-
Notifications
You must be signed in to change notification settings - Fork 1
/
recommender_all.py
369 lines (327 loc) · 13.5 KB
/
recommender_all.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
# -*- coding: utf-8 -*-
#recommender2
import pickle
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
import heapq
import numpy
import ConfigParser
import MySQLdb
import json
from sklearn.metrics.pairwise import cosine_similarity
import httplib
import re
import Stemmer
import time
import datetime
import redis
import threading
from multiprocessing import Pool
print 'Start at {}'.format(datetime.datetime.now())
start_time = time.time()
r = redis.StrictRedis(host='localhost', port=6379, db=0)
config = ConfigParser.ConfigParser()
config.readfp(open('my.cfg'))
headers = {"User-Agent": "hh-recommender"}
conn = httplib.HTTPSConnection("api.hh.ru")
conn.request("GET", "https://api.hh.ru/dictionaries", headers=headers)
r1 = conn.getresponse()
if r1.status != 200:
conn.close()
conn = httplib.HTTPSConnection("api.hh.ru")
conn.request("GET", "https://api.hh.ru/dictionaries", headers=headers)
r1 = conn.getresponse()
dictionaries = r1.read()
conn.close()
dictionaries_json = json.loads(dictionaries)
currencies = dictionaries_json['currency']
currency_rates = {}
for currency in currencies:
currency_rates[currency['code']] = currency['rate']
#areas
conn = httplib.HTTPSConnection("api.hh.ru")
conn.request("GET", "https://api.hh.ru/areas", headers=headers)
r1 = conn.getresponse()
if r1.status != 200:
conn.close()
conn = httplib.HTTPSConnection("api.hh.ru")
conn.request("GET", "https://api.hh.ru/areas", headers=headers)
r1 = conn.getresponse()
areas = r1.read()
conn.close()
areas_json = json.loads(areas)
areas_map = {}
def build_areas_map(areas, areas_map):
for area in areas:
if area['id'] == '1':#msk
parent_id = '2019'
elif area['id'] == '2':#spb
parent_id = '145'
elif area['id'] == '115':#kiev
parent_id = '2164'
elif area['id'] == '1002':#minsk
parent_id = '2237'
else:
parent_id = area['parent_id']
areas_map[area['id']] = parent_id
build_areas_map(area['areas'], areas_map)
build_areas_map(areas_json, areas_map)
with open( "count_vectorizer.p", "rb" ) as f:
count_vectorizer = pickle.load(f)
with open( "tfidf_transformer.p", "rb" ) as f:
tfidf_transformer = pickle.load(f)
def get_resumes():
db = MySQLdb.connect(host="127.0.0.1",
port=config.getint('mysqld', 'port'),
user=config.get('mysqld', 'user'),
passwd=config.get('mysqld', 'password'),
db=config.get('mysqld', 'database') )
db.autocommit(True)
db.set_character_set('utf8')
cursor = db.cursor()
cursor.execute('SET NAMES utf8;')
cursor.execute('SET CHARACTER SET utf8;')
cursor.execute('SET character_set_connection=utf8;')
cursor.close()
salaries = []
features = []
ids = []
areas = []
specializations = []
stemmer = Stemmer.Stemmer('russian')
cursor = db.cursor()
cursor.execute("""
SELECT r.item
FROM resumes r
WHERE r.is_active=1 """)
for item in cursor:
resume_json = json.loads(item[0])
feature = []
#description
p_doc = ''
if resume_json['skills'] != None:
doc = re.sub('<[^>]*>', '', resume_json['skills'].lower())
doc = re.sub('"', '', doc)
doc = re.sub(ur'[^a-zа-я]+', ' ', doc, re.UNICODE)
words = re.split(r'\s{1,}', doc.strip())
for word in words:
word = stemmer.stemWord(word.strip())
if len(word.strip()) > 1:
p_doc = p_doc + " " + word
#title
p_title = ''
if resume_json['title'] != None:
title = re.sub(ur'[^a-zа-я]+', ' ', resume_json['title'].lower(), re.UNICODE)
words = re.split(r'\s{1,}', title.strip())
for title_word in words:
title_word = stemmer.stemWord(title_word)
if len(title_word.strip()) > 1:
p_title = p_title + " " + title_word.strip()
#keyskills
p_skills = ''
