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score_card.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jun 27 09:49:17 2017
@author: Hank Kuang
@title: 评分卡生成
"""
import numpy as np
import statsmodels.api as sm
# import re
import pandas as pd
def creditCards(paramsEst,
woe_maps,
bin_maps,
red_maps,
basepoints=600,
odds=60,
PDO=20):
"""
output credit card for each var in model
--------------------------------------------
ParamsEst: pandas Series, params estimate results in logistic model,
index is param names,value is estimate results
bin_maps: dict, key is varname of paramEst , value is pandas dataframe contains map table
red_maps: dict, key is varname of paramEst, value is pandas dataframe contains redCats table
woe_maps: dict, key is varname of paramEst, value is also a dict contain binNumber--woe
basepoints: expect base points
odds: reciprocal of Odds
PDO: double coefficients
-------------------------------------------
Return
creditCard: pandas dataframe
"""
# 计算A&B
alpha, beta = _score_cal(basepoints, odds, PDO)
# 计算基础分
points_0 = round(alpha - beta * paramsEst['const'])
# 根据各段woe，计算相应得分
points = pd.DataFrame()
for k in woe_maps.keys():
d = pd.DataFrame(woe_maps[k], index=[k]).T
d['points'] = round(-beta * d.ix[:, k] * paramsEst[k])
if k in bin_maps.keys():
bin_map = bin_maps[k]
bin_map = bin_map.drop(['total', 'newbin'], axis=1)
bin_map['range'] = bin_map.apply(lambda x: str(x[0]) + '--' + str(x[1]), axis=1)
bin_map = bin_map.drop(['lower', 'upper'], axis=1)
d = pd.merge(d, bin_map, left_index=True, right_index=True)
elif k in red_maps.keys():
red_map = red_maps[k]
s = tableTranslate(red_map)
s = pd.DataFrame(s.T, columns=['range'])
d = pd.merge(d, s, left_index=True, right_index=True)
else:
d['range'] = d.index
n = len(d)
ind_0 = []
i = 0
while i < n:
ind_0.append(k)
i += 1
d.index = [ind_0, list(d.index)]
d = d.drop(k, axis=1)
points = pd.concat([points, d], axis=0)
# 输出评分卡
points_0 = pd.DataFrame([[points_0, '-']],
index=[['basePoints'], ['-']],
columns=['points', 'range'])
credit_card = pd.concat([points_0, points], axis=0)
credit_card.index.names = ["varname", "binCode"]
return credit_card
def tableTranslate(red_map):
"""
table tranlate for red_map
---------------------------
Params
red_map: pandas dataframe,reduceCats results
---------------------------
Return
res: pandas series
"""
l = red_map['bin'].unique()
res = pd.Series(index=l)
for i in l:
value = red_map[red_map['bin'] == i].index
value = list(value.map(lambda x: str(x) + ';'))
value = "".join(value)
res[i] = value
return res
def _score_cal(basepoints, odds, PDO):
"""
cal alpha&beta for score formula,
score = alpha + beta * log(odds)
---------------------------------------
Params
basepoints: expect base points
odds: cal by logit model
PDO: points of double odds
---------------------------------------
Return
alpha, beta
"""
beta = PDO / np.log(2)
alpha = basepoints - beta * np.log(odds)
return alpha, beta