845 lines
30 KiB
Python
845 lines
30 KiB
Python
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import pickle
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from io import BytesIO
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import numpy as np
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import scipy.sparse
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import pytest
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from sklearn.datasets import load_digits, load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import cross_val_score
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from sklearn.utils._testing import assert_almost_equal
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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_raises
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from sklearn.utils._testing import assert_raise_message
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from sklearn.utils._testing import assert_warns
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from sklearn.utils._testing import assert_no_warnings
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from sklearn.naive_bayes import GaussianNB, BernoulliNB
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from sklearn.naive_bayes import MultinomialNB, ComplementNB
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from sklearn.naive_bayes import CategoricalNB
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from sklearn.naive_bayes import BaseNB, BaseDiscreteNB
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# Data is just 6 separable points in the plane
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X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
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y = np.array([1, 1, 1, 2, 2, 2])
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# A bit more random tests
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rng = np.random.RandomState(0)
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X1 = rng.normal(size=(10, 3))
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y1 = (rng.normal(size=(10)) > 0).astype(np.int)
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# Data is 6 random integer points in a 100 dimensional space classified to
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# three classes.
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X2 = rng.randint(5, size=(6, 100))
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y2 = np.array([1, 1, 2, 2, 3, 3])
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def test_gnb():
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# Gaussian Naive Bayes classification.
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# This checks that GaussianNB implements fit and predict and returns
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# correct values for a simple toy dataset.
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clf = GaussianNB()
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y_pred = clf.fit(X, y).predict(X)
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assert_array_equal(y_pred, y)
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y_pred_proba = clf.predict_proba(X)
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y_pred_log_proba = clf.predict_log_proba(X)
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assert_array_almost_equal(np.log(y_pred_proba), y_pred_log_proba, 8)
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# Test whether label mismatch between target y and classes raises
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# an Error
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# FIXME Remove this test once the more general partial_fit tests are merged
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assert_raises(ValueError, GaussianNB().partial_fit, X, y, classes=[0, 1])
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def test_gnb_prior():
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# Test whether class priors are properly set.
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clf = GaussianNB().fit(X, y)
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assert_array_almost_equal(np.array([3, 3]) / 6.0,
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clf.class_prior_, 8)
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clf.fit(X1, y1)
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# Check that the class priors sum to 1
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assert_array_almost_equal(clf.class_prior_.sum(), 1)
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def test_gnb_sample_weight():
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"""Test whether sample weights are properly used in GNB. """
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# Sample weights all being 1 should not change results
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sw = np.ones(6)
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clf = GaussianNB().fit(X, y)
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clf_sw = GaussianNB().fit(X, y, sw)
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assert_array_almost_equal(clf.theta_, clf_sw.theta_)
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assert_array_almost_equal(clf.sigma_, clf_sw.sigma_)
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# Fitting twice with half sample-weights should result
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# in same result as fitting once with full weights
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sw = rng.rand(y.shape[0])
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clf1 = GaussianNB().fit(X, y, sample_weight=sw)
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clf2 = GaussianNB().partial_fit(X, y, classes=[1, 2], sample_weight=sw / 2)
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clf2.partial_fit(X, y, sample_weight=sw / 2)
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assert_array_almost_equal(clf1.theta_, clf2.theta_)
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assert_array_almost_equal(clf1.sigma_, clf2.sigma_)
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# Check that duplicate entries and correspondingly increased sample
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# weights yield the same result
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ind = rng.randint(0, X.shape[0], 20)
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sample_weight = np.bincount(ind, minlength=X.shape[0])
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clf_dupl = GaussianNB().fit(X[ind], y[ind])
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clf_sw = GaussianNB().fit(X, y, sample_weight)
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assert_array_almost_equal(clf_dupl.theta_, clf_sw.theta_)
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assert_array_almost_equal(clf_dupl.sigma_, clf_sw.sigma_)
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def test_gnb_neg_priors():
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"""Test whether an error is raised in case of negative priors"""
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clf = GaussianNB(priors=np.array([-1., 2.]))
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assert_raises(ValueError, clf.fit, X, y)
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def test_gnb_priors():
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"""Test whether the class prior override is properly used"""
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clf = GaussianNB(priors=np.array([0.3, 0.7])).fit(X, y)
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assert_array_almost_equal(clf.predict_proba([[-0.1, -0.1]]),
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np.array([[0.825303662161683,
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0.174696337838317]]), 8)
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assert_array_almost_equal(clf.class_prior_, np.array([0.3, 0.7]))
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def test_gnb_priors_sum_isclose():
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# test whether the class prior sum is properly tested"""
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X = np.array([[-1, -1], [-2, -1], [-3, -2], [-4, -5], [-5, -4],
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[1, 1], [2, 1], [3, 2], [4, 4], [5, 5]])
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priors = np.array([0.08, 0.14, 0.03, 0.16, 0.11, 0.16, 0.07, 0.14,
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0.11, 0.0])
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Y = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
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clf = GaussianNB(priors=priors)
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# smoke test for issue #9633
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clf.fit(X, Y)
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def test_gnb_wrong_nb_priors():
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""" Test whether an error is raised if the number of prior is different
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from the number of class"""
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clf = GaussianNB(priors=np.array([.25, .25, .25, .25]))
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assert_raises(ValueError, clf.fit, X, y)
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def test_gnb_prior_greater_one():
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"""Test if an error is raised if the sum of prior greater than one"""
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clf = GaussianNB(priors=np.array([2., 1.]))
