1200 lines
42 KiB
Python
1200 lines
42 KiB
Python
"""
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Testing for the tree module (sklearn.tree).
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"""
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import pickle
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from functools import partial
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from itertools import product
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import platform
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import numpy as np
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from scipy.sparse import csc_matrix
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from scipy.sparse import csr_matrix
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from scipy.sparse import coo_matrix
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from sklearn.random_projection import sparse_random_matrix
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from sklearn.utils.random import sample_without_replacement
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import mean_squared_error
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_in
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_greater
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from sklearn.utils.testing import assert_greater_equal
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from sklearn.utils.testing import assert_less
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import raises
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from sklearn.utils.validation import check_random_state
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.tree import DecisionTreeRegressor
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from sklearn.tree import ExtraTreeClassifier
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from sklearn.tree import ExtraTreeRegressor
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from sklearn import tree
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from sklearn.tree.tree import SPARSE_SPLITTERS
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from sklearn.tree._tree import TREE_LEAF
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from sklearn import datasets
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from sklearn.preprocessing._weights import _balance_weights
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CLF_CRITERIONS = ("gini", "entropy")
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REG_CRITERIONS = ("mse", )
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CLF_TREES = {
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"DecisionTreeClassifier": DecisionTreeClassifier,
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"Presort-DecisionTreeClassifier": partial(DecisionTreeClassifier,
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splitter="presort-best"),
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"ExtraTreeClassifier": ExtraTreeClassifier,
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}
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REG_TREES = {
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"DecisionTreeRegressor": DecisionTreeRegressor,
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"Presort-DecisionTreeRegressor": partial(DecisionTreeRegressor,
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splitter="presort-best"),
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"ExtraTreeRegressor": ExtraTreeRegressor,
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}
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ALL_TREES = dict()
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ALL_TREES.update(CLF_TREES)
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ALL_TREES.update(REG_TREES)
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SPARSE_TREES = [name for name, Tree in ALL_TREES.items()
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if Tree().splitter in SPARSE_SPLITTERS]
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X_small = np.array([
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[0, 0, 4, 0, 0, 0, 1, -14, 0, -4, 0, 0, 0, 0, ],
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[0, 0, 5, 3, 0, -4, 0, 0, 1, -5, 0.2, 0, 4, 1, ],
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[-1, -1, 0, 0, -4.5, 0, 0, 2.1, 1, 0, 0, -4.5, 0, 1, ],
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[-1, -1, 0, -1.2, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 1, ],
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[-1, -1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, ],
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[-1, -2, 0, 4, -3, 10, 4, 0, -3.2, 0, 4, 3, -4, 1, ],
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[2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -3, 1, ],
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[2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1, ],
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[2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1, ],
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[2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -1, 0, ],
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[2, 8, 5, 1, 0.5, -4, 10, 0, 1, -5, 3, 0, 2, 0, ],
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[2, 0, 1, 1, 1, -1, 1, 0, 0, -2, 3, 0, 1, 0, ],
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[2, 0, 1, 2, 3, -1, 10, 2, 0, -1, 1, 2, 2, 0, ],
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[1, 1, 0, 2, 2, -1, 1, 2, 0, -5, 1, 2, 3, 0, ],
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[3, 1, 0, 3, 0, -4, 10, 0, 1, -5, 3, 0, 3, 1, ],
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[2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 0.5, 0, -3, 1, ],
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[2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 1.5, 1, -1, -1, ],
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[2.11, 8, -6, -0.5, 0, 10, 0, 0, -3.2, 6, 0.5, 0, -1, -1, ],
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[2, 0, 5, 1, 0.5, -2, 10, 0, 1, -5, 3, 1, 0, -1, ],
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[2, 0, 1, 1, 1, -2, 1, 0, 0, -2, 0, 0, 0, 1, ],
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[2, 1, 1, 1, 2, -1, 10, 2, 0, -1, 0, 2, 1, 1, ],
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[1, 1, 0, 0, 1, -3, 1, 2, 0, -5, 1, 2, 1, 1, ],
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[3, 1, 0, 1, 0, -4, 1, 0, 1, -2, 0, 0, 1, 0, ]])
