From df557c6f6ca0e4208fc5536dc4d0f8658bb6fb9a Mon Sep 17 00:00:00 2001
From: P3ngSaM <61768364+P3ngSaM@users.noreply.github.com>
Date: Wed, 9 Feb 2022 16:07:12 +0800
Subject: [PATCH] =?UTF-8?q?update=20=E4=BF=AE=E6=94=B9=E6=89=93=E5=88=86?=
 =?UTF-8?q?=E9=80=BB=E8=BE=91?=
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---
 Rating/scripts/financial_score.py | 82 ++++++-------------------------
 1 file changed, 16 insertions(+), 66 deletions(-)

diff --git a/Rating/scripts/financial_score.py b/Rating/scripts/financial_score.py
index 9353882..7edf239 100644
--- a/Rating/scripts/financial_score.py
+++ b/Rating/scripts/financial_score.py
@@ -33,41 +33,34 @@ def financial_score(param1, param2):
         else:
             refer = list(map(float, refers[key]))
             weight = weights[key]
-            # 判断打分是正相关还是反相关
+            standard = [weight, weight*0.8, weight*0.6, weight*0.4, weight*0.2]
+            # 判断正反相关
             if refer[0] > refer[1]:
-                # 正相关判断
                 if value >= refer[0]:
                     score = weight
                 elif value > refer[1]:
-                    standard_score = round(weight * 1, 2)
-                    score = linear_correlation_type_positive(value, standard_score, refer[0])
+                    score = linear_correlation_type(value, standard[0], standard[1], refer[0], refer[1])
                 elif value > refer[2]:
-                    standard_score = round(weight * 0.8, 2)
-                    score = linear_correlation_type_positive(value, standard_score, refer[1])
+                    score = linear_correlation_type(value, standard[1], standard[2], refer[1], refer[2])
                 elif value > refer[3]:
-                    standard_score = round(weight * 0.5, 2)
-                    score = linear_correlation_type_positive(value, standard_score, refer[2])
+                    score = linear_correlation_type(value, standard[2], standard[3], refer[2], refer[3])
                 elif value > refer[4]:
-                    standard_score = round(weight * 0.25, 2)
-                    score = linear_correlation_type_positive(value, standard_score, refer[3])
+                    score = linear_correlation_type(value, standard[3], standard[4], refer[3], refer[4])
                 else:
                     score = 0
             else:
                 if value <= refer[0]:
                     score = weight
                 elif value < refer[1]:
-                    standard_score = round(weight * 1, 2)
-                    score = linear_correlation_type_anti(value, standard_score, refer[0])
+                    score = linear_correlation_type(value, standard[0], standard[1], refer[0], refer[1])
                 elif value < refer[2]:
-                    standard_score = round(weight * 0.8, 2)
-                    score = linear_correlation_type_anti(value, standard_score, refer[1])
+                    score = linear_correlation_type(value, standard[1], standard[2], refer[1], refer[2])
                 elif value < refer[3]:
-                    standard_score = round(weight * 0.5, 2)
-                    score = linear_correlation_type_anti(value, standard_score, refer[2])
+                    score = linear_correlation_type(value, standard[2], standard[3], refer[2], refer[3])
                 elif value < refer[4]:
-                    standard_score = round(weight * 0.25, 2)
-                    score = linear_correlation_type_anti(value, standard_score, refer[3])
-
+                    score = linear_correlation_type(value, standard[3], standard[4], refer[3], refer[4])
+                else:
+                    score = 0
             scores[key] = round(score, 2)
 
     result = dict()
@@ -93,63 +86,20 @@ def financial_score(param1, param2):
     return result
 
 
-def linear_correlation_type(value, standard_score, refer1, refer2):
+def linear_correlation_type(value, standard1, standard2, refer1, refer2):
     """
     线性相关类型 正相关/反相关
     Parameters
         value float 指标值
-        standard_score float 标准分
+        standard1 float 标准分1
+        standard2 float 标准分2
         refer1 阈值1
         refer2 阈值2
     Returns
         score float 得分
     """
     # main
-    # 正相关打分
-    if refer1 > refer2:
-        if value <= refer2:
-            score = 0
-        elif value >= refer1:
-            score = standard_score
-        else:
-            score = standard_score * (1 - (refer1 - value) / (refer1 - refer2))
-    # 反相关打分
-    else:
-        if value <= refer2:
-            score = standard_score
-        elif value >= refer1:
-            score = 0
-        else:
-            score = standard_score * (1 - (value - refer2) / (refer1 - refer2))
+    score = standard2 + (value - refer2) / (refer1 - refer2) * (standard1 - standard2)
 
     return score
 
-
-def linear_correlation_type_positive(value, standard_score, refer1):
-    """
-    线性相关类型 正相关/反相关
-    Parameters
-        value float 指标值
-        standard_score float 标准分
-        refer1 阈值1
-    Returns
-        score float 得分
-    """
-    score = standard_score * (value / refer1)
-
-    return score
-
-
-def linear_correlation_type_anti(value, standard_score, refer1):
-    """
-    线性相关类型 正相关/反相关
-    Parameters
-        value float 指标值
-        standard_score float 标准分
-        refer1 阈值1
-    Returns
-        score float 得分
-    """
-    score = standard_score - (standard_score * (value / refer1) - standard_score)
-
-    return score