def test_poissonnb_prior():
"""Test whether class priors are properly set. """
Xp = X + np.array([2, 2])
clf = PoissonNB().fit(Xp, y)
assert_array_almost_equal(np.array([3, 3]) / 6.0,
clf.class_prior_, 8)
clf.fit(X2, y2)
assert_array_almost_equal(clf.class_prior_.sum(), 1)
# Verify that np.log(clf.predict_proba(X)) gives the same results as
# clf.predict_log_proba(X)
y_pred_proba = clf.predict_proba(X2)
y_pred_log_proba = clf.predict_log_proba(X2)
assert_array_almost_equal(np.log(y_pred_proba), y_pred_log_proba, 8)
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
digits = load_digits()
X, y = digits.data, digits.target
binary_3v8 = np.logical_or(digits.target == 3, digits.target == 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_greater(scores.mean(), 0.86)
scores = cross_val_score(MultinomialNB(alpha=10), X_3v8, y_3v8, cv=10)
assert_greater(scores.mean(), 0.94)
# Bernoulli NB
scores = cross_val_score(BernoulliNB(alpha=10), X > 4, y, cv=10)
assert_greater(scores.mean(), 0.83)
scores = cross_val_score(BernoulliNB(alpha=10), X_3v8 > 4, y_3v8, cv=10)
assert_greater(scores.mean(), 0.92)
# Gaussian NB
scores = cross_val_score(GaussianNB(), X, y, cv=10)
assert_greater(scores.mean(), 0.77)
scores = cross_val_score(GaussianNB(), X_3v8, y_3v8, cv=10)
assert_greater(scores.mean(), 0.86)
# Poisson NB
scores = cross_val_score(PoissonNB(), X, y, cv=10)
assert_greater(scores.mean(), 0.85)
scores = cross_val_score(PoissonNB(), X_3v8, y_3v8, cv=10)
assert_greater(scores.mean(), 0.95)
def test_poissonnb_prior():
"""Test whether class priors are properly set. """
Xp = X + np.array([2, 2])
clf = PoissonNB().fit(Xp, y)
assert_array_almost_equal(np.array([3, 3]) / 6.0, clf.class_prior_, 8)
clf.fit(X2, y2)
assert_array_almost_equal(clf.class_prior_.sum(), 1)
# Verify that np.log(clf.predict_proba(X)) gives the same results as
# clf.predict_log_proba(X)
y_pred_proba = clf.predict_proba(X2)
y_pred_log_proba = clf.predict_log_proba(X2)
assert_array_almost_equal(np.log(y_pred_proba), y_pred_log_proba, 8)
def test_poissonnb():
clf = PoissonNB()
assert_raises(ValueError, clf.fit, -X2, y2)
y_pred = clf.fit(X2, y2).predict(X2)
assert_array_equal(y_pred, y2)
def test_poissonnb():
clf = PoissonNB()
assert_raises(ValueError, clf.fit, -X2, y2)
y_pred = clf.fit(X2, y2).predict(X2)
assert_array_equal(y_pred, y2)
