时值蚂蚁上市之际，马云在上海滩发表演讲。马云的核心逻辑其实只有一个，在全球数字经济时代，有且只有一种金融优势，那就是基于消费者大数据的纯信用！

我们不妨称之为数据信用，它比抵押更靠谱，它比担保更保险，它比监管更高明，它是一种面向未来的财产权，它是数字货币背后核心的抵押资产，它决定了数字货币时代信用创造的方向、速度和规模。一句话，谁掌握了数据信用，谁就控制了数字货币的发行权！

数据信用判断依靠的就是金融风控模型。更准确的说谁能掌握风控模型知识，谁就掌握了数字货币的发行权！欢迎各位同学学习python信用评分卡建模（附代码）视频系列教程
地址为：https://edu.csdn.net/course/detail/30611

作者介绍

作者Toby：持牌照消费金融模型专家，发明金融风控模型算法相关专利，和中科院，中科大教授保持长期项目合作；和同盾，聚信立等外部数据源公司有项目对接。熟悉消费金融场景业务，线上线下业务，包括现金贷，商品贷，医美，反欺诈，汽车金融等等。模型项目200+，擅长Python机器学习建模，对于变量筛选，衍生变量构造，变量缺失率高，正负样本不平衡，共线性高，多算法比较，调参等疑难问题有良好解决方法。

课程目录
章节1前言
章节1Python环境搭建
课时1 建评分卡模型，python，R，SAS谁最好？
课时2 Anaconda快速入门指南
课时3 Anaconda下载和安装
课时4 canopy下载和安装
课时5 Anaconda Navigato导航器
课时6 python安装第三方包：pip和conda install
课时7 Python非官方扩展包下载地址
课时8 Anaconda安装不同版本python
课时9 jupyter1_为什么使用jupyter notebook？
课时10 jupyter2_jupyter基本文本编辑操作
课时11 如何用jupyter notebook打开指定文件夹内容？
课时12 jupyter4_jupyter转换PPT实操
课时13 jupyter notebook用matplotlib不显示图片解决方案

章节2 python编程基础知识
课时14 Python文件基本操作
课时15 变量_表达式_运算符_值
课时16 字符串string
课时17 列表list
课时18 程序的基本构架（条件，循环）
课时19 数据类型_函数_面向对象编程
课时20 python2和3的区别
课时21 编程技巧和学习方法

章节3 python机器学习基础
课时22 UCI机器学习常用数据库介绍
课时23 机器学习书籍推荐
课时24 如何选择算法
课时25 机器学习语法速查表
课时26 python数据科学常用的库
课时27 python数据科学入门介绍(选修)

章节4 德国信用评分数据下载和介绍
课时28 35德国信用评分数据下载和介绍

章节5信用评分卡开发流程（上）
课时29 评分卡开发流程概述
课时30 第一步：数据收集
课时31 第二步：数据准备
课时32 变量可视化分析
课时33 样本量需要多少？
课时34 坏客户定义
课时35 第三步：变量筛选
课时36 变量重要性评估_iv和信息增益混合方法
课时37 衍生变量05:01
课时38 第四步：变量分箱01:38

章节6信用评分卡开发流程（下）
课时39 第五步：建立逻辑回归模型
课时40 odds赔率
课时41 woe计算
课时42 变量系数
课时43 A和B计算
课时44 Excel手动计算坏客户概率
课时45 Python脚本计算坏客户概率
课时46 客户评分
课时47 评分卡诞生-变量分数计算
课时48 拒绝演绎reject inference
课时49 第六步：模型验证
课时50 第七步：模型部署
课时51 常见模型部署问题

章节7 Python信用评分卡-逻辑回归脚本
课时52 Python信用评分卡脚本运行演示
课时53 描述性统计脚本_缺失率和共线性分析
课时54 woe脚本(kmean分箱)
课时55 iv计算独家脚本
课时56 Excel手动推导变量woe和iv值
课时57 评分卡脚本1（sklearn）
课时58 评分卡脚本2（statsmodel）
课时59 生成评分卡脚本
课时60 模型验证脚本

