Merge branch 'YDL-REC-XGB' into 'master'

增加基于树模型XGBoost排序

See merge request chaipengfei/ydl_ai_recommender!1

environment.yaml

@@ -10,3 +10,5 @@ dependencies:
   - pip
   - pip:
     - -r requirements.txt
+    - -i http://pypi.mirrors.ustc.edu.cn/simple/
+    - --trusted-host pypi.mirrors.ustc.edu.cn

model_data/bak/doctor_encoder_json_data.json

+{"region_encoder": {"\u6c5f\u897f\u7701": 17, "\u5c71\u897f\u7701": 12, "\u6d59\u6c5f\u7701": 20, "\u5b89\u5fbd\u7701": 10, "\u5c71\u4e1c\u7701": 11, "\u5e7f\u4e1c\u7701": 13, "\u8fbd\u5b81\u7701": 29, "\u4e0a\u6d77\u5e02": 0, "\u56db\u5ddd\u7701": 7, "\u5409\u6797\u7701": 6, "\u7518\u8083\u7701": 25, "\u5317\u4eac\u5e02": 4, "\u9655\u897f\u7701": 31, "\u6e56\u5317\u7701": 22, "\u6e56\u5357\u7701": 23, "\u6c5f\u82cf\u7701": 16, "\u6cb3\u5357\u7701": 19, "\u9ed1\u9f99\u6c5f\u7701": 34, "\u5929\u6d25\u5e02": 8, "\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a": 14, "NaN": 35, "\u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a": 15, "\u6d77\u5357\u7701": 21, "\u6cb3\u5317\u7701": 18, "\u798f\u5efa\u7701": 26, "\u91cd\u5e86\u5e02": 30, "\u5185\u8499\u53e4\u81ea\u6cbb\u533a": 3, "\u9752\u6d77\u7701": 32, "\u8d35\u5dde\u7701": 28, "\u4e91\u5357\u7701": 2, "\u4e0d\u660e": 1, "\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a": 9, "\u53f0\u6e7e\u7701": 5, "\u897f\u85cf\u81ea\u6cbb\u533a": 27, 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\ No newline at end of file

model_data/bak/user_encoder_json_data.json

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model_data/doctor_encoder_json_data.json

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model_data/user_encoder_json_data.json

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"620800": 290, "620700": 289, "230400": 60, "610200": 276, "SAADL00": 366, "650800": 309, "MEXMEX00": 349, "150800": 33, "421300": 176, "JPN1300": 343, "651700": 317, "46A2700": 237, "FRAPAR00": 334, "350800": 118, "469005": 234, "620400": 286, "621200": 294, "TXAUS00": 372, "429004": 177, "PAPHL00": 359, "630500": 299, "TH-100000": 370, "650600": 307, "VNMHI00": 375, "NVLAS00": 355, "621100": 293, "630400": 298, "KAZAKS00": 345, "THA1000": 371, "SCTARD00": 367, "ENGLND00": 331, "TXDAL00": 373, "NZLAUK00": 357, "42A2100": 180, "651600": 316, "46A3400": 240, "MABZD00": 348, "CANLOD00": 323, "429005": 178, "PHLMNL00": 361, "ITAROM00": 340, "CALAX00": 322, "JK0000": 342, "650400": 305, "46A3100": 238, "IDMJL00": 336, "SCTEDH00": 368, "ILCHI00": 337, "JPN2700": 344, "OR0000": 358, "WA0000": 376, "CANVAN00": 328, "QLDBNE00": 363, "MNG6100": 350, "NLDAMS00": 352, "46A3300": 239, "CHLRM00": 329, "ESPMAD00": 333, "810100": 318, "810300": 319, "46A2500": 236, "260000": 69, "ITAMIL00": 339, "QLDTWB00": 364, "WAPER00": 377, "SPE0000": 369, "RUSMOW00": 365, "ENGTRU00": 332, "46A3500": 241, "49SEO00": 243, "46A3600": 242, "CANOTT00": 325, "NSHF00": 353, "AZPHX00": 321, "MOSTL00": 351, "IRLDB00": 338}, "cate_id_1_encoder": {"0": 0, "452": 7, "26": 5, "23": 3, "25": 4, "22": 2, "699": 8, "1": 1, "27": 6}, "cate_id_2_encoder": {"0": 0, "25": 4, "452": 7, "699": 8, "26": 5, "1": 1, "23": 3, "22": 2, "27": 6}, "cate_id_3_encoder": {"0": 0, "22": 2, "27": 6, "25": 4, "23": 3, "452": 7, "1": 1, "26": 5, "699": 8}, "cate_id_4_encoder": {"0": 0, "26": 5, "699": 8, "452": 7, "1": 1, "25": 4, "22": 2, "23": 3, "27": 6}, "cate_id_5_encoder": {"0": 0, "1": 1, "23": 3, "25": 4, "699": 8, "27": 6, "22": 2, "26": 5, "452": 7}}
\ No newline at end of file