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assert_raises(ValueError, clf.fit, X, y)
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def test_gnb_prior_large_bias():
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"""Test if good prediction when class prior favor largely one class"""
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clf = GaussianNB(priors=np.array([0.01, 0.99]))
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clf.fit(X, y)
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assert clf.predict([[-0.1, -0.1]]) == np.array([2])
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def test_gnb_check_update_with_no_data():
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""" Test when the partial fit is called without any data"""
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# Create an empty array
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prev_points = 100
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mean = 0.
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var = 1.
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x_empty = np.empty((0, X.shape[1]))
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tmean, tvar = GaussianNB._update_mean_variance(prev_points, mean,
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var, x_empty)
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assert tmean == mean
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assert tvar == var
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def test_gnb_pfit_wrong_nb_features():
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"""Test whether an error is raised when the number of feature changes
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between two partial fit"""
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clf = GaussianNB()
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# Fit for the first time the GNB
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clf.fit(X, y)
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# Partial fit a second time with an incoherent X
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assert_raises(ValueError, clf.partial_fit, np.hstack((X, X)), y)
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def test_gnb_partial_fit():
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clf = GaussianNB().fit(X, y)
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clf_pf = GaussianNB().partial_fit(X, y, np.unique(y))
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assert_array_almost_equal(clf.theta_, clf_pf.theta_)
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assert_array_almost_equal(clf.sigma_, clf_pf.sigma_)
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assert_array_almost_equal(clf.class_prior_, clf_pf.class_prior_)
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clf_pf2 = GaussianNB().partial_fit(X[0::2, :], y[0::2], np.unique(y))
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clf_pf2.partial_fit(X[1::2], y[1::2])
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assert_array_almost_equal(clf.theta_, clf_pf2.theta_)
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assert_array_almost_equal(clf.sigma_, clf_pf2.sigma_)
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assert_array_almost_equal(clf.class_prior_, clf_pf2.class_prior_)
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def test_gnb_naive_bayes_scale_invariance():
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# Scaling the data should not change the prediction results
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iris = load_iris()
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X, y = iris.data, iris.target
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labels = [GaussianNB().fit(f * X, y).predict(f * X)
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for f in [1E-10, 1, 1E10]]
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assert_array_equal(labels[0], labels[1])
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assert_array_equal(labels[1], labels[2])
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@pytest.mark.parametrize("cls", [MultinomialNB, BernoulliNB, CategoricalNB])
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def test_discretenb_prior(cls):
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# Test whether class priors are properly set.
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clf = cls().fit(X2, y2)
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assert_array_almost_equal(np.log(np.array([2, 2, 2]) / 6.0),
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clf.class_log_prior_, 8)
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@pytest.mark.parametrize("cls", [MultinomialNB, BernoulliNB, CategoricalNB])
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def test_discretenb_partial_fit(cls):
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clf1 = cls()
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clf1.fit([[0, 1], [1, 0], [1, 1]], [0, 1, 1])
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clf2 = cls()
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clf2.partial_fit([[0, 1], [1, 0], [1, 1]], [0, 1, 1], classes=[0, 1])
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assert_array_equal(clf1.class_count_, clf2.class_count_)
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if cls is CategoricalNB:
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for i in range(len(clf1.category_count_)):
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assert_array_equal(clf1.category_count_[i],
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clf2.category_count_[i])
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else:
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assert_array_equal(clf1.feature_count_, clf2.feature_count_)