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y_small = [1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,
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0, 0]
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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,
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0.0, 1.2, 2, 11, 0, 0, 4.5, 0.201, 1.06, 0.9, 0]
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# toy sample
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
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y = [-1, -1, -1, 1, 1, 1]
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T = [[-1, -1], [2, 2], [3, 2]]
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true_result = [-1, 1, 1]
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# also load the iris dataset
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# and randomly permute it
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iris = datasets.load_iris()
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rng = np.random.RandomState(1)
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perm = rng.permutation(iris.target.size)
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iris.data = iris.data[perm]
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iris.target = iris.target[perm]
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# also load the boston dataset
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# and randomly permute it
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boston = datasets.load_boston()
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perm = rng.permutation(boston.target.size)
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boston.data = boston.data[perm]
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boston.target = boston.target[perm]
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digits = datasets.load_digits()
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perm = rng.permutation(digits.target.size)
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digits.data = digits.data[perm]
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digits.target = digits.target[perm]
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random_state = check_random_state(0)
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X_multilabel, y_multilabel = datasets.make_multilabel_classification(
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random_state=0, return_indicator=True, n_samples=30, n_features=10)
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X_sparse_pos = random_state.uniform(size=(20, 5))
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X_sparse_pos[X_sparse_pos <= 0.8] = 0.
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y_random = random_state.randint(0, 4, size=(20, ))
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X_sparse_mix = sparse_random_matrix(20, 10, density=0.25, random_state=0)
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DATASETS = {
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"iris": {"X": iris.data, "y": iris.target},
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"boston": {"X": boston.data, "y": boston.target},
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"digits": {"X": digits.data, "y": digits.target},
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"toy": {"X": X, "y": y},
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"clf_small": {"X": X_small, "y": y_small},
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"reg_small": {"X": X_small, "y": y_small_reg},
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"multilabel": {"X": X_multilabel, "y": y_multilabel},
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"sparse-pos": {"X": X_sparse_pos, "y": y_random},
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"sparse-neg": {"X": - X_sparse_pos, "y": y_random},
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"sparse-mix": {"X": X_sparse_mix, "y": y_random},
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"zeros": {"X": np.zeros((20, 3)), "y": y_random}
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}
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for name in DATASETS:
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DATASETS[name]["X_sparse"] = csc_matrix(DATASETS[name]["X"])
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def assert_tree_equal(d, s, message):
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assert_equal(s.node_count, d.node_count,
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"{0}: inequal number of node ({1} != {2})"
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"".format(message, s.node_count, d.node_count))
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assert_array_equal(d.children_right, s.children_right,
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message + ": inequal children_right")
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assert_array_equal(d.children_left, s.children_left,
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message + ": inequal children_left")
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external = d.children_right == TREE_LEAF
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internal = np.logical_not(external)
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assert_array_equal(d.feature[internal], s.feature[internal],
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message + ": inequal features")
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assert_array_equal(d.threshold[internal], s.threshold[internal],
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message + ": inequal threshold")
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assert_array_equal(d.n_node_samples.sum(), s.n_node_samples.sum(),
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message + ": inequal sum(n_node_samples)")
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assert_array_equal(d.n_node_samples, s.n_node_samples,
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message + ": inequal n_node_samples")
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assert_almost_equal(d.impurity, s.impurity,
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err_msg=message + ": inequal impurity")
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assert_array_almost_equal(d.value[external], s.value[external],
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err_msg=message + ": inequal value")
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def test_classification_toy():
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"""Check classification on a toy dataset."""