章节8PSI(population stability index)稳定指标
课时61 拿破仑远征欧洲失败/华尔街股灾真凶-PSI模型稳定指标揭秘
课时62 excel推导PSI的计算公式
课时63 PSI计算公式原理_独家秘密
课时64 PSI的python脚本讲解

章节9难点1_坏客户定义
课时65 坏客户定义错误，全盘皆输
课时66 不同场景坏客户定义不一样，坏客户定义具有反复性
课时67 坏客户占比不能太低
课时68 vintage源于葡萄酒酿造
课时69 vintage用于授信策略优化

章节10难点2_woe分箱
课时70 ln对数函数
课时71 excel手动计算woe值
课时72 python计算woe脚本
课时73 Iv计算推导
课时74 woe正负符号意义
课时75 WOE计算就这么简单？你想多了
课时76 Kmean算法原理
课时77 python kmean实现粗分箱脚本
课时78 自动化比较变量不同分箱的iv值
课时79 woe分箱第三方包脚本

章节11难点3_逻辑回归是最佳算法吗？
课时80 逻辑回归是最优算法吗？No
课时81 xgboost_支持脚本下载
课时82 随机森林randomForest_支持脚本下载
课时83 支持向量SVM_支持脚本下载
课时84 神经网络neural network_支持脚本下载
课时85 多算法比较重要性_模型竞赛，百万奖金任你拿

章节12难点4_变量缺失数据处理
课时86 imputer-缺失数据处理
课时87 xgboost简单处理缺失数据
课时88 catboost处理缺失数据最简单

章节13难点5.模型验证
课时89 模型需要验证码？
课时90 商业银行资本管理办法(试行)
课时91 模型验证_信用风险内部评级体系监管要求
课时92 模型验证主要指标概述
课时93 交叉验证cross validation
课时94 groupby分类统计函数
课时95 KS_模型区分能力指标
课时96 混淆矩阵（accuracy,precision，recall，f1 score）

章节14难点6.逻辑回归调参
课时97 菜鸟也能轻松调参
课时98 调参1_Penalty正则化选择参数
课时99 调参2_classWeight类别权重
课时100 调参3_solver优化算法选择参数
课时101 调参4_n_jobs
课时102 L-BFGS算法演化历史
课时103 次要参数一览

章节16 风控管理和诈骗中介（选修）
课时104 网络信贷发展史
课时105 诈骗中介
课时106 风控管理
课时107 告别套路贷，高利贷，选择正确贷款方式

章节17 2018-2019消费金融市场行情
课时108 揭秘：近年消费金融火爆发展根本原因
课时109 持牌照消费金融公司盈利排行榜
课时110 消费金融，风控技术是瓶颈
课时111 谁能笑到最后：2018-2019消费金融公司注册资本
课时112 萝卜加大棒：中央政策监管趋势独家预测
课时113 信用是金融交易的基石_P2P倒闭潮秘密

章节18 2018-2019年全球宏观经济
课时114 专家不会告诉你的秘密：美元和黄金真实关系
课时115 宏观经济主要指标：债务率和失业率
课时116 2019年中国宏观经济分析_赠人民银行发布2018n年中国金融稳定报告
课时117 2019年G20国家宏观经济信息汇总_供下载
课时118 全球系统金融风险
课时119 基尼系数_贫富差异指标
课时120 GDP_利率_通货膨胀
课时121 失业率_债务率
课时122 贸易差额_中美贸易战根本原因
课时123 信用评级_阿根廷金融危机独家解读

课程目的
Minimization of risk and maximization of profit on behalf of the bank.
To minimize loss from the bank’s perspective, the bank needs a decision rule regarding who to give approval of the loan and who not to. An applicant’s demographic and socio-economic profiles are considered by loan managers before a decision is taken regarding his/her loan application.
The German Credit Data contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for 1000 loan applicants. Here is a link to the German Credit data (right-click and “save as” ). A predictive model developed on this data is expected to provide a bank manager guidance for making a decision whether to approve a loan to a prospective applicant based on his/her profiles.
将银行风险最小化并将利润最大化
为了从银行的角度将损失降到最低，银行需要制定决策规则，确定谁批准贷款，谁不批准。 在决定贷款申请之前，贷款经理会考虑申请人的人口统计和社会经济概况。
德国信贷数据包含有关20个变量的数据，以及1000个贷款申请者被认为是好信用风险还是坏信用风险的分类。 这是指向德国信用数据的链接（右键单击并另存为）。 预期基于此数据开发的预测模型将为银行经理提供指导，以根据他/她的个人资料来决定是否批准准申请人的贷款。

