评分卡案例

数据预处理

%matplotlib inline
# 导入库 
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression as LR

# 加载数据
data = pd.read_csv("./rankingcard.csv", index_col=0)
data.head()

# 删除重复值
data.drop_duplicates(inplace=True)

# 查看缺失值占比
data.isnull().sum() / data.shape[0]

SeriousDlqin2yrs                        0.000000
RevolvingUtilizationOfUnsecuredLines    0.000000
age                                     0.000000
NumberOfTime30-59DaysPastDueNotWorse    0.000000
DebtRatio                               0.000000
MonthlyIncome                           0.195601
NumberOfOpenCreditLinesAndLoans         0.000000
NumberOfTimes90DaysLate                 0.000000
NumberRealEstateLoansOrLines            0.000000
NumberOfTime60-89DaysPastDueNotWorse    0.000000
NumberOfDependents                      0.025624
dtype: float64

# 删除缺失值
data.drop(index=data[data["NumberOfDependents"].isna() == True].index, inplace=True)

# 重建索引
data.index = range(data.shape[0])
data
type(data)

pandas.core.frame.DataFrame

# 封装填充缺失值函数
def fill_miss_rfr(x, y, to_fill: str, n_estimators: int) -> np.ndarray:
    """
    Args:
        x (DataFram): 要填补的特征矩阵
        y (Serise): 完整的，没有缺失值的标签
        to_fill (str): 字符串，要填补的那一列的名称
        n_estimators (int):随机森林树的数量
    """

    # 构建新特征矩阵和新标签
    df = x.copy()
    fill = df.loc[:, to_fill]
    df = pd.concat([df.loc[:, df.columns != to_fill], pd.DataFrame(y)], axis=1)

    # 划分训练集和测试集
    y_train = fill[fill.notna()]
    y_test = fill[fill.isna()]
    x_train = df.iloc[y_train.index, :]
    x_test = df.iloc[y_test.index, :]

    # 使用随机森林填充缺失值
    from sklearn.ensemble import RandomForestRegressor as RFR

    rfr = RFR(n_estimators=n_estimators)
    rfr.fit(x_train, y_train)
    y_predict = rfr.predict(x_test)

    return y_predict

# 准备参数
x = data.iloc[:, 1:]
y = data.iloc[:, 1]

# 获取缺失值MonthlyIncome
y_predict = fill_miss_rfr(x, y, "MonthlyIncome", 100)

# 填补缺失值MonthlyIncome
data.loc[data.loc[:, "MonthlyIncome"].isna(), "MonthlyIncome"] = y_predict

描述性统计处理异常值

data.describe([0.25, 0.5, 0.75, 0.90]).T

# 一个年龄为0的人，需要将他删掉
(data["age"] == 0).sum()
data = data[data["age"] != 0]

data = data[data.loc[:, "NumberOfTimes90DaysLate"] < 90]
# 重置索引
data.index = range(data.shape[0])

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 145354 entries, 0 to 145353
Data columns (total 11 columns):
 #   Column                                Non-Null Count   Dtype  
---  ------                                --------------   -----  
 0   SeriousDlqin2yrs                      145354 non-null  int64  
 1   RevolvingUtilizationOfUnsecuredLines  145354 non-null  float64
 2   age                                   145354 non-null  int64  
 3   NumberOfTime30-59DaysPastDueNotWorse  145354 non-null  int64  
 4   DebtRatio                             145354 non-null  float64
 5   MonthlyIncome                         145354 non-null  float64
 6   NumberOfOpenCreditLinesAndLoans       145354 non-null  int64  
 7   NumberOfTimes90DaysLate               145354 non-null  int64  
 8   NumberRealEstateLoansOrLines          145354 non-null  int64  
 9   NumberOfTime60-89DaysPastDueNotWorse  145354 non-null  int64  
 10  NumberOfDependents                    145354 non-null  float64
dtypes: float64(4), int64(7)
memory usage: 12.2 MB