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for name, Tree in CLF_TREES.items():
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clf = Tree(random_state=0)
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clf.fit(X, y)
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assert_array_equal(clf.predict(T), true_result,
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"Failed with {0}".format(name))
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clf = Tree(max_features=1, random_state=1)
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clf.fit(X, y)
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assert_array_equal(clf.predict(T), true_result,
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"Failed with {0}".format(name))
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def test_weighted_classification_toy():
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"""Check classification on a weighted toy dataset."""
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for name, Tree in CLF_TREES.items():
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clf = Tree(random_state=0)
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clf.fit(X, y, sample_weight=np.ones(len(X)))
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assert_array_equal(clf.predict(T), true_result,
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"Failed with {0}".format(name))
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clf.fit(X, y, sample_weight=np.ones(len(X)) * 0.5)
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assert_array_equal(clf.predict(T), true_result,
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"Failed with {0}".format(name))
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def test_regression_toy():
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"""Check regression on a toy dataset."""
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for name, Tree in REG_TREES.items():
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reg = Tree(random_state=1)
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reg.fit(X, y)
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assert_almost_equal(reg.predict(T), true_result,
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err_msg="Failed with {0}".format(name))
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clf = Tree(max_features=1, random_state=1)
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clf.fit(X, y)
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assert_almost_equal(reg.predict(T), true_result,
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err_msg="Failed with {0}".format(name))
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def test_xor():
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"""Check on a XOR problem"""
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y = np.zeros((10, 10))
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y[:5, :5] = 1
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y[5:, 5:] = 1
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gridx, gridy = np.indices(y.shape)
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X = np.vstack([gridx.ravel(), gridy.ravel()]).T
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y = y.ravel()
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for name, Tree in CLF_TREES.items():
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clf = Tree(random_state=0)
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clf.fit(X, y)
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assert_equal(clf.score(X, y), 1.0,
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"Failed with {0}".format(name))
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clf = Tree(random_state=0, max_features=1)
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clf.fit(X, y)
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assert_equal(clf.score(X, y), 1.0,
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"Failed with {0}".format(name))
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def test_iris():
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"""Check consistency on dataset iris."""
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for (name, Tree), criterion in product(CLF_TREES.items(), CLF_CRITERIONS):
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clf = Tree(criterion=criterion, random_state=0)
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clf.fit(iris.data, iris.target)
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score = accuracy_score(clf.predict(iris.data), iris.target)
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assert_greater(score, 0.9,
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"Failed with {0}, criterion = {1} and score = {2}"
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"".format(name, criterion, score))
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clf = Tree(criterion=criterion, max_features=2, random_state=0)
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clf.fit(iris.data, iris.target)
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score = accuracy_score(clf.predict(iris.data), iris.target)
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assert_greater(score, 0.5,
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"Failed with {0}, criterion = {1} and score = {2}"
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"".format(name, criterion, score))
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def test_boston():
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"""Check consistency on dataset boston house prices."""
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for (name, Tree), criterion in product(REG_TREES.items(), REG_CRITERIONS):
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reg = Tree(criterion=criterion, random_state=0)
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reg.fit(boston.data, boston.target)
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score = mean_squared_error(boston.target, reg.predict(boston.data))
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assert_less(score, 1,
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"Failed with {0}, criterion = {1} and score = {2}"
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"".format(name, criterion, score))
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# using fewer features reduces the learning ability of this tree,
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# but reduces training time.
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reg = Tree(criterion=criterion, max_features=6, random_state=0)
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reg.fit(boston.data, boston.target)
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score = mean_squared_error(boston.target, reg.predict(boston.data))
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assert_less(score, 2,
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"Failed with {0}, criterion = {1} and score = {2}"
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"".format(name, criterion, score))
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def test_probability():
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"""Predict probabilities using DecisionTreeClassifier."""
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for name, Tree in CLF_TREES.items():
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clf = Tree(max_depth=1, max_features=1, random_state=42)
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clf.fit(iris.data, iris.target)
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prob_predict = clf.predict_proba(iris.data)
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assert_array_almost_equal(np.sum(prob_predict, 1),
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np.ones(iris.data.shape[0]),
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err_msg="Failed with {0}".format(name))
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assert_array_equal(np.argmax(prob_predict, 1),
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clf.predict(iris.data),
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err_msg="Failed with {0}".format(name))
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assert_almost_equal(clf.predict_proba(iris.data),
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np.exp(clf.predict_log_proba(iris.data)), 8,
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err_msg="Failed with {0}".format(name))
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def test_arrayrepr():
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"""Check the array representation."""
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# Check resize
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X = np.arange(10000)[:, np.newaxis]
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y = np.arange(10000)
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for name, Tree in REG_TREES.items():
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reg = Tree(max_depth=None, random_state=0)
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reg.fit(X, y)
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def test_pure_set():
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"""Check when y is pure."""
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
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y = [1, 1, 1, 1, 1, 1]
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for name, TreeClassifier in CLF_TREES.items():
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clf = TreeClassifier(random_state=0)
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clf.fit(X, y)
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assert_array_equal(clf.predict(X), y,
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err_msg="Failed with {0}".format(name))
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for name, TreeRegressor in REG_TREES.items():
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reg = TreeRegressor(random_state=0)
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reg.fit(X, y)
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assert_almost_equal(clf.predict(X), y,
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err_msg="Failed with {0}".format(name))
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def test_numerical_stability():
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"""Check numerical stability."""
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X = np.array([
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[152.08097839, 140.40744019, 129.75102234, 159.90493774],
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[142.50700378, 135.81935120, 117.82884979, 162.75781250],
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[127.28772736, 140.40744019, 129.75102234, 159.90493774],
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[132.37025452, 143.71923828, 138.35694885, 157.84558105],
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[103.10237122, 143.71928406, 138.35696411, 157.84559631],
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[127.71276855, 143.71923828, 138.35694885, 157.84558105],
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[120.91514587, 140.40744019, 129.75102234, 159.90493774]])
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y = np.array(
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[1., 0.70209277, 0.53896582, 0., 0.90914464, 0.48026916, 0.49622521])
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with np.errstate(all="raise"):
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for name, Tree in REG_TREES.items():
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reg = Tree(random_state=0)
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reg.fit(X, y)
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reg.fit(X, -y)
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reg.fit(-X, y)
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reg.fit(-X, -y)
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def test_importances():
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"""Check variable importances."""
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X, y = datasets.make_classification(n_samples=2000,
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n_features=10,
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n_informative=3,
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n_redundant=0,
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n_repeated=0,
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shuffle=False,
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random_state=0)
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for name, Tree in CLF_TREES.items():
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clf = Tree(random_state=0)
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clf.fit(X, y)
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importances = clf.feature_importances_
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n_important = np.sum(importances > 0.1)
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assert_equal(importances.shape[0], 10, "Failed with {0}".format(name))
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assert_equal(n_important, 3, "Failed with {0}".format(name))
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X_new = clf.transform(X, threshold="mean")
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assert_less(0, X_new.shape[1], "Failed with {0}".format(name))
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assert_less(X_new.shape[1], X.shape[1], "Failed with {0}".format(name))
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# Check on iris that importances are the same for all builders
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clf = DecisionTreeClassifier(random_state=0)
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clf.fit(iris.data, iris.target)
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clf2 = DecisionTreeClassifier(random_state=0,
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max_leaf_nodes=len(iris.data))
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clf2.fit(iris.data, iris.target)
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assert_array_equal(clf.feature_importances_,
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clf2.feature_importances_)
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@raises(ValueError)
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def test_importances_raises():
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"""Check if variable importance before fit raises ValueError. """
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clf = DecisionTreeClassifier()
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clf.feature_importances_
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def test_importances_gini_equal_mse():
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"""Check that gini is equivalent to mse for binary output variable"""
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X, y = datasets.make_classification(n_samples=2000,
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n_features=10,
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n_informative=3,
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n_redundant=0,
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n_repeated=0,
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shuffle=False,
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random_state=0)
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# The gini index and the mean square error (variance) might differ due
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# to numerical instability. Since those instabilities mainly occurs at
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# high tree depth, we restrict this maximal depth.
