matplotlib隐式保存ROC图片

青旅半醒 2023-10-05 08:12 22阅读 0赞

最近在跑模型的时候，想要获取一个ROC曲线的图，但是由于在Ubuntu的服务器上，没有可视化界面，当然就无法直接显示出图片来，由于自己之前没有这么使用过，因此将自己查询资料后的模板总结如下，方便自己或者有需要的人拿来使用。

import numpy as np
    import matplotlib
    matplotlib.use('Agg')
    from matplotlib.pyplot import plot,savefig
    
    import numpy as np
    import matplotlib.pyplot as plt
    from itertools import cycle
    
    from sklearn import svm, datasets
    from sklearn.metrics import roc_curve, auc
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import label_binarize
    from sklearn.multiclass import OneVsRestClassifier
    from scipy import interp
    from sklearn.metrics import roc_auc_score
    
    # Import some data to play with
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    
    # Binarize the output
    y = label_binarize(y, classes=[0, 1, 2])
    n_classes = y.shape[1]
    
    # 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 = train_test_split(X, y, test_size=.5,
                                                        random_state=0)
    
    # Learn to predict each class against the other
    classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
                                     random_state=random_state))
    y_score = classifier.fit(X_train, y_train).decision_function(X_test)
    
    # Compute ROC curve and ROC area for each class
    fpr = dict()
    tpr = dict()
    roc_auc = dict()
    for i in range(n_classes):
        fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
        roc_auc[i] = auc(fpr[i], tpr[i])
    
    # Compute micro-average ROC curve and ROC area
    fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
    roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
    #x=np.linspace(-4,4,30)
    #y=np.sin(x);
    #plot(x,y,'--*b')
    
    
    plt.figure()
    lw = 2
    plt.plot(fpr[2], tpr[2], color='darkorange',
             lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])
    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()
    
    savefig('E:/bert/ROC.jpg')   # 保存路径

参考：[https://scikit-learn.org/stable/auto\_examples/model\_selection/plot\_roc.html][https_scikit-learn.org_stable_auto_examples_model_selection_plot_roc.html]

[https://blog.csdn.net/lyb3b3b/article/details/84819931][https_blog.csdn.net_lyb3b3b_article_details_84819931]

[https://blog.csdn.net/sinat\_26917383/article/details/75199996?utm\_medium=distribute.pc\_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel\_param&depth\_1-utm\_source=distribute.pc\_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel\_param][https_blog.csdn.net_sinat_26917383_article_details_75199996_utm_medium_distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param_depth_1-utm_source_distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param]

[https://blog.csdn.net/GAN\_player/article/details/78543288][https_blog.csdn.net_GAN_player_article_details_78543288]

[https_scikit-learn.org_stable_auto_examples_model_selection_plot_roc.html]: https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
[https_blog.csdn.net_lyb3b3b_article_details_84819931]: https://blog.csdn.net/lyb3b3b/article/details/84819931
[https_blog.csdn.net_sinat_26917383_article_details_75199996_utm_medium_distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param_depth_1-utm_source_distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param]: https://blog.csdn.net/sinat_26917383/article/details/75199996?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param
[https_blog.csdn.net_GAN_player_article_details_78543288]: https://blog.csdn.net/GAN_player/article/details/78543288