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clf3 = cls()
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# all categories have to appear in the first partial fit
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clf3.partial_fit([[0, 1]], [0], classes=[0, 1])
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clf3.partial_fit([[1, 0]], [1])
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clf3.partial_fit([[1, 1]], [1])
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assert_array_equal(clf1.class_count_, clf3.class_count_)
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if cls is CategoricalNB:
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# the categories for each feature of CategoricalNB are mapped to an
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# index chronologically with each call of partial fit and therefore
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# the category_count matrices cannot be compared for equality
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for i in range(len(clf1.category_count_)):
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assert_array_equal(clf1.category_count_[i].shape,
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clf3.category_count_[i].shape)
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assert_array_equal(np.sum(clf1.category_count_[i], axis=1),
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np.sum(clf3.category_count_[i], axis=1))
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# assert category 0 occurs 1x in the first class and 0x in the 2nd
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# class
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assert_array_equal(clf1.category_count_[0][0], np.array([1, 0]))
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# assert category 1 occurs 0x in the first class and 2x in the 2nd
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# class
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assert_array_equal(clf1.category_count_[0][1], np.array([0, 2]))
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# assert category 0 occurs 0x in the first class and 1x in the 2nd
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# class
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assert_array_equal(clf1.category_count_[1][0], np.array([0, 1]))
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# assert category 1 occurs 1x in the first class and 1x in the 2nd
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# class
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assert_array_equal(clf1.category_count_[1][1], np.array([1, 1]))
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else:
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assert_array_equal(clf1.feature_count_, clf3.feature_count_)
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@pytest.mark.parametrize('cls', [BernoulliNB, MultinomialNB, GaussianNB,
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CategoricalNB])
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def test_discretenb_pickle(cls):
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# Test picklability of discrete naive Bayes classifiers
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clf = cls().fit(X2, y2)
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y_pred = clf.predict(X2)
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store = BytesIO()
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pickle.dump(clf, store)
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clf = pickle.load(BytesIO(store.getvalue()))
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assert_array_equal(y_pred, clf.predict(X2))
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# Test pickling of estimator trained with partial_fit
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clf2 = cls().partial_fit(X2[:3], y2[:3], classes=np.unique(y2))
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clf2.partial_fit(X2[3:], y2[3:])
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store = BytesIO()
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pickle.dump(clf2, store)
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clf2 = pickle.load(BytesIO(store.getvalue()))
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assert_array_equal(y_pred, clf2.predict(X2))
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@pytest.mark.parametrize('cls', [BernoulliNB, MultinomialNB, GaussianNB,
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CategoricalNB])
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def test_discretenb_input_check_fit(cls):
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# Test input checks for the fit method
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# check shape consistency for number of samples at fit time
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assert_raises(ValueError, cls().fit, X2, y2[:-1])
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# check shape consistency for number of input features at predict time
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clf = cls().fit(X2, y2)
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assert_raises(ValueError, clf.predict, X2[:, :-1])
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@pytest.mark.parametrize('cls', [BernoulliNB, MultinomialNB, CategoricalNB])
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def test_discretenb_input_check_partial_fit(cls):
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# check shape consistency
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assert_raises(ValueError, cls().partial_fit, X2, y2[:-1],
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classes=np.unique(y2))
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# classes is required for first call to partial fit
