class FeatureContainer:
def __init__(self, word_dict, keyword_dict=None):
# Load word list
self.word_list = []
self.word_dict = {}
l = [(value, key) for key, value in word_dict.items()]
l = sorted(l, reverse=True)
for idx, (value, key) in enumerate(l):
self.word_list.append(key)
self.word_dict[key.decode('utf8')] = idx
self.fb = FeatureBuilder(self.word_dict, keyword_dict)
self.smb = SimhashBuilder(self.word_list)
self.mnb = MinhashBuilder()
print 'FeatureContainer OK'
def compute_feature(self, token_list):
new_words = []
for token in token_list:
if not token in self.word_dict:
new_words.append(token)
if len(new_words) != 0:
# Update word_list and word_dict
self.fb.update_words(new_words)
self.smb.update_words([word.encode('utf8') for word in new_words])
self.word_dict = self.fb.word_dict
self.word_list.extend([word.encode('utf8') for word in new_words])
feature_vec = self.fb.compute(token_list)
sim_hash, hash_vec = self.smb.sim_hash_nonzero(feature_vec)
min_hash = self.mnb.min_hash(hash_vec)
return feature_vec, sim_hash, min_hash
class FeatureContainer:
def __init__(self, word_dict_path):
# Load word list
self.word_dict_path = word_dict_path
self.word_list = []
with open(word_dict_path, 'r') as ins:
for line in ins.readlines():
self.word_list.append(line.split()[1])
self.word_dict = {}
for idx, ascword in enumerate(self.word_list):
self.word_dict[ascword.decode('utf8')] = idx
self.fb = FeatureBuilder(self.word_dict)
self.smb = SimhashBuilder(self.word_list)
print 'Loaded ', len(self.word_list), 'words'
def compute_feature(self, token_list):
new_words = []
for token in token_list:
if not token in self.word_dict:
new_words.append(token)
if len(new_words) != 0:
# Update word_list and word_dict
self.fb.update_words(new_words)
self.smb.update_words([word.encode('utf8') for word in new_words])
self.word_dict = self.fb.word_dict
self.word_list.extend([word.encode('utf8') for word in new_words])
feature_vec = self.fb.compute(token_list)
return feature_vec, self.smb.sim_hash(feature_vec)
class FeatureContainer:
def __init__(self, word_dict_path):
# Load word list
self.word_dict_path = word_dict_path
self.word_list = []
with open(word_dict_path, "r") as ins:
for line in ins.readlines():
self.word_list.append(line.split()[1])
self.word_dict = {}
for idx, ascword in enumerate(self.word_list):
self.word_dict[ascword.decode("utf8")] = idx
self.fb = FeatureBuilder(self.word_dict)
self.smb = SimhashBuilder(self.word_list)
print "Loaded ", len(self.word_list), "words"
def compute_feature(self, token_list):
new_words = []
for token in token_list:
if not token in self.word_dict:
new_words.append(token)
if len(new_words) != 0:
# Update word_list and word_dict
self.fb.update_words(new_words)
self.smb.update_words([word.encode("utf8") for word in new_words])
self.word_dict = self.fb.word_dict
self.word_list.extend([word.encode("utf8") for word in new_words])
feature_vec = self.fb.compute(token_list)
return feature_vec, self.smb.sim_hash(feature_vec)
# 分词 tokens返回分词后的数组
doc_token_1 = jt.tokens(doc_data_1)
print 'Loading word dict...'
# 加载字典并构建词典
word_list = []
with open(word_dict, 'r') as ins:
for line in ins.readlines():
word_list.append(line.split()[1])
word_dict = {}
for idx, ascword in enumerate(word_list):
word_dict[ascword.decode('utf8')] = idx
# 构建非0特征向量
fb = FeatureBuilder(word_dict)
doc_feat_1 = fb.compute(
doc_token_1
) # return feature_nonzero得到一个非0 长度的向量 ，元素为(idx,value)且 value > 0
#使得字典中的每个值都有一个hash值，
smb = SimhashBuilder(word_list)
doc_fl_1 = DocFeatLoader(smb, doc_feat_1)
#测试文件,用于调研算法
out_file = open('/home/lin.xiong/lsh_data/out.file', 'w')
#fp_set = set()
fp_arr = []
fp_post_id_dict = {}
with open('/home/lin.xiong/lsh_data/lsh_clear.fingerprint', 'r') as fp:
for line in fp:
fp_post_id_dict[long(line.split('\t')[1])] = line.split('\t')[0]
fp_arr.append(long(line.split('\t')[1]))
comment = []
with open('/home/lin.xiong/lsh_data/lsh.data', 'r') as comment_file:
doc_token_2 = jt.tokens(doc_data_2)
print 'Loading word dict...'