信用评分数据下载地址
http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)
account balance 账户余额
duration of credit持卡时间

数据信息Data Set Information:

Two datasets are provided. the original dataset, in the form provided by Prof. Hofmann, contains categorical/symbolic attributes and is in the file “german.data”.

For algorithms that need numerical attributes, Strathclyde University produced the file “german.data-numeric”. This file has been edited and several indicator variables added to make it suitable for algorithms which cannot cope with categorical variables. Several attributes that are ordered categorical (such as attribute 17) have been coded as integer. This was the form used by StatLog.

This dataset requires use of a cost matrix (see below)

提供了两个数据集。 原始数据集以Hofmann教授提供的形式包含类别/符号属性，并且位于文件“ german.data”中。

对于需要数字属性的算法，斯特拉斯克莱德大学产生了文件“ german.data-numeric”。 该文件已经过编辑，并添加了一些指标变量，以使其适用于无法处理分类变量的算法。 几个按类别排序的属性（例如属性17）已编码为整数。 这是StatLog使用的形式。
This dataset requires use of a cost matrix (see below)

提供了两个数据集。 原始数据集以Hofmann教授提供的形式包含类别/符号属性，并且位于文件“ german.data”中。

对于需要数字属性的算法，斯特拉斯克莱德大学产生了文件“ german.data-numeric”。 该文件已经过编辑，并添加了一些指标变量，以使其适用于无法处理分类变量的算法。 几个按类别排序的属性（例如属性17）已编码为整数。 这是StatLog使用的形式。

该数据集需要使用成本矩阵（请参见下文）
… 1 2
1 0 1

2 5 0

(1 = Good, 2 = Bad)

The rows represent the actual classification and the columns the predicted classification.

It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1).

Attribute Information:

Attribute 1: (qualitative)
Status of existing checking account
A11 : … < 0 DM
A12 : 0 <= … < 200 DM
A13 : … >= 200 DM / salary assignments for at least 1 year
A14 : no checking account

Attribute 2: (numerical)
Duration in month

Attribute 3: (qualitative)
Credit history
A30 : no credits taken/ all credits paid back duly
A31 : all credits at this bank paid back duly
A32 : existing credits paid back duly till now
A33 : delay in paying off in the past
A34 : critical account/ other credits existing (not at this bank)

Attribute 4: (qualitative)
Purpose
A40 : car (new)
A41 : car (used)
A42 : furniture/equipment
A43 : radio/television
A44 : domestic appliances
A45 : repairs
A46 : education
A47 : (vacation - does not exist?)
A48 : retraining
A49 : business
A410 : others

Attribute 5: (numerical)
Credit amount

Attibute 6: (qualitative)
Savings account/bonds
A61 : … < 100 DM
A62 : 100 <= … < 500 DM
A63 : 500 <= … < 1000 DM
A64 : … >= 1000 DM
A65 : unknown/ no savings account

Attribute 7: (qualitative)
Present employment since
A71 : unemployed
A72 : … < 1 year
A73 : 1 <= … < 4 years
A74 : 4 <= … < 7 years
A75 : … >= 7 years

Attribute 8: (numerical)
Installment rate in percentage of disposable income

Attribute 9: (qualitative)
Personal status and sex
A91 : male : divorced/separated
A92 : female : divorced/separated/married
A93 : male : single
A94 : male : married/widowed
A95 : female : single

Attribute 10: (qualitative)
Other debtors / guarantors
A101 : none
A102 : co-applicant
A103 : guarantor

