python大赛评分_python评分卡之分数计算

#计算分数函数

def get_score(

,woe,B):

scores=[]

for w in woe:

score=round(

*w*B,0)

scores.append(score)

return scores

#根据变量计算分数

def compute_score(series,cut,score):

list = []

i = 0

while i < len(series):

value = series[i]

j = len(cut) - 2

m = len(cut) - 2

while j > 0: # 有的代码写的是“>=”，但实际转换后发现有的变量出现错误

if value >= cut[j]:

j = -1

else:

j -= 1

m -= 1

list.append(score[m])

i += 1

return list

#计算分数

#coe为逻辑回归模型的系数

coe=[

]

# 我们取600分为基础分值，PDO为20(每高20分好坏比翻一倍)，好坏比取60。

B = 20 / math.log(2)

A = 600 -B*log(60)

# B= 20 / math.log(2)

#A= 600 + B*math.log(1/60)

baseScore = round(A+ B * coe[0], 0)

#baseScore = round(A+ B * coe[0], 0)

注：若odds是坏客户概率/好客户概率,odds应取倒数，再经过ln转换则B前面是‘—’。woe也要改变。

x1 = get_score(coe[1], woex1, B) #,woe,

要对应

x2 = get_score(coe[2], woex2, B)

x3 = get_score(coe[3], woex3, B)

#计算分数

test1 = pd.read_csv('TestData.csv')

test1['BaseScore']=Series(np.zeros(len(test1)))+baseScore

test1['x1'] = Series(compute_score(test1['变量名'], cutx1, x1))

test1['x2'] = Series(compute_score(test1['变量名'], cutx2, x2))

test1['x3'] = Series(compute_score(test1['变量名'], cutx3, x3))

test1['Score'] = test1['x1'] + test1['x2'] + test1['x3'] + baseScore