def fit_transform(self, raw_documents, y=None):
        """Learn vocabulary and idf, return term-document matrix.
        This is equivalent to fit followed by transform, but more efficiently
        implemented.
        Parameters
        ----------
        raw_documents : iterable
            an iterable which yields either str, unicode or file objects
        Returns
        -------
        X : sparse matrix, [n_samples, n_features]
            Tf-idf-weighted document-term matrix.
        """
        documents = super(TfidfVectorizer, self).fit_transform(
            raw_documents=raw_documents, y=y)
        count = CountVectorizer(encoding=self.encoding,
                                decode_error=self.decode_error,
                                strip_accents=self.strip_accents,
                                lowercase=self.lowercase,
                                preprocessor=self.preprocessor,
                                tokenizer=self.tokenizer,
                                stop_words=self.stop_words,
                                token_pattern=self.token_pattern,
                                ngram_range=self.ngram_range,
                                analyzer=self.analyzer,
                                max_df=self.max_df,
                                min_df=self.min_df,
                                max_features=self.max_features,
                                vocabulary=self.vocabulary_,
                                binary=self.binary,
                                dtype=self.dtype)
        count.fit_transform(raw_documents=raw_documents, y=y)
        self.period_ = count.period_
        self.df_ = count.df_
        self.n = count.n
        return documents
def fit_transform(self, raw_documents, y=None):
        """Learn vocabulary and idf, return term-document matrix.
        This is equivalent to fit followed by transform, but more efficiently
        implemented.
        Parameters
        ----------
        raw_documents : iterable
            an iterable which yields either str, unicode or file objects
        Returns
        -------
        X : sparse matrix, [n_samples, n_features]
            Tf-idf-weighted document-term matrix.
        """
        documents = super(TfidfVectorizer, self).fit_transform(
            raw_documents=raw_documents, y=y)
        count = CountVectorizer(encoding=self.encoding,
                                decode_error=self.decode_error,
                                strip_accents=self.strip_accents,
                                lowercase=self.lowercase,
                                preprocessor=self.preprocessor,
                                tokenizer=self.tokenizer,
                                stop_words=self.stop_words,
                                token_pattern=self.token_pattern,
                                ngram_range=self.ngram_range,
                                analyzer=self.analyzer,
                                max_df=self.max_df,
                                min_df=self.min_df,
                                max_features=self.max_features,
                                vocabulary=self.vocabulary_,
                                binary=self.binary,
                                dtype=self.dtype)
        count.fit_transform(raw_documents=raw_documents, y=y)
        self.period_ = count.period_
        self.df_ = count.df_
        self.n = count.n
        return documents
def fit_transform ( self , raw_documents , y = None ) : documents = super ( TfidfVectorizer , self ) . fit_transform ( raw_documents = raw_documents , y = y ) count = CountVectorizer ( encoding = self . encoding , decode_error = self . decode_error , strip_accents = self . strip_accents , lowercase = self . lowercase , preprocessor = self . preprocessor , tokenizer = self . tokenizer , stop_words = self . stop_words , token_pattern = self . token_pattern , ngram_range = self . ngram_range , analyzer = self . analyzer , max_df = self . max_df , min_df = self . min_df , max_features = self . max_features , vocabulary = self . vocabulary_ , binary = self . binary , dtype = self . dtype ) count . fit_transform ( raw_documents = raw_documents , y = y ) self . period_ = count . period_ self . df_ = count . df_ self . n = count . n return documents
Learn vocabulary and idf, return term-document matrix.
        This is equivalent to fit followed by transform, but more efficiently
        implemented.
        Parameters
        ----------
        raw_documents : iterable
            an iterable which yields either str, unicode or file objects
        Returns
        -------
        X : sparse matrix, [n_samples, n_features]
            Tf-idf-weighted document-term matrix.
Learn vocabulary and idf return term - document matrix . This is equivalent to fit followed by transform but more efficiently implemented . Parameters ---------- raw_documents : iterable an iterable which yields either str unicode or file objects Returns ------- X : sparse matrix [ n_samples n_features ] Tf - idf - weighted document - term matrix .