preface:最近《生物信息学》多次谈到AUC,ROC这两个指标,正在做的project,要求画ROC曲线,sklearn里面有相应的函数,故学习学习。
AUC:
ROC:
具体使用参考sklearn:
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html
http://www.tuicool.com/articles/b22eYz(博友博客)
#coding:utf-8 print(__doc__) import numpy as np from scipy import interp import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.cross_validation import StratifiedKFold ############################################################################### # Data IO and generation,导入iris数据,做数据准备 # import some data to play with iris = datasets.load_iris() X = iris.data y = iris.target X, y = X[y != 2], y[y != 2] n_samples, n_features = X.shape # Add noisy features random_state = np.random.RandomState(0) X = np.c_[X, random_state.randn(n_samples, 200 * n_features)] ############################################################################### # Classification and ROC analysis #分类,做ROC分析 # Run classifier with cross-validation and plot ROC curves #使用6折交叉验证,并且画ROC曲线 cv = StratifiedKFold(y, n_folds=6) classifier = svm.SVC(kernel='linear', probability=True, random_state=random_state) mean_tpr = 0.0 mean_fpr = np.linspace(0, 1, 100) all_tpr = [] for i, (train, test) in enumerate(cv): #通过训练数据,使用svm线性核建立模型,并对测试集进行测试,求出预测得分 probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test]) # Compute ROC curve and area the curve #通过roc_curve()函数,求出fpr和tpr,以及阈值 fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1]) mean_tpr += interp(mean_fpr, fpr, tpr) #对mean_tpr在mean_fpr处进行插值,通过scipy包调用interp()函数 mean_tpr[0] = 0.0 #初始处为0 roc_auc = auc(fpr, tpr) #画图,只需要plt.plot(fpr,tpr),变量roc_auc只是记录auc的值,通过auc()函数能计算出来 plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc)) #画对角线 plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck') mean_tpr /= len(cv) #在mean_fpr100个点,每个点处插值插值多次取平均 mean_tpr[-1] = 1.0 #坐标最后一个点为(1,1) mean_auc = auc(mean_fpr, mean_tpr) #计算平均AUC值 #画平均ROC曲线 #print mean_fpr,len(mean_fpr) #print mean_tpr plt.plot(mean_fpr, mean_tpr, 'k--', label='Mean ROC (area = %0.2f)' % mean_auc, lw=2) plt.xlim([-0.05, 1.05]) plt.ylim([-0.05, 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()
时间: 2024-10-07 16:50:01