查看样本均衡问题

x = data.iloc[:, 1:]
y = data.iloc[:, 0]

sample_n = y.value_counts().sum()
sample_0 = y.value_counts()[0]
sample_1 = y.value_counts()[1]

print(
    "样本总数: {}, 样本0占比:{:.2%}, 样本1占比:{:.2%}".format(
        sample_n, sample_0 / sample_n, sample_1 / sample_n
    )
)

样本总数: 145354, 样本0占比:93.32%, 样本1占比:6.68%

# 增加样本1的占比
from imblearn.over_sampling import SMOTE

sm = SMOTE(random_state=42)
x, y = sm.fit_resample(x, y)

sample_n = y.value_counts().sum()
sample_0 = y.value_counts()[0]
sample_1 = y.value_counts()[1]
print(
    "样本总数: {}, 样本0占比:{:.2%}, 样本1占比:{:.2%}".format(
        sample_n, sample_0 / sample_n, sample_1 / sample_n
    )
)

样本总数: 271296, 样本0占比:50.00%, 样本1占比:50.00%

data

划分测试集和训练集并为分箱做准备

from sklearn.model_selection import train_test_split as tts

x_train, x_vali, y_train, y_vali = tts(x, y, test_size=0.3, random_state=420)
model_data = pd.concat([y_train, x_train], axis=1)
model_data.index = range(model_data.shape[0])
model_data.columns = data.columns

model_vali = pd.concat([y_vali, x_vali], axis=1)
model_vali.index = range(model_vali.shape[0])
model_vali.columns = data.columns

model_data.to_csv("./model_data.csv")
model_vali.to_csv("./model_vali.csv")

分箱

等频分箱

# 按照等频对箱子进行分箱
model_data["qcut"], updown = pd.qcut(model_data["age"], retbins=True, q=20)

coount_0 = (
    model_data[model_data["SeriousDlqin2yrs"] == 0]
    .groupby(by=model_data["qcut"])
    .count()["SeriousDlqin2yrs"]
)

coount_1 = (
    model_data[model_data["SeriousDlqin2yrs"] == 1]
    .groupby(by=model_data["qcut"])
    .count()["SeriousDlqin2yrs"]
)

num_bins = [*zip(updown, updown[1:], coount_0, coount_1)]

num_bins

[(21.0, 28.0, 4121, 7178),
 (28.0, 31.0, 3476, 5783),
 (31.0, 34.0, 3933, 6662),
 (34.0, 36.0, 2896, 4652),
 (36.0, 39.0, 5108, 7417),
 (39.0, 41.0, 3963, 5843),
 (41.0, 43.0, 3957, 5558),
 (43.0, 45.0, 4317, 5691),
 (45.0, 46.0, 2342, 3240),
 (46.0, 48.0, 4864, 6150),
 (48.0, 50.0, 4861, 6038),
 (50.0, 52.0, 4621, 5523),
 (52.0, 54.0, 4560, 4623),
 (54.0, 56.0, 4498, 4010),
 (56.0, 58.0, 4468, 3359),
 (58.0, 61.0, 6451, 4746),
 (61.0, 63.0, 4712, 2228),
 (63.0, 67.0, 6974, 2578),
 (67.0, 73.0, 6975, 2041),
 (73.0, 107.0, 7998, 1492)]

定义 WOE 和 IV 函数

# 获取WOE
columns = ["min", "max", "count_0", "count_1"]
df = pd.DataFrame(num_bins, columns=columns)
df["tatol"] = df.count_0 + df.count_1
df["percentage"] = df.tatol / df.tatol.sum()
df["bad_rate"] = df.count_1 / df.tatol
df["good%"] = df.count_0 / df.count_0.sum()
df["bad%"] = df.count_1 / df.count_1.sum()
df["woe"] = np.log(df["good%"] / df["bad%"])

