画像更新逻辑线均分，后衰减

eda/smart_rank/dist_update_user_portrait_service.py

@@ -41,11 +41,11 @@ def get_user_service_portrait(cl_id, all_word_tags, all_tag_tag_type, all_3tag_2
             user_df_service["tag2_type"] = user_df_service.apply(lambda x: all_tag_tag_type.get(x["tag2"]), axis=1)
             # 算分及比例
             user_df_service["tag_score"] = user_df_service.apply(
-                lambda x: compute_henqiang(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time) if x.score_type == "henqiang" else (
-                    compute_jiaoqiang(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time) if x.score_type == "jiaoqiang" else (
-                        compute_ai_scan(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time) if x.score_type == "ai_scan" else (
-                        compute_ruoyixiang(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time) if x.score_type == "ruoyixiang" else
-                        compute_validate(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time)))), axis=1)
+                lambda x: compute_henqiang(x.days_diff_now, exponential=1, action_tag_count=get_action_tag_count(user_df_service, x.time)) if x.score_type == "henqiang" else (
+                    compute_jiaoqiang(x.days_diff_now, exponential=1, action_tag_count=get_action_tag_count(user_df_service, x.time)) if x.score_type == "jiaoqiang" else (
+                        compute_ai_scan(x.days_diff_now, exponential=1, action_tag_count=get_action_tag_count(user_df_service, x.time)) if x.score_type == "ai_scan" else (
+                        compute_ruoyixiang(x.days_diff_now, exponential=1, action_tag_count=get_action_tag_count(user_df_service, x.time)) if x.score_type == "ruoyixiang" else
+                        compute_validate(x.days_diff_now, exponential=1, action_tag_count=get_action_tag_count(user_df_service, x.time))))), axis=1)
             tag_score_sum = user_df_service.groupby(by=["tag2", "tag2_type"]).agg(
                 {'tag_score': 'sum', 'cl_id': 'first', 'action': 'first'}).reset_index().sort_values(by=["tag_score"],
                                                                                                      ascending=False)

eda/smart_rank/tool.py

@@ -236,52 +236,52 @@ def get_tag2_from_tag3(tag3, all_3tag_2tag, user_log_df_tag2_list):
         print(e)
 
 
-def compute_henqiang(x, decay_days=30, exponential=0):
+def compute_henqiang(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 15 - 1.1**alpha * ((15-0.5)/decay_days)
+        score = 15/action_tag_count - 1.1**alpha * ((15-0.5)/decay_days)
     else:
-        score = 15 - x * ((15-0.5)/decay_days)
+        score = 15/action_tag_count - x * ((15-0.5)/decay_days)
     if score > 0.5:
         return score
     else:
         return 0.5
-def compute_jiaoqiang(x, decay_days=30, exponential=0):
+def compute_jiaoqiang(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 12 - 1.1**alpha * ((12-0.5)/decay_days)
+        score = 12/action_tag_count - 1.1**alpha * ((12-0.5)/decay_days)
     else:
-        score = 12 - x * ((12-0.5)/decay_days)
+        score = 12/action_tag_count - x * ((12-0.5)/decay_days)
     if score > 0.5:
         return score
     else:
         return 0.5
-def compute_ruoyixiang(x, decay_days=30, exponential=0):
+def compute_ruoyixiang(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 5 - 1.1**alpha * ((5-0.5)/decay_days)
+        score = 5/action_tag_count - 1.1**alpha * ((5-0.5)/decay_days)
     else:
-        score = 5 - x * ((5-0.5)/decay_days)
+        score = 5/action_tag_count - x * ((5-0.5)/decay_days)
     if score > 0.5:
         return score
     else:
         return 0.5
-def compute_validate(x, decay_days=30, exponential=0):
+def compute_validate(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 10 - 1.1**alpha * ((10-0.5)/decay_days)
+        score = 10/action_tag_count - 1.1**alpha * ((10-0.5)/decay_days)
     else:
-        score = 10 - x * ((10-0.5)/decay_days)
+        score = 10/action_tag_count - x * ((10-0.5)/decay_days)
     if score > 0.5:
         return score
     else:
         return 0.5
-def compute_ai_scan(x, decay_days=30, exponential=0):
+def compute_ai_scan(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 2 - 1.1**alpha * ((2-0.5)/decay_days)
+        score = 2/action_tag_count - 1.1**alpha * ((2-0.5)/decay_days)
     else:
-        score = 2 - x * ((2-0.5)/decay_days)
+        score = 2/action_tag_count - x * ((2-0.5)/decay_days)
     if score > 0.5:
         return score
     else: