用Python绘制ROC曲线

最新推荐文章于 2025-04-18 15:28:33 发布

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本文介绍如何利用支持向量机(SVM)进行二分类任务，并通过ROC曲线和AUC值评估模型性能。以鸢尾花数据集为例，通过对数据集的预处理、特征选择及模型训练，展示了如何绘制ROC曲线并计算AUC。

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# 如果Anaconda中没有安装sklearn包，需要先导入sklearn包等

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc  ###计算roc和auc
from sklearn import cross_validation
 
# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
 
##变为2分类
X, y = X[y != 2], y[y != 2]
 
# Add noisy features to make the problem harder
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
 
# shuffle and split training and test sets
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=.3,random_state=0)
 
# Learn to predict each class against the other
svm = svm.SVC(kernel='linear', probability=True,random_state=random_state)
 
###通过decision_function()计算得到的y_score的值，用在roc_curve()函数中
y_score = svm.fit(X_train, y_train).decision_function(X_test)
 
# Compute ROC curve and ROC area for each class
fpr,tpr,threshold = roc_curve(y_test, y_score) ###计算真正率和假正率
roc_auc = auc(fpr,tpr) ###计算auc的值
 
plt.figure()
lw = 2
plt.figure(figsize=(10,10))
plt.plot(fpr, tpr, color='darkorange',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) ###假正率为横坐标，真正率为纵坐标做曲线
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()