# 获取IV
rate = df["good%"] - df["bad%"]
iv = np.sum(rate * df["woe"])

# 封装 WOE 和 IV 函数
def get_woe(data: list) -> pd.DataFrame:
    """通过 num_bins 数据计算 woe

    Args:
        data (list): _description_

    Returns:
        pd.DataFrame: _description_
    """
    df = data.copy()
    columns = ["min", "max", "count_0", "count_1"]
    df = pd.DataFrame(df, columns=columns)
    df["tatol"] = df.count_0 + df.count_1
    df["percentage"] = df.tatol / df.tatol.sum()
    df["bad_rate"] = df.count_1 / df.tatol
    df["good%"] = df.count_0 / df.count_0.sum()
    df["bad%"] = df.count_1 / df.count_1.sum()
    df["woe"] = np.log(df["good%"] / df["bad%"])
    return df


def get_iv(data: pd.DataFrame) -> float:
    """计算IV值

    Args:
        data (pd.DataFrame): _description_

    Returns:
        float: _description_
    """
    rate = data["good%"] - data["bad%"]
    iv = np.sum(rate * data["woe"])
    return iv

get_iv(get_woe(num_bins))

0.3451906890667462

num_bins_ = num_bins.copy()
[*zip(range(len(num_bins_)), num_bins_)]

[(0, (21.0, 28.0, 4121, 7178)),
 (1, (28.0, 31.0, 3476, 5783)),
 (2, (31.0, 34.0, 3933, 6662)),
 (3, (34.0, 36.0, 2896, 4652)),
 (4, (36.0, 39.0, 5108, 7417)),
 (5, (39.0, 41.0, 3963, 5843)),
 (6, (41.0, 43.0, 3957, 5558)),
 (7, (43.0, 45.0, 4317, 5691)),
 (8, (45.0, 46.0, 2342, 3240)),
 (9, (46.0, 48.0, 4864, 6150)),
 (10, (48.0, 50.0, 4861, 6038)),
 (11, (50.0, 52.0, 4621, 5523)),
 (12, (52.0, 54.0, 4560, 4623)),
 (13, (54.0, 56.0, 4498, 4010)),
 (14, (56.0, 58.0, 4468, 3359)),
 (15, (58.0, 61.0, 6451, 4746)),
 (16, (61.0, 63.0, 4712, 2228)),
 (17, (63.0, 67.0, 6974, 2578)),
 (18, (67.0, 73.0, 6975, 2041)),
 (19, (73.0, 107.0, 7998, 1492))]

卡方检验，合并箱体，画出 IV 曲线

# 卡方检验
import scipy.stats as ss

x1 = num_bins_[0][2:]
x2 = num_bins_[1][2:]
pvs = ss.chi2_contingency([x1, x2])[1]

pvs_list = []
for i in range(len(num_bins_) - 1):
    x1 = num_bins_[i][2:]
    x2 = num_bins_[i + 1][2:]
    pvs = ss.chi2_contingency([x1, x2])[1]
    pvs_list.append(pvs)
pvs_list
pvs_list

[0.11728724900429034,
 0.5508163222314948,
 0.09053121716062464,
 0.0007530919909110494,
 0.5874212014912026,
 0.10044784335748964,
 0.029701216027069655,
 0.15850408664347226,
 0.007103486255692831,
 0.5224541867703053,
 0.16902174569691014,
 1.2731738769229555e-08,
 2.1014725165904846e-05,
 6.866926043454617e-08,
 0.476802189877255,
 1.944497287524474e-43,
 1.0901328872969334e-12,
 8.002706776208699e-12,
 6.800451443744261e-33]

# 合并卡方检验相似的两个箱子
i = pvs_list.index(max(pvs_list))
num_bins_[i : i + 2] = [
    (
        num_bins_[i][0],
        num_bins_[i + 2][0],
        num_bins_[i][2] + num_bins_[i + 1][2],
        num_bins_[i][3] + num_bins_[i + 1][3],
    )
]

import matplotlib.pyplot as plt

IV = []
axisx = []
while len(num_bins_) > 2:
    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 = ss.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 = get_woe(num_bins_)
    axisx.append(len(num_bins_))
    IV.append(get_iv(bins_df))
plt.figure()
plt.plot(axisx, IV)
plt.xticks(axisx)
plt.xlabel("number of box")
plt.ylabel("IV")
plt.show()

