新增咨询师->咨询师索引关系

bin/test.py

@@ -76,7 +76,7 @@ def evaluation(result_detail):
             for top_n, counselor in enumerate(rd['recall_counselors']):
                 if counselor['counselor'] == rd['supplier_id']:
                     from_info = counselor['from'].split(' ')
-                    if from_info[0] == 'top_100':
+                    if from_info[0] == 'default':
                         metrics['default_recall_cnt'] += 1
                     else:
                         from_id = from_info[1]
@@ -122,6 +122,8 @@ def evaluation(result_detail):
     for i, n in enumerate(top_n_list):
         logger.info('top {:<2} 的召回数 {:<4} 召回率 {:.2%}'.format(n, metrics['top_n_recall_cnt'][i], _sd(metrics['top_n_recall_cnt'][i], metrics['all_test_cnt'])))
 
+    return metrics
+
 
 def do_test(args):
     user_profile_dict = load_local_user_profile()
@@ -147,7 +149,7 @@ def do_test(args):
             continue
 
         is_merge = args.mode == 0
-        size = 10 if args.mode == 0 else 0
+        size = 100 if args.mode == 0 else 0
         recommend_result = recommender.recommend_with_profile(profile, size=size, is_merge=is_merge)
         recall_resons = []
         for rr in recommend_result:
@@ -216,6 +218,104 @@ def update_test_data(args):
     logger.info('测试数据更新完成')
     
