Python TfidfVectorizer.max_df方法代碼示例

# 需要導入模塊: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
# 或者: from sklearn.feature_extraction.text.TfidfVectorizer import max_df [as 別名]
def test_vectorizer():
    # raw documents as an iterator
    train_data = iter(ALL_FOOD_DOCS[:-1])
    test_data = [ALL_FOOD_DOCS[-1]]
    n_train = len(ALL_FOOD_DOCS) - 1

    # test without vocabulary
    v1 = CountVectorizer(max_df=0.5)
    counts_train = v1.fit_transform(train_data)
    if hasattr(counts_train, 'tocsr'):
        counts_train = counts_train.tocsr()
    assert_equal(counts_train[0, v1.vocabulary_["pizza"]], 2)

    # build a vectorizer v1 with the same vocabulary as the one fitted by v1
    v2 = CountVectorizer(vocabulary=v1.vocabulary_)

    # compare that the two vectorizer give the same output on the test sample
    for v in (v1, v2):
        counts_test = v.transform(test_data)
        if hasattr(counts_test, 'tocsr'):
            counts_test = counts_test.tocsr()

        vocabulary = v.vocabulary_
        assert_equal(counts_test[0, vocabulary["salad"]], 1)
        assert_equal(counts_test[0, vocabulary["tomato"]], 1)
        assert_equal(counts_test[0, vocabulary["water"]], 1)

        # stop word from the fixed list
        assert_false("the" in vocabulary)

        # stop word found automatically by the vectorizer DF thresholding
        # words that are high frequent across the complete corpus are likely
        # to be not informative (either real stop words of extraction
        # artifacts)
        assert_false("copyright" in vocabulary)

        # not present in the sample
        assert_equal(counts_test[0, vocabulary["coke"]], 0)
        assert_equal(counts_test[0, vocabulary["burger"]], 0)
        assert_equal(counts_test[0, vocabulary["beer"]], 0)
        assert_equal(counts_test[0, vocabulary["pizza"]], 0)

    # test tf-idf
    t1 = TfidfTransformer(norm='l1')
    tfidf = t1.fit(counts_train).transform(counts_train).toarray()
    assert_equal(len(t1.idf_), len(v1.vocabulary_))
    assert_equal(tfidf.shape, (n_train, len(v1.vocabulary_)))

    # test tf-idf with new data
    tfidf_test = t1.transform(counts_test).toarray()
    assert_equal(tfidf_test.shape, (len(test_data), len(v1.vocabulary_)))

    # test tf alone
    t2 = TfidfTransformer(norm='l1', use_idf=False)
    tf = t2.fit(counts_train).transform(counts_train).toarray()
    assert_equal(t2.idf_, None)

    # test idf transform with unlearned idf vector
    t3 = TfidfTransformer(use_idf=True)
    assert_raises(ValueError, t3.transform, counts_train)

    # test idf transform with incompatible n_features
    X = [[1, 1, 5],
         [1, 1, 0]]
    t3.fit(X)
    X_incompt = [[1, 3],
                 [1, 3]]
    assert_raises(ValueError, t3.transform, X_incompt)

    # L1-normalized term frequencies sum to one
    assert_array_almost_equal(np.sum(tf, axis=1), [1.0] * n_train)

    # test the direct tfidf vectorizer
    # (equivalent to term count vectorizer + tfidf transformer)
    train_data = iter(ALL_FOOD_DOCS[:-1])
    tv = TfidfVectorizer(norm='l1')
    assert_false(tv.fixed_vocabulary)

    tv.max_df = v1.max_df
    tfidf2 = tv.fit_transform(train_data).toarray()
    assert_array_almost_equal(tfidf, tfidf2)

    # test the direct tfidf vectorizer with new data
    tfidf_test2 = tv.transform(test_data).toarray()
    assert_array_almost_equal(tfidf_test, tfidf_test2)

    # test transform on unfitted vectorizer with empty vocabulary
    v3 = CountVectorizer(vocabulary=None)
    assert_raises(ValueError, v3.transform, train_data)