Attribute 11: (numerical)
Present residence since

Attribute 12: (qualitative)
Property
A121 : real estate
A122 : if not A121 : building society savings agreement/ life insurance
A123 : if not A121/A122 : car or other, not in attribute 6
A124 : unknown / no property

Attribute 13: (numerical)
Age in years

Attribute 14: (qualitative)
Other installment plans
A141 : bank
A142 : stores
A143 : none

Attribute 15: (qualitative)
Housing
A151 : rent
A152 : own
A153 : for free

Attribute 16: (numerical)
Number of existing credits at this bank

Attribute 17: (qualitative)
Job
A171 : unemployed/ unskilled - non-resident
A172 : unskilled - resident
A173 : skilled employee / official
A174 : management/ self-employed/
highly qualified employee/ officer

Attribute 18: (numerical)
Number of people being liable to provide maintenance for

Attribute 19: (qualitative)
Telephone
A191 : none
A192 : yes, registered under the customers name

Attribute 20: (qualitative)
foreign worker
A201 : yes
A202 : no

It is worse to class a customer as good when they are bad (5),
than it is to class a customer as bad when they are good (1).

模型变量重要性排序结果


python建模脚本
randomForest.py
random forest with 1000 trees:
accuracy on the training subset:1.000
accuracy on the test subset:0.772
准确性高于决策树

# -*- coding: utf-8 -*-
"""
python信用评分卡建模（附代码）：
https://edu.csdn.net/course/detail/30611
博主微信公众号：pythonEducation
随机森林不需要预处理数据
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_splittrees=1000
#读取文件
readFileName="German_credit.xlsx"
#读取excel
df=pd.read_excel(readFileName)
list_columns=list(df.columns[:-1])
X=df.ix[:,:-1]
y=df.ix[:,-1]
names=X.columns
x_train,x_test,y_train,y_test=train_test_split(X,y,random_state=0)
#n_estimators表示树的个数，测试中100颗树足够
forest=RandomForestClassifier(n_estimators=trees,random_state=0)
forest.fit(x_train,y_train)
print("random forest with %d trees:"%trees) 
print("accuracy on the training subset:{:.3f}".format(forest.score(x_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(forest.score(x_test,y_test)))
print('Feature importances:{}'.format(forest.feature_importances_))
n_features=X.shape[1]
plt.barh(range(n_features),forest.feature_importances_,align='center')
plt.yticks(np.arange(n_features),names)
plt.title("random forest with %d trees:"%trees)
plt.xlabel('Feature Importance')
plt.ylabel('Feature')
plt.show()

比较之前

课程支持决策树可视化

实验决策树准确率不高，且严重过度拟合
accuracy on the training subset:0.991
accuracy on the test subset:0.680

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pydotplus
from IPython.display import Image
import graphviz
from sklearn.tree import export_graphviz
from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_splittrees=1000
#读取文件
readFileName="German_credit.xlsx"
#读取excel
df=pd.read_excel(readFileName)
list_columns=list(df.columns[:-1])
x=df.ix[:,:-1]
y=df.ix[:,-1]
names=x.columns
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=0)
#调参
list_average_accuracy=[]
depth=range(1,30)
for i in depth:#max_depth=4限制决策树深度可以降低算法复杂度，获取更精确值tree= DecisionTreeClassifier(max_depth=i,random_state=0)tree.fit(x_train,y_train)accuracy_training=tree.score(x_train,y_train)accuracy_test=tree.score(x_test,y_test)average_accuracy=(accuracy_training+accuracy_test)/2.0#print("average_accuracy:",average_accuracy)list_average_accuracy.append(average_accuracy)max_value=max(list_average_accuracy)
#索引是0开头，结果要加1
best_depth=list_average_accuracy.index(max_value)+1
print("best_depth:",best_depth)
best_tree= DecisionTreeClassifier(max_depth=best_depth,random_state=0)
best_tree.fit(x_train,y_train)
accuracy_training=best_tree.score(x_train,y_train)
accuracy_test=best_tree.score(x_test,y_test)
print("decision tree:")   
print("accuracy on the training subset:{:.3f}".format(best_tree.score(x_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(best_tree.score(x_test,y_test)))n_features=x.shape[1]
plt.barh(range(n_features),best_tree.feature_importances_,align='center')
plt.yticks(np.arange(n_features),names)
plt.title("Decision Tree:")
plt.xlabel('Feature Importance')
plt.ylabel('Feature')
plt.show()#生成一个dot文件，以后用cmd形式生成图片
export_graphviz(best_tree,out_file="creditTree.dot",class_names=['bad','good'],feature_names=names,impurity=False,filled=True)
'''
best_depth: 12
decision tree:
accuracy on the training subset:0.991
accuracy on the test subset:0.680
'''

