update

eda/smart_rank/tool.py

@@ -235,50 +235,50 @@ def get_tag2_from_tag3(tag3, all_3tag_2tag, user_log_df_tag2_list):
         print(e)
 
 
-def compute_henqiang(x, decay_days=30, normalization_size=7, exponential=0):
+def compute_henqiang(x, decay_days=30, exponential=0):
     if exponential:
-        alpha = exponential_decay(x, decay_days, normalization_size)
-        score = 15 - 2**alpha * ((15-0.5)/decay_days)
+        alpha = exponential_decay(x, decay_days)
+        score = 15 - 1.1**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, decay_days=30, normalization_size=7, exponential=0):
+def compute_jiaoqiang(x, decay_days=30, exponential=0):
     if exponential:
-        alpha = exponential_decay(x, decay_days, normalization_size)
-        score = 12 - 2**alpha * ((12-0.5)/decay_days)
+        alpha = exponential_decay(x, decay_days)
+        score = 12 - 1.1**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, decay_days=30, normalization_size=7, exponential=0):
+def compute_ruoyixiang(x, decay_days=30, exponential=0):
     if exponential:
-        alpha = exponential_decay(x, decay_days, normalization_size)
-        score = 5 - 2**alpha * ((5-0.5)/decay_days)
+        alpha = exponential_decay(x, decay_days)
+        score = 5 - 1.1**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, decay_days=30, normalization_size=7, exponential=0):
+def compute_validate(x, decay_days=30, exponential=0):
     if exponential:
-        alpha = exponential_decay(x, decay_days, normalization_size)
-        score = 10 - 2**alpha * ((10-0.5)/decay_days)
+        alpha = exponential_decay(x, decay_days)
+        score = 10 - 1.1**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, decay_days=30, normalization_size=7, exponential=0):
+def compute_ai_scan(x, decay_days=30, exponential=0):
     if exponential:
-        alpha = exponential_decay(x, decay_days, normalization_size)
-        score = 2 - 2**alpha * ((2-0.5)/decay_days)
+        alpha = exponential_decay(x, decay_days)
+        score = 2 - 1.1**alpha * ((2-0.5)/decay_days)
     else:
         score = 2 - x * ((2-0.5)/decay_days)
     if score > 0.5:
@@ -295,10 +295,10 @@ def get_action_tag_count(df, action_time):
         print(e)
 
 
-def exponential_decay(days_diff, decay_days=30, 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))
+def exponential_decay(days_diff, decay_days=30):
+    # 天数差归一化到[0, decay_days]
+    x = np.arange(1, 180+1, 1)
+    a = (decay_days - 0) * (days_diff - min(x)) / (max(x) - min(x))
     return a