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clf = DecisionTreeClassifier(criterion="gini", max_depth=5,
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random_state=0).fit(X, y)
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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(Exception, 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():
|
|
# Invalid values for parameters
|
|
assert_raises(ValueError, TreeEstimator(min_samples_leaf=-1).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(max_depth=-1).fit, X, y)
|
|
assert_raises(ValueError, TreeEstimator(max_features=42).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(Exception, 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)
|
|
|
|
clf = TreeEstimator()
|
|
clf.fit(X, y)
|
|
assert_raises(ValueError, clf.predict, Xt)
|
|
|
|
|
|
def test_min_samples_leaf():
|
|
"""Test if leaves contain more than leaf_count training examples"""
|
|
X = np.asfortranarray(iris.data.astype(tree._tree.DTYPE))
|
|
y = iris.target
|
|
|
|
# test both DepthFirstTreeBuilder and BestFirstTreeBuilder
|
|
# by setting max_leaf_nodes
|
|
for max_leaf_nodes in (None, 1000):
|
|
for name, TreeEstimator in ALL_TREES.items():
|
|
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))
|
|
|
|
|
|
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))
|
|
|
|
|
|
def test_min_weight_fraction_leaf():
|
|
# Check on dense input
|
|
for name in ALL_TREES:
|
|
yield check_min_weight_fraction_leaf, name, "iris"
|
|
|
|
# Check on sparse input
|
|
for name in SPARSE_TREES:
|
|
yield check_min_weight_fraction_leaf, name, "multilabel", True
|
|
|
|
|
|
def test_pickle():
|
|
"""Check that tree estimator are pickable """
|
|
for name, TreeClassifier in CLF_TREES.items():
|
|
clf = TreeClassifier(random_state=0)
|
|
clf.fit(iris.data, iris.target)
|
|
score = clf.score(iris.data, iris.target)
|
|
|
|
serialized_object = pickle.dumps(clf)
|
|
clf2 = pickle.loads(serialized_object)
|
|
assert_equal(type(clf2), clf.__class__)
|
|
score2 = clf2.score(iris.data, iris.target)
|
|
assert_equal(score, score2, "Failed to generate same score "
|
|
"after pickling (classification) "
|
|
"with {0}".format(name))
|
|
|
|
for name, TreeRegressor in REG_TREES.items():
|
|
reg = TreeRegressor(random_state=0)
|
|
reg.fit(boston.data, boston.target)
|
|
score = reg.score(boston.data, boston.target)
|
|
|
|
serialized_object = pickle.dumps(reg)
|
|
reg2 = pickle.loads(serialized_object)
|
|
assert_equal(type(reg2), reg.__class__)
|
|
score2 = reg2.score(boston.data, boston.target)
|
|
assert_equal(score, score2, "Failed to generate same score "
|
|
"after pickling (regression) "
|
|
"with {0}".format(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 = _balance_weights(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 est.splitter in SPARSE_SPLITTERS:
|
|
# 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], 200)
|
|
|
|
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 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 preceedence 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
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non-regression for #2726
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"""
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for attr in ['n_classes', 'value', 'children_left', 'children_right',
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'threshold', 'impurity', 'feature', 'n_node_samples']:
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value = getattr(DecisionTreeClassifier().fit([[0]], [0]).tree_, attr)
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# if pointing to freed memory, contents may be arbitrary
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assert_true(-2 <= value.flat[0] < 2,
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'Array points to arbitrary memory')
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def test_only_constant_features():
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random_state = check_random_state(0)
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X = np.zeros((10, 20))
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y = random_state.randint(0, 2, (10, ))
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for name, TreeEstimator in ALL_TREES.items():
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est = TreeEstimator(random_state=0)
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est.fit(X, y)
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assert_equal(est.tree_.max_depth, 0)
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def test_with_only_one_non_constant_features():
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X = np.hstack([np.array([[1.], [1.], [0.], [0.]]),
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np.zeros((4, 1000))])
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y = np.array([0., 1., 0., 1.0])
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for name, TreeEstimator in CLF_TREES.items():
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est = TreeEstimator(random_state=0, max_features=1)
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est.fit(X, y)
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assert_equal(est.tree_.max_depth, 1)
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assert_array_equal(est.predict_proba(X), 0.5 * np.ones((4, 2)))
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for name, TreeEstimator in REG_TREES.items():
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est = TreeEstimator(random_state=0, max_features=1)
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est.fit(X, y)
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assert_equal(est.tree_.max_depth, 1)
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assert_array_equal(est.predict(X), 0.5 * np.ones((4, )))
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def test_big_input():
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"""Test if the warning for too large inputs is appropriate."""
