# 导入第三方包
import pandas as pdimport matplotlib.pyplot as plt# 读入数据
default = pd.read_excel(r'F:\\python_Data_analysis_and_mining\\14\\default of credit card clients.xls')print(default.shape)print(default.head())print(default.columns)# 数据集中是否违约的客户比例# 为确保绘制的饼图为圆形，需执行如下代码plt.axes(aspect = 'equal')# 中文乱码和坐标轴负号的处理plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = False# 统计客户是否违约的频数default['y']=default['default payment next month']counts = default.y.value_counts()# 绘制饼图plt.pie(x = counts, # 绘图数据 labels=pd.Series(counts.index).map({0:'不违约',1:'违约'}), # 添加文字标签 autopct='%.1f%%' # 设置百分比的格式，这里保留一位小数 )# 显示图形plt.show()
# 将数据集拆分为训练集和测试集
# 导入第三方包from sklearn import model_selectionfrom sklearn import ensemblefrom sklearn import metrics# 排除数据集中的ID变量和因变量，剩余的数据用作自变量X
X = default.drop(['ID','y','default payment next month'], axis = 1)y = default.y# 数据拆分X_train,X_test,y_train,y_test = model_selection.train_test_split(X,y,test_size = 0.25, random_state = 1234)# 构建AdaBoost算法的类AdaBoost1 = ensemble.AdaBoostClassifier()# 算法在训练数据集上的拟合AdaBoost1.fit(X_train,y_train)# 算法在测试数据集上的预测pred1 = AdaBoost1.predict(X_test)# 返回模型的预测效果
print('模型的准确率为：\n',metrics.accuracy_score(y_test, pred1))print('模型的评估报告：\n',metrics.classification_report(y_test, pred1))# 计算客户违约的概率值，用于生成ROC曲线的数据
y_score = AdaBoost1.predict_proba(X_test)[:,1]fpr,tpr,threshold = metrics.roc_curve(y_test, y_score)# 计算AUC的值roc_auc = metrics.auc(fpr,tpr)print(roc_auc)# 绘制面积图plt.stackplot(fpr, tpr, color='steelblue', alpha = 0.5, edgecolor = 'black')# 添加边际线plt.plot(fpr, tpr, color='black', lw = 1)# 添加对角线plt.plot([0,1],[0,1], color = 'red', linestyle = '--')# 添加文本信息plt.text(0.5,0.3,'ROC curve (area = %0.2f)' % roc_auc)# 添加x轴与y轴标签plt.xlabel('1-Specificity')plt.ylabel('Sensitivity')# 显示图形plt.show()# 自变量的重要性排序importance = pd.Series(AdaBoost1.feature_importances_, index = X.columns)importance.sort_values().plot(kind = 'barh')plt.show()# 取出重要性比较高的自变量建模predictors = list(importance[importance>0.02].index)print(predictors)# 通过网格搜索法选择基础模型所对应的合理参数组合
# 导入第三方包from sklearn.model_selection import GridSearchCVfrom sklearn.tree import DecisionTreeClassifiermax_depth = [3,4,5,6]
params1 = {'base_estimator__max_depth':max_depth}base_model = GridSearchCV(estimator = ensemble.AdaBoostClassifier(base_estimator = DecisionTreeClassifier()), param_grid= params1, scoring = 'roc_auc', cv = 5, n_jobs = 4, verbose = 1)base_model.fit(X_train[predictors],y_train)# 返回参数的最佳组合和对应AUC值print(base_model.best_params_, base_model.best_score_)
