) assert_raise_message(ValueError, msg, est.fit, X, y) def test_decision_path_hardcoded(): X = iris.data y = iris.target est = DecisionTreeClassifier(random_state=0, max_depth=1).fit(X, y) node_indicator = est.decision_path(X[:2]).toarray() assert_array_equal(node_indicator, [[1, 1, 0], [1, 0, 1]]) def check_decision_path(name): X = iris.data y = iris.target n_samples = X.shape[0] TreeEstimator = ALL_TREES[name] est = TreeEstimator(random_state=0, max_depth=2) est.fit(X, y) node_indicator_csr = est.decision_path(X) node_indicator = node_indicator_csr.toarray() assert_equal(node_indicator.shape, (n_samples, est.tree_.node_count)) # Assert that leaves index are correct leaves = est.apply(X) leave_indicator = [node_indicator[i, j] for i, j in enumerate(leaves)] assert_array_almost_equal(leave_indicator, np.ones(shape=n_samples)) # Ensure only one leave node per sample all_leaves = est.tree_.children_left == TREE_LEAF assert_array_almost_equal(np.dot(node_indicator, all_leaves), np.ones(shape=n_samples)) # Ensure max depth is consistent with sum of indicator max_depth = node_indicator.sum(axis=1).max() assert_less_equal(est.tree_.max_depth, max_depth) @pytest.mark.parametrize("name", ALL_TREES) def test_decision_path(name): check_decision_path(name) def check_no_sparse_y_support(name): X, y = X_multilabel, csr_matrix(y_multilabel) TreeEstimator = ALL_TREES[name] assert_raises(TypeError, TreeEstimator(random_state=0).fit, X, y) @pytest.mark.parametrize("name", ALL_TREES) def test_no_sparse_y_support(name): # Currently we don't support sparse y check_no_sparse_y_support(name) def test_mae(): """Check MAE criterion produces correct results on small toy dataset: ------------------ | X | y | weight | ------------------ | 3 | 3 | 0.1 | | 5 | 3 | 0.3 | | 8 | 4 | 1.0 | | 3 | 6 | 0.6 | | 5 | 7 | 0.3 | ------------------ |sum wt:| 2.3 | ------------------ Because we are dealing with sample weights, we cannot find the median by simply choosing/averaging the centre value(s), instead we consider the median where 50% of the cumulative weight is found (in a y sorted data set) . Therefore with regards to this test data, the cumulative weight is >= 50% when y = 4. Therefore: Median = 4 For all the samples, we can get the total error by summing: Absolute(Median - y) * weight I.e., total error = (Absolute(4 - 3) * 0.1) + (Absolute(4 - 3) * 0.3) + (Absolute(4 - 4) * 1.0) + (Absolute(4 - 6) * 0.6) + (Absolute(4 - 7) * 0.3) = 2.5 Impurity = Total error / total weight = 2.5 / 2.3 = 1.08695652173913 ------------------ From this root node, the next best split is between X values of 3 and 5. Thus, we have left and right child nodes: LEFT RIGHT ------------------ ------------------ | X | y | weight | | X | y | weight | ------------------ ------------------ | 3 | 3 | 0.1 | | 5 | 3 | 0.3 | | 3 | 6 | 0.6 | | 8 | 4 | 1.0 | ------------------ | 5 | 7 | 0.3 | |sum wt:| 0.7 | ------------------ ------------------ |sum wt:| 1.6 | ------------------ Impurity is found in the same way: Left node Median = 6 Total error = (Absolute(6 - 3) * 0.1) + (Absolute(6 - 6) * 0.6) = 0.3 Left Impurity = Total error / total weight = 0.3 / 0.7 = 0.428571428571429 ------------------- Likewise for Right node: Right node Median = 4 Total error = (Absolute(4 - 3) * 0.3) + (Absolute(4 - 4) * 1.0) + (Absolute(4 - 7) * 0.3) = 1.2 Right Impurity = Total error / total weight = 1.2 / 1.6 = 0.75 ------ """ dt_mae = DecisionTreeRegressor(random_state=0, criterion="mae", max_leaf_nodes=2) # Test MAE where sample weights are non-uniform (as illustrated above): dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3], sample_weight=[0.6, 0.3, 0.1, 1.0, 0.3]) assert_allclose(dt_mae.tree_.impurity, [2.5 / 2.3, 0.3 / 0.7, 1.2 / 1.6]) assert_array_equal(dt_mae.tree_.value.flat, [4.0, 6.0, 4.0]) # Test MAE where all sample weights are uniform: dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3], sample_weight=np.ones(5)) assert_array_equal(dt_mae.tree_.impurity, [1.4, 1.5, 4.0 / 3.0]) assert_array_equal(dt_mae.tree_.value.flat, [4, 4.5, 4.0]) # Test MAE where a `sample_weight` is not explicitly provided. # This is equivalent to providing uniform sample weights, though # the internal logic is different: dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3]) assert_array_equal(dt_mae.tree_.impurity, [1.4, 1.5, 4.0 / 3.0]) assert_array_equal(dt_mae.tree_.value.flat, [4, 4.5, 4.0]) def test_criterion_copy(): # Let's check whether copy of our criterion has the same type # and properties as original n_outputs = 3 n_classes = np.arange(3, dtype=np.intp) n_samples = 100 def _pickle_copy(obj): return pickle.loads(pickle.dumps(obj)) for copy_func in [copy.copy, copy.deepcopy, _pickle_copy]: for _, typename in CRITERIA_CLF.items(): criteria = typename(n_outputs, n_classes) result = copy_func(criteria).__reduce__() typename_, (n_outputs_, n_classes_), _ = result assert_equal(typename, typename_) assert_equal(n_outputs, n_outputs_) assert_array_equal(n_classes, n_classes_) for _, typename in CRITERIA_REG.items(): criteria = typename(n_outputs, n_samples) result = copy_func(criteria).__reduce__() typename_, (n_outputs_, n_samples_), _ = result assert_equal(typename, typename_) assert_equal(n_outputs, n_outputs_) assert_equal(n_samples, n_samples_) def test_empty_leaf_infinite_threshold(): # try to make empty leaf by using near infinite value. data = np.random.RandomState(0).randn(100, 11) * 2e38 data = np.nan_to_num(data.astype('float32')) X_full = data[:, :-1] X_sparse = csc_matrix(X_full) y = data[:, -1] for X in [X_full, X_sparse]: tree = DecisionTreeRegressor(random_state=0).fit(X, y) terminal_regions = tree.apply(X) left_leaf = set(np.where(tree.tree_.children_left == TREE_LEAF)[0]) empty_leaf = left_leaf.difference(terminal_regions) infinite_threshold = np.where(~np.isfinite(tree.tree_.threshold))[0] assert len(infinite_threshold) == 0 assert len(empty_leaf) == 0