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X = np.repeat(10 ** 40., 4).astype(np.float64).reshape(-1, 1)
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clf = DecisionTreeClassifier()
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try:
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clf.fit(X, [0, 1, 0, 1])
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except ValueError as e:
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assert_in("float32", str(e))
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def test_realloc():
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from sklearn.tree._tree import _realloc_test
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assert_raises(MemoryError, _realloc_test)
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def test_huge_allocations():
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n_bits = int(platform.architecture()[0].rstrip('bit'))
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X = np.random.randn(10, 2)
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y = np.random.randint(0, 2, 10)
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# Sanity check: we cannot request more memory than the size of the address
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# space. Currently raises OverflowError.
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huge = 2 ** (n_bits + 1)
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clf = DecisionTreeClassifier(splitter='best', max_leaf_nodes=huge)
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assert_raises(Exception, clf.fit, X, y)
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# Non-regression test: MemoryError used to be dropped by Cython
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# because of missing "except *".
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huge = 2 ** (n_bits - 1) - 1
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clf = DecisionTreeClassifier(splitter='best', max_leaf_nodes=huge)
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assert_raises(MemoryError, clf.fit, X, y)
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def check_sparse_input(tree, dataset, max_depth=None):
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TreeEstimator = ALL_TREES[tree]
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X = DATASETS[dataset]["X"]
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X_sparse = DATASETS[dataset]["X_sparse"]
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y = DATASETS[dataset]["y"]
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# Gain testing time
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if dataset in ["digits", "boston"]:
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n_samples = X.shape[0] // 5
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X = X[:n_samples]
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X_sparse = X_sparse[:n_samples]
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y = y[:n_samples]
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for sparse_format in (csr_matrix, csc_matrix, coo_matrix):
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X_sparse = sparse_format(X_sparse)
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# Check the default (depth first search)
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d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y)
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s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y)
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assert_tree_equal(d.tree_, s.tree_,
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"{0} with dense and sparse format gave different "
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"trees".format(tree))
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y_pred = d.predict(X)
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if tree in CLF_TREES:
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y_proba = d.predict_proba(X)
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y_log_proba = d.predict_log_proba(X)
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for sparse_matrix in (csr_matrix, csc_matrix, coo_matrix):
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X_sparse_test = sparse_matrix(X_sparse, dtype=np.float32)
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assert_array_almost_equal(s.predict(X_sparse_test), y_pred)
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if tree in CLF_TREES:
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assert_array_almost_equal(s.predict_proba(X_sparse_test),
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y_proba)
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assert_array_almost_equal(s.predict_log_proba(X_sparse_test),
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y_log_proba)
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def test_sparse_input():
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for tree, dataset in product(SPARSE_TREES,
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("clf_small", "toy", "digits", "multilabel",
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"sparse-pos", "sparse-neg", "sparse-mix",
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"zeros")):
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max_depth = 3 if dataset == "digits" else None
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yield (check_sparse_input, tree, dataset, max_depth)
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# Due to numerical instability of MSE and too strict test, we limit the
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# maximal depth
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for tree, dataset in product(REG_TREES, ["boston", "reg_small"]):
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if tree in SPARSE_TREES:
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yield (check_sparse_input, tree, dataset, 2)
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def check_sparse_parameters(tree, dataset):
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TreeEstimator = ALL_TREES[tree]
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X = DATASETS[dataset]["X"]
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X_sparse = DATASETS[dataset]["X_sparse"]
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y = DATASETS[dataset]["y"]
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# Check max_features
