增加分数指数衰减的函数

eda/smart_rank/tool.py

@@ -174,33 +174,53 @@ def get_tag2_from_tag3(tag3, all_3tag_2tag, user_log_df_tag2_list):
         print(e)
 
 
-def compute_henqiang(x):
-    score = 15-x*((15-0.5)/180)
-    if score>0.5:
+def compute_henqiang(x, decay_days=180, normalization_size=7, exponential=1):
+    if exponential:
+        alpha = exponential_decay(x, decay_days, normalization_size)
+        score = 15 - 2**alpha * ((15-0.5)/decay_days)
+    else:
+        score = 15 - x * ((15-0.5)/decay_days)
+    if score > 0.5:
         return score
     else:
         return 0.5
-def compute_jiaoqiang(x):
-    score = 12-x*(12/180)
-    if score>0.5:
+def compute_jiaoqiang(x, decay_days=180, normalization_size=7, exponential=1):
+    if exponential:
+        alpha = exponential_decay(x, decay_days, normalization_size)
+        score = 12 - 2**alpha * ((12-0.5)/decay_days)
+    else:
+        score = 12 - x * ((12-0.5)/decay_days)
+    if score > 0.5:
         return score
     else:
         return 0.5
-def compute_ruoyixiang(x):
-    score = 5-x*((5-0.5)/180)
-    if score>0.5:
+def compute_ruoyixiang(x, decay_days=180, normalization_size=7, exponential=1):
+    if exponential:
+        alpha = exponential_decay(x, decay_days, normalization_size)
+        score = 5 - 2**alpha * ((5-0.5)/decay_days)
+    else:
+        score = 5 - x * ((5-0.5)/decay_days)
+    if score > 0.5:
         return score
     else:
         return 0.5
-def compute_validate(x):
-    score = 10-x*((10-0.5)/180)
-    if score>0.5:
+def compute_validate(x, decay_days=180, normalization_size=7, exponential=1):
+    if exponential:
+        alpha = exponential_decay(x, decay_days, normalization_size)
+        score = 10 - 2**alpha * ((10-0.5)/decay_days)
+    else:
+        score = 10 - x * ((10-0.5)/decay_days)
+    if score > 0.5:
         return score
     else:
         return 0.5
-def compute_ai_scan(x):
-    score = 2 - x * ((2 - 0.5) / 180)
-    if score>0.5:
+def compute_ai_scan(x, decay_days=180, normalization_size=7, exponential=1):
+    if exponential:
+        alpha = exponential_decay(x, decay_days, normalization_size)
+        score = 2 - 2**alpha * ((2-0.5)/decay_days)
+    else:
+        score = 2 - x * ((2-0.5)/decay_days)
+    if score > 0.5:
         return score
     else:
         return 0.5
@@ -212,3 +232,10 @@ def get_action_tag_count(df, action_time):
             return 1
     except Exception as e:
         print(e)
+
+
+def exponential_decay(days_diff, decay_days=180, normalization_size=7):
+    x = np.arange(1, decay_days+1, 1)
+    # 天数差归一化到[0, normalization_size]
+    a = (normalization_size - 0) * (days_diff - min(x)) / (max(x) - min(x))
+    return a