 
+def batch_test(args):
+    user_profile_dict = load_local_user_profile()
+
+    try:
+        old_users, test_orders = load_test_data()
+    except Exception as e:
+        logger.error('测试数据加载出错，请确认测试数据已经下载到本地，或启动命令中增加参数 "--do_update_test_data"', exc_info=True)
+        return
+
+    def _test(case):
+        logger.info('##' * 20 + ' 测试case ' + '##' * 20)
+        logger.info(json.dumps(case, ensure_ascii=False))
+        logger.info('##' * 20 + ' 测试case ' + '##' * 20)
+
+        recommender = UserCFRecommender(**case)
+
+        result_detail = []
+        for index, order_info in test_orders.iterrows():
+            if args.max_test > 0:
+                if index >= args.max_test:
+                    break
+
+            uid = order_info['uid']
+            profile = user_profile_dict.get(uid)
+            if profile is None:
+                continue
+
+            is_merge = args.mode == 0
+            size = 100 if args.mode == 0 else 0
+            recommend_result = recommender.recommend_with_profile(profile, size=size, is_merge=is_merge)
+            recall_resons = []
+            for rr in recommend_result:
+                if rr['counselor'] == order_info['supplier_id']:
+                    recall_resons.append(rr['from'])
+
+            result_detail.append({
+                'uid': uid,
+                'supplier_id': order_info['supplier_id'],
+                'is_old_user': uid in old_users,
+                'recall_counselors': recommend_result,
+                'is_recall': len(recall_resons) > 0,
+                'recall_reason': '|'.join(recall_resons),
+                'update_time': order_info['update_time'],
+            })
+        return result_detail
+
+
+    def _evaluation(result_detail):
+        metrics = evaluation(result_detail)
+        metrics['all_recall_ratio'] = _sd(metrics['all_recall_cnt'], metrics['all_test_cnt'])
+        metrics['old_user_recall_ratio'] = _sd(metrics['old_user_recall_cnt'], metrics['old_user_test_cnt'])
+        metrics['new_user_recall_ratio'] = _sd(metrics['new_user_recall_cnt'], metrics['new_user_test_cnt'])
+        metrics['top1'] = _sd(metrics['top_n_recall_cnt'][0], metrics['all_test_cnt'])
+        metrics['top10'] = _sd(metrics['top_n_recall_cnt'][3], metrics['all_test_cnt'])
+        metrics['top50'] = _sd(metrics['top_n_recall_cnt'][5], metrics['all_test_cnt'])
+        return metrics
+
+
+    test_cases = [
+        {'top_n': 2, 'k': 50, 'u2c': 'combination', 'c2c': None, 'is_use_db': False},
+        {'top_n': 5, 'k': 20, 'u2c': 'combination', 'c2c': None, 'is_use_db': False},
+        {'top_n': 10, 'k': 10, 'u2c': 'combination', 'c2c': None, 'is_use_db': False},
+        {'top_n': 20, 'k': 5, 'u2c': 'combination', 'c2c': None, 'is_use_db': False},
+        {'top_n': 2, 'k': 50, 'u2c': 'chat', 'c2c': None, 'is_use_db': False},
+        {'top_n': 5, 'k': 20, 'u2c': 'chat', 'c2c': None, 'is_use_db': False},
+        {'top_n': 10, 'k': 10, 'u2c': 'chat', 'c2c': None, 'is_use_db': False},
+        {'top_n': 20, 'k': 5, 'u2c': 'chat', 'c2c': None, 'is_use_db': False},
+        {'top_n': 2, 'k': 50, 'u2c': 'order', 'c2c': None, 'is_use_db': False},
+        {'top_n': 5, 'k': 20, 'u2c': 'order', 'c2c': None, 'is_use_db': False},
+        {'top_n': 10, 'k': 10, 'u2c': 'order', 'c2c': None, 'is_use_db': False},
+        {'top_n': 20, 'k': 5, 'u2c': 'order', 'c2c': None, 'is_use_db': False},
+        {'top_n': 2, 'k': 50, 'u2c': 'combination', 'c2c': True, 'is_use_db': False},
+        {'top_n': 5, 'k': 20, 'u2c': 'combination', 'c2c': True, 'is_use_db': False},
+        {'top_n': 10, 'k': 10, 'u2c': 'combination', 'c2c': True, 'is_use_db': False},
+        {'top_n': 20, 'k': 5, 'u2c': 'combination', 'c2c': True, 'is_use_db': False},
+        {'top_n': 2, 'k': 50, 'u2c': 'chat', 'c2c': True, 'is_use_db': False},
+        {'top_n': 5, 'k': 20, 'u2c': 'chat', 'c2c': True, 'is_use_db': False},
+        {'top_n': 10, 'k': 10, 'u2c': 'chat', 'c2c': True, 'is_use_db': False},
+        {'top_n': 20, 'k': 5, 'u2c': 'chat', 'c2c': True, 'is_use_db': False},
+        {'top_n': 2, 'k': 50, 'u2c': 'order', 'c2c': True, 'is_use_db': False},
+        {'top_n': 5, 'k': 20, 'u2c': 'order', 'c2c': True, 'is_use_db': False},
+        {'top_n': 10, 'k': 10, 'u2c': 'order', 'c2c': True, 'is_use_db': False},
+        {'top_n': 20, 'k': 5, 'u2c': 'order', 'c2c': True, 'is_use_db': False},
+    ]
+
+    all_test_result = []
+    for case in test_cases:
+        result_detail = _test(case)
+        metrics = _evaluation(result_detail)
+        all_test_result.append((case, metrics))
+
+    with open('batch_test_result.tsv', 'w', encoding='utf-8') as f:
+        for (case, metrics) in all_test_result:
+            items = [case['k'], case['top_n'], case['u2c'], case['c2c']]
+            items.extend([metrics['all_recall_ratio'], metrics['old_user_recall_ratio'], metrics['new_user_recall_ratio']])
+            items.extend([metrics['top1'], metrics['top10'], metrics['top50']])
+            f.write('\t'.join(map(lambda x: str(x), items)) + '\n')
+
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
@@ -227,6 +327,8 @@ if __name__ == '__main__':
     parser.add_argument('--save_test_result', default=False, action='store_true', help='保存测试详情结果')
     parser.add_argument('--show_result_by_day', default=False, action='store_true', help='测试结果是否按天展示')
     
+    parser.add_argument('--do_batch_test', default=False, action='store_true', help='批量测试')
+    
     parser.add_argument('--do_update_test_data', default=False, action='store_true', help='是否更新测试数据')
     parser.add_argument('--start_date', default='-1', type=str, help='测试订单创建的开始时间，可以是"%Y-%m-%d"格式，也可以是 -3 表示前3天')
 
@@ -238,5 +340,9 @@ if __name__ == '__main__':
         logger.info('测试数据创建时间 %s', args.start_date)
         update_test_data(args)
 
