【模型评估（2天）】 记录5个模型（逻辑回归、SVM、决策树、随机森林、XGBoost）关于accuracy、precision，recall和F1-score、auc值的评分表格，并画出ROC曲线。
from sklearn.metrics import accuracy_score, recall_score, f1_score, roc_auc_score, roc_curvefrom matplotlib import pyplot as plt
# 定义评估函数
def model_metrics(clf, X_train, X_test, y_train, y_test):
# 预测
y_train_pred = clf.predict(X_train)
y_test_pred = clf.predict(X_test)
y_train_pred_proba = clf.predict_proba(X_train)[:, 1]
y_test_pred_proba = clf.predict_proba(X_test)[:, 1]
# 评估
# 准确性
print('准确性：')
print('Train：{:.4f}'.format(accuracy_score(y_train, y_train_pred)))
print('Test：{:.4f}'.format(accuracy_score(y_test, y_test_pred)))
# 召回率
print('召回率：')
print('Train：{:.4f}'.format(recall_score(y_train, y_train_pred)))
print('Test：{:.4f}'.format(recall_score(y_test, y_test_pred)))
# f1_score
print('f1_score：')
print('Train：{:.4f}'.format(f1_score(y_train, y_train_pred)))
print('Test：{:.4f}'.format(f1_score(y_test, y_test_pred)))
# roc_auc
print('roc_auc：')
print('Train：{:.4f}'.format(roc_auc_score(y_train, y_train_pred_proba)))
print('Test：{:.4f}'.format(roc_auc_score(y_test, y_test_pred_proba)))
# 描绘 ROC 曲线
fpr_tr, tpr_tr, _ = roc_curve(y_train, y_train_pred_proba)
fpr_te, tpr_te, _ = roc_curve(y_test, y_test_pred_proba)
# KS
print('KS：')
print('Train：{:.4f}'.format(max(abs((fpr_tr - tpr_tr)))))
print('Test：{:.4f}'.format(max(abs((fpr_te - tpr_te)))))
# 绘图
plt.plot(fpr_tr, tpr_tr, 'r-',
7label="Train:AUC: {:.3f} KS:{:.3f}".format(roc_auc_score(y_train, y_train_pred_proba),
max(abs((fpr_tr - tpr_tr)))))
plt.plot(fpr_te, tpr_te, 'g-',
label="Test:AUC: {:.3f} KS:{:.3f}".format(roc_auc_score(y_test, y_test_pred_proba),
max(abs((fpr_tr - tpr_tr)))))
plt.plot([0, 1], [0, 1], 'd--')
plt.legend(loc='best')
plt.title("ROC curse")
plt.show()