Это пример.Сначала вам нужно скопировать matutils.py и utils.py из gensim , а каталогу должно понравиться pic blow.
utils.py
matutils.py
doc_s аналог.py
модель (dir)
data (dir)
Удар кода должен быть в doc_simil.py .Затем просто переместите ваш data_file в каталог данных и измените fname в функции main.
#coding:utf-8
from gensim import corpora, models, similarities
import cPickle
import logging
import utils
import os
import numpy as np
import scipy
import matutils
from collections import defaultdict
data_dir = os.path.join(os.getcwd(), 'data')
work_dir = os.path.join(os.getcwd(), 'model', os.path.basename(__file__).rstrip('.py'))
if not os.path.exists(work_dir):
os.mkdir(work_dir)
os.chdir(work_dir)
logger = logging.getLogger('text_similar')
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
# convert to unicode
def to_unicode(text):
if not isinstance(text, unicode):
text = text.decode('utf-8')
return text
class TextSimilar(utils.SaveLoad):
def __init__(self):
self.conf = {}
def _preprocess(self):
docs = [to_unicode(doc.strip()).split()[1:] for doc in file(self.fname)]
cPickle.dump(docs, open(self.conf['fname_docs'], 'wb'))
dictionary = corpora.Dictionary(docs)
dictionary.save(self.conf['fname_dict'])
corpus = [dictionary.doc2bow(doc) for doc in docs]
corpora.MmCorpus.serialize(self.conf['fname_corpus'], corpus)
return docs, dictionary, corpus
def _generate_conf(self):
fname = self.fname[self.fname.rfind('/') + 1:]
self.conf['fname_docs'] = '%s.docs' % fname
self.conf['fname_dict'] = '%s.dict' % fname
self.conf['fname_corpus'] = '%s.mm' % fname
def train(self, fname, is_pre=True, method='lsi', **params):
self.fname = fname
self.method = method
self._generate_conf()
if is_pre:
self.docs, self.dictionary, corpus = self._preprocess()
else:
self.docs = cPickle.load(open(self.conf['fname_docs']))
self.dictionary = corpora.Dictionary.load(self.conf['fname_dict'])
corpus = corpora.MmCorpus(self.conf['fname_corpus'])
if params is None:
params = {}
logger.info("training TF-IDF model")
self.tfidf = models.TfidfModel(corpus, id2word=self.dictionary)
corpus_tfidf = self.tfidf[corpus]
if method == 'lsi':
logger.info("training LSI model")
self.lsi = models.LsiModel(corpus_tfidf, id2word=self.dictionary, **params)
self.similar_index = similarities.MatrixSimilarity(self.lsi[corpus_tfidf])
self.para = self.lsi[corpus_tfidf]
elif method == 'lda_tfidf':
logger.info("training LDA model")
self.lda = models.LdaMulticore(corpus_tfidf, id2word=self.dictionary, workers=8, **params)
self.similar_index = similarities.MatrixSimilarity(self.lda[corpus_tfidf])
self.para = self.lda[corpus_tfidf]
elif method == 'lda':
logger.info("training LDA model")
self.lda = models.LdaMulticore(corpus, id2word=self.dictionary, workers=8, **params)
self.similar_index = similarities.MatrixSimilarity(self.lda[corpus])
self.para = self.lda[corpus]
elif method == 'logentropy':
logger.info("training a log-entropy model")
self.logent = models.LogEntropyModel(corpus, id2word=self.dictionary)
self.similar_index = similarities.MatrixSimilarity(self.logent[corpus])
self.para = self.logent[corpus]
else:
msg = "unknown semantic method %s" % method
logger.error(msg)
raise NotImplementedError(msg)
def doc2vec(self, doc):
bow = self.dictionary.doc2bow(to_unicode(doc).split())
if self.method == 'lsi':
return self.lsi[self.tfidf[bow]]
elif self.method == 'lda':
return self.lda[bow]
elif self.method == 'lda_tfidf':
return self.lda[self.tfidf[bow]]
elif self.method == 'logentropy':
return self.logent[bow]
def find_similar(self, doc, n=10):
vec = self.doc2vec(doc)
sims = self.similar_index[vec]
sims = sorted(enumerate(sims), key=lambda item: -item[1])
for elem in sims[:n]:
idx, value = elem
print ' '.join(self.docs[idx]), value
def get_vectors(self):
return self._get_vector(self.para)
def _get_vector(self, corpus):
def get_max_id():
maxid = -1
for document in corpus:
maxid = max(maxid, max([-1] + [fieldid for fieldid, _ in document])) # [-1] to avoid exceptions from max(empty)
return maxid
num_features = 1 + get_max_id()
index = np.empty(shape=(len(corpus), num_features), dtype=np.float32)
for docno, vector in enumerate(corpus):
if docno % 1000 == 0:
print("PROGRESS: at document #%i/%i" % (docno, len(corpus)))
if isinstance(vector, np.ndarray):
pass
elif scipy.sparse.issparse(vector):
vector = vector.toarray().flatten()
else:
vector = matutils.unitvec(matutils.sparse2full(vector, num_features))
index[docno] = vector
return index
def cluster(vectors, ts, k=30):
from sklearn.cluster import k_means
X = np.array(vectors)
cluster_center, result, inertia = k_means(X.astype(np.float), n_clusters=k, init="k-means++")
X_Y_dic = defaultdict(set)
for i, pred_y in enumerate(result):
X_Y_dic[pred_y].add(''.join(ts.docs[i]))
print 'len(X_Y_dic): ', len(X_Y_dic)
with open(data_dir + '/cluser.txt', 'w') as fo:
for Y in X_Y_dic:
fo.write(str(Y) + '\n')
fo.write('{word}\n'.format(word='\n'.join(list(X_Y_dic[Y])[:100])))
def main(is_train=True):
fname = data_dir + '/brand'
num_topics = 100
method = 'lda'
ts = TextSimilar()
if is_train:
ts.train(fname, method=method ,num_topics=num_topics, is_pre=True, iterations=100)
ts.save(method)
else:
ts = TextSimilar().load(method)
index = ts.get_vectors()
cluster(index, ts, k=num_topics)
if __name__ == '__main__':
is_train = True if len(sys.argv) > 1 else False
main(is_train)