​

​

# 封装分箱函数
def get_bin(num_bins_: list, n: str) -> list:
    """分箱函数

    Args:
        num_bins (list): _description_
        n (str): _description_

    Returns:
        list: _description_
    """
    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 = ss.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],
            )
        ]
    return num_bins_

get_woe(get_bin(num_bins, 5))

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: 初始分箱的个数
    graph: 是否要画出IV图像
    区间为前开后闭 (]
    """
    global bins_df
    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", "count_0", "count_1"]
        df = pd.DataFrame(num_bins, columns=columns)
        df["total"] = df.count_0 + df.count_1
        df["percentage"] = df.total / df.total.sum()
        df["bad_rate"] = df.count_1 / df.total
        df["good%"] = df.count_0 / df.count_0.sum()
        df["bad%"] = df.count_1 / df.count_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 = []
        for i in range(len(num_bins) - 1):
            x1 = num_bins[i][2:]
            x2 = num_bins[i + 1][2:]
            pv = scipy.stats.chi2_contingency([x1, x2])[1]
            pvs.append(pv)
        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

dfs = []
for i in model_data.columns[1:-1]:
    print(i)
    a = graphforbestbin(DF=model_data, X=i, Y="SeriousDlqin2yrs", graph=False)
    dfs.append(a)

RevolvingUtilizationOfUnsecuredLines
age
NumberOfTime30-59DaysPastDueNotWorse
DebtRatio
MonthlyIncome
NumberOfOpenCreditLinesAndLoans
NumberOfTimes90DaysLate
NumberRealEstateLoansOrLines
NumberOfTime60-89DaysPastDueNotWorse
NumberOfDependents

for i in dfs:
    print(i)

        min           max  count_0  count_1  total  percentage  bad_rate  \
0  0.000000      0.102453    42746     4731  47477    0.250001  0.099648   
1  0.102453      0.224921    13207     5784  18991    0.100002  0.304565   
2  0.224921      0.553524    17987    19994  37981    0.199998  0.526421   
3  0.553524      1.000000    19729    53108  72837    0.383540  0.729135   
4  1.000000  29110.000000     1426    11195  12621    0.066459  0.887014   

      good%      bad%       woe  
0  0.449508  0.049899  2.198159  
1  0.138882  0.061005  0.822671  
2  0.189148  0.210880 -0.108763  
3  0.207466  0.560140 -0.993218  
4  0.014996  0.118076 -2.063574  
    min    max  count_0  count_1  total  percentage  bad_rate     good%  \
0  21.0   36.0    14426    24275  38701    0.203789  0.627245  0.151701   
1  36.0   52.0    34033    45460  79493    0.418589  0.571874  0.357884   
2  52.0   61.0    19977    16738  36715    0.193331  0.455890  0.210074   
3  61.0   73.0    18661     6847  25508    0.134318  0.268426  0.196235   
4  73.0  107.0     7998     1492   9490    0.049972  0.157218  0.084105   

       bad%       woe  
0  0.256033 -0.523395  
1  0.479475 -0.292483  
2  0.176539  0.173920  
3  0.072217  0.999645  
4  0.015736  1.676094  
    min    max  count_0  count_1  total  percentage  bad_rate     good%  \
0  21.0   36.0    14426    24275  38701    0.203789  0.627245  0.151701   
1  36.0   52.0    34033    45460  79493    0.418589  0.571874  0.357884   
2  52.0   61.0    19977    16738  36715    0.193331  0.455890  0.210074   
3  61.0   73.0    18661     6847  25508    0.134318  0.268426  0.196235   
4  73.0  107.0     7998     1492   9490    0.049972  0.157218  0.084105   

