Home Credit Default Risk(2) —初步探索

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匿名技术用户   2020-12-27 06:41   11   0

上篇中已经给出了application_{train|test}.csv数据表字段的基本含义，本篇对其进行基本的数据分析，包活异常数据处理，特征变换等，最后给出仅考虑此数据文件，应用logistic回归和random forest两种模型分别训练模型的方式。

加载数据并初步预览

# 导入需要的依赖包
import os
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import numpy as np
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, Imputer
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier

plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号

dir_path = 'XXX/dataset'#替换为数据文件所在目录
print(os.listdir(dir_path))

结果如下：
[‘application_test.csv’, ‘application_train.csv’, ‘bureau.csv’, ‘bureau_balance.csv’, ‘credit_card_balance.csv’, ‘HomeCredit_columns_description.csv’, ‘installments_payments.csv’, ‘POS_CASH_balance.csv’, ‘previous_application.csv’, ‘sample_submission.csv’]

# 加载训练数据和测试数据
application_train_file = dir_path + '/application_train.csv'
application_test_file = dir_path + '/application_test.csv'
app_train = pd.read_csv(application_train_file)
app_test = pd.read_csv(application_test_file)
# 初步预览
print('Training data shape: ', app_train.shape)
app_train.head()

结果如下：
Training data shape: (307511, 122)

SK_ID_CURR TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY ... FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK AMT_REQ_CREDIT_BUREAU_MON AMT_REQ_CREDIT_BUREAU_QRT AMT_REQ_CREDIT_BUREAU_YEAR
0 100002 1 Cash loans M N Y 0 202500.0 406597.5 24700.5 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
1 100003 0 Cash loans F N N 0 270000.0 1293502.5 35698.5 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
2 100004 0 Revolving loans M Y Y 0 67500.0 135000.0 6750.0 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
3 100006 0 Cash loans F N Y 0 135000.0 312682.5 29686.5 ... 0 0 0 0 NaN NaN NaN NaN NaN NaN
4 100007 0 Cash loans M N Y 0 121500.0 513000.0 21865.5 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 122 columns

1.根据TARGET在不同分类下的数量绘制整体风险直方图

# 1.根据TARGET在不同分类下的数量绘制整体风险直方图
app_train.TARGET.value_counts().plot(kind='bar')
plt.title("是否有偿还风险（1-有风险，0- 无风险）")
plt.ylabel("人数")
plt.show()

在这里插入图片描述

2.检查缺失数据整体情况(对于缺失值的处理可以舍弃，补中位数，补均值，RF预测，XGBoost预测等)

# 2.检查缺失数据整体情况
def examine_missing_data(df):
    missing = df.isnull().sum().sort_values(ascending=False)
    missing_percent = (100 * df.isnull().sum() / len(df)).sort_values(ascending=False)
    missing_table = pd.concat([missing, missing_percent], axis=1)
    missing_table = missing_table.rename(columns={0: '缺失值数量', 1: '缺失值占比(%)'})
    missing_table = missing_table[missing_table.iloc[:, 1] != 0]
    print("数据总共有%d个特征，其中存在缺失值的特征数为%d" % (df.shape[1], missing_table.shape[0]))
    return missing_table


missing_values = examine_missing_data(app_train)
missing_values.head(10)

结果如下：

数据总共有122个特征，其中存在缺失值的特征数为67

缺失值数量 缺失值占比(%)
COMMONAREA_MEDI 214865 69.872297
COMMONAREA_AVG 214865 69.872297
COMMONAREA_MODE 214865 69.872297
NONLIVINGAPARTMENTS_MODE 213514 69.432963
NONLIVINGAPARTMENTS_MEDI 213514 69.432963
NONLIVINGAPARTMENTS_AVG 213514 69.432963
FONDKAPREMONT_MODE 210295 68.386172
LIVINGAPARTMENTS_MEDI 210199 68.354953
LIVINGAPARTMENTS_MODE 210199 68.354953
LIVINGAPARTMENTS_AVG 210199 68.354953

