9b39c4c4d20eef7a2b0b8420945f09d3731e1b67,sklearn/feature_extraction/tests/test_text.py,,test_countvectorizer_empty_vocabulary,#,328

Before Change

def test_countvectorizer_empty_vocabulary():
    try:
        vect = CountVectorizer(vocabulary=[])
        vect.fit(["foo"])
        assert False, "we shouldn"t get here"
    except ValueError as e:
        assert "empty vocabulary" in str(e).lower()

    try:
        v = CountVectorizer(max_df=1.0, stop_words="english")
        // fit on stopwords only
        v.fit(["to be or not to be", "and me too", "and so do you"])

After Change

def test_countvectorizer_empty_vocabulary():
    with pytest.raises(ValueError, match="empty vocabulary"):
        vect = CountVectorizer(vocabulary=[])
        vect.fit(["foo"])

    with pytest.raises(ValueError, match="empty vocabulary"):
        v = CountVectorizer(max_df=1.0, stop_words="english")
        // fit on stopwords only
        v.fit(["to be or not to be", "and me too", "and so do you"])


def test_fit_countvectorizer_twice():
    cv = CountVectorizer()
    X1 = cv.fit_transform(ALL_FOOD_DOCS[:5])
    X2 = cv.fit_transform(ALL_FOOD_DOCS[5:])

Instances