实验中发现SVN 支持向量建模可以得到最高预测率
accuracy on the scaled training subset:0.867
accuracy on the scaled test subset:0.800效果高于随机森林0.8-0.772=0.028

#标准化数据
from sklearn import preprocessing
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd#读取文件
readFileName="German_credit.xlsx"
#读取excel
df=pd.read_excel(readFileName)
list_columns=list(df.columns[:-1])
x=df.ix[:,:-1]
y=df.ix[:,-1]
names=x.columns
#random_state 相当于随机数种子
X_train,x_test,y_train,y_test=train_test_split(x,y,stratify=y,random_state=42)
svm=SVC()
svm.fit(X_train,y_train)
print("accuracy on the training subset:{:.3f}".format(svm.score(X_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(svm.score(x_test,y_test)))
'''
accuracy on the training subset:1.000
accuracy on the test subset:0.700'''
#观察数据是否标准化
plt.plot(X_train.min(axis=0),'o',label='Min')
plt.plot(X_train.max(axis=0),'v',label='Max')
plt.xlabel('Feature Index')
plt.ylabel('Feature magnitude in log scale')
plt.yscale('log')
plt.legend(loc='upper right')#标准化数据
X_train_scaled = preprocessing.scale(X_train)
x_test_scaled = preprocessing.scale(x_test)
svm1=SVC()
svm1.fit(X_train_scaled,y_train)
print("accuracy on the scaled training subset:{:.3f}".format(svm1.score(X_train_scaled,y_train)))
print("accuracy on the scaled test subset:{:.3f}".format(svm1.score(x_test_scaled,y_test)))
'''
accuracy on the scaled training subset:0.867
accuracy on the scaled test subset:0.800
'''
#改变C参数，调优,kernel表示核函数，用于平面转换，probability表示是否需要计算概率
svm2=SVC(C=10,gamma="auto",kernel='rbf',probability=True)
svm2.fit(X_train_scaled,y_train)
print("after c parameter=10,accuracy on the scaled training subset:{:.3f}".format(svm2.score(X_train_scaled,y_train)))
print("after c parameter=10,accuracy on the scaled test subset:{:.3f}".format(svm2.score(x_test_scaled,y_test)))
'''
after c parameter=10,accuracy on the scaled training subset:0.972
after c parameter=10,accuracy on the scaled test subset:0.716
'''
#计算样本点到分割超平面的函数距离
#print (svm2.decision_function(X_train_scaled))
#print (svm2.decision_function(X_train_scaled)[:20]>0)
#支持向量机分类
#print(svm2.classes_)
#malignant和bening概率计算,输出结果包括恶性概率和良性概率
#print(svm2.predict_proba(x_test_scaled))
#判断数据属于哪一类，0或1表示
#print(svm2.predict(x_test_scaled))

实验中，发现神经网络模型效果不如支持向量和随机森林
最好概率为：accuracy on the training subset:0.916
accuracy on the test subset:0.720
下图是神经网络因子重要程度可视化