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assert_raises(ValueError, cls().partial_fit, X2, y2)
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# check consistency of consecutive classes values
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clf = cls()
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clf.partial_fit(X2, y2, classes=np.unique(y2))
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assert_raises(ValueError, clf.partial_fit, X2, y2,
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classes=np.arange(42))
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# check consistency of input shape for partial_fit
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assert_raises(ValueError, clf.partial_fit, X2[:, :-1], y2)
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# check consistency of input shape for predict
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assert_raises(ValueError, clf.predict, X2[:, :-1])
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def test_discretenb_predict_proba():
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# Test discrete NB classes' probability scores
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# The 100s below distinguish Bernoulli from multinomial.
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# FIXME: write a test to show this.
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X_bernoulli = [[1, 100, 0], [0, 1, 0], [0, 100, 1]]
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X_multinomial = [[0, 1], [1, 3], [4, 0]]
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# test binary case (1-d output)
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y = [0, 0, 2] # 2 is regression test for binary case, 02e673
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for cls, X in zip([BernoulliNB, MultinomialNB],
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[X_bernoulli, X_multinomial]):
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clf = cls().fit(X, y)
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assert clf.predict(X[-1:]) == 2
|
||
|
|
assert clf.predict_proba([X[0]]).shape == (1, 2)
|
||
|
|
assert_array_almost_equal(clf.predict_proba(X[:2]).sum(axis=1),
|
||
|
|
np.array([1., 1.]), 6)
|
||
|
|
|
||
|
|
# test multiclass case (2-d output, must sum to one)
|
||
|
|
y = [0, 1, 2]
|
||
|
|
for cls, X in zip([BernoulliNB, MultinomialNB],
|
||
|
|
[X_bernoulli, X_multinomial]):
|
||
|
|
clf = cls().fit(X, y)
|
||
|
|
assert clf.predict_proba(X[0:1]).shape == (1, 3)
|
||
|
|
assert clf.predict_proba(X[:2]).shape == (2, 3)
|
||
|
|
assert_almost_equal(np.sum(clf.predict_proba([X[1]])), 1)
|
||
|
|
assert_almost_equal(np.sum(clf.predict_proba([X[-1]])), 1)
|
||
|
|
assert_almost_equal(np.sum(np.exp(clf.class_log_prior_)), 1)
|
||
|
|
assert_almost_equal(np.sum(np.exp(clf.intercept_)), 1)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('cls', [BernoulliNB, MultinomialNB, CategoricalNB])
|
||
|
|
def test_discretenb_uniform_prior(cls):
|
||
|
|
# Test whether discrete NB classes fit a uniform prior
|
||
|
|
# when fit_prior=False and class_prior=None
|
||
|
|
|
||
|
|
clf = cls()
|
||
|
|
clf.set_params(fit_prior=False)
|
||
|
|
clf.fit([[0], [0], [1]], [0, 0, 1])
|
||
|
|
prior = np.exp(clf.class_log_prior_)
|
||
|
|
assert_array_almost_equal(prior, np.array([.5, .5]))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('cls', [BernoulliNB, MultinomialNB, CategoricalNB])
|
||
|
|
def test_discretenb_provide_prior(cls):
|
||
|
|
# Test whether discrete NB classes use provided prior
|
||
|
|
|
||
|
|
clf = cls(class_prior=[0.5, 0.5])
|
||
|
|
clf.fit([[0], [0], [1]], [0, 0, 1])
|
||
|
|
prior = np.exp(clf.class_log_prior_)
|
||
|
|
assert_array_almost_equal(prior, np.array([.5, .5]))
|
||
|
|
|
||
|
|
# Inconsistent number of classes with prior
|
||
|
|
assert_raises(ValueError, clf.fit, [[0], [1], [2]], [0, 1, 2])
|
||
|
|
assert_raises(ValueError, clf.partial_fit, [[0], [1]], [0, 1],
|
||
|
|
classes=[0, 1, 1])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('cls', [BernoulliNB, MultinomialNB, CategoricalNB])
|
||
|
|
def test_discretenb_provide_prior_with_partial_fit(cls):
|
||
|
|
# Test whether discrete NB classes use provided prior
|
||
|
|
# when using partial_fit
|
||
|
|
|
||
|
|
iris = load_iris()
|
||
|
|
iris_data1, iris_data2, iris_target1, iris_target2 = train_test_split(
|
||
|
|
iris.data, iris.target, test_size=0.4, random_state=415)
|
||
|
|
|
||
|
|
for prior in [None, [0.3, 0.3, 0.4]]:
|
||
|
|
clf_full = cls(class_prior=prior)
|
||
|
|
clf_full.fit(iris.data, iris.target)
|
||
|
|
clf_partial = cls(class_prior=prior)
|
||
|
|
clf_partial.partial_fit(iris_data1, iris_target1,
|
||
|
|
classes=[0, 1, 2])
|
||
|
|
clf_partial.partial_fit(iris_data2, iris_target2)
|
||
|
|
assert_array_almost_equal(clf_full.class_log_prior_,
|
||
|
|
clf_partial.class_log_prior_)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('cls', [BernoulliNB, MultinomialNB, CategoricalNB])
|
||
|
|
def test_discretenb_sample_weight_multiclass(cls):
|
||
|
|
# check shape consistency for number of samples at fit time
|
||
|
|
X = [
|
||
|
|
[0, 0, 1],
|
||
|
|
[0, 1, 1],
|
||
|
|
[0, 1, 1],
|
||
|
|
[1, 0, 0],
|
||
|
|
]
|
||
|
|
y = [0, 0, 1, 2]
|
||
|
|
sample_weight = np.array([1, 1, 2, 2], dtype=np.float64)
|
||
|
|
sample_weight /= sample_weight.sum()
|
||
|
|
clf = cls().fit(X, y, sample_weight=sample_weight)
|
||
|
|
assert_array_equal(clf.predict(X), [0, 1, 1, 2])
|
||
|
|
|
||
|
|
# Check sample weight using the partial_fit method
|
||
|
|
clf = cls()
|
||
|
|
clf.partial_fit(X[:2], y[:2], classes=[0, 1, 2],
|
||
|
|
sample_weight=sample_weight[:2])
|
||
|
|
clf.partial_fit(X[2:3], y[2:3], sample_weight=sample_weight[2:3])
|
||
|
|
clf.partial_fit(X[3:], y[3:], sample_weight=sample_weight[3:])
|
||
