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TST Changes assert to pytest style in tests/test_naive_bayes.py #19768

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Merged
merged 12 commits into from
Apr 1, 2021
Merged

TST Changes assert to pytest style in tests/test_naive_bayes.py #19768

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89ac17b
Fix first half of tests
Mar 25, 2021
00eddc3
Fix the rest of the nb tests
Mar 26, 2021
8dbd016
Fix message variable names
Apr 1, 2021
a1ce7de
Add check record length for pytest.warns
Apr 1, 2021
79416a8
Update sklearn/tests/test_naive_bayes.py
azihna Apr 1, 2021
9bf03eb
Update sklearn/tests/test_naive_bayes.py
azihna Apr 1, 2021
ebc220b
Update sklearn/tests/test_naive_bayes.py
azihna Apr 1, 2021
54f2746
Update sklearn/tests/test_naive_bayes.py
azihna Apr 1, 2021
5adafd8
Update sklearn/tests/test_naive_bayes.py
azihna Apr 1, 2021
dbd2a45
Update sklearn/tests/test_naive_bayes.py
azihna Apr 1, 2021
72da64b
Update sklearn/tests/test_naive_bayes.py
azihna Apr 1, 2021
79223e4
Update sklearn/tests/test_naive_bayes.py
azihna Apr 1, 2021
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125 changes: 82 additions & 43 deletions sklearn/tests/test_naive_bayes.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import re

import numpy as np
import scipy.sparse
Expand All @@ -11,10 +12,6 @@
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_raises
from sklearn.utils._testing import assert_raise_message
from sklearn.utils._testing import assert_warns
from sklearn.utils._testing import assert_no_warnings
from sklearn.utils._testing import ignore_warnings

from sklearn.naive_bayes import GaussianNB, BernoulliNB
Expand Down Expand Up @@ -118,7 +115,10 @@ def test_gnb_sample_weight():
def test_gnb_neg_priors():
"""Test whether an error is raised in case of negative priors"""
clf = GaussianNB(priors=np.array([-1., 2.]))
assert_raises(ValueError, clf.fit, X, y)

msg = 'Priors must be non-negative'
with pytest.raises(ValueError, match=msg):
clf.fit(X, y)


def test_gnb_priors():
Expand Down Expand Up @@ -146,13 +146,19 @@ def test_gnb_wrong_nb_priors():
""" Test whether an error is raised if the number of prior is different
from the number of class"""
clf = GaussianNB(priors=np.array([.25, .25, .25, .25]))
assert_raises(ValueError, clf.fit, X, y)

msg = 'Number of priors must match number of classes'
with pytest.raises(ValueError, match=msg):
clf.fit(X, y)


def test_gnb_prior_greater_one():
"""Test if an error is raised if the sum of prior greater than one"""
clf = GaussianNB(priors=np.array([2., 1.]))
assert_raises(ValueError, clf.fit, X, y)

msg = 'The sum of the priors should be 1'
with pytest.raises(ValueError, match=msg):
clf.fit(X, y)


def test_gnb_prior_large_bias():
Expand Down Expand Up @@ -339,9 +345,13 @@ def test_discretenb_provide_prior(DiscreteNaiveBayes):
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])
msg = 'Number of priors must match number of classes'
with pytest.raises(ValueError, match=msg):
clf.fit([[0], [1], [2]], [0, 1, 2])

msg = 'is not the same as on last call to partial_fit'
with pytest.raises(ValueError, match=msg):
clf.partial_fit([[0], [1]], [0, 1], classes=[0, 1, 1])


@pytest.mark.parametrize('DiscreteNaiveBayes', DISCRETE_NAIVE_BAYES_CLASSES)
Expand Down Expand Up @@ -470,7 +480,10 @@ def test_mnnb(kind):

# Check the ability to predict the learning set.
clf = MultinomialNB()
assert_raises(ValueError, clf.fit, -X, y2)

msg = 'Negative values in data passed to'
with pytest.raises(ValueError, match=msg):
clf.fit(-X, y2)
y_pred = clf.fit(X, y2).predict(X)

assert_array_equal(y_pred, y2)
Expand Down Expand Up @@ -518,18 +531,18 @@ def test_mnb_prior_unobserved_targets():

clf = MultinomialNB()

assert_no_warnings(
clf.partial_fit, X, y, classes=[0, 1, 2]
)
with pytest.warns(None) as record:
clf.partial_fit(X, y, classes=[0, 1, 2])
assert len(record) == 0

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]
)
with pytest.warns(None) as record:
clf.partial_fit([[1, 1]], [2])
assert len(record) == 0

assert clf.predict([[0, 1]]) == 0
assert clf.predict([[1, 0]]) == 1
Expand Down Expand Up @@ -666,7 +679,10 @@ def test_cnb():

# Verify inputs are nonnegative.
clf = ComplementNB(alpha=1.0)
assert_raises(ValueError, clf.fit, -X, Y)

msg = re.escape('Negative values in data passed to ComplementNB (input X)')
with pytest.raises(ValueError, match=msg):
clf.fit(-X, Y)

clf.fit(X, Y)

Expand Down Expand Up @@ -700,9 +716,13 @@ def test_categoricalnb():
# 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)
error_msg = re.escape(
"Negative values in data passed to CategoricalNB (input X)"
)
with pytest.raises(ValueError, match=error_msg):
clf.predict(X)
with pytest.raises(ValueError, match=error_msg):
clf.fit(X, y)

# Test alpha
X3_test = np.array([[2, 5]])
Expand Down Expand Up @@ -794,52 +814,67 @@ def test_alpha():
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)
msg = (
"alpha too small will result in numeric errors,"
" setting alpha = 1.0e-10"
)
with pytest.warns(UserWarning, match=msg):
nb.partial_fit(X, y, classes=[0, 1])
with pytest.warns(UserWarning, match=msg):
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)
with pytest.warns(UserWarning, match=msg):
nb.partial_fit(X, y, classes=[0, 1])
with pytest.warns(UserWarning, match=msg):
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)
with pytest.warns(UserWarning, match=msg):
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)
with pytest.warns(UserWarning, match=msg):
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)
with pytest.warns(UserWarning, match=msg):
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.')
expected_msg = re.escape(
'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)
with pytest.raises(ValueError, match=expected_msg):
b_nb.fit(X, y)
with pytest.raises(ValueError, match=expected_msg):
m_nb.fit(X, y)
with pytest.raises(ValueError, match=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])
with pytest.raises(ValueError, match=expected_msg):
b_nb.partial_fit(X, y, classes=[0, 1])
with pytest.raises(ValueError, match=expected_msg):
m_nb.partial_fit(X, y, classes=[0, 1])


def test_alpha_vector():
Expand All @@ -862,10 +897,12 @@ def test_alpha_vector():

# 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)
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
Expand All @@ -879,9 +916,11 @@ def test_alpha_vector():
# 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)
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_check_accuracy_on_digits():
Expand Down