Python text.TfidfVectorizer方法代碼示例

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def text_to_graph(text):
    import networkx as nx
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.neighbors import kneighbors_graph

    # use tfidf to transform texts into feature vectors
    vectorizer = TfidfVectorizer()
    vectors = vectorizer.fit_transform(text)

    # build the graph which is full-connected
    N = vectors.shape[0]
    mat = kneighbors_graph(vectors, N, metric='cosine', mode='distance', include_self=True)
    mat.data = 1 - mat.data  # to similarity

    g = nx.from_scipy_sparse_matrix(mat, create_using=nx.Graph())

    return g 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def load(self):
        categories = ['comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware']
        newsgroups_train = fetch_20newsgroups(
            subset='train', remove=('headers', 'footers', 'quotes'), categories=categories)
        newsgroups_test = fetch_20newsgroups(
            subset='test', remove=('headers', 'footers', 'quotes'), categories=categories)
        vectorizer = TfidfVectorizer(stop_words='english', min_df=0.001, max_df=0.20)
        vectors = vectorizer.fit_transform(newsgroups_train.data)
        vectors_test = vectorizer.transform(newsgroups_test.data)
        x1 = vectors
        y1 = newsgroups_train.target
        x2 = vectors_test
        y2 = newsgroups_test.target
        x = np.array(np.r_[x1.todense(), x2.todense()])
        y = np.r_[y1, y2]
        return x, y 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def train(self, training_data) -> None:
        questions = training_data[0]
        answers = training_data[1]
        answer_docs = defaultdict(str)
        for q, ans in zip(questions, answers):
            text = ' '.join(q)
            answer_docs[ans] += ' ' + text

        x_array = []
        y_array = []
        for ans, doc in answer_docs.items():
            x_array.append(doc)
            y_array.append(ans)

        self.i_to_ans = {i: ans for i, ans in enumerate(y_array)}
        self.tfidf_vectorizer = TfidfVectorizer(
            ngram_range=(1, 3), min_df=2, max_df=.9
        ).fit(x_array)
        self.tfidf_matrix = self.tfidf_vectorizer.transform(x_array) 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def test_ShapLinearExplainer(self):
        corpus, y = shap.datasets.imdb()
        corpus_train, corpus_test, y_train, y_test = train_test_split(corpus, y, test_size=0.2, random_state=7)

        vectorizer = TfidfVectorizer(min_df=10)
        X_train = vectorizer.fit_transform(corpus_train)
        X_test = vectorizer.transform(corpus_test)

        model = sklearn.linear_model.LogisticRegression(penalty="l1", C=0.1, solver='liblinear')
        model.fit(X_train, y_train)

        shapexplainer = LinearExplainer(model, X_train, feature_dependence="independent")
        shap_values = shapexplainer.explain_instance(X_test)
        print("Invoked Shap LinearExplainer")

    # comment this test as travis runs out of resources 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def extract_bow(train_file="../data/train.csv", test_file="../data/test.csv", analyzer='char', ngram_range=(1, 1), stop_words=[], min_df=1, max_features=10000,use_idf=True, to_preprocess=True):
    """return 4 tensors of train_q1,q2 and test_q1,q2"""
    df_train = pd.read_csv(train_file, usecols=['title1_zh', 'title2_zh']).fillna("")
    df_test = pd.read_csv(test_file, usecols=['title1_zh', 'title2_zh']).fillna("")
    df = pd.DataFrame()
    df['text'] = pd.Series(df_train['title1_zh'].tolist() + df_train['title2_zh'].tolist() + df_test['title1_zh'].tolist() + df_test['title2_zh'].tolist()).unique()
        
    if to_preprocess:
        df['text'] = df['text'].map(lambda x: preprocess(x))
        df_train['title1_zh'] = df_train['title1_zh'].apply(preprocess)
        df_train['title2_zh'] = df_train['title2_zh'].apply(preprocess)
        df_test['title1_zh'] = df_test['title1_zh'].apply(preprocess)
        df_test['title2_zh'] = df_test['title2_zh'].apply(preprocess)
        
