update

rov_train.py

@@ -332,6 +332,30 @@ def predict_test():
     # 清空修改ROV的视频数据
     redis_helper.del_keys(key_name=config_.UPDATE_ROV_KEY_NAME_APP)
 
+    # ####### appType: [18, 19] 应用数据更新
+    for app_type in [config_.APP_TYPE['LAO_HAO_KAN_VIDEO'], config_.APP_TYPE['ZUI_JING_QI']]:
+        log_.info(f"app_type = {app_type}")
+        videos_temp = random.sample(filtered_videos, 300)
+        redis_data_temp = {}
+        csv_data_temp = []
+        for video_id in videos_temp:
+            score = random.uniform(0, 100)
+            redis_data_temp[video_id] = score
+            csv_data_temp.append({'video_id': video_id, 'rov_score': score})
+        # 打包预测结果存入csv
+        predict_result_filename = f'predict_{app_type}.csv'
+        pack_list_result_to_csv(filename=predict_result_filename,
+                                data=csv_data_temp,
+                                columns=['video_id', 'rov_score'],
+                                sort_columns=['rov_score'],
+                                ascending=False)
+
+        # 上传redis
+        key_name = f"{config_.RECALL_KEY_NAME_PREFIX_APP_TYPE}{app_type}.{time.strftime('%Y%m%d')}"
+        redis_helper = RedisHelper()
+        redis_helper.add_data_with_zset(key_name=key_name, data=redis_data_temp)
+        log_.info('data to redis finished!')
+
 
 def predict_18_19():
     """预测 app_type:[18, 19]"""
@@ -388,14 +412,14 @@ def predict_18_19():
 
 
 if __name__ == '__main__':
-    log_.info('rov model train start...')
-    train_start = time.time()
-    train_filename = config_.TRAIN_DATA_FILENAME
-    X, Y, videos, fea = process_data(filename=train_filename)
-    log_.info('X_shape = {}, Y_sahpe = {}'.format(X.shape, Y.shape))
-    train(X, Y, features=fea)
-    train_end = time.time()
-    log_.info('rov model train end, execute time = {}ms'.format((train_end - train_start)*1000))
+    # log_.info('rov model train start...')
+    # train_start = time.time()
+    # train_filename = config_.TRAIN_DATA_FILENAME
+    # X, Y, videos, fea = process_data(filename=train_filename)
+    # log_.info('X_shape = {}, Y_sahpe = {}'.format(X.shape, Y.shape))
+    # train(X, Y, features=fea)
+    # train_end = time.time()
+    # log_.info('rov model train end, execute time = {}ms'.format((train_end - train_start)*1000))
 
     log_.info('rov model predict start...')
     predict_start = time.time()