src/core/recommender.py

@@ -5,9 +5,12 @@ import json
 import configparser
 from typing import List
 
+import xgboost as xgb
+import time
 import faiss
 import requests
 import numpy as np
+import pandas as pd
 
 from ydl_ai_recommender.src.core.indexer import (
     UserCounselorChatIndexer,
@@ -17,11 +20,24 @@ from ydl_ai_recommender.src.core.indexer import (
     CounselorCounselorCFIndexer,
 )
 from ydl_ai_recommender.src.core.profile import encode_user_profile
-from ydl_ai_recommender.src.utils import get_conf_path, get_data_path
+from ydl_ai_recommender.src.utils import get_conf_path, get_data_path, read_user_encoder_dict, read_counselors, get_project_path
 from ydl_ai_recommender.src.utils.log import create_logger
 
+logger = create_logger(__name__, 'recommender.log', is_rotating=True)
 
-class Recommender():
+
+def cost_time(desc):
+    def the_func_cost_time(func):
+        def fun(*args,**kwargs):
+            t = time.perf_counter()
+            result = func(*args, **kwargs)
+            logger.info('函数:{},{} 耗时:{} ms'.format(str(func.__name__), desc, round((time.perf_counter()-t)*1000, 2)))
+            return result
+        return fun
+    return the_func_cost_time
+
+
+class Recommender:
 
     def __init__(self) -> None:
         self.logger = create_logger(__name__, 'recommender.log')
@@ -241,6 +257,162 @@ class ItemCFRecommender(Recommender):
         return counselors
 