3. 查看特征类型（非数值类型需要LabelEncoder或者one-hot Encoder编码成数值型）

对于离散型非数值特征，例如工作性质（工人，教师，商人，医生）需要编码成计算机能识别的数值类型，通常有两种方式，label encoder简单的说就是编码成连续连续的数值，例如（工人— 0，教师 — 1，商人— 2，医生—4）。而one-hot（独热）编码，直观来说就是有多少个状态就有多少比特，而且只有一个比特为1，其他全为0的一种码制，例如（工人—1000，教师—0100，商人—0010，医生—0001）。可以看出one-hot编码长度就是该特征可以取到的所有的状态的数量，对于状态数很多的情况下，可能会导致维数灾难，可以结合PCA算法优化。对于状态数只有二个的情况，LabelEncoder的0，1二值就足够表示，不需要one-hot来扩充编码位数了。

# 3.查看特征类型（非数值类型需要LabelEncoder或者one-hot编码成数值型）
print(app_train.dtypes.value_counts())

结果如下：
float64 65
int64 41
object 16
dtype: int64
可以看出有16个非数值型(object)特征。

# 统计非数值型数据的unique数
print(app_train.select_dtypes(include='object').apply(func=pd.Series.nunique, axis=0))

NAME_CONTRACT_TYPE             2
CODE_GENDER                    3
FLAG_OWN_CAR                   2
FLAG_OWN_REALTY                2
NAME_TYPE_SUITE                7
NAME_INCOME_TYPE               8
NAME_EDUCATION_TYPE            5
NAME_FAMILY_STATUS             6
NAME_HOUSING_TYPE              6
OCCUPATION_TYPE               18
WEEKDAY_APPR_PROCESS_START     7
ORGANIZATION_TYPE             58
FONDKAPREMONT_MODE             4
HOUSETYPE_MODE                 3
WALLSMATERIAL_MODE             7
EMERGENCYSTATE_MODE            2
dtype: int64

# 可以看出ORGANIZATION_TYPE的类别特别多，有58个，
print(app_train['ORGANIZATION_TYPE'].unique())

['Business Entity Type 3' 'School' 'Government' 'Religion' 'Other' 'XNA'
 'Electricity' 'Medicine' 'Business Entity Type 2' 'Self-employed'
 'Transport: type 2' 'Construction' 'Housing' 'Kindergarten'
 'Trade: type 7' 'Industry: type 11' 'Military' 'Services'
 'Security Ministries' 'Transport: type 4' 'Industry: type 1' 'Emergency'
 'Security' 'Trade: type 2' 'University' 'Transport: type 3' 'Police'
 'Business Entity Type 1' 'Postal' 'Industry: type 4' 'Agriculture'
 'Restaurant' 'Culture' 'Hotel' 'Industry: type 7' 'Trade: type 3'
 'Industry: type 3' 'Bank' 'Industry: type 9' 'Insurance' 'Trade: type 6'
 'Industry: type 2' 'Transport: type 1' 'Industry: type 12' 'Mobile'
 'Trade: type 1' 'Industry: type 5' 'Industry: type 10' 'Legal Services'
 'Advertising' 'Trade: type 5' 'Cleaning' 'Industry: type 13'
 'Trade: type 4' 'Telecom' 'Industry: type 8' 'Realtor' 'Industry: type 6']

# 由于类别在后面会Encoder成多个特征，冗余的类别会导致特征数量过多，这里先简单的使用正则化根据大类合并
def organization_type_convert(df):
    df['ORGANIZATION_TYPE'].replace(
        regex=[r'Business Entity[\s\S]*', r'Transport[\s\S]*', r'Industry[\s\S]*', r'Trade[\s\S]*'],
        value=['Business Entity', 'Transport', 'Industry', 'Trade'],
        inplace=True)

organization_type_convert(app_train)
organization_type_convert(app_test)

#观察ORGANIZATION_TYPE的类别显著减少
print(app_train['ORGANIZATION_TYPE'].unique())