神经网络建模脚本如下：

#Multi-layer Perceptron 多层感知机
from sklearn.neural_network import MLPClassifier
#标准化数据，否则神经网络结果不准确，和SVM类似
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import mglearn
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd#读取文件
readFileName="German_credit.xlsx"#读取excel
df=pd.read_excel(readFileName)
list_columns=list(df.columns[:-1])
x=df.ix[:,:-1]
y=df.ix[:,-1]
names=x.columns#random_state 相当于随机数种子
x_train,x_test,y_train,y_test=train_test_split(x,y,stratify=y,random_state=42)
mlp=MLPClassifier(random_state=42)
mlp.fit(x_train,y_train)
print("neural network:")   
print("accuracy on the training subset:{:.3f}".format(mlp.score(x_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(mlp.score(x_test,y_test)))scaler=StandardScaler()
x_train_scaled=scaler.fit(x_train).transform(x_train)
x_test_scaled=scaler.fit(x_test).transform(x_test)mlp_scaled=MLPClassifier(max_iter=1000,random_state=42)
mlp_scaled.fit(x_train_scaled,y_train)
print("neural network after scaled:")   
print("accuracy on the training subset:{:.3f}".format(mlp_scaled.score(x_train_scaled,y_train)))
print("accuracy on the test subset:{:.3f}".format(mlp_scaled.score(x_test_scaled,y_test)))mlp_scaled2=MLPClassifier(max_iter=1000,alpha=1,random_state=42)
mlp_scaled2.fit(x_train_scaled,y_train)
print("neural network after scaled and alpha change to 1:")   
print("accuracy on the training subset:{:.3f}".format(mlp_scaled2.score(x_train_scaled,y_train)))
print("accuracy on the test subset:{:.3f}".format(mlp_scaled2.score(x_test_scaled,y_test)))#绘制颜色图,热图
plt.figure(figsize=(20,5))
plt.imshow(mlp_scaled.coefs_[0],interpolation="None",cmap="GnBu")
plt.yticks(range(30),names)
plt.xlabel("columns in weight matrix")
plt.ylabel("input feature")
plt.colorbar()'''
neural network:
accuracy on the training subset:0.700
accuracy on the test subset:0.700
neural network after scaled:
accuracy on the training subset:1.000
accuracy on the test subset:0.704
neural network after scaled and alpha change to 1:
accuracy on the training subset:0.916
accuracy on the test subset:0.720
'''

实验发现xgboost区分能力还可以，模型性能指标如下：
AUC: 0.8134
ACC: 0.7720
Recall: 0.9521
F1-score: 0.8480
Precesion: 0.7644

import xgboost as xgb
from sklearn.cross_validation import train_test_split
import pandas as pd
import matplotlib.pylab as plt
#读取文件
readFileName="German_credit.xlsx"
#读取excel
df=pd.read_excel(readFileName)
list_columns=list(df.columns[:-1])
x=df.ix[:,:-1]
y=df.ix[:,-1]
names=x.columns
train_x, test_x, train_y, test_y=train_test_split(x,y,random_state=0)dtrain=xgb.DMatrix(train_x,label=train_y)
dtest=xgb.DMatrix(test_x)params={'booster':'gbtree',#'objective': 'reg:linear','objective': 'binary:logistic','eval_metric': 'auc','max_depth':4,'lambda':10,'subsample':0.75,'colsample_bytree':0.75,'min_child_weight':2,'eta': 0.025,'seed':0,'nthread':8,'silent':1}
watchlist = [(dtrain,'train')]
bst=xgb.train(params,dtrain,num_boost_round=100,evals=watchlist)
ypred=bst.predict(dtest)
# 设置阈值, 输出一些评价指标
y_pred = (ypred >= 0.5)*1
#模型校验
from sklearn import metrics
print ('AUC: %.4f' % metrics.roc_auc_score(test_y,ypred))
print ('ACC: %.4f' % metrics.accuracy_score(test_y,y_pred))
print ('Recall: %.4f' % metrics.recall_score(test_y,y_pred))
print ('F1-score: %.4f' %metrics.f1_score(test_y,y_pred))
print ('Precesion: %.4f' %metrics.precision_score(test_y,y_pred))
metrics.confusion_matrix(test_y,y_pred)print("xgboost:") 
#print("accuracy on the training subset:{:.3f}".format(bst.get_score(train_x,train_y)))
#print("accuracy on the test subset:{:.3f}".format(bst.get_score(test_x,test_y)))
print('Feature importances:{}'.format(bst.get_fscore()))'''
AUC: 0.8135
ACC: 0.7640
Recall: 0.9641
F1-score: 0.8451
Precesion: 0.7523#特征重要性和随机森林差不多
Feature importances:{'Account Balance': 80, 'Duration of Credit (month)': 119,'Most valuable available asset': 54, 'Payment Status of Previous Credit': 84,'Value Savings/Stocks': 66, 'Age (years)': 94, 'Credit Amount': 149,'Type of apartment': 20, 'Instalment per cent': 37,'Length of current employment': 70, 'Sex & Marital Status': 29,'Purpose': 67, 'Occupation': 13, 'Duration in Current address': 25,'Telephone': 15, 'Concurrent Credits': 23, 'No of Credits at this Bank': 7,'Guarantors': 28, 'No of dependents': 6}
'''

