import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import VarianceThreshold

model_data = pd.read_csv('path'+model_data.csv')
vali_data = pd.read_csv('path'+vali_data.csv')
test_data = pd.read_csv('path'+test.csv')

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy

def graphforbestbin(DF,X,Y,n=5,q=20,graph=True):
    """
    自动最优分箱函数，基于卡方检验的分箱

    参数：
    DF：需要输入的数据
    X：需要分箱的列名
    Y：分箱数据对应的标签Y列名
    n:保留分箱个数
    q:初始分箱的格式
    graph:是否画出iv图像

    区间为前开后闭（]

    """
    DF = DF[[X,Y]].copy()
    
    DF["qcut"],bins = pd.qcut(DF[X], retbins=True, q=q, duplicates="drop")
    coount_y0 = DF.loc[DF[Y]==0].groupby(by="qcut").count()[Y]
    coount_y1 = DF.loc[DF[Y]==1].groupby(by="qcut").count()[Y]
    num_bins = [*zip(bins,bins[1:],coount_y0,coount_y1)]
    
    for i in range(q):
        if 0 in num_bins[0][2:]:
            num_bins[0:2] = [(
                num_bins[0][0],
                num_bins[1][1],
                num_bins[0][2]+num_bins[1][2],
                num_bins[0][3]+num_bins[1][3])]
            continue
            
        for i in range(len(num_bins)):
            if 0 in num_bins[i][2:]:
                num_bins[i-1:i+1] = [(
                    num_bins[i-1][0],
                    num_bins[i][1],
                    num_bins[i-1][2]+num_bins[i][2],
                    num_bins[i-1][3]+num_bins[i][3])]
                break
        else:
            break
            
    def get_woe(num_bins):
        columns = ["min","max","coount_0","coount_1"]
        df = pd.DataFrame(num_bins,columns=columns)
        df["total"]=df.coount_0+df.coount_1
        df["percentage"]=df.total/df.total.sum()
        df["bad_rate"]=df.coount_1/df.total
        df["good%"]=df.coount_0/df.coount_0.sum()
        df["bad%"]=df.coount_1/df.coount_1.sum()
        df["woe"]=np.log(df["good%"]/df["bad%"])
        return df
    
    def get_iv(df):
        rate = df["good%"] - df["bad%"]
        iv = np.sum(rate * df.woe)
        return iv
    
    iv = []
    axisx = []
    while len(num_bins) > n:
        pvs = []
        #获取num_bins_两两之间的卡方检验的置信度（或卡方值）
        for i in range(len(num_bins)-1):
            x1 = num_bins[i][2:]
            x2 = num_bins[i+1][2:]
            #0返回chi2值，1返回p值
            pv = scipy.stats.chi2_contingency([x1,x2])[1]
            #chi2=scipy.stats.chi2_contingency([x1,x2])[0]
            pvs.append(pv)

        #通过p值进行处理，合并p值最大的两组
        i = pvs.index(max(pvs))
        num_bins[i:i+2] = [(
            num_bins[i][0],
            num_bins[i+1][1],
            num_bins[i][2]+num_bins[i+1][2],
            num_bins[i][3]+num_bins[i+1][3])]
   

        bins_df = pd.DataFrame(get_woe(num_bins))
        axisx.append(len(num_bins))
        iv.append(get_iv(bins_df))
 
    if graph:    
        plt.figure()
        plt.plot(axisx,iv)
        plt.xticks(axisx)
        plt.xlabel("number of box")
        plt.ylabel("iv")
        plt.show()
    return bins_df
    
for i in model_data.columns[27:-1]:
    print(i)
graphforbestbin(model_data,i,"isDefault",n=2,q=20,graph=True)
    
#可以分箱的放在这里，后面的数字为之前得到的最优箱子数
auto_col_bins = {"loanAmnt":6,
                 "interestRate":6,
                 "installment":8,
                 "employmentTitle":7,
                 "annualIncome":7,
                 "issueDate":8,
                 "dti":6,
                 "delinquency_2years":3,
                 "ficoRangeLow":7,
                 "ficoRangeHigh":7,
                 "openAcc":9,
                 "revolBal":4,
                 "revolUtil":4,
                 "totalAcc":11,
                 "earliesCreditLine":8,
                 "title":5,
                 "n0":3,
                 "n1":6,
                 "n2":4,
                 "n3":4,
                 "n4":6,
                 "n5":8,
                 "n6":8,
                 "n7":7,
                 "n8":9,
                 "n9":4,
                 "n10":7,
                 "n14":5
                }

#不可以分箱的放在这里面
hand_bins = {
             "grade":[1,2,3,4,5,6,7],
             "homeOwnership":[0,1,2,3,4,5],
             "verificationStatus":[0,1,2],
             "initialListStatus":[0,1],
             "applicationType":[0,1],
    
}

#左右极限变成无穷，以便映射
hand_bins = {k:[-np.inf,*v[:-1],np.inf]for k,v in hand_bins.items()}

#计算woe的函数
def get_woe(df,col,y,bins):
    df = df[[col,y]].copy()
    df["cut"] = pd.cut(df[col],bins)
    bins_df = df.groupby("cut")[y].value_counts().unstack()
    woe = bins_df["woe"] = np.log((bins_df[0]/bins_df[0].sum())/(bins_df[1]/bins_df[1].sum()))
    return woe
    
#将所有woe存储到字典

woeall = {}
for col in bins_of_col:
    woeall[col] = get_woe(model_data,col,"isDefault",bins_of_col[col])
    
model_woe = pd.DataFrame(index=model_data.index)

for col in bins_of_col:
    model_woe[col] = pd.cut(model_data[col],bins_of_col[col]).map(woeall[col])

#剩余标签放进去
for col in [ 'isDefault', 'term', 'grade', 'employmentLength','purpose','pubRec', 'pubRecBankruptcies',  'n13']:
    model_woe[col] = model_data[col]
    
#得到分箱后的数据集model_woe,之前的数据集为model_data
 
#处理测试集

vali_woe = pd.DataFrame(index=vali_data.index)

for col in bins_of_col:
    vali_woe[col] = pd.cut(vali_data[col],bins_of_col[col]).map(woeall[col])
    
for col in [ 'isDefault', 'term', 'grade', 'employmentLength','purpose','pubRec', 'pubRecBankruptcies',  'n13']:
    vali_woe[col] = vali_data[col]
    
vali_x = vali_woe.drop(["isDefault"],axis=1)
vali_y = vali_woe["isDefault"]

#训练集
x = model_woe.drop(["isDefault"],axis=1)
y = model_woe["isDefault"]

#然后就可以用逻辑回归，xgboost等分类了
#我用的是xgboost

x_copy = x.copy()
vali_x_copy = vali_x.copy()

#需要将category变成float64
for col in bins_of_col:
    vali_x_copy[[col]] = vali_x_copy[[col]].astype('float64')
    x_copy[[col]] = x_copy[[col]].astype('float64')
    
import xgboost
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score as CVS

clf = XGBClassifier().fit(x_copy,y)
CVS(clf,x_copy,y,cv=2)
clf.score(vali_x_copy,vali_y)

#说实话，我感觉这个正确率还挺高的,能达到86%，可能也跟我之前用随机森林处理缺失值有关系
#但是用这个模型去testA预测，结果AUC很低
    

数据集来自阿里云天池贷款违约预测。