['Business Entity' 'School' 'Government' 'Religion' 'Other' 'XNA'
 'Electricity' 'Medicine' 'Self-employed' 'Transport' 'Construction'
 'Housing' 'Kindergarten' 'Trade' 'Industry' 'Military' 'Services'
 'Security Ministries' 'Emergency' 'Security' 'University' 'Police'
 'Postal' 'Agriculture' 'Restaurant' 'Culture' 'Hotel' 'Bank' 'Insurance'
 'Mobile' 'Legal Services' 'Advertising' 'Cleaning' 'Telecom' 'Realtor']

# 对非数值型数据且unique数小于等于2的用 LabelEncoder进行编码转换
def label_encode_unique_under_two(train, test):
    label_encoder = LabelEncoder()
    for col in train:
        if train[col].dtype == 'object' and len(train[col].unique()) <= 2:
            label_encoder.fit(train[col])
            train[col] = label_encoder.transform(train[col])
            test[col] = label_encoder.transform(test[col])
    return train, test


# 对非数值型数据且unique数大于2的用 one-hot进行编码转换
def onehot_encode_unique_over_two(train, test):
    train = pd.get_dummies(train)
    test = pd.get_dummies(test)
    return train, test

app_train, app_test = label_encode_unique_under_two(app_train, app_test)
app_train, app_test = onehot_encode_unique_over_two(app_train, app_test)
print(app_train.shape)
print(app_test.shape)

(307511, 220)
(48744, 216)

# 4.对齐训练和测试数据的列数（训练数据只应该比测试数据多出TARGET一列）
train_target = app_train['TARGET']
app_train, app_test = app_train.align(other=app_test, join='inner', axis=1)
app_train['TARGET'] = train_target
print(app_train.shape)
print(app_test.shape)

(307511, 217)
(48744, 216)

# 5.异常数据检测
def plot_day_employ(train):
    print(train['DAYS_EMPLOYED'].describe())
    train['DAYS_EMPLOYED'].plot.hist(title='从业时间分布直方图（以天为单位）')
    plt.xlabel('从业时间')
    plt.ylabel('人数')
    plt.show()
    
plot_day_employ(app_train)

count    307511.000000
mean      63815.045904
std      141275.766519
min      -17912.000000
25%       -2760.000000
50%       -1213.000000
75%        -289.000000
max      365243.000000
Name: DAYS_EMPLOYED, dtype: float64

在这里插入图片描述

# 由于从业时间记录的是距离申请当日的天数，应该为负值，所以上图中右边区域的数据显然是异常的
anoms = app_train[app_train['DAYS_EMPLOYED'] > 300000]
anoms_one = app_train[app_train['DAYS_EMPLOYED'] == 365243]
non_anoms = app_train[app_train['DAYS_EMPLOYED'] != 365243]
print(anoms.shape)
print(anoms_one.shape)

(55374, 217)
(55374, 217)

# 可以看出异常数据均为一个恒定值365243，推测有可能是人为批量修改数据导致的
print('异常数据的偿还风险均值为%0.2f%%' % (100 * anoms['TARGET'].mean()))
print('正常数据的偿还风险均值为%0.2f%%' % (100 * non_anoms['TARGET'].mean()))

异常数据的偿还风险均值为5.40%
正常数据的偿还风险均值为8.66%

# 可以看出异常数据的偿还风险更低，所以不能简单的将异常数据删除，
# 这里将从业时间拆成两个特征，正常从业时间和异常从业时间，处理方式如下
app_train['DAYS_EMPLOYED_ANOM'] = app_train['DAYS_EMPLOYED'] == 365243
app_train['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace=True)
app_test['DAYS_EMPLOYED_ANOM'] = app_test['DAYS_EMPLOYED'] == 365243
app_test['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace=True)
plot_day_employ(app_train)

count    252137.000000
mean      -2384.169325
std        2338.360162
min      -17912.000000
25%       -3175.000000
50%       -1648.000000
75%        -767.000000
max           0.000000
Name: DAYS_EMPLOYED, dtype: float64

