import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import pickle
from sklearn.metrics import classification_report
from imblearn.over_sampling import SMOTE

# 加载数据
data = pd.read_csv('finance数据集.csv')

# 显示前五行的数据
print(data.head())

# 选择自变量和因变量
X = data.drop(['SeriousDlqin2yrs', 'Unnamed: 0'], axis=1)
y = data['SeriousDlqin2yrs']

# 分割训练集和测试集（测试集20%）
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练Logistic回归模型（最大迭代次数为1000次）
model = LogisticRegression(max_iter=1000)
#训练 Logistic 回归模型
model.fit(X_train, y_train)

# 保存模型
with open('2.2.1_model.pkl', 'wb') as file:
    pickle.dump(model, file)

# 预测并保存结果
y_pred = model.predict(X_test)
pd.DataFrame(y_pred, columns=['预测结果']).to_csv('2.2.1_results.txt', index=False)

# 生成测试报告
report = classification_report(y_test, y_pred, zero_division=1)
with open('2.2.1_report.txt', 'w') as file:
    file.write(report)

# 分析测试结果
accuracy = (y_test == y_pred).mean()
print(f"模型准确率: {accuracy:.2f}")

# 处理数据不平衡
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X_train, y_train)

# 重新训练模型
model.fit(X_resampled, y_resampled)
# 重新预测
y_pred_resampled = model.predict(X_test)

# 保存新结果
pd.DataFrame(y_pred_resampled, columns=['预测结果']).to_csv('2.2.1_results_xg.txt', index=False)

# 生成新的测试报告
report_resampled = classification_report(y_test, y_pred_resampled, zero_division=1)
with open('2.2.1_report_xg.txt', 'w') as file:
    file.write(report_resampled)

# 分析新的测试结果
accuracy_resampled =  (y_test == y_pred_resampled).mean()
print(f"重新采样后的模型准确率: {accuracy_resampled:.2f}")

   Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \
0           1                 1                              0.766127   45   
1           2                 0                              0.957151   40   
2           3                 0                              0.658180   38   
3           4                 0                              0.233810   30   
4           5                 0                              0.907239   49   

   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \
0                                     2   0.802982         9120.0   
1                                     0   0.121876         2600.0   
2                                     1   0.085113         3042.0   
3                                     0   0.036050         3300.0   
4                                     1   0.024926        63588.0   

   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \
0                               13                        0   
1                                4                        0   
2                                2                        1   
3                                5                        0   
4                                7                        0   

   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \
0                             6                                     0   
1                             0                                     0   
2                             0                                     0   
3                             0                                     0   
4                             1                                     0   

   NumberOfDependents  
0                 2.0  
1                 1.0  
2                 0.0  
3                 0.0  
4                 0.0  

C:\ProgramData\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

模型准确率: 0.94