Python utils.to_unicode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 to_unicode [as 別名]
def load_from_text(fname):
        """
        Load a previously stored Dictionary from a text file.
        Mirror function to `save_as_text`.
        """
        result = Dictionary()
        with utils.smart_open(fname) as f:
            for lineno, line in enumerate(f):
                line = utils.to_unicode(line)
                try:
                    wordid, word, docfreq = line[:-1].split('\t')
                except Exception:
                    raise ValueError("invalid line in dictionary file %s: %s"
                                     % (fname, line.strip()))
                wordid = int(wordid)
                if word in result.token2id:
                    raise KeyError('token %s is defined as ID %d and as ID %d' % (word, wordid, result.token2id[word]))
                result.token2id[word] = wordid
                result.dfs[wordid] = int(docfreq)
        return result 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def load_word_topics(self):
        logger.info("loading assigned topics from %s" % self.fstate())
        wordtopics = numpy.zeros((self.num_topics, self.num_terms), dtype=numpy.float32)
        with utils.smart_open(self.fstate()) as fin:
            _ = next(fin)  # header
            self.alpha = numpy.array([float(val) for val in next(fin).split()[2:]])
            assert len(self.alpha) == self.num_topics, "mismatch between MALLET vs. requested topics"
            _ = next(fin)  # beta
            for lineno, line in enumerate(fin):
                line = utils.to_unicode(line)
                doc, source, pos, typeindex, token, topic = line.split()
                tokenid = self.id2word.token2id[token] if hasattr(self.id2word, 'token2id') else int(token)
                wordtopics[int(topic), tokenid] += 1
        logger.info("loaded assigned topics for %i tokens" % wordtopics.sum())
        self.wordtopics = wordtopics
        self.print_topics(15) 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def setUp(self):
        """setup lee test corpora"""
        global bg_corpus, corpus, human_sim_vector, bg_corpus2, corpus2

        pre_path = os.path.join(os.path.dirname(__file__), 'test_data')
        bg_corpus_file = 'lee_background.cor'
        corpus_file = 'lee.cor'
        sim_file = 'similarities0-1.txt'

        # read in the corpora
        latin1 = lambda line: utils.to_unicode(line, encoding='latin1')
        with utils.smart_open(os.path.join(pre_path, bg_corpus_file)) as f:
            bg_corpus = preprocess_documents(latin1(line) for line in f)
        with utils.smart_open(os.path.join(pre_path, corpus_file)) as f:
            corpus = preprocess_documents(latin1(line) for line in f)
        with utils.smart_open(os.path.join(pre_path, bg_corpus_file)) as f:
            bg_corpus2 = [preprocess_string(latin1(s), filters=DEFAULT_FILTERS[:-1]) for s in f]
        with utils.smart_open(os.path.join(pre_path, corpus_file)) as f:
            corpus2 = [preprocess_string(latin1(s), filters=DEFAULT_FILTERS[:-1]) for s in f]

        # read the human similarity data
        sim_matrix = np.loadtxt(os.path.join(pre_path, sim_file))
        sim_m_size = np.shape(sim_matrix)[0]
        human_sim_vector = sim_matrix[matutils.triu_indices(sim_m_size, 1)] 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def tokenize(content, token_min_len=2, token_max_len=100, lower=True):
    content = re.sub(EMAIL_PATTERN, ' ', content)  # remove email pattern
    content = re.sub(URL_PATTERN, ' ', content) # remove url pattern
    content = re.sub(WIKI_REMOVE_CHARS, ' ', content)  # remove unnecessary chars
    content = re.sub(WIKI_SPACE_CHARS, ' ', content)
    content = re.sub(MULTIPLE_SPACES, ' ', content)
    tokens = content.replace(", )", "").split(" ")
    result = []
    for token in tokens:
        if not token.startswith('_'):
            token_candidate = to_unicode(re.sub(WIKI_REMOVE_TOKEN_CHARS, '', token))
        else:
            token_candidate = ""
        if len(token_candidate) > 0:
            result.append(token_candidate)
    return result 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def __init__(self, input, transposed=True):
        """
        Initialize the matrix reader.

        The `input` refers to a file on local filesystem, which is expected to
        be in the sparse (coordinate) Matrix Market format. Documents are assumed
        to be rows of the matrix (and document features are columns).

        `input` is either a string (file path) or a file-like object that supports
        `seek()` (e.g. gzip.GzipFile, bz2.BZ2File).
        """
        logger.info("initializing corpus reader from %s" % input)
        self.input, self.transposed = input, transposed
        with utils.file_or_filename(self.input) as lines:
            try:
                header = utils.to_unicode(next(lines)).strip()
                if not header.lower().startswith('%%matrixmarket matrix coordinate real general'):
                    raise ValueError("File %s not in Matrix Market format with coordinate real general; instead found: \n%s" %
                                    (self.input, header))
            except StopIteration:
                pass

            self.num_docs = self.num_terms = self.num_nnz = 0
            for lineno, line in enumerate(lines):
                line = utils.to_unicode(line)
                if not line.startswith('%'):
                    self.num_docs, self.num_terms, self.num_nnz = map(int, line.split())
                    if not self.transposed:
                        self.num_docs, self.num_terms = self.num_terms, self.num_docs
                    break