在这里插入图片描述

# 6.将年龄和从业天数都转化为正值，且以年为单位
app_train['DAYS_BIRTH'] = app_train['DAYS_BIRTH'].abs() / 365
app_train['DAYS_EMPLOYED'] = app_train['DAYS_EMPLOYED'].abs() / 365
app_test['DAYS_BIRTH'] = app_test['DAYS_BIRTH'].abs() / 365
app_test['DAYS_EMPLOYED'] = app_test['DAYS_EMPLOYED'].abs() / 365
app_train.rename(columns={'DAYS_BIRTH': 'YEARS_BIRTH', 'DAYS_EMPLOYED': 'YEARS_EMPLOYED'}, inplace=True)
app_test.rename(columns={'DAYS_BIRTH': 'YEARS_BIRTH', 'DAYS_EMPLOYED': 'YEARS_EMPLOYED'}, inplace=True)
print(app_train['YEARS_BIRTH'].describe())

count    307511.000000
mean         43.936973
std          11.956133
min          20.517808
25%          34.008219
50%          43.150685
75%          53.923288
max          69.120548
Name: YEARS_BIRTH, dtype: float64

# 7. 将离散的年龄数据转为年龄区间
age_data = app_train.loc[:,['TARGET', 'YEARS_BIRTH']]
age_data.loc[:, 'YEARS_BINNED'] = pd.cut(age_data.loc[:,'YEARS_BIRTH'], bins=np.linspace(20, 70, num=11))
age_group = age_data.groupby('YEARS_BINNED').mean()
print(age_group.index)

CategoricalIndex([(20.0, 25.0], (25.0, 30.0], (30.0, 35.0], (35.0, 40.0],
                  (40.0, 45.0], (45.0, 50.0], (50.0, 55.0], (55.0, 60.0],
                  (60.0, 65.0], (65.0, 70.0]],
                 categories=[(20.0, 25.0], (25.0, 30.0], (30.0, 35.0], (35.0, 40.0], (40.0, 45.0], (45.0, 50.0], (50.0, 55.0], (55.0, 60.0], ...], ordered=True, name='YEARS_BINNED', dtype='category')

plt.bar(age_group.index.astype(str), age_group['TARGET'])
plt.title('按年龄分组的偿还风险')
plt.xticks(rotation=75)
plt.xlabel('年龄段')
plt.ylabel('偿还风险')
plt.show()

在这里插入图片描述

# 8.这里简单的根据相关系数矩阵找到和TARGET最相关的特征（也可以在Random forest算法中学习出来，后面会介绍）
correlations = app_train.corr()['TARGET'].sort_values(ascending=False)
print('最正相关的十个特征为：\n', correlations.head(10))
print('最负相关的十个特征为：\n', correlations.tail(10))

最正相关的十个特征为：
 TARGET                                               1.000000
REGION_RATING_CLIENT_W_CITY                          0.060893
REGION_RATING_CLIENT                                 0.058899
NAME_INCOME_TYPE_Working                             0.057481
DAYS_LAST_PHONE_CHANGE                               0.055218
CODE_GENDER_M                                        0.054713
DAYS_ID_PUBLISH                                      0.051457
REG_CITY_NOT_WORK_CITY                               0.050994
NAME_EDUCATION_TYPE_Secondary / secondary special    0.049824
FLAG_EMP_PHONE                                       0.045982
Name: TARGET, dtype: float64
最负相关的十个特征为：
 DAYS_EMPLOYED_ANOM                     -0.045987
ORGANIZATION_TYPE_XNA                  -0.045987
NAME_INCOME_TYPE_Pensioner             -0.046209
CODE_GENDER_F                          -0.054704
NAME_EDUCATION_TYPE_Higher education   -0.056593
YEARS_EMPLOYED                         -0.074958
YEARS_BIRTH                            -0.078239
EXT_SOURCE_1                           -0.155317
EXT_SOURCE_2                           -0.160472
EXT_SOURCE_3                           -0.178919
Name: TARGET, dtype: float64

