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])
m_nb = MultinomialNB(alpha=alpha)
expected_msg = (
'Smoothing parameter alpha = -1.0e-01. alpha should be > 0.')
with pytest.raises(ValueError, match=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 = re.escape(
'alpha should be a scalar or a numpy array with shape [n_features]')
with pytest.raises(ValueError, match=expected_msg):
m_nb.fit(X, y)
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_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, -0.1])
m_nb = MultinomialNB(alpha=alpha)
expected_msg = "All values in alpha must be greater than 0."
with pytest.raises(ValueError, match=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([1.0, 2.0, 3.0])
m_nb = MultinomialNB(alpha=alpha)
expected_msg = "When alpha is an array, it should contains `n_features`"
with pytest.raises(ValueError, match=expected_msg):
m_nb.fit(X, y)