|
|
assert_array_equal(clf.predict(X), [0, 1, 1, 2])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('cls', [BernoulliNB, MultinomialNB])
|
||
|
|
def test_discretenb_coef_intercept_shape(cls):
|
||
|
|
# coef_ and intercept_ should have shapes as in other linear models.
|
||
|
|
# Non-regression test for issue #2127.
|
||
|
|
X = [[1, 0, 0], [1, 1, 1]]
|
||
|
|
y = [1, 2] # binary classification
|
||
|
|
clf = cls()
|
||
|
|
|
||
|
|
clf.fit(X, y)
|
||
|
|
assert clf.coef_.shape == (1, 3)
|
||
|
|
assert clf.intercept_.shape == (1,)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('kind', ('dense', 'sparse'))
|
||
|
|
def test_mnnb(kind):
|
||
|
|
# Test Multinomial Naive Bayes classification.
|
||
|
|
# This checks that MultinomialNB implements fit and predict and returns
|
||
|
|
# correct values for a simple toy dataset.
|
||
|
|
|
||
|
|
if kind == 'dense':
|
||
|
|
X = X2
|
||
|
|
elif kind == 'sparse':
|
||
|
|
X = scipy.sparse.csr_matrix(X2)
|
||
|
|
|
||
|
|
# Check the ability to predict the learning set.
|
||
|
|
clf = MultinomialNB()
|
||
|
|
assert_raises(ValueError, clf.fit, -X, y2)
|
||
|
|
y_pred = clf.fit(X, y2).predict(X)
|
||
|
|
|
||
|
|
assert_array_equal(y_pred, y2)
|
||
|
|
|
||
|
|
# Verify that np.log(clf.predict_proba(X)) gives the same results as
|
||
|
|
# clf.predict_log_proba(X)
|
||
|
|
y_pred_proba = clf.predict_proba(X)
|
||
|
|
y_pred_log_proba = clf.predict_log_proba(X)
|
||
|
|
assert_array_almost_equal(np.log(y_pred_proba), y_pred_log_proba, 8)
|
||
|
|
|
||
|
|
# Check that incremental fitting yields the same results
|
||
|
|
clf2 = MultinomialNB()
|
||
|
|
clf2.partial_fit(X[:2], y2[:2], classes=np.unique(y2))
|
||
|
|
clf2.partial_fit(X[2:5], y2[2:5])
|
||
|
|
clf2.partial_fit(X[5:], y2[5:])
|
||
|
|
|
||
|
|
y_pred2 = clf2.predict(X)
|
||
|
|
assert_array_equal(y_pred2, y2)
|
||
|
|
|
||
|
|
y_pred_proba2 = clf2.predict_proba(X)
|
||
|
|
y_pred_log_proba2 = clf2.predict_log_proba(X)
|
||
|
|
assert_array_almost_equal(np.log(y_pred_proba2), y_pred_log_proba2, 8)
|
||
|
|
assert_array_almost_equal(y_pred_proba2, y_pred_proba)
|
||
|
|
assert_array_almost_equal(y_pred_log_proba2, y_pred_log_proba)
|
||
|
|
|
||
|
|
# Partial fit on the whole data at once should be the same as fit too
|
||
|
|
clf3 = MultinomialNB()
|
||
|
|
clf3.partial_fit(X, y2, classes=np.unique(y2))
|
||
|
|
|
||
|
|
y_pred3 = clf3.predict(X)
|
||
|
|
assert_array_equal(y_pred3, y2)
|
||
|
|
y_pred_proba3 = clf3.predict_proba(X)
|
||
|
|
y_pred_log_proba3 = clf3.predict_log_proba(X)
|
||
|
|
assert_array_almost_equal(np.log(y_pred_proba3), y_pred_log_proba3, 8)
|
||
|
|
assert_array_almost_equal(y_pred_proba3, y_pred_proba)
|
||
|
|
assert_array_almost_equal(y_pred_log_proba3, y_pred_log_proba)
|
||
|
|
|
||
|
|
|
||
|
|
def test_mnb_prior_unobserved_targets():
|
||
|
|
# test smoothing of prior for yet unobserved targets
|
||
|
|
|
||
|
|
# Create toy training data
|
||
|
|
X = np.array([[0, 1], [1, 0]])
|
||
|
|
y = np.array([0, 1])
|
||
|
|
|
||
|
|
clf = MultinomialNB()
|
||
|
|
|
||
|
|
assert_no_warnings(
|
||
|
|
clf.partial_fit, X, y, classes=[0, 1, 2]
|
||
|
|
)
|
||
|
|
|
||
|
|
assert clf.predict([[0, 1]]) == 0
|
||
|
|
assert clf.predict([[1, 0]]) == 1
|
||
|
|
assert clf.predict([[1, 1]]) == 0
|
||
|
|
|
||
|
|
# add a training example with previously unobserved class
|
||
|
|
assert_no_warnings(
|
||
|
|
clf.partial_fit, [[1, 1]], [2]
|
||
|
|
)
|
||
|
|
|
||
|
|
assert clf.predict([[0, 1]]) == 0
|
||
|
|
assert clf.predict([[1, 0]]) == 1
|
||
|
|
assert clf.predict([[1, 1]]) == 2
|
||
|
|
|
||
|
|
|
||
|
|
def test_mnb_sample_weight():
|
||
|
|
clf = MultinomialNB()
|
||
|
|
clf.fit([[1, 2], [1, 2], [1, 0]],
|
||
|
|
[0, 0, 1],
|
||
|
|
sample_weight=[1, 1, 4])
|
||
|
|
assert_array_equal(clf.predict([[1, 0]]), [1])
|
||
|
|
positive_prior = np.exp(clf.intercept_[0])
|
||
|
|
assert_array_almost_equal([1 - positive_prior, positive_prior],
|
||
|
|
[1 / 3., 2 / 3.])
|
||
|
|
|
||
|
|
|
||
|
|
def test_bnb():
|
||
|
|
# Tests that BernoulliNB when alpha=1.0 gives the same values as
|
||
|
|
# those given for the toy example in Manning, Raghavan, and
|
||
|
|
# Schuetze's "Introduction to Information Retrieval" book:
|
||
|
|
# https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
|
||
|
|
|
||
|
|
# Training data points are:
|
||
|
|
# Chinese Beijing Chinese (class: China)
|
||
|
|
# Chinese Chinese Shanghai (class: China)
|
||
|
|
# Chinese Macao (class: China)
|
||
|
|
# Tokyo Japan Chinese (class: Japan)
|
||
|
|
|
||
|
|
# Features are Beijing, Chinese, Japan, Macao, Shanghai, and Tokyo
|
||
|
|
X = np.array([[1, 1, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0, 1]])
|
||
|
|
|
||
|
|
# Classes are China (0), Japan (1)
|
||
|
|
Y = np.array([0, 0, 0, 1])
|
||
|
|
|
||
|
|
# Fit BernoulliBN w/ alpha = 1.0
|
||
|
|
clf = BernoulliNB(alpha=1.0)
|
||
|
|
clf.fit(X, Y)
|
||
|
|
|
||
|
|
# Check the class prior is correct
|
||
|
|
class_prior = np.array([0.75, 0.25])
|
||
|
|
assert_array_almost_equal(np.exp(clf.class_log_prior_), class_prior)
|
||
|
|
|
||
|
|
# Check the feature probabilities are correct
|
||
|
|
feature_prob = np.array([[0.4, 0.8, 0.2, 0.4, 0.4, 0.2],
|
||
|
|
[1 / 3.0, 2 / 3.0, 2 / 3.0, 1 / 3.0, 1 / 3.0,
|
||
|
|
2 / 3.0]])
|
||
|
|
assert_array_almost_equal(np.exp(clf.feature_log_prob_), feature_prob)
|
||
|
|
|
||
|
|
# Testing data point is:
|
||
|
|
# Chinese Chinese Chinese Tokyo Japan
|
||
|
|
X_test = np.array([[0, 1, 1, 0, 0, 1]])
|
||
|
|
|
||
|
|
# Check the predictive probabilities are correct
|
||
|
|
unnorm_predict_proba = np.array([[0.005183999999999999,
|
||
|
|
0.02194787379972565]])
|
||
|
|
predict_proba = unnorm_predict_proba / np.sum(unnorm_predict_proba)
|
||
|
|
assert_array_almost_equal(clf.predict_proba(X_test), predict_proba)
|
||
|
|
|
||
|
|
|
||
|
|
def test_bnb_feature_log_prob():