xgboost 有时候特征重要性分析比随机森林还准确，可见其强大之处
随机森林重要因子排序 xgboost权重指数
Credit amount信用保证金 149
age 年龄 94
account balance 账户余额 80
duration of credit持卡时间 119 （信用卡逾期时间，每个银行有所不同，以招商银行为例，两个月就会被停卡）

2018-9-18数据更新
逻辑回归验证数据和catboost验证数据差不多，可见逻辑回归稳定性

import math
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import cross_val_score
import statsmodels.api as sm
#混淆矩阵计算
from sklearn import metrics
from sklearn.metrics import roc_curve, auc,roc_auc_score
from sklearn.metrics import precision_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score#df_german=pd.read_excel("german_woe.xlsx")
df_german=pd.read_excel("german_credit.xlsx")
#df_german=pd.read_excel("df_after_vif.xlsx")
y=df_german["target"]
x=df_german.ix[:,"Account Balance":"Foreign Worker"]
#x=df_german.ix[:,"Credit Amount":"Purpose"]
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)classifier = LogisticRegression()
classifier.fit(X_train, y_train)
predictions = classifier.predict(X_test)#验证
print("accuracy on the training subset:{:.3f}".format(classifier.score(X_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(classifier.score(X_test,y_test)))#得分公式
'''
P0 = 50
PDO = 10
theta0 = 1.0/20
B = PDO/np.log(2)
A = P0 + B*np.log(theta0)
'''
def Score(probability):#底数是escore = A-B*np.log(probability/(1-probability))return score
#批量获取得分
def List_score(pos_probablity_list):list_score=[]for probability in pos_probablity_list:score=Score(probability)list_score.append(score)return list_scoreP0 = 50
PDO = 10
theta0 = 1.0/20
B = PDO/np.log(2)
A = P0 + B*np.log(theta0)
print("A:",A)
print("B:",B)
list_coef = list(classifier.coef_[0])
intercept= classifier.intercept_#获取所有x数据的预测概率,包括好客户和坏客户，0为好客户，1为坏客户
probablity_list=classifier.predict_proba(x)
#获取所有x数据的坏客户预测概率
pos_probablity_list=[i[1] for i in probablity_list]
#获取所有客户分数
list_score=List_score(pos_probablity_list)
list_predict=classifier.predict(x)
df_result=pd.DataFrame({"label":y,"predict":list_predict,"pos_probablity":pos_probablity_list,"score":list_score})df_result.to_excel("score_proba.xlsx")#变量名列表
list_vNames=df_german.columns
#去掉第一个变量名target
list_vNames=list_vNames[1:]
df_coef=pd.DataFrame({"variable_names":list_vNames,"coef":list_coef})
df_coef.to_excel("coef.xlsx")y_true=y_test
y_pred=classifier.predict(X_test)
accuracyScore = accuracy_score(y_true, y_pred)
print('model accuracy is:',accuracyScore)#precision,TP/(TP+FP) （真阳性）/（真阳性+假阳性）
precision=precision_score(y_true, y_pred)
print('model precision is:',precision)#recall（sensitive）敏感度，(TP)/（TP+FN）
sensitivity=recall_score(y_true, y_pred)
print('model sensitivity is:',sensitivity)#F1 = 2 x (精确率 x 召回率) / (精确率 + 召回率)
#F1 分数会同时考虑精确率和召回率，以便计算新的分数。可将 F1 分数理解为精确率和召回率的加权平均值，其中 F1 分数的最佳值为 1、最差值为 0：
f1Score=f1_score(y_true, y_pred)
print("f1_score:",f1Score)def AUC(y_true, y_scores):auc_value=0#auc第二种方法是通过fpr,tpr，通过auc(fpr,tpr)来计算AUCfpr, tpr, thresholds = metrics.roc_curve(y_true, y_scores, pos_label=1)auc_value= auc(fpr,tpr) ###计算auc的值#print("fpr:",fpr)#print("tpr:",tpr)#print("thresholds:",thresholds)if auc_value<0.5:auc_value=1-auc_valuereturn auc_valuedef Draw_roc(auc_value):fpr, tpr, thresholds = metrics.roc_curve(y, list_score, pos_label=0)#画对角线plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Diagonal line')plt.plot(fpr,tpr,label='ROC curve (area = %0.2f)' % auc_value)plt.title('ROC curve') plt.legend(loc="lower right")#评价AUC表现
def AUC_performance(AUC):if AUC >=0.7:print("good classifier")if 0.7>AUC>0.6:print("not very good classifier")if 0.6>=AUC>0.5:print("useless classifier")if 0.5>=AUC:print("bad classifier,with sorting problems")#Auc验证，数据采用测试集数据
auc_value=AUC(y, list_score)
print("AUC:",auc_value)
#评价AUC表现
AUC_performance(auc_value)
#绘制ROC曲线
Draw_roc(auc_value)