# Load word list from word_dict
word_list = []
with open(word_dict, 'r') as ins:
for line in ins.readlines():
word_list.append(line.split()[1])
# Build unicode string word dict
word_dict = {}
for idx, ascword in enumerate(word_list):
word_dict[ascword.decode('utf8')] = idx
# Build nonzero-feature
fb = FeatureBuilder(word_dict)
doc_feat_1 = fb.compute(doc_token_1)
doc_feat_2 = fb.compute(doc_token_2)
# Init simhash_builder
smb = SimhashBuilder(word_list)
doc_fl_1 = DocFeatLoader(smb, doc_feat_1)
doc_fl_2 = DocFeatLoader(smb, doc_feat_2)
if mode == '-c':
print 'Matching by VSM + cosine distance'
dist = cosine_distance_nonzero(doc_fl_1.feat_vec, doc_fl_2.feat_vec, norm=False)
if dist > float(threshold):
print 'Matching Result:\t<True:%s>' % dist
else:
print 'Matching Result:\t<False:%s>' % dist
# For exp: load document content
doc_list = []
with open(sys.argv[4], 'r') as ins:
for line in ins.readlines():
doc_list.append(line.strip())
# Detection process begins
min_sim = 64
min_docid = 0
with open(sys.argv[5], 'r') as ins:
for lineidx, line in enumerate(ins.readlines()):
if lineidx != 642:
continue
# Tokenize
tokens = jt.tokens(line.strip().decode('utf8'))
# Compute text feature
feature = fb.compute(tokens)
# Compute simhash
fingerprint = smb.sim_hash(feature)
result_list = []
for idx, fp in enumerate(fingerprint_list):
sim = hamming_distance(fingerprint, fp, 64)
result_list.append((sim, idx))
result_list = sorted(result_list, cmp=lambda x,y: cmp(x[0],y[0]))
if result_list[0][0] < min_sim:
min_sim, min_docid = result_list[0][0], lineidx
#'''
with open(sys.argv[6], 'w') as outs:
outs.write(line.strip()+os.linesep)
for sim, idx in result_list:
outs.write('%s\t%s%s' %(sim, doc_list[idx], os.linesep))
#'''
# For exp: load document content
doc_list = []
with open(sys.argv[4], 'r') as ins:
for line in ins.readlines():
doc_list.append(line.strip())
# Detection process begins
min_sim = 64
min_docid = 0
with open(sys.argv[5], 'r') as ins:
for lineidx, line in enumerate(ins.readlines()):
if lineidx != 642:
continue
# Tokenize
tokens = jt.tokens(line.strip().decode('utf8'))
# Compute text feature
feature = fb.compute(tokens)
# Compute simhash
fingerprint = smb.sim_hash(feature)
result_list = []
for idx, fp in enumerate(fingerprint_list):
sim = hamming_distance(fingerprint, fp, 64)
result_list.append((sim, idx))
result_list = sorted(result_list, cmp=lambda x, y: cmp(x[0], y[0]))
if result_list[0][0] < min_sim:
min_sim, min_docid = result_list[0][0], lineidx
#'''
with open(sys.argv[6], 'w') as outs:
outs.write(line.strip() + os.linesep)
for sim, idx in result_list:
outs.write('%s\t%s%s' % (sim, doc_list[idx], os.linesep))
#'''
doc_token_2 = jt.tokens(doc_data_2)
print 'Loading word dict...'
# Load word list from word_dict
word_list = []
with open(word_dict, 'r') as ins:
for line in ins.readlines():
word_list.append(line.split()[1])
# Build unicode string word dict
word_dict = {}
for idx, ascword in enumerate(word_list):
word_dict[ascword.decode('utf8')] = idx
# Build nonzero-feature
fb = FeatureBuilder(word_dict)
doc_feat_1 = fb.compute(doc_token_1)
doc_feat_2 = fb.compute(doc_token_2)
# Init simhash_builder
smb = SimhashBuilder(word_list)
doc_fl_1 = DocFeatLoader(smb, doc_feat_1)
doc_fl_2 = DocFeatLoader(smb, doc_feat_2)
if mode == '-c':
print 'Matching by VSM + cosine distance'
dist = cosine_distance_nonzero(doc_fl_1.feat_vec,
doc_fl_2.feat_vec,
norm=False)
if dist > float(threshold):
print 'Matching Result:\t<True:%s>' % dist