# 通过网格搜索法选择提升树的合理参数组合
# 导入第三方包from sklearn.model_selection import GridSearchCVn_estimators = [100,200,300]
learning_rate = [0.01,0.05,0.1,0.2]params2 = {'n_estimators':n_estimators,'learning_rate':learning_rate}adaboost = GridSearchCV(estimator = ensemble.AdaBoostClassifier(base_estimator = DecisionTreeClassifier(max_depth = 3)), param_grid= params2, scoring = 'roc_auc', cv = 5, n_jobs = 4, verbose = 1)adaboost.fit(X_train[predictors] ,y_train)# 返回参数的最佳组合和对应AUC值print(adaboost.best_params_, adaboost.best_score_)
# 使用最佳的参数组合构建AdaBoost模型
AdaBoost2 = ensemble.AdaBoostClassifier(base_estimator = DecisionTreeClassifier(max_depth = 3), n_estimators = 300, learning_rate = 0.01)# 算法在训练数据集上的拟合AdaBoost2.fit(X_train[predictors],y_train)# 算法在测试数据集上的预测pred2 = AdaBoost2.predict(X_test[predictors])# 返回模型的预测效果
print('模型的准确率为：\n',metrics.accuracy_score(y_test, pred2))print('模型的评估报告：\n',metrics.classification_report(y_test, pred2))
# 计算正例的预测概率，用于生成ROC曲线的数据
y_score = AdaBoost2.predict_proba(X_test[predictors])[:,1]fpr,tpr,threshold = metrics.roc_curve(y_test, y_score)# 计算AUC的值roc_auc = metrics.auc(fpr,tpr)print(roc_auc)# 绘制面积图plt.stackplot(fpr, tpr, color='steelblue', alpha = 0.5, edgecolor = 'black')# 添加边际线plt.plot(fpr, tpr, color='black', lw = 1)# 添加对角线plt.plot([0,1],[0,1], color = 'red', linestyle = '--')# 添加文本信息plt.text(0.5,0.3,'ROC curve (area = %0.2f)' % roc_auc)# 添加x轴与y轴标签plt.xlabel('1-Specificity')plt.ylabel('Sensitivity')# 显示图形plt.show()
# 运用网格搜索法选择梯度提升树的合理参数组合
learning_rate = [0.01,0.05,0.1,0.2]n_estimators = [100,300,500]max_depth = [3,4,5,6]params = {'learning_rate':learning_rate,'n_estimators':n_estimators,'max_depth':max_depth}gbdt_grid = GridSearchCV(estimator = ensemble.GradientBoostingClassifier(), param_grid= params, scoring = 'roc_auc', cv = 5, n_jobs = 4, verbose = 1)gbdt_grid.fit(X_train[predictors],y_train)# 返回参数的最佳组合和对应AUC值print(gbdt_grid.best_params_, gbdt_grid.best_score_)# 基于最佳参数组合的GBDT模型，对测试数据集进行预测
pred = gbdt_grid.predict(X_test[predictors])# 返回模型的预测效果print('模型的准确率为：\n',metrics.accuracy_score(y_test, pred))print('模型的评估报告：\n',metrics.classification_report(y_test, pred))# 计算违约客户的概率值，用于生成ROC曲线的数据
y_score = gbdt_grid.predict_proba(X_test[predictors])[:,1]fpr,tpr,threshold = metrics.roc_curve(y_test, y_score)# 计算AUC的值roc_auc = metrics.auc(fpr,tpr)print(roc_auc)# 绘制面积图
plt.stackplot(fpr, tpr, color='steelblue', alpha = 0.5, edgecolor = 'black')# 添加边际线plt.plot(fpr, tpr, color='black', lw = 1)# 添加对角线plt.plot([0,1],[0,1], color = 'red', linestyle = '--')# 添加文本信息plt.text(0.5,0.3,'ROC curve (area = %0.2f)' % roc_auc)# 添加x轴与y轴标签plt.xlabel('1-Specificity')plt.ylabel('Sensitivity')# 显示图形plt.show()import numpy as np
import pandas as pdimport matplotlib.pyplot as plt# 读入数据
