Python utils.tokenize方法代碼示例

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import tokenize [as 別名]
def get_texts(self):
        """
        Iterate over the collection, yielding one document at a time. A document
        is a sequence of words (strings) that can be fed into `Dictionary.doc2bow`.

        Override this function to match your input (parse input files, do any
        text preprocessing, lowercasing, tokenizing etc.). There will be no further
        preprocessing of the words coming out of this function.
        """
        # Instead of raising NotImplementedError, let's provide a sample implementation:
        # assume documents are lines in a single file (one document per line).
        # Yield each document as a list of lowercase tokens, via `utils.tokenize`.
        lineno = -1
        with self.getstream() as lines:
            for lineno, line in enumerate(lines):
                if self.metadata:
                    yield utils.tokenize(line, lowercase=True), (lineno,)
                else:
                    yield utils.tokenize(line, lowercase=True)
            self.length = lineno + 1 # will be 0 if loop never executes 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import tokenize [as 別名]
def simple_preprocess(
    doc: str,
    lower: bool = False,
    deacc: bool = False,
    min_len: int = 2,
    max_len: int = 15,
) -> List[str]:
    r"""
    Gensim's simple_preprocess adding a 'lower' param to indicate wether or not to
    lower case all the token in the texts

    For more informations see: https://radimrehurek.com/gensim/utils.html
    """
    tokens = [
        token
        for token in tokenize(doc, lower=False, deacc=deacc, errors="ignore")
        if min_len <= len(token) <= max_len and not token.startswith("_")
    ]
    return tokens 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import tokenize [as 別名]
def tokenize(self, content):
        raise NotImplementedError('Abstract Base Class') 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import tokenize [as 別名]
def characters(self, text):
            # for text, we only care about tokens directly within the <p> tag
            if self.path[-1] == 'p':
                tokens = [token.encode('utf8') for token in utils.tokenize(text, errors = 'ignore') if not token.isdigit()]
                self.tokens.extend(tokens)
    #endclass ArxmlivHandler 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import tokenize [as 別名]
def process_article(args):
    """
    Parse a wikipedia article, returning its content as a list of tokens
    (utf8-encoded strings).
    """
    text, lemmatize, title, pageid = args
    text = filter_wiki(text)
    if lemmatize:
        result = utils.lemmatize(text)
    else:
        result = tokenize(text)
    return result, title, pageid 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import tokenize [as 別名]
def tokenize(content):
    """
    Tokenize a piece of text from wikipedia. The input string `content` is assumed
    to be mark-up free (see `filter_wiki()`).

    Return list of tokens as utf8 bytestrings. Ignore words shorted than 2 or longer
    that 15 characters (not bytes!).
    """
    # TODO maybe ignore tokens with non-latin characters? (no chinese, arabic, russian etc.)
    return [token.encode('utf8') for token in utils.tokenize(content, lower=True, errors='ignore')
            if 2 <= len(token) <= 15 and not token.startswith('_')] 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import tokenize [as 別名]
def get_texts(self, metadata=None):
        """Iterate over the lines of "The Complete Works of William Shakespeare".

        This yields lists of strings (**texts**) rather than vectors (vectorized bags-of-words).
        And the **texts** yielded are lines rather than entire plays or sonnets.
        If you want vectors, use the corpus interface instead of this method.

        >>> shakes = ShakesCorpus(lowercase=True)
        >>> for i, tokens in enumerate(shakes.get_texts()):
        ...     print(i, tokens)
        ...     if i >= 4:
        ...         break
        (0, [])
        (1, [])
        (2, [u'the', u'sonnets'])
        (3, [])
        (4, [u'by', u'william', u'shakespeare'])
        """
        if metadata is None:
            metadata = self.metadata
        self.input_file = gzip.GzipFile(self.input_file_path)
        volume_num = 0
        with self.input_file as lines:
            for lineno, line in enumerate(lines):
                if volume_num >= len(self.book_meta['volumes']):
                    raise StopIteration()
                if lineno < self.book_meta['volumes'][volume_num]['start']:
                    continue
                if lineno < self.book_meta['volumes'][volume_num]['stop']:
                    # act_num, scene_num = 0, 0  # FIXME: use self.book_meta['volumes'][volume_num]['sections']
                    if metadata:
                        # FIXME: use self.lemmatize
                        toks = self.tokenize(line, lowercase=self.lowercase)
                        yield (toks, (lineno,))
                    else:
                        toks = self.tokenize(line, lowercase=self.lowercase)
                        yield toks
                else:
                    volume_num += 1  # don't yield the "THE END" line? 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import tokenize [as 別名]
def tokenize(self, line, **kwargs):
        return list(utils.tokenize(line, **kwargs)) 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import tokenize [as 別名]
def tokenize_tr(content,token_min_len=2,token_max_len=50,lower=True):
	if lower:
		lowerMap = {ord(u'A'): u'a',ord(u'A'): u'a',ord(u'B'): u'b',ord(u'C'): u'c',ord(u'Ç'): u'ç',ord(u'D'): u'd',ord(u'E'): u'e',ord(u'F'): u'f',ord(u'G'): u'g',ord(u'Ğ'): u'ğ',ord(u'H'): u'h',ord(u'I'): u'ı',ord(u'İ'): u'i',ord(u'J'): u'j',ord(u'K'): u'k',ord(u'L'): u'l',ord(u'M'): u'm',ord(u'N'): u'n',ord(u'O'): u'o',ord(u'Ö'): u'ö',ord(u'P'): u'p',ord(u'R'): u'r',ord(u'S'): u's',ord(u'Ş'): u'ş',ord(u'T'): u't',ord(u'U'): u'u',ord(u'Ü'): u'ü',ord(u'V'): u'v',ord(u'Y'): u'y',ord(u'Z'): u'z'}
		content = content.translate(lowerMap)
	return [
	utils.to_unicode(token) for token in utils.tokenize(content, lower=False, errors='ignore')
	if token_min_len <= len(token) <= token_max_len and not token.startswith('_')
	]