+    if args.do_batch_test:
+        logger.info('执行批量测试')
+        batch_test(args)
+    else:
         logger.info('开始执行测试任务，测试模式为 %s', args.mode)
         do_test(args)
\ No newline at end of file

bin/update.py

@@ -91,10 +91,10 @@ if __name__ == '__main__':
 
     if args.task == 'make_index':
         indexers = [
-            ['[用户->咨询师]兜底关系索引', UserCounselorDefaultIndexer()],
-            ['基于订单数据的[用户->咨询师]关系索引', UserCounselorOrderIndexer()],
-            ['基于询单数据的[用户->咨询师]关系索引', UserCounselorChatIndexer()],
-            ['基于多种数据组合的[用户->咨询师]关系索引', UserCounselorCombinationIndexer()],
+            ['[用户->咨询师]兜底关系索引', UserCounselorDefaultIndexer(logger=logger)],
+            ['基于订单数据的[用户->咨询师]关系索引', UserCounselorOrderIndexer(logger=logger)],
+            ['基于询单数据的[用户->咨询师]关系索引', UserCounselorChatIndexer(logger=logger)],
+            ['基于多种数据组合的[用户->咨询师]关系索引', UserCounselorCombinationIndexer(0.8, 0.2, logger=logger)],
         ]
 
         logger.info('')

src/core/recommender.py

@@ -7,8 +7,13 @@ from typing import List, Dict
 import faiss
 import numpy as np
 
-from ydl_ai_recommender.src.core.indexer import UserCounselorDefaultIndexer
-from ydl_ai_recommender.src.core.indexer import UserCounselorCombinationIndexer
+from ydl_ai_recommender.src.core.indexer import (
+    UserCounselorChatIndexer,
+    UserCounselorOrderIndexer,
+    UserCounselorDefaultIndexer,
+    UserCounselorCombinationIndexer,
+    CounselorCounselorCFIndexer,
+)
 from ydl_ai_recommender.src.core.profile import encode_profile
 from ydl_ai_recommender.src.data.mysql_client import MySQLClient
 from ydl_ai_recommender.src.utils import get_conf_path, get_data_path
@@ -26,7 +31,15 @@ class Recommender():
 
 class UserCFRecommender(Recommender):
 
-    def __init__(self, top_n=5, k=20, is_use_db=True) -> None:
+    def __init__(self, top_n=5, k=20, is_use_db=True, u2c='combination', c2c=None) -> None:
+        """
+        params:
+            top_n: 每个召回的用户获取的相关咨询师个数
+            k: 召回的相似用户数
+            is_use_db: 是否使用数据库
+            u2c: [用户->咨询师] 索引方法
+            c2c: [咨询师->咨询师] 索引方法，None 表示不使用咨询师拓展
+        """
         super().__init__()
         # 召回 top_n 个相似用户
         self.top_n = top_n
@@ -41,9 +54,21 @@ class UserCFRecommender(Recommender):
 
         self.default_indexer = UserCounselorDefaultIndexer(self.logger)
         self.default_indexer.load_index_data()
+
+        if u2c == 'chat':
+            self.indexer = UserCounselorChatIndexer(self.logger)
+        elif u2c == 'order':
+            self.indexer = UserCounselorOrderIndexer(self.logger)
+        else:
             self.indexer = UserCounselorCombinationIndexer(self.logger)
         self.indexer.load_index_data()
 
+        if c2c:
+            self.c2c_indexer = CounselorCounselorCFIndexer(self.logger)
+            self.c2c_indexer.load_index_data()
+        else:
+            self.c2c_indexer = None
+
         self.local_file_dir = get_data_path()
         self.load_data()
 
@@ -100,7 +125,18 @@ class UserCFRecommender(Recommender):
                 'score': score / max(0.01, float(simi_score)),
                 'from': 'similar_users {}'.format(similar_user_id),
             } for (c_id, score) in similar_user_counselor]
-            counselors.extend(recommend_data)
+
+            supplement_data = []
+            if self.c2c_indexer:
+                for ro in recommend_data:
+                    supplement_data.extend([{
+                        'counselor': sc_id,
+                        'score': ro['score'] * score,
+                        'from': '{} supplement {}'.format(ro['from'], ro['counselor']),
+                    } for (sc_id, score) in self.c2c_indexer.index(ro['counselor'], count=int(self.top_n))])
+                    # } for (sc_id, score) in self.c2c_indexer.index(ro['counselor'], count=int(self.top_n / len(recommend_data)))])
+
+            counselors.extend(recommend_data + supplement_data)
 
         counselors.sort(key=lambda x: x['score'], reverse=True)
         return counselors

src/service/recommend_service.py

@@ -14,7 +14,7 @@ from ydl_ai_recommender.src.core.recommender import UserCFRecommender
 
 
 logger = create_logger(__name__, 'service.log', is_rotating=True)
-recommender = UserCFRecommender(top_n=5, k=5)
+recommender = UserCFRecommender(top_n=5, k=20, c2c=True)
 
 
 class RecommendHandler(tornado.web.RequestHandler):