       bad%       woe  
0  0.256033 -0.523395  
1  0.479475 -0.292483  
2  0.176539  0.173920  
3  0.072217  0.999645  
4  0.015736  1.676094  
          min            max  count_0  count_1   total  percentage  bad_rate  \
0    0.000000       0.017546     7816     1680    9496    0.050003  0.176917   
1    0.017546       0.490044    52595    51853  104448    0.549996  0.496448   
2    0.490044       1.120358    14828    23153   37981    0.199998  0.609594   
3    1.120358     837.000000     8988    10006   18994    0.100017  0.526798   
4  837.000000  329664.000000    10868     8120   18988    0.099986  0.427639   

      good%      bad%       woe  
0  0.082191  0.017719  1.534399  
1  0.553079  0.546903  0.011228  
2  0.155928  0.244199 -0.448587  
3  0.094516  0.105535 -0.110275  
4  0.114286  0.085643  0.288512  
           min           max  count_0  count_1  total  percentage  bad_rate  \
0     0.000000  9.000000e-02     6585     3001   9586    0.050477  0.313061   
1     0.090000  1.419000e+03    13754    14643  28397    0.149531  0.515653   
2  1419.000000  4.667000e+03    27842    38638  66480    0.350066  0.581197   
3  4667.000000  6.238526e+03    13996    14476  28472    0.149926  0.508429   
4  6238.526439  3.008750e+06    32918    24054  56972    0.299999  0.422207   

      good%      bad%       woe  
0  0.069247  0.031652  0.782868  
1  0.144634  0.154442 -0.065613  
2  0.292781  0.407522 -0.330671  
3  0.147179  0.152681 -0.036701  
4  0.346159  0.253702  0.310738  
    min   max  count_0  count_1   total  percentage  bad_rate     good%  \
0   0.0   1.0     3104     7758   10862    0.057196  0.714233  0.032641   
1   1.0   3.0     9412    13478   22890    0.120533  0.588816  0.098975   
2   3.0   5.0    15485    16514   31999    0.168498  0.516079  0.162837   
3   5.0  17.0    61589    53679  115268    0.606971  0.465689  0.647658   
4  17.0  58.0     5505     3383    8888    0.046802  0.380626  0.057889   

       bad%       woe  
0  0.081825 -0.919013  
1  0.142155 -0.362054  
2  0.174176 -0.067317  
3  0.566163  0.134481  
4  0.035681  0.483913  
    min   max  count_0  count_1   total  percentage  bad_rate     good%  \
0   0.0   1.0     3104     7758   10862    0.057196  0.714233  0.032641   
1   1.0   3.0     9412    13478   22890    0.120533  0.588816  0.098975   
2   3.0   5.0    15485    16514   31999    0.168498  0.516079  0.162837   
3   5.0  17.0    61589    53679  115268    0.606971  0.465689  0.647658   
4  17.0  58.0     5505     3383    8888    0.046802  0.380626  0.057889   

       bad%       woe  
0  0.081825 -0.919013  
1  0.142155 -0.362054  
2  0.174176 -0.067317  
3  0.566163  0.134481  
4  0.035681  0.483913  
    min   max  count_0  count_1   total  percentage  bad_rate     good%  \
0   0.0   1.0     3104     7758   10862    0.057196  0.714233  0.032641   
1   1.0   3.0     9412    13478   22890    0.120533  0.588816  0.098975   
2   3.0   5.0    15485    16514   31999    0.168498  0.516079  0.162837   
3   5.0  17.0    61589    53679  115268    0.606971  0.465689  0.647658   
4  17.0  58.0     5505     3383    8888    0.046802  0.380626  0.057889   

       bad%       woe  
0  0.081825 -0.919013  
1  0.142155 -0.362054  
2  0.174176 -0.067317  
3  0.566163  0.134481  
4  0.035681  0.483913  
    min   max  count_0  count_1   total  percentage  bad_rate     good%  \
0   0.0   1.0     3104     7758   10862    0.057196  0.714233  0.032641   
1   1.0   3.0     9412    13478   22890    0.120533  0.588816  0.098975   
2   3.0   5.0    15485    16514   31999    0.168498  0.516079  0.162837   
3   5.0  17.0    61589    53679  115268    0.606971  0.465689  0.647658   
4  17.0  58.0     5505     3383    8888    0.046802  0.380626  0.057889   

