from porter_stemer import PorterStemmer words = 'it\'s those dogs i liked' stem = PorterStemmer() print stem.stem(words, 0, len(words)-1)
def __init__(self):
self.stemmer = PorterStemmer()
self.STOP_WORDS_FILE = '%s/../Data/english.stop' % os.path.dirname(
os.path.realpath(__file__))
with codecs.open(self.STOP_WORDS_FILE, 'r') as f:
self.stopwords = f.read().split()
class LSA(object):
stemmer = None
stopwords = []
def __init__(self):
self.stemmer = PorterStemmer()
self.STOP_WORDS_FILE = '%s/../Data/english.stop' % os.path.dirname(
os.path.realpath(__file__))
with codecs.open(self.STOP_WORDS_FILE, 'r') as f:
self.stopwords = f.read().split()
def remove_stop_wrods(self, word_list):
return [word for word in word_list if word not in self.stopwords]
def _pre_solve_1(self, sentence):
sentence = sentence.replace(unicode('’', 'utf-8'), '\'')
return sentence.lower()
def _pre_solve_stemmer(self, sentence):
words_list = []
for w in sentence.split(' '):
words_list.append(self.stemmer.stem(w, 0, len(w) - 1))
return ' '.join(words_list)
def _pre_solve_filter_stopwords(self, sentence):
word_list = sentence.split(' ')
return ' '.join(self.remove_stop_wrods(word_list))
def pre_solve(self, sentence):
sentence = self._pre_solve_1(sentence)
sentence = self._pre_solve_stemmer(sentence)
return self._pre_solve_filter_stopwords(sentence)
def get_documents(self, file_path):
documents = []
documents_words = []
line_num = 0
with codecs.open(file_path, 'r') as fopen:
fopen.readline()
line_num += 1
while fopen:
line = fopen.readline()
#print 'line_num:', line_num
line_num += 1
if len(line) == 0:
break
line = line.strip().split(' ')
#print 'line[3] = ', line[3]
#pre solve
sentence = self.pre_solve(line[3])
documents_words.append(sentence.split(' '))
documents.append(sentence)
return documents, documents_words
def main(self,
query,
target_id,
sorurce_file_path,
documents,
documents_words,
num_topics=20):
#documents, documents_words = self.get_documents(sorurce_file_path)
dictionary = corpora.Dictionary(documents_words)
corpus = [
dictionary.doc2bow(documents_word)
for documents_word in documents_words
]
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
lsi = models.LsiModel(corpus_tfidf,
id2word=dictionary,
num_topics=num_topics)
corpus_lsi = lsi[corpus_tfidf]
index = similarities.MatrixSimilarity(lsi[corpus])
query = self.pre_solve(query)
query_word_list = query.split(' ')
query_bow = dictionary.doc2bow(query_word_list)
query_lsi = lsi[query_bow]
sims = list(index[query_lsi])
#print 'sims[', target_id, ']=', sims[target_id]
return sims[target_id]
def __init__(self): self.stemmer = PorterStemmer() self.STOP_WORDS_FILE = '%s/../Data/english.stop' % os.path.dirname(os.path.realpath(__file__)) with codecs.open(self.STOP_WORDS_FILE, 'r') as f: self.stopwords = f.read().split()
class LSA(object):
stemmer = None
stopwords = []
def __init__(self):
self.stemmer = PorterStemmer()
self.STOP_WORDS_FILE = '%s/../Data/english.stop' % os.path.dirname(os.path.realpath(__file__))
with codecs.open(self.STOP_WORDS_FILE, 'r') as f:
self.stopwords = f.read().split()
def remove_stop_wrods(self, word_list):
return [word for word in word_list if word not in self.stopwords]
def _pre_solve_1(self, sentence):
sentence = sentence.replace(unicode('’', 'utf-8'), '\'')
return sentence.lower()
def _pre_solve_stemmer(self, sentence):
words_list = []
for w in sentence.split(' '):
words_list.append(self.stemmer.stem(w, 0, len(w)-1))
return ' '.join(words_list)
def _pre_solve_filter_stopwords(self, sentence):
word_list = sentence.split(' ')
return ' '.join(self.remove_stop_wrods(word_list))
def pre_solve(self, sentence):
sentence = self._pre_solve_1(sentence)
sentence = self._pre_solve_stemmer(sentence)
return self._pre_solve_filter_stopwords(sentence)
def get_documents(self, file_path):
documents = []
documents_words = []
line_num = 0
with codecs.open(file_path, 'r') as fopen:
fopen.readline()
line_num += 1
while fopen:
line = fopen.readline()
#print 'line_num:', line_num
line_num += 1
if len(line) == 0:
break
line = line.strip().split(' ')
#print 'line[3] = ', line[3]
#pre solve
sentence = self.pre_solve(line[3])
documents_words.append(sentence.split(' '))
documents.append(sentence)
return documents, documents_words
def main(self, query, target_id, sorurce_file_path, documents, documents_words, num_topics=20):
#documents, documents_words = self.get_documents(sorurce_file_path)
dictionary = corpora.Dictionary(documents_words)
corpus = [dictionary.doc2bow(documents_word) for documents_word in documents_words]
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=num_topics)
corpus_lsi = lsi[corpus_tfidf]
index = similarities.MatrixSimilarity(lsi[corpus])
query = self.pre_solve(query)
query_word_list = query.split(' ')
query_bow = dictionary.doc2bow(query_word_list)
query_lsi = lsi[query_bow]
sims = list(index[query_lsi])
#print 'sims[', target_id, ']=', sims[target_id]
return sims[target_id]
from porter_stemer import PorterStemmer words = 'it\'s those dogs i liked' stem = PorterStemmer() print stem.stem(words, 0, len(words) - 1)