    # ascii preprocessor?
    v3.set_params(strip_accents='ascii', lowercase=False)
    assert_equal(v3.build_preprocessor(), strip_accents_ascii)

    # error on bad strip_accents param
    v3.set_params(strip_accents='_gabbledegook_', preprocessor=None)
    assert_raises(ValueError, v3.build_preprocessor)

    # error with bad analyzer type
    v3.set_params = '_invalid_analyzer_type_'
#.........這裏部分代碼省略.........

# 需要導入模塊: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
# 或者: from sklearn.feature_extraction.text.TfidfVectorizer import max_df [as 別名]
def test_vectorizer():
    # raw documents as an iterator
    train_data = iter(ALL_FOOD_DOCS[:-1])
    test_data = [ALL_FOOD_DOCS[-1]]
    n_train = len(ALL_FOOD_DOCS) - 1

    # test without vocabulary
    v1 = CountVectorizer(max_df=0.5)
    counts_train = v1.fit_transform(train_data)
    if hasattr(counts_train, 'tocsr'):
        counts_train = counts_train.tocsr()
    assert_equal(counts_train[0, v1.vocabulary_[u"pizza"]], 2)

    # build a vectorizer v1 with the same vocabulary as the one fitted by v1
    v2 = CountVectorizer(vocabulary=v1.vocabulary_)

    # compare that the two vectorizer give the same output on the test sample
    for v in (v1, v2):
        counts_test = v.transform(test_data)
        if hasattr(counts_test, 'tocsr'):
            counts_test = counts_test.tocsr()

        vocabulary = v.vocabulary_
        assert_equal(counts_test[0, vocabulary[u"salad"]], 1)
        assert_equal(counts_test[0, vocabulary[u"tomato"]], 1)
        assert_equal(counts_test[0, vocabulary[u"water"]], 1)

        # stop word from the fixed list
        assert_false(u"the" in vocabulary)

        # stop word found automatically by the vectorizer DF thresholding
        # words that are high frequent across the complete corpus are likely
        # to be not informative (either real stop words of extraction
        # artifacts)
        assert_false(u"copyright" in vocabulary)

        # not present in the sample
        assert_equal(counts_test[0, vocabulary[u"coke"]], 0)
        assert_equal(counts_test[0, vocabulary[u"burger"]], 0)
        assert_equal(counts_test[0, vocabulary[u"beer"]], 0)
        assert_equal(counts_test[0, vocabulary[u"pizza"]], 0)

    # test tf-idf
    t1 = TfidfTransformer(norm='l1')
    tfidf = t1.fit(counts_train).transform(counts_train).toarray()
    assert_equal(len(t1.idf_), len(v1.vocabulary_))
    assert_equal(tfidf.shape, (n_train, len(v1.vocabulary_)))

    # test tf-idf with new data
    tfidf_test = t1.transform(counts_test).toarray()
    assert_equal(tfidf_test.shape, (len(test_data), len(v1.vocabulary_)))

    # test tf alone
    t2 = TfidfTransformer(norm='l1', use_idf=False)
    tf = t2.fit(counts_train).transform(counts_train).toarray()
    assert_equal(t2.idf_, None)

    # L1-normalized term frequencies sum to one
    assert_array_almost_equal(np.sum(tf, axis=1), [1.0] * n_train)

    # test the direct tfidf vectorizer
    # (equivalent to term count vectorizer + tfidf transformer)
    train_data = iter(ALL_FOOD_DOCS[:-1])
    tv = TfidfVectorizer(norm='l1')
    assert_false(tv.fixed_vocabulary)

    tv.max_df = v1.max_df
    tfidf2 = tv.fit_transform(train_data).toarray()
    assert_array_almost_equal(tfidf, tfidf2)

    # test the direct tfidf vectorizer with new data
    tfidf_test2 = tv.transform(test_data).toarray()
    assert_array_almost_equal(tfidf_test, tfidf_test2)

    # test transform on unfitted vectorizer with empty vocabulary
    v3 = CountVectorizer(vocabulary=None)
    assert_raises(ValueError, v3.transform, train_data)