# 从而可以看出来EXT_SOURCE_3是和TARGET最相关的特征（绝对值最大），这里可以绘制出KDE曲线
def plot_kde_curve(var_name, df):
    """
    绘制变量和目标关联关系的核密度曲线
    :param var_name: 变量名称
    :param df: DataFrame
    :return:
    """
    # 计算皮尔逊相关系数
    corr = df.loc[:,'TARGET'].corr(df.loc[:,var_name], method='pearson')
    # 分别计算target=0和target=1时variable的中位数
    avg_paid = df.loc[df['TARGET'] == 0, var_name].median()
    avg_not_paid = df.loc[df['TARGET'] == 1, var_name].median()
    print('%s 和TARGET的相关系数为 %0.4f' % (var_name, corr))
    print('无偿还风险的变量中位数为 %0.4f' % avg_paid)
    print('有偿还风险的变量中位数系数为 %0.4f ' % avg_not_paid)

    plt.figure(figsize=(12, 6))
    # 绘制KDE曲线（distplot默认绘制直方图和核密度曲线）
    sns.distplot(df.loc[df['TARGET'] == 0, var_name], label='TARGET==0(无风险)')
    sns.distplot(df.loc[df['TARGET'] == 1, var_name], label='TARGET==1(有风险)')
    plt.xlabel(var_name)
    plt.ylabel('核密度估计')
    plt.title('%s 分布' % var_name)
    plt.legend()
    plt.show()
    
plot_kde_curve('YEARS_BIRTH', app_train)

YEARS_BIRTH 和TARGET的相关系数为 -0.0782
无偿还风险的变量中位数为 43.4986
有偿还风险的变量中位数系数为 39.1288

在这里插入图片描述

# 8.基准模型-LR
def base_lr_train(train_data, test_data):
    target = train_data['TARGET']
    train = train_data.drop(columns=['TARGET'])
    test = test_data.copy()
    # 用中位数填充缺失值
    imputer = Imputer(strategy='median')
    imputer.fit(train)
    train = imputer.transform(train)
    test = imputer.transform(test)
    # 归一化到[0,1]区间
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaler.fit(train)
    train = scaler.transform(train)
    test = scaler.transform(test)
    print('Training data shape', train.shape)
    print('Test data shape', test.shape)
    # 使用Logistic回归建模
    log_reg = LogisticRegression(C=0.001)
    log_reg.fit(train, target)
    log_reg_predict_not_paid = log_reg.predict_proba(test)[:, 1]
    submit = test_data[['SK_ID_CURR']]
    submit.loc[:, 'TARGET'] = log_reg_predict_not_paid
    print(submit.head(5))
    submit.to_csv(dir_path + '/result/log_reg_baseline.csv', index=False)

base_lr_train(app_train, app_test)

# 9.随机森林模型-RF
def base_rf_train(train_data, test_data):
    target = train_data['TARGET']
    train = train_data.drop(columns=['TARGET'])
    test = test_data.copy()
    # 用中位数填充缺失值
    imputer = Imputer(strategy='median')
    imputer.fit(train)
    train = imputer.transform(train)
    test = imputer.transform(test)
    # 归一化到[0,1]区间
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaler.fit(train)
    train = scaler.transform(train)
    test = scaler.transform(test)
    print('Training data shape', train.shape)
    print('Test data shape', test.shape)
    random_forest = RandomForestClassifier(n_estimators=100,random_state=42,verbose=1,n_jobs=-1)
    random_forest.fit(train,target)
    random_forest_predict_not_paid = random_forest.predict_proba(test)[:, 1]
    submit = test_data[['SK_ID_CURR']]
    submit.loc[:, 'TARGET'] = random_forest_predict_not_paid
    print(submit.head(5))
    submit.to_csv(dir_path + '/result/random_forest_baseline.csv', index=False)
    random_forest.max_features

base_rf_train(app_train, app_test)
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