def set_params(self, **kwargs): CountVectorizer.set_params(self, **kwargs) CountVectorizer.__init__(self, preprocessor=get_preprocessor( self.column, self.size, self.terminator), ngram_range=(1, self.size), analyzer='char', binary=self.binary)
def set_params(self, **kwargs): CountVectorizer.set_params(self, **kwargs) CountVectorizer.__init__(self, preprocessor=get_preprocessor(self.column, self.size, self.terminator), ngram_range=(1, self.size), analyzer='char', binary=self.binary)
def __init__(self, lang, **kwargs): CountVectorizer.__init__(self, kwargs) try: self.stemmer = SnowballStemmer(lang.lower()).stem self.vect = CountVectorizer() self.analyzer = self.analyzer_nltk except ValueError: pass
def __init__(self, large_file=False): if large_file: HashingVectorizer.__init__(self) else: # Over ride the built in string processing by assigning the # tokenizer, preprocessor and lowercase parameters as below. CountVectorizer.__init__(self, tokenizer=identity, preprocessor=None, lowercase=False)
def __init__(self, column, binary=False, size=3, terminator='$'): self.column = column self.size = size self.terminator = terminator CountVectorizer.__init__(self, preprocessor=get_preprocessor( self.column, self.size, self.terminator), ngram_range=(1, size), analyzer='char', binary=binary)
def __init__(self, column, binary=False, size=3, terminator='$'): self.column = column self.size = size self.terminator = terminator CountVectorizer.__init__(self, preprocessor=get_preprocessor(self.column, self.size, self.terminator), ngram_range=(1, size), analyzer='char', binary=binary)
def __init__( self, lowercase: Boolean(), stopwords_remove: Boolean(), binary: Boolean(), inner_tokenizer: algorithm(Sentence(), List(Word())), inner_stemmer: algorithm(Word(), Stem()), inner_stopwords: algorithm(List(Word()), List(Word())), ): self.stopwords_remove = stopwords_remove self.inner_tokenizer = inner_tokenizer self.inner_stemmer = inner_stemmer self.inner_stopwords = inner_stopwords SklearnTransformer.__init__(self) _CountVectorizer.__init__(self, lowercase=lowercase, binary=binary)
def __init__(self, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern="(?u)\b\w\w+\b", ngram_range=(1, 1), analyzer="word", max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=numpy.int64, progress_bar_resolution_seconds=.333, progress_bar_clear_when_done=False): CountVectorizer.__init__(self, input=input, encoding=encoding, decode_error=decode_error, strip_accents=strip_accents, lowercase=lowercase, preprocessor=preprocessor, tokenizer=tokenizer, stop_words=stop_words, token_pattern=token_pattern, ngram_range=ngram_range, analyzer=analyzer, max_df=max_df, min_df=min_df, max_features=max_features, vocabulary=vocabulary, binary=binary, dtype=dtype) ProgressBarVectorizer.__init__(self, progress_bar_resolution_seconds, progress_bar_clear_when_done)
def __init__(self, stopwords_list=None, max_features=None): CountVectorizer.__init__(self,analyzer="word",\ strip_accents="unicode",\ stop_words=stopwords_list,\ max_features=max_features) self.en_lemmatizer = nltk.stem.WordNetLemmatizer()
def __init__(self, analyzer=BOWAnalyzer, max_df=None): CountVectorizer.__init__(self, analyzer=analyzer, max_df=max_df)
def __init__(self, n_grams=1, first_last_sentence_only=False): CountVectorizer.__init__(self, ngram_range=(n_grams, n_grams)) self.first_last_sentence_only = first_last_sentence_only self.term_dict = {}
def __init__(self, **kwargs): CountVectorizer.__init__(self, **kwargs)
def __init__(self): CountVectorizer.__init__(self, binary=True, tokenizer=identity, preprocessor=None, lowercase=False)
def __init__(self, window=None, sentence_splitter="\n", directional=False, **args): self.window = window self.sentence_splitter = sentence_splitter self.directional = directional CountVectorizer.__init__(self, **args)