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tfse-model-api-v0.2
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update 修改打分逻辑

This commit is contained in:
P3ngSaM 2022-02-09 16:07:12 +08:00
parent d2282e4756
commit df557c6f6c
1 changed files with 16 additions and 66 deletions
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82
Rating/scripts/financial_score.py
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@ -33,41 +33,34 @@ def financial_score(param1, param2):
else: else:
refer = list(map(float, refers[key])) refer = list(map(float, refers[key]))
weight = weights[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]: if refer[0] > refer[1]:
# 正相关判断
if value >= refer[0]: if value >= refer[0]:
score = weight score = weight
elif value > refer[1]: elif value > refer[1]:
standard_score = round(weight * 1, 2) score = linear_correlation_type(value, standard[0], standard[1], refer[0], refer[1])
score = linear_correlation_type_positive(value, standard_score, refer[0])
elif value > refer[2]: elif value > refer[2]:
standard_score = round(weight * 0.8, 2) score = linear_correlation_type(value, standard[1], standard[2], refer[1], refer[2])
score = linear_correlation_type_positive(value, standard_score, refer[1])
elif value > refer[3]: elif value > refer[3]:
standard_score = round(weight * 0.5, 2) score = linear_correlation_type(value, standard[2], standard[3], refer[2], refer[3])
score = linear_correlation_type_positive(value, standard_score, refer[2])
elif value > refer[4]: elif value > refer[4]:
standard_score = round(weight * 0.25, 2) score = linear_correlation_type(value, standard[3], standard[4], refer[3], refer[4])
score = linear_correlation_type_positive(value, standard_score, refer[3])
else: else:
score = 0 score = 0
else: else:
if value <= refer[0]: if value <= refer[0]:
score = weight score = weight
elif value < refer[1]: elif value < refer[1]:
standard_score = round(weight * 1, 2) score = linear_correlation_type(value, standard[0], standard[1], refer[0], refer[1])
score = linear_correlation_type_anti(value, standard_score, refer[0])
elif value < refer[2]: elif value < refer[2]:
standard_score = round(weight * 0.8, 2) score = linear_correlation_type(value, standard[1], standard[2], refer[1], refer[2])
score = linear_correlation_type_anti(value, standard_score, refer[1])
elif value < refer[3]: elif value < refer[3]:
standard_score = round(weight * 0.5, 2) score = linear_correlation_type(value, standard[2], standard[3], refer[2], refer[3])
score = linear_correlation_type_anti(value, standard_score, refer[2])
elif value < refer[4]: elif value < refer[4]:
standard_score = round(weight * 0.25, 2) score = linear_correlation_type(value, standard[3], standard[4], refer[3], refer[4])
score = linear_correlation_type_anti(value, standard_score, refer[3]) else:
score = 0
scores[key] = round(score, 2) scores[key] = round(score, 2)
result = dict() result = dict()
@ -93,63 +86,20 @@ def financial_score(param1, param2):
return result 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 线性相关类型 正相关/反相关
Parameters Parameters
value float 指标值 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 refer1 阈值1
refer2 阈值2 refer2 阈值2
Returns Returns
score float 得分 score float 得分
""" """
# main # main
# 正相关打分 score = standard2 + (value - refer2) / (refer1 - refer2) * (standard1 - standard2)
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))
return score 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