|
||
|
|
# Test for issue #4268.
|
||
|
|
# Tests that the feature log prob value computed by BernoulliNB when
|
||
|
|
# alpha=1.0 is equal to the expression given in Manning, Raghavan,
|
||
|
|
# and Schuetze's "Introduction to Information Retrieval" book:
|
||
|
|
# http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
|
||
|
|
|
||
|
|
X = np.array([[0, 0, 0], [1, 1, 0], [0, 1, 0], [1, 0, 1], [0, 1, 0]])
|
||
|
|
Y = np.array([0, 0, 1, 2, 2])
|
||
|
|
|
||
|
|
# Fit Bernoulli NB w/ alpha = 1.0
|
||
|
|
clf = BernoulliNB(alpha=1.0)
|
||
|
|
clf.fit(X, Y)
|
||
|
|
|
||
|
|
# Manually form the (log) numerator and denominator that
|
||
|
|
# constitute P(feature presence | class)
|
||
|
|
num = np.log(clf.feature_count_ + 1.0)
|
||
|
|
denom = np.tile(np.log(clf.class_count_ + 2.0), (X.shape[1], 1)).T
|
||
|
|
|
||
|
|
# Check manual estimate matches
|
||
|
|
assert_array_almost_equal(clf.feature_log_prob_, (num - denom))
|
||
|
|
|
||
|
|
|
||
|
|
def test_cnb():
|
||
|
|
# Tests ComplementNB when alpha=1.0 for the toy example in Manning,
|
||
|
|
# Raghavan, and Schuetze's "Introduction to Information Retrieval" book:
|
||
|
|
# https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
|
||
|
|
|
||
|
|
# Training data points are:
|
||
|
|
# Chinese Beijing Chinese (class: China)
|
||
|
|
# Chinese Chinese Shanghai (class: China)
|
||
|
|
# Chinese Macao (class: China)
|
||
|
|
# Tokyo Japan Chinese (class: Japan)
|
||
|
|
|
||
|
|
# Features are Beijing, Chinese, Japan, Macao, Shanghai, and Tokyo.
|
||
|
|
X = np.array([[1, 1, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0, 1]])
|
||
|
|
|
||
|
|
# Classes are China (0), Japan (1).
|
||
|
|
Y = np.array([0, 0, 0, 1])
|
||
|
|
|
||
|
|
# Check that weights are correct. See steps 4-6 in Table 4 of
|
||
|
|
# Rennie et al. (2003).
|
||
|
|
theta = np.array([
|
||
|
|
[
|
||
|
|
(0 + 1) / (3 + 6),
|
||
|
|
(1 + 1) / (3 + 6),
|
||
|
|
(1 + 1) / (3 + 6),
|
||
|
|
(0 + 1) / (3 + 6),
|
||
|
|
(0 + 1) / (3 + 6),
|
||
|
|
(1 + 1) / (3 + 6)
|
||
|
|
],
|
||
|
|
[
|
||
|
|
(1 + 1) / (6 + 6),
|
||
|
|
(3 + 1) / (6 + 6),
|
||
|
|
(0 + 1) / (6 + 6),
|
||
|
|
(1 + 1) / (6 + 6),
|
||
|
|
(1 + 1) / (6 + 6),
|
||
|
|
(0 + 1) / (6 + 6)
|
||
|
|
]])
|
||
|
|
|
||
|
|
weights = np.zeros(theta.shape)
|
||
|
|
normed_weights = np.zeros(theta.shape)
|
||
|
|
for i in range(2):
|
||
|
|
weights[i] = -np.log(theta[i])
|
||
|
|
normed_weights[i] = weights[i] / weights[i].sum()
|
||
|
|
|
||
|
|
# Verify inputs are nonnegative.
|
||
|
|
clf = ComplementNB(alpha=1.0)
|
||
|
|
assert_raises(ValueError, clf.fit, -X, Y)
|
||
|
|
|
||
|
|
clf.fit(X, Y)
|
||
|
|
|
||
|
|
# Check that counts/weights are correct.
|
||
|
|
feature_count = np.array([[1, 3, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1]])
|
||
|
|
assert_array_equal(clf.feature_count_, feature_count)
|
||
|
|
class_count = np.array([3, 1])
|
||
|
|
assert_array_equal(clf.class_count_, class_count)
|
||
|
|
feature_all = np.array([1, 4, 1, 1, 1, 1])
|
||
|
|
assert_array_equal(clf.feature_all_, feature_all)
|
||
|
|
assert_array_almost_equal(clf.feature_log_prob_, weights)
|
||
|
|
|
||
|
|
clf = ComplementNB(alpha=1.0, norm=True)
|
||
|
|
clf.fit(X, Y)
|
||
|
|
assert_array_almost_equal(clf.feature_log_prob_, normed_weights)
|
||
|
|
|
||
|
|
|
||
|
|
def test_categoricalnb():