    if analyzer == 'char':
        vect = TfidfVectorizer(analyzer=analyzer, ngram_range=ngram_range, stop_words=stop_words, min_df=min_df, max_features=max_features, use_idf=use_idf)
    else:
        vect = TfidfVectorizer(analyzer=analyzer, tokenizer=jieba.cut, ngram_range=ngram_range, stop_words=stop_words, min_df=min_df, max_features=max_features, use_idf=use_idf)
    vect.fit(df["text"].tolist())
    return vect.transform(df_train.title1_zh),vect.transform(df_train.title2_zh), vect.transform(df_test.title1_zh),vect.transform(df_test.title2_zh), vect 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def _init_word_ngram_tfidf(self, ngram, vocabulary=None):
        tfidf = TfidfVectorizer(min_df=3,
                                max_df=0.75,                                
                                max_features=None,
                                norm="l2",
                                strip_accents="unicode",
                                analyzer="word",
                                token_pattern=r"\w{1,}",
                                ngram_range=(1, ngram),
                                use_idf=1,
                                smooth_idf=1,
                                sublinear_tf=1,
                                # stop_words="english",
                                vocabulary=vocabulary)
        return tfidf

    ## char based 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def _init_char_ngram_tfidf(self, ngram, vocabulary=None):
        tfidf = TfidfVectorizer(min_df=3,
                                max_df=0.75, 
                                max_features=None, 
                                norm="l2",
                                strip_accents="unicode", 
                                analyzer="char",
                                token_pattern=r"\w{1,}",
                                ngram_range=(1, ngram), 
                                use_idf=1,
                                smooth_idf=1,
                                sublinear_tf=1, 
                                # stop_words="english",
                                vocabulary=vocabulary)
        return tfidf


# ------------------------ LSA ------------------------------- 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def create_ngram_model(params=None):
    def preprocessor(tweet):
        global emoticons_replaced
        tweet = tweet.lower()

        for k in emo_repl_order:
            tweet = tweet.replace(k, emo_repl[k])
        for r, repl in re_repl.iteritems():
            tweet = re.sub(r, repl, tweet)

        return tweet

    tfidf_ngrams = TfidfVectorizer(preprocessor=preprocessor,
                                   analyzer="word")
    clf = MultinomialNB()
    pipeline = Pipeline([('tfidf', tfidf_ngrams), ('clf', clf)])

    if params:
        pipeline.set_params(**params)

    return pipeline 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def create_union_model(params=None):
    def preprocessor(tweet):
        tweet = tweet.lower()

        for k in emo_repl_order:
            tweet = tweet.replace(k, emo_repl[k])
        for r, repl in re_repl.iteritems():
            tweet = re.sub(r, repl, tweet)

        return tweet.replace("-", " ").replace("_", " ")

    tfidf_ngrams = TfidfVectorizer(preprocessor=preprocessor,
                                   analyzer="word")
    ling_stats = LinguisticVectorizer()
    all_features = FeatureUnion(
        [('ling', ling_stats), ('tfidf', tfidf_ngrams)])
    #all_features = FeatureUnion([('tfidf', tfidf_ngrams)])
    #all_features = FeatureUnion([('ling', ling_stats)])
    clf = MultinomialNB()
    pipeline = Pipeline([('all', all_features), ('clf', clf)])

    if params:
        pipeline.set_params(**params)