 
+class RecommendByXgboost(Recommender):
+    def __init__(self, top_n=5, k=20, is_use_db=True, u2c='combination', c2c=None) -> None:
+        super().__init__()
+
+        config = configparser.RawConfigParser()
+        config.read(get_conf_path())
+        self.dmp_url = config.get('DMP', 'url')
+
+        select_items = ['uid', 'ffrom_login', 'user_login_city', 'user_preference_cate']
+        self.select_fields = {k: True for k in select_items}
+        self.user_encoder_convert = read_user_encoder_dict()
+
+        self.all_counselors = read_counselors()
+
+        #self.recommender = UserCFRecommender(top_n=top_n, k=k, u2c=u2c)
+        self.params = {'n_estimators': 150, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.9,
+                       'colsample_bytree': 0.5, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1,
+                       'max_delta_step': 0,
+                       'scale_pos_weight': 1}
+        self.model = xgb.XGBClassifier(objective='binary:logistic', nthread=-1, **self.params)
+        self.model.load_model(os.path.join(get_project_path(), 'model_data/xgb_model.bin'))
+
+    @cost_time(desc='召回咨询师')
+    def recall_data(self, user_id, size=0, is_merge=True):
+        return self.recommender.recommend(user_id, size=size, is_merge=True)
+
+    @cost_time(desc='模型推荐整个流程')
+    def recommend(self, user_id, size=0, is_merge=True):
+        user_profile = self.get_user_profile(user_id)
+        if not user_profile:
+            return self._recommend_top(size)
+
+        # recommend_result = self.recall_data(user_id, size=size, is_merge=True)
+        recommend_result = None
+        data_time = time.time()
+        predit_data = self.trans_feature_data(user_id, user_profile, recommend_result)
+        doctor_ids = predit_data.pop('doctor_id')
+        doctor_ids = doctor_ids.to_numpy()
+
+        pre_time = time.time()
+        predit_result = self.model.predict_proba(predit_data)[:, 1]
+        self.logger.info('predit_time:{}ms'.format(int((time.time()-pre_time)*1000)))
+        result_dict = dict(zip(doctor_ids, predit_result))
+        result_dict = sorted(result_dict.items(), key=lambda x:x[1], reverse=True)
+        recommend_data = [{
+            'counselor': c_id,
+            'score': round(float(proba), 4),
+            'from': 'similar_users {}'.format(user_id),
+        } for (c_id, proba) in result_dict[0:50]]
+        return recommend_data
+
+    @cost_time(desc='')
+    def trans_feature_data(self, user_id, user_profile, counselor_data):
+        user_feature_data = self.trans_user_feature_data(user_id, user_profile)
+        counselor_feature_data = self.trans_counselor_feature_data(counselor_data)
+        counselor_num = len(counselor_feature_data)
+
+        user_feature_data_dataframe = pd.DataFrame([user_feature_data]*counselor_num, columns=['ffrom_login_encoder'\
+            , 'user_login_city_encoder', 'cate_id_1_encoder', 'cate_id_2_encoder', 'cate_id_3_encoder'\
+            , 'cate_id_4_encoder', 'cate_id_5_encoder'])
+
+        predit_feature_data = pd.concat([user_feature_data_dataframe, counselor_feature_data], axis=1)
+        return predit_feature_data
+
+    def trans_user_feature_data(self, user_id, user_profile):
+        if not user_profile:
+            user_profile = self.get_user_profile(user_id)
+        from_login_encoder = self.get_encoder_from_dict('ffrom_login', user_profile['ffrom_login'])
+        user_login_city_encoder = self.get_encoder_from_dict('user_login_city', user_profile['user_login_city'])
+
+        user_preference_cate = user_profile['user_preference_cate']
+        user_preference_cate_top_5_encoder = self.process_user_preference_cate(user_preference_cate)
+
+        user_feature_data = [from_login_encoder, user_login_city_encoder]
+        user_feature_data.extend(user_preference_cate_top_5_encoder)
+
+        return user_feature_data
+
+    def trans_counselor_feature_data(self, counselor_data):
+        # counselor_ids = [str(item['counselor']) for item in counselor_data]
+        # counselor_profiles = self.all_counselors[self.all_counselors['doctor_id'].isin(counselor_ids)].reset_index(drop=True)
+        # return counselor_profiles
+        return self.all_counselors
+
+    @cost_time(desc='获取用户画像')
+    def get_user_profile(self, user_id):
+        if user_id == '0':
+            return []
+
+        headers = {
+            'X-App-Id': 'plough_cloud',
+            'Content-Type': 'application/json'
+        }
+
+        payload = {
+            "filter": {
+                "uid": user_id,
+            },
+            "fields": self.select_fields,
+            "limit": 10
+        }
+
+        try:
+            get_profile_time = time.time()
+            response = requests.request('POST', self.dmp_url, headers=headers, json=payload)
+            resp = response.json()
+            return resp['data']['objects'][0]
+        except Exception as e:
+            self.logger.error('获取用户画像数据失败: %s', e, exc_info=True)
+            try:
+                self.logger.exception('response json data %s', resp)
+            except:
+                pass
+            return []
+
+    def process_user_preference_cate(self, preference_cate):
+        result = [0, 0, 0, 0, 0]
+        ids = []
+        if isinstance(preference_cate, str):
+            pref_data = json.loads(preference_cate)
+            for info in pref_data:
+                ids.append(info['cate_id'])
+            ids = ids[0:min(5, len(ids))]
+        for ind, val in enumerate(ids):
+            result[ind] = val
+        encoder_result = []
+        for ind, val in enumerate(result):
+            value_convert_dict = self.user_encoder_convert.get('cate_id_{}_encoder'.format(ind+1))
+            if value_convert_dict is not None:
+                encoder_result.append(value_convert_dict.get(val, 0))
+        if len(encoder_result)<5:
+            encoder_result.extend([0]*(5-len(encoder_result)))
+        return encoder_result
+
+    def get_encoder_from_dict(self, feature_name, feature_value):
+        value_convert_dict = self.user_encoder_convert.get('{}_encoder'.format(feature_name))
+        if value_convert_dict is None:
+            return 0
+        return value_convert_dict.get(str(feature_value), 0)
+
+
 if __name__ == '__main__':
-    recommender = UserCFRecommender()
-    print(recommender.recommend('12047'))
+    # s_time = time.time()
+    # recommender1 = UserCFRecommender()
\ No newline at end of file
+    # recommender1.recommend('30004410')
+    # print('all cost time: {}'.format(time.time() - s_time), recommender1.recommend('12047'))
+
+    print()
+    print()
+    s_time = time.time()
+    recommender = RecommendByXgboost()
+    recommender.recommend('3251227')
+    print('all cost time: '.format(time.time()-s_time), recommender.recommend('12047'))
+
+    # print()
+    # print()
+    # s_time = time.time()
+    # recommender.recommend('37298')
+    # print('all cost time: '.format(time.time() - s_time), recommender.recommend('12047'))
\ No newline at end of file