       bad%       woe  
0  0.081825 -0.919013  
1  0.142155 -0.362054  
2  0.174176 -0.067317  
3  0.566163  0.134481  
4  0.035681  0.483913  
    min   max  count_0  count_1   total  percentage  bad_rate     good%  \
0   0.0   1.0     3104     7758   10862    0.057196  0.714233  0.032641   
1   1.0   3.0     9412    13478   22890    0.120533  0.588816  0.098975   
2   3.0   5.0    15485    16514   31999    0.168498  0.516079  0.162837   
3   5.0  17.0    61589    53679  115268    0.606971  0.465689  0.647658   
4  17.0  58.0     5505     3383    8888    0.046802  0.380626  0.057889   

       bad%       woe  
0  0.081825 -0.919013  
1  0.142155 -0.362054  
2  0.174176 -0.067317  
3  0.566163  0.134481  
4  0.035681  0.483913  

auto_col_bins = {
    "RevolvingUtilizationOfUnsecuredLines": 6,
    "age": 5,
    "DebtRatio": 4,
    "MonthlyIncome": 3,
    "NumberOfOpenCreditLinesAndLoans": 5,
}
# 不能使用自动分箱的变量
hand_bins = {
    "NumberOfTime30-59DaysPastDueNotWorse": [0, 1, 2, 13],
    "NumberOfTimes90DaysLate": [0, 1, 2, 17],
    "NumberRealEstateLoansOrLines": [0, 1, 2, 4, 54],
    "NumberOfTime60-89DaysPastDueNotWorse": [0, 1, 2, 8],
    "NumberOfDependents": [0, 1, 2, 3],
}
# 保证区间覆盖使用 np.inf替换最大值，用-np.inf替换最小值
hand_bins = {k: [-np.inf, *v[:-1], np.inf] for k, v in hand_bins.items()}

bins_of_col = {}
# 生成自动分箱的分箱区间和分箱后的 IV 值
for col in auto_col_bins:
    bins_df = graphforbestbin(
        model_data,
        col,
        "SeriousDlqin2yrs",
        n=auto_col_bins[col]
        # 使用字典的性质来取出每个特征所对应的箱的数量
        ,
        q=20,
        graph=False,
    )
    bins_list = sorted(set(bins_df["min"]).union(bins_df["max"]))
    # 保证区间覆盖使用 np.inf 替换最大值 -np.inf 替换最小值
    bins_list[0], bins_list[-1] = -np.inf, np.inf
    bins_of_col[col] = bins_list
    # 合并手动分箱数据
bins_of_col.update(hand_bins)

bins_of_col

{'RevolvingUtilizationOfUnsecuredLines': [-inf,
  0.1024533425,
  0.2249209732,
  0.5535236390041033,
  0.9829105421203392,
  0.9999999,
  inf],
 'age': [-inf, 36.0, 52.0, 61.0, 73.0, inf],
 'DebtRatio': [-inf, 0.017546052905140292, 0.4900438860450557, 837.0, inf],
 'MonthlyIncome': [-inf, 0.09, 6238.526438865782, inf],
 'NumberOfOpenCreditLinesAndLoans': [-inf, 1.0, 3.0, 5.0, 17.0, inf],
 'NumberOfTime30-59DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],
 'NumberOfTimes90DaysLate': [-inf, 0, 1, 2, inf],
 'NumberRealEstateLoansOrLines': [-inf, 0, 1, 2, 4, inf],
 'NumberOfTime60-89DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],
 'NumberOfDependents': [-inf, 0, 1, 2, inf]}

data = model_data.copy()

data["cut"] = pd.cut(data["age"], bins_of_col["age"])

data.groupby("cut")["SeriousDlqin2yrs"].value_counts().unstack()
bins_df = data.groupby("cut")["SeriousDlqin2yrs"].value_counts().unstack()
bins_df

bins_df[0]

cut
(-inf, 36.0]    14426
(36.0, 52.0]    34033
(52.0, 61.0]    19977
(61.0, 73.0]    18661
(73.0, inf]      7998
Name: 0, dtype: int64

bins_df["woe"] = np.log(
    (bins_df[0] / bins_df[0].sum()) / (bins_df[1] / bins_df[1].sum())
)

bins_df

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


# 将所有特征的WOE存储到字典当中
woeall = {}
for col in bins_of_col:
    woeall[col] = get_woe(model_data, col, "SeriousDlqin2yrs", bins_of_col[col])
woeall