    return pipeline 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def tfidf_fit(sentences):
    """
       tfidf相似度
    :param sentences: 
    :return: 
    """
    # tfidf計算
    model = TfidfVectorizer(ngram_range=(1, 2), # 3,5
                            stop_words=[' ', '\t', '\n'],  # 停用詞
                            max_features=10000,
                            token_pattern=r"(?u)\b\w+\b",  # 過濾停用詞
                            min_df=1,
                            max_df=0.9,
                            use_idf=1,  # 光滑
                            smooth_idf=1,  # 光滑
                            sublinear_tf=1, )  # 光滑
    matrix = model.fit_transform(sentences)
    return matrix 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def tfidf_fit(sentences):
    """
       tfidf相似度
    :param sentences: 
    :return: 
    """
    # tfidf計算
    model = TfidfVectorizer(ngram_range=(1, 2),  # 3,5
                            stop_words=[' ', '\t', '\n'],  # 停用詞
                            max_features=10000,
                            token_pattern=r"(?u)\b\w+\b",  # 過濾停用詞
                            min_df=1,
                            max_df=0.9,
                            use_idf=1,  # 光滑
                            smooth_idf=1,  # 光滑
                            sublinear_tf=1, )  # 光滑
    matrix = model.fit_transform(sentences)
    return matrix 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def tdidf_sim(sentences):
    """
       tfidf相似度
    :param sentences: 
    :return: 
    """
    # tfidf計算
    model = TfidfVectorizer(tokenizer=jieba.cut,
                            ngram_range=(1, 2), # 3,5
                            stop_words=[' ', '\t', '\n'],  # 停用詞
                            max_features=10000,
                            token_pattern=r"(?u)\b\w+\b",  # 過濾停用詞
                            min_df=1,
                            max_df=0.9,
                            use_idf=1,  # 光滑
                            smooth_idf=1,  # 光滑
                            sublinear_tf=1, )  # 光滑
    matrix = model.fit_transform(sentences)
    matrix_norm = TfidfTransformer().fit_transform(matrix)
    return matrix_norm 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def test_vectorizer_stop_words_inconsistent():
    lstr = "['and', 'll', 've']"
    message = ('Your stop_words may be inconsistent with your '
               'preprocessing. Tokenizing the stop words generated '
               'tokens %s not in stop_words.' % lstr)
    for vec in [CountVectorizer(),
                TfidfVectorizer(), HashingVectorizer()]:
        vec.set_params(stop_words=["you've", "you", "you'll", 'AND'])
        assert_warns_message(UserWarning, message, vec.fit_transform,
                             ['hello world'])
        # reset stop word validation
        del vec._stop_words_id
        assert _check_stop_words_consistency(vec) is False

    # Only one warning per stop list
    assert_no_warnings(vec.fit_transform, ['hello world'])
    assert _check_stop_words_consistency(vec) is None

    # Test caching of inconsistency assessment
    vec.set_params(stop_words=["you've", "you", "you'll", 'blah', 'AND'])
    assert_warns_message(UserWarning, message, vec.fit_transform,
                         ['hello world']) 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def build_language_classifier(texts, labels, verbose=False, random_state=None):
    """Train a text classifier with scikit-learn

    The text classifier is composed of two elements assembled in a pipeline:

    - A text feature extractor (`TfidfVectorizer`) that extract the relative
      frequencies of unigrams, bigrams and trigrams of characters in the text.

    - An instance of `SGDClassifier` for the classification it-self. To speed
      up training it is recommended to enable early stopping.

    `random_state` is passed to the underlying `SGDClassifier` instance.
    """
    language_classifier = make_pipeline(
        TfidfVectorizer(analyzer="char", ngram_range=(1, 3),
                        min_df=2, max_df=0.9, norm="l2", dtype=np.float32),
        SGDClassifier(early_stopping=True, validation_fraction=0.2,
                      n_iter_no_change=3, max_iter=1000, tol=1e-3,
                      alpha=1e-5, penalty="l2", verbose=verbose,
                      random_state=random_state)
    )
    return language_classifier.fit(texts, labels) 

# 需要導入模塊: from sklearn.feature_extraction import text [as 別名]
# 或者: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
def __init__(self, language=None):
        """ Initialize stoplist builder with option for language specific parameters
        :type language: str
        :param language : text from which to build the stoplist
        """
        if language:
            self.language = language.lower()
        self.numpy_installed = True  # Write utility for common import traps?
        self.sklearn_installed = True

        try:
            import numpy as np
            self.np = np
        except ImportError:
            self.numpy_installed = False

        try:
            from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
            # self.vectorizer = CountVectorizer(input='content') # Set df?
            # self.tfidf_vectorizer = TfidfVectorizer()
        except ImportError:
            self.sklearn_installed = False