python：实现二分类和多分类的ROC曲线

灰太狼 2022-03-14 10:18 774阅读 0赞

## 基本概念 ##

*  precision：预测为对的当中，原本为对的比例（越大越好，1为理想状态）
 *  recall：原本为对的当中，预测为对的比例（越大越好，1为理想状态）
 *  F-measure：F度量是对准确率和召回率做一个权衡（越大越好，1为理想状态，此时precision为1，recall为1）
 *  accuracy：预测对的（包括原本是对预测为对，原本是错的预测为错两种情形）占整个的比例（越大越好，1为理想状态）
 *  fp rate：原本是错的预测为对的比例（越小越好，0为理想状态）
 *  tp rate：原本是对的预测为对的比例（越大越好，1为理想状态）

**ROC曲线通常在Y轴上具有真阳性率，在X轴上具有假阳性率。这意味着图的左上角是“理想”点 - 误报率为零，真正的正率为1。这不太现实，但它确实意味着曲线下面积（AUC）通常更好。**

## 二分类问题：ROC曲线 ##

**ROC图如下所示：**  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NDEwNDI4_size_16_color_FFFFFF_t_70]

## 多分类问题：ROC曲线 ##

**ROC曲线通常用于二分类以研究分类器的输出。为了将ROC曲线和ROC区域扩展到多类或多标签分类，有必要对输出进行二值化。⑴可以每个标签绘制一条ROC曲线。⑵也可以通过将标签指示符矩阵的每个元素视为二元预测（微平均）来绘制ROC曲线。⑶另一种用于多类别分类的评估方法是宏观平均，它对每个标签的分类给予相同的权重。**

from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    import time
    start_time = time.time()
    import matplotlib.pyplot as plt
    from sklearn.metrics import roc_curve
    from sklearn.metrics import auc
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import recall_score,accuracy_score
    from sklearn.metrics import precision_score,f1_score
    from keras.optimizers import Adam,SGD,sgd
    from keras.models import load_model
    from itertools import cycle
    from scipy import interp
    from sklearn.preprocessing import label_binarize
    
    
    
    nb_classes = 5
    print('读取数据')
    X_train = np.load('x_train-resized_5.npy')
    Y_train = np.load('y_train-resized_5.npy')
    print(X_train.shape)
    print(Y_train.shape)
    
    
    print('获取测试数据和验证数据')
    X_train, X_valid, Y_train, Y_valid = train_test_split(X_train, Y_train, test_size=0.1, random_state=666)
    
    Y_train = np.asarray(Y_train,np.uint8)
    Y_valid = np.asarray(Y_valid,np.uint8)
    X_valid = np.asarray(X_valid, np.float32) / 255.
    
    
    print('获取模型')
    model = load_model('./model/SE-InceptionV3_model.h5')
    opt = Adam(lr=1e-4)
    model.compile(optimizer=opt, loss='categorical_crossentropy')
    
    
    print("Predicting")
    Y_pred = model.predict(X_valid)
    Y_pred = [np.argmax(y) for y in Y_pred]  # 取出y中元素最大值所对应的索引
    Y_valid = [np.argmax(y) for y in Y_valid]
    
    # Binarize the output
    Y_valid = label_binarize(Y_valid, classes=[i for i in range(nb_classes)])
    Y_pred = label_binarize(Y_pred, classes=[i for i in range(nb_classes)])
    
    
    # micro：多分类　　
    # weighted：不均衡数量的类来说，计算二分类metrics的平均
    # macro：计算二分类metrics的均值，为每个类给出相同权重的分值。
    precision = precision_score(Y_valid, Y_pred, average='micro')
    recall = recall_score(Y_valid, Y_pred, average='micro')
    f1_score = f1_score(Y_valid, Y_pred, average='micro')
    accuracy_score = accuracy_score(Y_valid, Y_pred)
    print("Precision_score:",precision)
    print("Recall_score:",recall)
    print("F1_score:",f1_score)
    print("Accuracy_score:",accuracy_score)
    
    
    # roc_curve:真正率（True Positive Rate , TPR）或灵敏度（sensitivity）
    # 横坐标：假正率（False Positive Rate , FPR）
    
    
    # Compute ROC curve and ROC area for each class
    fpr = dict()
    tpr = dict()
    roc_auc = dict()
    for i in range(nb_classes):
        fpr[i], tpr[i], _ = roc_curve(Y_valid[:, i], Y_pred[:, i])
        roc_auc[i] = auc(fpr[i], tpr[i])
    
    # Compute micro-average ROC curve and ROC area
    fpr["micro"], tpr["micro"], _ = roc_curve(Y_valid.ravel(), Y_pred.ravel())
    roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
    
    
    # Compute macro-average ROC curve and ROC area
    
    # First aggregate all false positive rates
    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(nb_classes)]))
    
    # Then interpolate all ROC curves at this points
    mean_tpr = np.zeros_like(all_fpr)
    for i in range(nb_classes):
        mean_tpr += interp(all_fpr, fpr[i], tpr[i])
    
    # Finally average it and compute AUC
    mean_tpr /= nb_classes
    
    fpr["macro"] = all_fpr
    tpr["macro"] = mean_tpr
    roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
    
    
    
    # Plot all ROC curves
    lw = 2
    plt.figure()
    plt.plot(fpr["micro"], tpr["micro"],
             label='micro-average ROC curve (area = {0:0.2f})'
                   ''.format(roc_auc["micro"]),
             color='deeppink', linestyle=':', linewidth=4)
    
    plt.plot(fpr["macro"], tpr["macro"],
             label='macro-average ROC curve (area = {0:0.2f})'
                   ''.format(roc_auc["macro"]),
             color='navy', linestyle=':', linewidth=4)
    
    colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
    for i, color in zip(range(nb_classes), colors):
        plt.plot(fpr[i], tpr[i], color=color, lw=lw,
                 label='ROC curve of class {0} (area = {1:0.2f})'
                 ''.format(i, roc_auc[i]))
    
    plt.plot([0, 1], [0, 1], 'k--', lw=lw)
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Some extension of Receiver operating characteristic to multi-class')
    plt.legend(loc="lower right")
    plt.savefig("../images/ROC/ROC_5分类.png")
    plt.show()
    
    
    print("--- %s seconds ---" % (time.time() - start_time))

**ROC图如下所示：**  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NDEwNDI4_size_16_color_FFFFFF_t_70 1]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NDEwNDI4_size_16_color_FFFFFF_t_70]: /images/20220313/d0964acce8dd48048be748b750aef73c.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NDEwNDI4_size_16_color_FFFFFF_t_70 1]: /images/20220313/0f195a3ea7ec470498c5a9f5b0e57b65.png