lgbm模型画ROC曲线

1、得到分类的概率

	import numpy as npimport pandas as pdimport lightgbm as lgbfrom sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = \train_test_split(data.iloc[:, 0:-1],  # featuredata.iloc[:, -1],  # labeltest_size=0.2,  # 训练集、测试集分割比例stratify=data.iloc[:, -1]  # 这里保证分割后y的比例分布与原数据一致)gbm = lgb.LGBMClassifier(colsample_bytree=0.7, max_depth=6, min_child_weight=0.0, min_child_samples=26,n_estimators=65, num_leaves=28, objective='binary', learning_rate=0.14,subsample=0.6)gbm.fit(X_train, y_train)gbm_y_pre = gbm.predict(X_test) # 分类的类别gbm_y_proba = gbm.predict_proba(X_test) # 分类的概率值

2、计算AUC，画出ROC

	import matplotlib.pyplot as pltfrom sklearn.metrics import roc_auc_score, roc_curvegbm_auc = roc_auc_score(y_test, gbm_y_proba[:, 1])  # 计算aucgbm_fpr, gbm_tpr, gbm_threasholds = roc_curve(y_test, gbm_y_proba[:, 1])  # 计算ROC的值plt.title("roc_curve of %s(AUC=%.4f)" % ('gbm', gbm_auc))plt.xlabel('Specificity')  # specificity = 1 - np.array(gbm_fpr))plt.ylabel('Sensitivity')  # sensitivity = gbm_tprplt.plot(list(1 - np.array(gbm_fpr)), gbm_tpr)plt.gca().invert_xaxis()  # 将X轴反转plt.show()