|
||
|
|
# Check the ability to predict the training set.
|
||
|
|
clf = CategoricalNB()
|
||
|
|
y_pred = clf.fit(X2, y2).predict(X2)
|
||
|
|
assert_array_equal(y_pred, y2)
|
||
|
|
|
||
|
|
X3 = np.array([[1, 4], [2, 5]])
|
||
|
|
y3 = np.array([1, 2])
|
||
|
|
clf = CategoricalNB(alpha=1, fit_prior=False)
|
||
|
|
|
||
|
|
clf.fit(X3, y3)
|
||
|
|
|
||
|
|
# Check error is raised for X with negative entries
|
||
|
|
X = np.array([[0, -1]])
|
||
|
|
y = np.array([1])
|
||
|
|
error_msg = "Negative values in data passed to CategoricalNB (input X)"
|
||
|
|
assert_raise_message(ValueError, error_msg, clf.predict, X)
|
||
|
|
assert_raise_message(ValueError, error_msg, clf.fit, X, y)
|
||
|
|
|
||
|
|
# Check error is raised for incorrect X
|
||
|
|
X = np.array([[1, 4, 1], [2, 5, 6]])
|
||
|
|
msg = "Expected input with 2 features, got 3 instead"
|
||
|
|
assert_raise_message(ValueError, msg, clf.predict, X)
|
||
|
|
|
||
|
|
# Test alpha
|
||
|
|
X3_test = np.array([[2, 5]])
|
||
|
|
# alpha=1 increases the count of all categories by one so the final
|
||
|
|
# probability for each category is not 50/50 but 1/3 to 2/3
|
||
|
|
bayes_numerator = np.array([[1/3*1/3, 2/3*2/3]])
|
||
|
|
bayes_denominator = bayes_numerator.sum()
|
||
|
|
assert_array_almost_equal(clf.predict_proba(X3_test),
|
||
|
|
bayes_numerator / bayes_denominator)
|
||
|
|
|
||
|
|
# Assert category_count has counted all features
|
||
|
|
assert len(clf.category_count_) == X3.shape[1]
|
||
|
|
|
||
|
|
# Check sample_weight
|
||
|
|
X = np.array([[0, 0], [0, 1], [0, 0], [1, 1]])
|
||
|
|
y = np.array([1, 1, 2, 2])
|
||
|
|
clf = CategoricalNB(alpha=1, fit_prior=False)
|
||
|
|
clf.fit(X, y)
|
||
|
|
assert_array_equal(clf.predict(np.array([[0, 0]])), np.array([1]))
|
||
|
|
|
||
|
|
for factor in [1., 0.3, 5, 0.0001]:
|
||
|
|
X = np.array([[0, 0], [0, 1], [0, 0], [1, 1]])
|
||
|
|
y = np.array([1, 1, 2, 2])
|
||
|
|
sample_weight = np.array([1, 1, 10, 0.1]) * factor
|
||
|
|
clf = CategoricalNB(alpha=1, fit_prior=False)
|
||
|
|
clf.fit(X, y, sample_weight=sample_weight)
|
||
|
|
assert_array_equal(clf.predict(np.array([[0, 0]])), np.array([2]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_alpha():
|
||
|
|
# Setting alpha=0 should not output nan results when p(x_i|y_j)=0 is a case
|
||
|
|
X = np.array([[1, 0], [1, 1]])
|
||
|
|
y = np.array([0, 1])
|
||
|
|
nb = BernoulliNB(alpha=0.)
|
||
|
|
assert_warns(UserWarning, nb.partial_fit, X, y, classes=[0, 1])
|
||
|
|
assert_warns(UserWarning, nb.fit, X, y)
|
||
|
|
prob = np.array([[1, 0], [0, 1]])
|
||
|
|
assert_array_almost_equal(nb.predict_proba(X), prob)
|
||
|
|
|
||
|
|
nb = MultinomialNB(alpha=0.)
|
||
|
|
assert_warns(UserWarning, nb.partial_fit, X, y, classes=[0, 1])
|
||
|
|
assert_warns(UserWarning, nb.fit, X, y)
|
||
|
|
prob = np.array([[2. / 3, 1. / 3], [0, 1]])
|
||
|
|
assert_array_almost_equal(nb.predict_proba(X), prob)
|
||
|
|
|
||
|
|
nb = CategoricalNB(alpha=0.)
|
||
|
|
assert_warns(UserWarning, nb.fit, X, y)
|
||
|
|
prob = np.array([[1., 0.], [0., 1.]])
|
||
|
|
assert_array_almost_equal(nb.predict_proba(X), prob)
|
||
|
|
|
||
|
|
# Test sparse X
|
||
|
|
X = scipy.sparse.csr_matrix(X)
|
||
|
|
nb = BernoulliNB(alpha=0.)
|
||
|
|
assert_warns(UserWarning, nb.fit, X, y)
|
||
|
|
prob = np.array([[1, 0], [0, 1]])
|
||
|
|
assert_array_almost_equal(nb.predict_proba(X), prob)
|
||
|
|
|
||
|
|
nb = MultinomialNB(alpha=0.)
|
||
|
|
assert_warns(UserWarning, nb.fit, X, y)
|
||
|
|
prob = np.array([[2. / 3, 1. / 3], [0, 1]])
|
||
|
|
assert_array_almost_equal(nb.predict_proba(X), prob)
|
||
|
|
|
||
|
|
# Test for alpha < 0
|
||
|
|
X = np.array([[1, 0], [1, 1]])
|
||
|
|
y = np.array([0, 1])
|
||
|
|
expected_msg = ('Smoothing parameter alpha = -1.0e-01. '
|
||
|
|
'alpha should be > 0.')