{'RevolvingUtilizationOfUnsecuredLines': cut
 (-inf, 0.102]     2.198159
 (0.102, 0.225]    0.822671
 (0.225, 0.554]   -0.108763
 (0.554, 0.983]   -1.156168
 (0.983, 1.0]     -0.483896
 (1.0, inf]       -2.063574
 dtype: float64,
 'age': cut
 (-inf, 36.0]   -0.523395
 (36.0, 52.0]   -0.292483
 (52.0, 61.0]    0.173920
 (61.0, 73.0]    0.999645
 (73.0, inf]     1.676094
 dtype: float64,
 'DebtRatio': cut
 (-inf, 0.0175]    1.534399
 (0.0175, 0.49]    0.011228
 (0.49, 837.0]    -0.333937
 (837.0, inf]      0.288512
 dtype: float64,
 'MonthlyIncome': cut
 (-inf, 0.09]        0.782868
 (0.09, 6238.526]   -0.200869
 (6238.526, inf]     0.310738
 dtype: float64,
 'NumberOfOpenCreditLinesAndLoans': cut
 (-inf, 1.0]   -0.919013
 (1.0, 3.0]    -0.362054
 (3.0, 5.0]    -0.067317
 (5.0, 17.0]    0.134481
 (17.0, inf]    0.483913
 dtype: float64,
 'NumberOfTime30-59DaysPastDueNotWorse': cut
 (-inf, 0.0]    0.351043
 (0.0, 1.0]    -0.868353
 (1.0, 2.0]    -1.363561
 (2.0, inf]    -1.482353
 dtype: float64,
 'NumberOfTimes90DaysLate': cut
 (-inf, 0.0]    0.235054
 (0.0, 1.0]    -1.737703
 (1.0, 2.0]    -2.281241
 (2.0, inf]    -2.377136
 dtype: float64,
 'NumberRealEstateLoansOrLines': cut
 (-inf, 0.0]   -0.408144
 (0.0, 1.0]     0.195483
 (1.0, 2.0]     0.650412
 (2.0, 4.0]     0.372983
 (4.0, inf]    -0.297756
 dtype: float64,
 'NumberOfTime60-89DaysPastDueNotWorse': cut
 (-inf, 0.0]    0.124264
 (0.0, 1.0]    -1.375012
 (1.0, 2.0]    -1.807985
 (2.0, inf]    -1.792286
 dtype: float64,
 'NumberOfDependents': cut
 (-inf, 0.0]    0.636441
 (0.0, 1.0]    -0.586082
 (1.0, 2.0]    -0.510146
 (2.0, inf]    -0.451625
 dtype: float64}

建模与模型验证

# 不希望覆盖掉原本的数据，创建一个新的DataFrame，索引和原始数据model_data一模一样
model_woe = pd.DataFrame(index=model_data.index)

# 将原数据分箱后，按箱的结果把WOE结构用map函数映射到数据中
model_woe["age"] = pd.cut(model_data["age"], bins_of_col["age"]).map(woeall["age"])
model_woe