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d = TreeEstimator(random_state=0, max_features=1, max_depth=2).fit(X, y)
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s = TreeEstimator(random_state=0, max_features=1,
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max_depth=2).fit(X_sparse, y)
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assert_tree_equal(d.tree_, s.tree_,
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"{0} with dense and sparse format gave different "
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"trees".format(tree))
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assert_array_almost_equal(s.predict(X), d.predict(X))
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# Check min_samples_split
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d = TreeEstimator(random_state=0, max_features=1,
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min_samples_split=10).fit(X, y)
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s = TreeEstimator(random_state=0, max_features=1,
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min_samples_split=10).fit(X_sparse, y)
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assert_tree_equal(d.tree_, s.tree_,
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"{0} with dense and sparse format gave different "
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"trees".format(tree))
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assert_array_almost_equal(s.predict(X), d.predict(X))
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# Check min_samples_leaf
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d = TreeEstimator(random_state=0,
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min_samples_leaf=X_sparse.shape[0] // 2).fit(X, y)
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s = TreeEstimator(random_state=0,
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min_samples_leaf=X_sparse.shape[0] // 2).fit(X_sparse, y)
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assert_tree_equal(d.tree_, s.tree_,
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"{0} with dense and sparse format gave different "
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"trees".format(tree))
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assert_array_almost_equal(s.predict(X), d.predict(X))
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# Check best-first search
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d = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X, y)
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s = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X_sparse, y)
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assert_tree_equal(d.tree_, s.tree_,
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"{0} with dense and sparse format gave different "
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"trees".format(tree))
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assert_array_almost_equal(s.predict(X), d.predict(X))
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def test_sparse_parameters():
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for tree, dataset in product(SPARSE_TREES,
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["sparse-pos", "sparse-neg", "sparse-mix",
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"zeros"]):
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yield (check_sparse_parameters, tree, dataset)
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def check_sparse_criterion(tree, dataset):
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TreeEstimator = ALL_TREES[tree]
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X = DATASETS[dataset]["X"]
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X_sparse = DATASETS[dataset]["X_sparse"]
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y = DATASETS[dataset]["y"]
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# Check various criterion
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CRITERIONS = REG_CRITERIONS if tree in REG_TREES else CLF_CRITERIONS
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for criterion in CRITERIONS:
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d = TreeEstimator(random_state=0, max_depth=3,
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criterion=criterion).fit(X, y)
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s = TreeEstimator(random_state=0, max_depth=3,
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criterion=criterion).fit(X_sparse, y)
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assert_tree_equal(d.tree_, s.tree_,
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"{0} with dense and sparse format gave different "
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"trees".format(tree))
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assert_array_almost_equal(s.predict(X), d.predict(X))
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def test_sparse_criterion():
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for tree, dataset in product(SPARSE_TREES,
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["sparse-pos", "sparse-neg", "sparse-mix",
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"zeros"]):
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yield (check_sparse_criterion, tree, dataset)
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def check_explicit_sparse_zeros(tree, max_depth=3,
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n_features=10):
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TreeEstimator = ALL_TREES[tree]
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# n_samples set n_feature to ease construction of a simultaneous
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# construction of a csr and csc matrix
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n_samples = n_features
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samples = np.arange(n_samples)
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# Generate X, y
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random_state = check_random_state(0)
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indices = []
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data = []
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offset = 0
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indptr = [offset]
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for i in range(n_features):