|
||
|
|
b_nb = BernoulliNB(alpha=-0.1)
|
||
|
|
m_nb = MultinomialNB(alpha=-0.1)
|
||
|
|
c_nb = CategoricalNB(alpha=-0.1)
|
||
|
|
assert_raise_message(ValueError, expected_msg, b_nb.fit, X, y)
|
||
|
|
assert_raise_message(ValueError, expected_msg, m_nb.fit, X, y)
|
||
|
|
assert_raise_message(ValueError, expected_msg, c_nb.fit, X, y)
|
||
|
|
|
||
|
|
b_nb = BernoulliNB(alpha=-0.1)
|
||
|
|
m_nb = MultinomialNB(alpha=-0.1)
|
||
|
|
assert_raise_message(ValueError, expected_msg, b_nb.partial_fit,
|
||
|
|
X, y, classes=[0, 1])
|
||
|
|
assert_raise_message(ValueError, expected_msg, m_nb.partial_fit,
|
||
|
|
X, y, classes=[0, 1])
|
||
|
|
|
||
|
|
|
||
|
|
def test_alpha_vector():
|
||
|
|
X = np.array([[1, 0], [1, 1]])
|
||
|
|
y = np.array([0, 1])
|
||
|
|
|
||
|
|
# Setting alpha=np.array with same length
|
||
|
|
# as number of features should be fine
|
||
|
|
alpha = np.array([1, 2])
|
||
|
|
nb = MultinomialNB(alpha=alpha)
|
||
|
|
nb.partial_fit(X, y, classes=[0, 1])
|
||
|
|
|
||
|
|
# Test feature probabilities uses pseudo-counts (alpha)
|
||
|
|
feature_prob = np.array([[1 / 2, 1 / 2], [2 / 5, 3 / 5]])
|
||
|
|
assert_array_almost_equal(nb.feature_log_prob_, np.log(feature_prob))
|
||
|
|
|
||
|
|
# Test predictions
|
||
|
|
prob = np.array([[5 / 9, 4 / 9], [25 / 49, 24 / 49]])
|
||
|
|
assert_array_almost_equal(nb.predict_proba(X), prob)
|
||
|
|
|
||
|
|
# Test alpha non-negative
|
||
|
|
alpha = np.array([1., -0.1])
|
||
|
|
expected_msg = ('Smoothing parameter alpha = -1.0e-01. '
|
||
|
|
'alpha should be > 0.')
|
||
|
|
m_nb = MultinomialNB(alpha=alpha)
|
||
|
|
assert_raise_message(ValueError, expected_msg, m_nb.fit, X, y)
|
||
|
|
|
||
|
|
# Test that too small pseudo-counts are replaced
|
||
|
|
ALPHA_MIN = 1e-10
|
||
|
|
alpha = np.array([ALPHA_MIN / 2, 0.5])
|
||
|
|
m_nb = MultinomialNB(alpha=alpha)
|
||
|
|
m_nb.partial_fit(X, y, classes=[0, 1])
|
||
|
|
assert_array_almost_equal(m_nb._check_alpha(),
|
||
|
|
[ALPHA_MIN, 0.5],
|
||
|
|
decimal=12)
|
||
|
|
|
||
|
|
# Test correct dimensions
|
||
|
|
alpha = np.array([1., 2., 3.])
|
||
|
|
m_nb = MultinomialNB(alpha=alpha)
|
||
|
|
expected_msg = ('alpha should be a scalar or a numpy array '
|
||
|
|
'with shape [n_features]')
|
||
|
|
assert_raise_message(ValueError, expected_msg, m_nb.fit, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_check_accuracy_on_digits():
|
||
|
|
# Non regression test to make sure that any further refactoring / optim
|
||
|
|
# of the NB models do not harm the performance on a slightly non-linearly
|
||
|
|
# separable dataset
|
||
|
|
X, y = load_digits(return_X_y=True)
|
||
|
|
binary_3v8 = np.logical_or(y == 3, y == 8)
|
||
|
|
X_3v8, y_3v8 = X[binary_3v8], y[binary_3v8]
|
||
|
|
|
||
|
|
# Multinomial NB
|
||
|
|
scores = cross_val_score(MultinomialNB(alpha=10), X, y, cv=10)
|
||
|
|
assert scores.mean() > 0.86
|
||
|
|
|
||
|
|
scores = cross_val_score(MultinomialNB(alpha=10), X_3v8, y_3v8, cv=10)
|
||
|
|
assert scores.mean() > 0.94
|
||
|
|
|
||
|
|
# Bernoulli NB
|
||
|
|
scores = cross_val_score(BernoulliNB(alpha=10), X > 4, y, cv=10)
|
||
|
|
assert scores.mean() > 0.83
|
||
|
|
|
||
|
|
scores = cross_val_score(BernoulliNB(alpha=10), X_3v8 > 4, y_3v8, cv=10)
|
||
|
|
assert scores.mean() > 0.92
|
||
|
|
|
||
|
|
# Gaussian NB
|
||
|
|
scores = cross_val_score(GaussianNB(), X, y, cv=10)
|
||
|
|
assert scores.mean() > 0.77
|
||
|
|
|
||
|
|
scores = cross_val_score(GaussianNB(var_smoothing=0.1), X, y, cv=10)
|
||
|
|
assert scores.mean() > 0.89
|
||
|
|
|
||
|
|
scores = cross_val_score(GaussianNB(), X_3v8, y_3v8, cv=10)
|
||
|
|
assert scores.mean() > 0.86
|
||
|
|
|
||
|
|
|
||
|
|
# TODO: remove in 0.24
|
||
|
|
def test_deprecations():
|
||
|
|
|
||
|
|
class A(BaseNB, GaussianNB):
|
||
|
|
pass
|
||
|
|
|
||
|
|
class B(BaseDiscreteNB, CategoricalNB):
|
||
|
|
pass
|
||
|
|
|
||
|
|
with pytest.warns(FutureWarning, match="is deprecated in version 0.22"):
|
||
|
|
A()
|
||
|
|
|
||
|
|
with pytest.warns(FutureWarning, match="is deprecated in version 0.22"):
|
||
|
|
B()
|