# 对所有特征操作可以写成：
for col in bins_of_col:
    model_woe[col] = pd.cut(model_data[col], bins_of_col[col]).map(woeall[col])

# 将标签补充到数据中
model_woe["SeriousDlqin2yrs"] = model_data["SeriousDlqin2yrs"]

vali_woe = pd.DataFrame(index=model_data.index)
for col in bins_of_col:
    vali_woe[col] = pd.cut(model_data[col], bins_of_col[col]).map(woeall[col])
vali_woe["SeriousDlqin2yrs"] = model_data["SeriousDlqin2yrs"]

vali_X = vali_woe.iloc[:, :-1]
vali_y = vali_woe.iloc[:, -1]

x = model_woe.iloc[:, :-1]
y = model_woe.iloc[:, -1]

x.columns

Index(['age', 'RevolvingUtilizationOfUnsecuredLines', 'DebtRatio',
       'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans',
       'NumberOfTime30-59DaysPastDueNotWorse', 'NumberOfTimes90DaysLate',
       'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',
       'NumberOfDependents'],
      dtype='object')

age = vali_X.pop("age")
age

/Users/chenhao/anaconda3/envs/machine_learn/lib/python3.10/site-packages/IPython/lib/pretty.py:778: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.
  output = repr(obj)





0         0.173920
1         0.999645
2        -0.292483
3         0.173920
4        -0.292483
            ...   
189902    0.173920
189903   -0.292483
189904   -0.523395
189905    0.999645
189906   -0.523395
Name: age, Length: 189907, dtype: category
Categories (5, float64): [-0.523395 < -0.292483 < 0.173920 < 0.999645 < 1.676094]

vali_X.insert(0, column="age", value=age)

vali_X.columns

Index(['age', 'RevolvingUtilizationOfUnsecuredLines', 'DebtRatio',
       'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans',
       'NumberOfTime30-59DaysPastDueNotWorse', 'NumberOfTimes90DaysLate',
       'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',
       'NumberOfDependents'],
      dtype='object')

from sklearn.linear_model import LogisticRegression as LR

lr = LR().fit(x, y)
lr.score(vali_X, vali_y)

0.7838626274966167

from sklearn.model_selection import cross_val_score

c_1 = np.linspace(0.01, 1, 20)
c_2 = np.linspace(0.01, 0.2, 20)
score_list_1 = []
score_list_2 = []
for i in range(20):
    lr1 = LR(penalty="l2", solver="liblinear", C=c_1[i])
    lr1.fit(x, y)
    score_list_1.append(cross_val_score(lr1, vali_X, vali_y, cv=10).mean())
    lr2 = LR(penalty="l2", solver="liblinear", C=c_2[i])
    lr2.fit(x, y)
    score_list_2.append(cross_val_score(lr2, vali_X, vali_y, cv=10).mean())
plt.figure(figsize=[20, 5])
plt.plot(range(20), score_list_1, label="c_1")
plt.plot(range(20), score_list_2, label="c_2")
plt.legend()
plt.show()

​

​

import scikitplot as skplt

vali_proba_df = pd.DataFrame(lr.predict_proba(vali_X))
skplt.metrics.plot_roc(
    vali_y, vali_proba_df, plot_micro=False, figsize=(6, 6), plot_macro=False
)

<AxesSubplot: title={'center': 'ROC Curves'}, xlabel='False Positive Rate', ylabel='True Positive Rate'>

​

​

制作评分卡

B = 20 / np.log(2)
A = 600 + B * np.log(1 / 60)
B, A

(28.85390081777927, 481.8621880878296)

base_score = A - B * lr.intercept_
base_score
score_age = woeall["age"] * (-B * lr.coef_[0][0])
score_age

cut
(-inf, 36.0]    -4.180160
(36.0, 52.0]    -2.335948
(52.0, 61.0]     1.389030
(61.0, 73.0]     7.983784
(73.0, inf]     13.386325
dtype: float64

file = "./ScoreData.csv"

# 首先写入基准分数
# 之后使用循环，每次生成一组score_age类似的分档和分数，不断写入文件之中
with open(file, "w") as fdata:
    fdata.write("base_score,{}\n".format(base_score))
for i, col in enumerate(x.columns):
    score = woeall[col] * (-B * lr.coef_[0][i])
    score.name = "Score"
    score.index.name = col
    score.to_csv(file, header=True, mode="a")