108dd7b00095a1265e6f0c4db0a69d620f590400,sklearn/tests/test_naive_bayes.py,,test_categoricalnb,#,687

Before Change

    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)

    // Test alpha
    X3_test = np.array([[2, 5]])

After Change

    // Check error is raised for X with negative entries
    X = np.array([[0, -1]])
    y = np.array([1])
    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]])
    // 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]])

Instances