res_skills = resume_json['skill_set']
for skill in res_skills:
words = re.split(r'\s{1,}', skill.lower().strip())
for word in words:
word = stemmer.stemWord(word)
if len(word.strip()) > 1:
p_skills = p_skills + " " + word.strip()
#salary
salary = None
if resume_json['salary'] != None and resume_json['salary']['amount'] != None:
salary = resume_json['salary']['amount']/currency_rates[resume_json['salary']['currency']]
max_salary = 500000.0
if salary >= max_salary:
salary = max_salary
#experience
if resume_json['experience'] != None and len(resume_json['experience'])> 0 and resume_json['experience'][0]['description'] != None:
experience_description = resume_json['experience'][0]['description']
doc = re.sub('<[^>]*>', '', experience_description.lower())
doc = re.sub('"', '', doc)
doc = re.sub(ur'[^a-zа-я]+', ' ', doc, re.UNICODE)
words = re.split(r'\s{1,}', doc.strip())
for word in words:
word = stemmer.stemWord(word.strip())
if len(word.strip()) > 1:
p_doc = p_doc + " " + word
#areas
res_areas = []
if resume_json['area'] == None:
res_areas.append(areas_map["1"])
else :
res_areas.append(areas_map[resume_json['area']['id']])
for area in resume_json['relocation']['area']:
res_areas.append(areas_map[area['id']])
areas.append(res_areas)
#specializations
res_specializations = set()
try:
if resume_json['specialization'] != None:
for spec in resume_json['specialization']:
res_specializations.add(spec['profarea_id'])
except KeyError:
print 'cant find specialization'
specializations.append(res_specializations)
p_doc = p_doc + " " + p_title + " " + p_skills
feature_p_doc = count_vectorizer.transform([p_doc])
feature = tfidf_transformer.transform(feature_p_doc)
features.append(feature.toarray())
salaries.append(salary)
ids.append(resume_json['id'])
cursor.close()
db.close()
return features, salaries, ids, areas, specializations
resume_features, resume_salaries, resume_ids, resume_areas, resume_specializations = get_resumes()
lock = threading.Lock()
def process_vacancy_ids(vacancies):
pre_vacancy_similarities = {}
pre_vacancy_ids = {}
for idx, val in enumerate(resume_features):
new_vacancy_features = []
new_vacancy_ids = []
new_vacancy_specializations = []
for vac_id, vac_data in vacancies.iteritems():
if resume_areas[idx][0] == vac_data['area'] and (resume_salaries[idx] == None or vac_data['salary'] == 'None'):
new_vacancy_features.append(json.loads(vac_data['features'].decode('zlib')))
new_vacancy_ids.append(vac_id)
if 'specializations' in vac_data:
new_vacancy_specializations.append(vac_data['specializations'])
else:
new_vacancy_specializations.append(None)
elif resume_areas[idx][0] == vac_data['area']:
min_resume_salary = resume_salaries[idx] - (resume_salaries[idx] * 0.2)
max_resume_salary = resume_salaries[idx] + (resume_salaries[idx] * 0.8)
vac_salary = float(vac_data['salary'])
if vac_salary >= min_resume_salary and vac_salary <= max_resume_salary:
new_vacancy_features.append(json.loads(vac_data['features'].decode('zlib')))
new_vacancy_ids.append(vac_id)
if 'specializations' in vac_data:
new_vacancy_specializations.append(vac_data['specializations'])
else:
new_vacancy_specializations.append(None)
similarities = []
ids = []
if len(new_vacancy_features) > 0:
c_result = cosine_similarity(resume_features[idx], new_vacancy_features)
for s_id, s_val in enumerate(c_result[0]):
if new_vacancy_specializations[s_id] != None:
found = False
for vac_spec_id in new_vacancy_specializations[s_id]:
for res_spec_id in resume_specializations[idx]:
if vac_spec_id == res_spec_id:
found = True
break;
if found:
c_result[0][s_id] = c_result[0][s_id] + (c_result[0][s_id]*1.0)
break;
res = heapq.nlargest(20, range(len(c_result[0])), c_result[0].take)
for j in res:
similarities.append(c_result[0][j])
ids.append(new_vacancy_ids[j])
lock.acquire()
try:
if resume_ids[idx] not in pre_vacancy_similarities:
pre_vacancy_similarities[resume_ids[idx]] = similarities
pre_vacancy_ids[resume_ids[idx]] = ids
else:
pre_vacancy_similarities[resume_ids[idx]] = pre_vacancy_similarities[resume_ids[idx]] + similarities
pre_vacancy_ids[resume_ids[idx]] = pre_vacancy_ids[resume_ids[idx]] + ids
finally:
lock.release()
return len(vacancies), pre_vacancy_similarities, pre_vacancy_ids
tp_res = []
tpool = Pool(3)
def iterate_ids(start):
cnt = 500
rcursor = r.scan(cursor=start, count=cnt)
vacancies = {}
for vac_id in rcursor[1]:
vacancies[vac_id] = r.hgetall(vac_id)
tres = tpool.apply_async(process_vacancy_ids, (vacancies,))
tp_res.append(tres)
while (rcursor[0] != 0):
rcursor = r.scan(cursor=rcursor[0], count=cnt)
vacancies = {}
for vac_id in rcursor[1]:
vacancies[vac_id] = r.hgetall(vac_id)
tres = tpool.apply_async(process_vacancy_ids, (vacancies,))
tp_res.append(tres)
iterate_ids(0)
c = 0
pre_vacancy_similarities = {}
pre_vacancy_ids = {}
for tr in tp_res:
cnt, p_vacancy_similarities, p_vacancy_ids = tr.get()
for resume_id in p_vacancy_similarities.keys():
if resume_id not in pre_vacancy_similarities:
pre_vacancy_similarities[resume_id] = p_vacancy_similarities[resume_id]
pre_vacancy_ids[resume_id] = p_vacancy_ids[resume_id]
else:
pre_vacancy_similarities[resume_id] = pre_vacancy_similarities[resume_id]+p_vacancy_similarities[resume_id]
pre_vacancy_ids[resume_id] = pre_vacancy_ids[resume_id]+p_vacancy_ids[resume_id]
c = c+cnt
print 'processed {}'.format(c)
def finalize_recommendations(resume_id):
result = []
similarities = pre_vacancy_similarities[resume_id]
ids = pre_vacancy_ids[resume_id]
max_similarities = heapq.nlargest(20, range(len(numpy.asarray(similarities))), numpy.asarray(similarities).take)
db = MySQLdb.connect(host="127.0.0.1",
port=config.getint('mysqld', 'port'),
user=config.get('mysqld', 'user'),
passwd=config.get('mysqld', 'password'),
db=config.get('mysqld', 'database') )
db.autocommit(True)
db.set_character_set('utf8')
cursor = db.cursor()
cursor.execute('SET NAMES utf8;')
cursor.execute('SET CHARACTER SET utf8;')
cursor.execute('SET character_set_connection=utf8;')
cursor.close()
cursor = db.cursor()
try:
cursor.execute("""UPDATE recommendations SET is_active=0 WHERE resume_id='{}'""".format(resume_id))
except BaseException as ex:
print ex
finally:
cursor.close()
for ind in max_similarities:
conn = httplib.HTTPSConnection("api.hh.ru")
conn.request("GET", "https://api.hh.ru/vacancies/{}".format(ids[ind]), headers=headers)
r1 = conn.getresponse()
if r1.status != 200:
conn.close()
conn = httplib.HTTPSConnection("api.hh.ru")
conn.request("GET", "https://api.hh.ru/vacancies/{}".format(ids[ind]), headers=headers)
r1 = conn.getresponse()
t_vacancy = r1.read()
conn.close()
t_vacancy_json = json.loads(t_vacancy)
try:
title = t_vacancy_json['name'].encode('utf-8').strip()
except KeyError as ex:
print ex
title = 'Title temporary not found'
cursor = db.cursor()
try:
cursor.execute("""
INSERT INTO recommendations (resume_id, vacancy_id, updated, is_active, similarity, vacancy_title)
VALUES ('{}', {}, now(), 1, {}, '{}')
""".format(resume_id, ids[ind], similarities[ind], title))
except BaseException as err:
print err
finally:
cursor.close()
result.append('{}. for {} similarity is {}'.format(resume_id, ids[ind], similarities[ind]))
db.close()
return result
p_res = []
pool = Pool(7)
for resume_id in pre_vacancy_similarities.keys():
res = pool.apply_async(finalize_recommendations, (resume_id,))
p_res.append(res)
for t in p_res:
res = t.get()
for s in res:
print s
print 'total time {} sec\n'.format(time.time()-start_time)