        logger.info("accepted corpus with %i documents, %i features, %i non-zero entries" %
                     (self.num_docs, self.num_terms, self.num_nnz)) 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def line2doc(self, line):
        """
        Create a document from a single line (string) in SVMlight format
        """
        line = utils.to_unicode(line)
        line = line[: line.find('#')].strip()
        if not line:
            return None # ignore comments and empty lines
        parts = line.split()
        if not parts:
            raise ValueError('invalid line format in %s' % self.fname)
        target, fields = parts[0], [part.rsplit(':', 1) for part in parts[1:]]
        doc = [(int(p1) - 1, float(p2)) for p1, p2 in fields if p1 != 'qid'] # ignore 'qid' features, convert 1-based feature ids to 0-based
        return doc, target 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def filter_wiki(raw):
    """
    Filter out wiki mark-up from `raw`, leaving only text. `raw` is either unicode
    or utf-8 encoded string.
    """
    # parsing of the wiki markup is not perfect, but sufficient for our purposes
    # contributions to improving this code are welcome :)
    text = utils.to_unicode(raw, 'utf8', errors='ignore')
    text = utils.decode_htmlentities(text) # ' ' --> '\xa0'
    return remove_markup(text) 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def line2doc(self, line):
        l = [word for word in utils.to_unicode(line).strip().split(' ') if word]
        docid, doclang, words = l[0], l[1], l[2:]

        doc = super(MalletCorpus, self).line2doc(' '.join(words))

        if self.metadata:
            return doc, (docid, doclang)
        else:
            return doc 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def split_on_space(s):
    return [word for word in utils.to_unicode(s).strip().split(' ') if word] 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def save_corpus(fname, corpus, id2word=None, metadata=False):
        """
        Save a corpus in the List-of-words format.

        This function is automatically called by `LowCorpus.serialize`; don't
        call it directly, call `serialize` instead.
        """
        if id2word is None:
            logger.info("no word id mapping provided; initializing from corpus")
            id2word = utils.dict_from_corpus(corpus)

        logger.info("storing corpus in List-Of-Words format into %s" % fname)
        truncated = 0
        offsets = []
        with utils.smart_open(fname, 'wb') as fout:
            fout.write(utils.to_utf8('%i\n' % len(corpus)))
            for doc in corpus:
                words = []
                for wordid, value in doc:
                    if abs(int(value) - value) > 1e-6:
                        truncated += 1
                    words.extend([utils.to_unicode(id2word[wordid])] * int(value))
                offsets.append(fout.tell())
                fout.write(utils.to_utf8('%s\n' % ' '.join(words)))

        if truncated:
            logger.warning("List-of-words format can only save vectors with "
                            "integer elements; %i float entries were truncated to integer value" %
                            truncated)
        return offsets 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def from_corpus(corpus, id2word=None):
        """
        Create Dictionary from an existing corpus. This can be useful if you only
        have a term-document BOW matrix (represented by `corpus`), but not the
        original text corpus.

        This will scan the term-document count matrix for all word ids that
        appear in it, then construct and return Dictionary which maps each
        `word_id -> id2word[word_id]`.

        `id2word` is an optional dictionary that maps the `word_id` to a token. In
        case `id2word` isn't specified the mapping `id2word[word_id] = str(word_id)`
        will be used.
        """

        result = Dictionary()
        max_id = -1
        for docno, document in enumerate(corpus):
            if docno % 10000 == 0:
                logger.info("adding document #%i to %s" % (docno, result))
            result.num_docs += 1
            result.num_nnz += len(document)
            for wordid, word_freq in document:
                max_id = max(wordid, max_id)
                result.num_pos += word_freq
                result.dfs[wordid] = result.dfs.get(wordid, 0) + 1

        if id2word is None:
            # make sure length(result) == get_max_id(corpus) + 1
            result.token2id = dict((unicode(i), i) for i in xrange(max_id + 1))
        else:
            # id=>word mapping given: simply copy it
            result.token2id = dict((utils.to_unicode(token), id) for id, token in iteritems(id2word))
        for id in itervalues(result.token2id):
            # make sure all token ids have a valid `dfs` entry
            result.dfs[id] = result.dfs.get(id, 0)

        logger.info("built %s from %i documents (total %i corpus positions)" %
                     (result, result.num_docs, result.num_pos))
        return result
#endclass Dictionary 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def line2doc(self, line):
        parts = utils.to_unicode(line).split()
        if int(parts[0]) != len(parts) - 1:
            raise ValueError("invalid format in %s: %s" % (self.fname, repr(line)))
        doc = [part.rsplit(':', 1) for part in parts[1:]]
        doc = [(int(p1), float(p2)) for p1, p2 in doc]
        return doc 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def remove_stopwords(s):
    s = utils.to_unicode(s)
    return " ".join(w for w in s.split() if w not in STOPWORDS) 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def strip_punctuation(s):
    s = utils.to_unicode(s)
    return RE_PUNCT.sub(" ", s)


# unicode.translate cannot delete characters like str can 

# 需要導入模塊: from gensim import utils [as 別名]
# 或者: from gensim.utils import to_unicode [as 別名]
def strip_tags(s):
    s = utils.to_unicode(s)
    return RE_TAGS.sub("",s)