catboost脚本

import catboost as cb
import math
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import cross_val_score
import statsmodels.api as sm
#混淆矩阵计算
from sklearn import metrics
from sklearn.metrics import roc_curve, auc,roc_auc_score
from sklearn.metrics import precision_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score#df_german=pd.read_excel("german_woe.xlsx")
df_german=pd.read_excel("german_credit.xlsx")
#df_german=pd.read_excel("df_after_vif.xlsx")
y=df_german["target"]
x=df_german.ix[:,"Account Balance":"Foreign Worker"]
#x=df_german.ix[:,"Credit Amount":"Purpose"]
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)classifier = cb.CatBoostClassifier()
classifier.fit(X_train, y_train)list_score=classifier.predict_proba(X_test)
list_score=[i[1] for i in list_score]#验证
print("accuracy on the training subset:{:.3f}".format(classifier.score(X_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(classifier.score(X_test,y_test)))list_predict=classifier.predict(x)
y_true=y_test
y_pred=classifier.predict(X_test)
accuracyScore = accuracy_score(y_true, y_pred)
print('model accuracy is:',accuracyScore)#precision,TP/(TP+FP) （真阳性）/（真阳性+假阳性）
precision=precision_score(y_true, y_pred)
print('model precision is:',precision)#recall（sensitive）敏感度，(TP)/（TP+FN）
sensitivity=recall_score(y_true, y_pred)
print('model sensitivity is:',sensitivity)#F1 = 2 x (精确率 x 召回率) / (精确率 + 召回率)
#F1 分数会同时考虑精确率和召回率，以便计算新的分数。可将 F1 分数理解为精确率和召回率的加权平均值，其中 F1 分数的最佳值为 1、最差值为 0：
f1Score=f1_score(y_true, y_pred)
print("f1_score:",f1Score)def AUC(y_true, y_scores):auc_value=0#auc第二种方法是通过fpr,tpr，通过auc(fpr,tpr)来计算AUCfpr, tpr, thresholds = metrics.roc_curve(y_true, y_scores, pos_label=1)auc_value= auc(fpr,tpr) ###计算auc的值#print("fpr:",fpr)#print("tpr:",tpr)#print("thresholds:",thresholds)if auc_value<0.5:auc_value=1-auc_valuereturn auc_value#Auc验证，数据采用测试集数据
auc_value=AUC(y_test, list_score)
print("AUC:",auc_value)