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n_nonzero_i = random_state.binomial(n_samples, 0.5)
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indices_i = random_state.permutation(samples)[:n_nonzero_i]
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indices.append(indices_i)
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data_i = random_state.binomial(3, 0.5, size=(n_nonzero_i, )) - 1
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data.append(data_i)
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offset += n_nonzero_i
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indptr.append(offset)
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indices = np.concatenate(indices)
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data = np.array(np.concatenate(data), dtype=np.float32)
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X_sparse = csc_matrix((data, indices, indptr),
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shape=(n_samples, n_features))
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X = X_sparse.toarray()
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X_sparse_test = csr_matrix((data, indices, indptr),
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shape=(n_samples, n_features))
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X_test = X_sparse_test.toarray()
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y = random_state.randint(0, 3, size=(n_samples, ))
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# Ensure that X_sparse_test owns its data, indices and indptr array
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X_sparse_test = X_sparse_test.copy()
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# Ensure that we have explicit zeros
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assert_greater((X_sparse.data == 0.).sum(), 0)
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assert_greater((X_sparse_test.data == 0.).sum(), 0)
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# Perform the comparison
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d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y)
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s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y)
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assert_tree_equal(d.tree_, s.tree_,
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"{0} with dense and sparse format gave different "
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"trees".format(tree))
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Xs = (X_test, X_sparse_test)
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for X1, X2 in product(Xs, Xs):
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assert_array_almost_equal(s.tree_.apply(X1), d.tree_.apply(X2))
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assert_array_almost_equal(s.predict(X1), d.predict(X2))
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if tree in CLF_TREES:
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assert_array_almost_equal(s.predict_proba(X1),
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d.predict_proba(X2))
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def test_explicit_sparse_zeros():
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for tree in SPARSE_TREES:
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yield (check_explicit_sparse_zeros, tree)
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def check_raise_error_on_1d_input(name):
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TreeEstimator = ALL_TREES[name]
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X = iris.data[:, 0].ravel()
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X_2d = iris.data[:, 0].reshape((-1, 1))
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y = iris.target
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assert_raises(ValueError, TreeEstimator(random_state=0).fit, X, y)
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est = TreeEstimator(random_state=0)
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est.fit(X_2d, y)
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assert_raises(ValueError, est.predict, X)
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def test_1d_input():
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for name in ALL_TREES:
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yield check_raise_error_on_1d_input, name
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def _check_min_weight_leaf_split_level(TreeEstimator, X, y, sample_weight):
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# Private function to keep pretty printing in nose yielded tests
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est = TreeEstimator(random_state=0)
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est.fit(X, y, sample_weight=sample_weight)
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assert_equal(est.tree_.max_depth, 1)
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est = TreeEstimator(random_state=0, min_weight_fraction_leaf=0.4)
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est.fit(X, y, sample_weight=sample_weight)
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assert_equal(est.tree_.max_depth, 0)
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def check_min_weight_leaf_split_level(name):
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TreeEstimator = ALL_TREES[name]
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X = np.array([[0], [0], [0], [0], [1]])
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y = [0, 0, 0, 0, 1]
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sample_weight = [0.2, 0.2, 0.2, 0.2, 0.2]
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_check_min_weight_leaf_split_level(TreeEstimator, X, y, sample_weight)
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if TreeEstimator().splitter in SPARSE_SPLITTERS:
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_check_min_weight_leaf_split_level(TreeEstimator, csc_matrix(X), y,
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sample_weight)
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def test_min_weight_leaf_split_level():
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for name in ALL_TREES:
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yield check_min_weight_leaf_split_level, name
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