src/service/recommend_service.py

 # -*- coding: utf-8 -*-
 
 import json
+import time
+
 from concurrent.futures import ThreadPoolExecutor
 
 import tornado.web
@@ -11,11 +13,12 @@ from tornado.concurrent import run_on_executor
 
 from ydl_ai_recommender.src.utils.log import create_logger
 from ydl_ai_recommender.src.core.recommender import UserCFRecommender
+from ydl_ai_recommender.src.core.recommender import RecommendByXgboost
 
 
 logger = create_logger(__name__, 'service.log', is_rotating=True)
 recommender = UserCFRecommender(top_n=2, k=50, u2c='order')
-
+recommenderByXgb = RecommendByXgboost()
 
 class RecommendHandler(tornado.web.RequestHandler):
     executor = ThreadPoolExecutor(1)
@@ -73,13 +76,71 @@ class RecommendHandler(tornado.web.RequestHandler):
         logger.info('response@@uid=%s@@ret=%s', uid, ret_str)
         return ret
 
+class RecommendXgbHandler(tornado.web.RequestHandler):
+    executor = ThreadPoolExecutor(1)
+
+    @tornado.gen.coroutine
+    def get(self):
+        uid = self.get_argument('uid', None)
+        if uid is None:
+            logger.warn('请求参数不正确，无uid')
+
+        size = self.get_argument('size', 100)
+        try:
+            size = int(size)
+        except Exception as e:
+            logger.warn('size=%s 不是数字', size)
+            size = 100
+
+        ret = yield self.run(uid, size)
+        self.write(ret)
+
+
+    @tornado.gen.coroutine
+    def post(self):
+        param = json.loads(self.request.body.decode('utf-8'))
+        uid = param.get('uid', None)
+        size = param.get('size', 100)
+        if uid is None:
+            logger.warn('请求参数不正确，无uid')
+
+        ret = yield self.run(uid, size)
+        self.write(ret)
+
+
+    @run_on_executor
+    def run(self, uid, size=100):
+        logger.info('request@@uid=%s@@size=%s', uid, size)
+        try:
+            start_time = time.time()
+            recommend_result = recommenderByXgb.recommend(uid, size=size, is_merge=True)
+            logger.info('request@@uid=%s@@size=%s, cost %s ms', uid, size, (time.time()-start_time)*1000)
+            ret = {
+                'status': 'success',
+                'code': 0,
+                'data': recommend_result,
+                'total_count': len(recommend_result),
+            }
+        except Exception as e:
+            logger.error('执行推荐函数报错', exc_info=True)
+            ret = {
+                'status': 'error',
+                'code': 1,
+                'data': [],
+                'total_count': 0,
+            }
+        ret_str = json.dumps(ret, ensure_ascii=False)
+        logger.info('response@@uid=%s@@ret=%s', uid, ret_str)
+        return ret
 
 if __name__ == '__main__':
 
     tornado.options.define('port', default=8868, type=int, help='服务启动的端口号')
 
     tornado.options.parse_command_line()
-    app = tornado.web.Application(handlers=[(r'/ai_counselor_recommend', RecommendHandler)], autoreload=False, debug=False)
+    app = tornado.web.Application(handlers=[(r'/ai_counselor_recommend', RecommendHandler),
+                                            (r'/ai_counselor_recommend/xgb/v1', RecommendXgbHandler)]
+                                  , autoreload=True, debug=False)
     http_server = tornado.httpserver.HTTPServer(app)
     http_server.listen(tornado.options.options.port)
     tornado.ioloop.IOLoop.instance().start()

src/utils/__init__.py

 # -*- coding: utf-8 -*-
 
 import os
+import json
+import pandas as pd
 
 
 def get_project_path():
@@ -21,4 +23,15 @@ def get_conf_path():
 
 def get_model_path():
     project_path = get_project_path()
-    return os.path.join(project_path, 'model')
+    return os.path.join(project_path, 'model')
\ No newline at end of file
+
+def read_user_encoder_dict():
+    user_encoder_json_data = None
+    project_path = get_project_path()
+    with open(os.path.join(project_path, 'model_data/user_encoder_json_data.json'), 'r') as f:
+        user_encoder_json_data = json.load(f)
+    return user_encoder_json_data
+
+def read_counselors():
+    project_path = get_project_path()
+    return pd.read_csv(os.path.join(project_path, 'model_data/doctor_profile_selected_feature.csv'), sep=',', index_col=0, dtype={'doctor_id':str})