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
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 print('读取数据') X_train = np.load('x_train-rotate_2.npy') Y_train = np.load('y_train-rotate_2.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.array(X_valid, np.float32) / 255. print('获取模型') model = load_model('./model/InceptionV3_model.h5') opt = Adam(lr=1e-4) model.compile(optimizer=opt, loss='binary_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] # 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='weighted') recall = recall_score(Y_valid, Y_pred, average='weighted') f1_score = f1_score(Y_valid, Y_pred, average='weighted') 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) # 二分类 ROC曲线 # roc_curve:真正率(True Positive Rate , TPR)或灵敏度(sensitivity) # 横坐标:假正率(False Positive Rate , FPR) fpr, tpr, thresholds_keras = roc_curve(Y_valid, Y_pred) auc = auc(fpr, tpr) print("AUC : ", auc) plt.figure() plt.plot([0, 1], [0, 1], 'k--') plt.plot(fpr, tpr, label='Keras (area = {:.3f})'.format(auc)) plt.xlabel('False positive rate') plt.ylabel('True positive rate') plt.title('ROC curve') plt.legend(loc='best') plt.savefig("../images/ROC/ROC_2分类.png") plt.show() print("--- %s seconds ---" % (time.time() - start_time))
ROC图如下所示:
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:
以上这篇python实现二分类和多分类的ROC曲线教程就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。
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