creditcard = pd.read_csv(r'F:\\python_Data_analysis_and_mining\\14\\creditcard.csv')print(creditcard.shape)print(creditcard.head())# 为确保绘制的饼图为圆形，需执行如下代码# 中文乱码和坐标轴负号的处理plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = Falseplt.axes(aspect = 'equal')# 统计交易是否为欺诈的频数counts = creditcard.Class.value_counts()# 绘制饼图plt.pie(x = counts, # 绘图数据 labels=pd.Series(counts.index).map({0:'正常',1:'欺诈'}), # 添加文字标签 autopct='%.2f%%' # 设置百分比的格式，这里保留一位小数 )# 显示图形plt.show()
from sklearn import model_selection
# 将数据拆分为训练集和测试集
# 删除自变量中的Time变量X = creditcard.drop(['Time','Class'], axis = 1)print(X.columns)y = creditcard.Class# 数据拆分X_train,X_test,y_train,y_test = model_selection.train_test_split(X,y,test_size = 0.3, random_state = 1234)# 导入第三方包
from imblearn.over_sampling import SMOTE# 运用SMOTE算法实现训练数据集的平衡
over_samples = SMOTE(random_state=1234) over_samples_X,over_samples_y = over_samples.fit_sample(X_train, y_train)# over_samples_X,over_samples_y = over_samples.fit_sample(X_train.values,y_train.values.ravel())# 重抽样前的类别比例print(y_train.value_counts()/len(y_train))# 重抽样后的类别比例print(pd.Series(over_samples_y).value_counts()/len(over_samples_y))
# 导入第三方包
import xgboostimport numpy as np# 构建XGBoost分类器
xgboost = xgboost.XGBClassifier()# 使用重抽样后的数据，对其建模xgboost.fit(over_samples_X,over_samples_y)# 将模型运用到测试数据集中resample_pred = xgboost.predict(np.array(X_test))# 返回模型的预测效果
print('模型的准确率为：\n',metrics.accuracy_score(y_test, resample_pred))print('模型的评估报告：\n',metrics.classification_report(y_test, resample_pred))# 计算欺诈交易的概率值，用于生成ROC曲线的数据
y_score = xgboost.predict_proba(np.array(X_test))[:,1]fpr,tpr,threshold = metrics.roc_curve(y_test, y_score)# 计算AUC的值roc_auc = metrics.auc(fpr,tpr)# 绘制面积图
plt.stackplot(fpr, tpr, color='steelblue', alpha = 0.5, edgecolor = 'black')# 添加边际线plt.plot(fpr, tpr, color='black', lw = 1)# 添加对角线plt.plot([0,1],[0,1], color = 'red', linestyle = '--')# 添加文本信息plt.text(0.5,0.3,'ROC curve (area = %0.2f)' % roc_auc)# 添加x轴与y轴标签plt.xlabel('1-Specificity')plt.ylabel('Sensitivity')# 显示图形plt.show()# 构建XGBoost分类器
xgboost2 = xgboost.XGBClassifier()# 使用非平衡的训练数据集拟合模型xgboost2.fit(X_train,y_train)# 基于拟合的模型对测试数据集进行预测pred2 = xgboost2.predict(X_test)# 混淆矩阵pd.crosstab(pred2,y_test)# 返回模型的预测效果
print('模型的准确率为：\n',metrics.accuracy_score(y_test, pred2))print('模型的评估报告：\n',metrics.classification_report(y_test, pred2))# 计算欺诈交易的概率值，用于生成ROC曲线的数据
y_score = xgboost2.predict_proba(X_test)[:,1]fpr,tpr,threshold = metrics.roc_curve(y_test, y_score)# 计算AUC的值roc_auc = metrics.auc(fpr,tpr)# 绘制面积图
plt.stackplot(fpr, tpr, color='steelblue', alpha = 0.5, edgecolor = 'black')# 添加边际线plt.plot(fpr, tpr, color='black', lw = 1)# 添加对角线plt.plot([0,1],[0,1], color = 'red', linestyle = '--')# 添加文本信息plt.text(0.5,0.3,'ROC curve (area = %0.2f)' % roc_auc)# 添加x轴与y轴标签plt.xlabel('1-Specificity')plt.ylabel('Sensitivity')# 显示图形plt.show()