基本概念

precision：預測為對的當中，原本為對的比例（越大越好，1為理想狀態）

recall：原本為對的當中，預測為對的比例（越大越好，1為理想狀態）

F-measure：F度量是對準確率和召回率做一個權衡（越大越好，1為理想狀態，此時precision為1，recall為1）

accuracy：預測對的（包括原本是對預測為對，原本是錯的預測為錯兩種情形）占整個的比例（越大越好，1為理想狀態）

fp rate：原本是錯的預測為對的比例（越小越好，0為理想狀態）

tp rate：原本是對的預測為對的比例（越大越好，1為理想狀態）

ROC曲線通常在Y軸上具有真陽性率，在X軸上具有假陽性率。這意味著圖的左上角是“理想”點 - 誤報率為零，真正的正率為1。這不太現實，但它確實意味著曲線下面積（AUC）通常更好。

二分類問題：ROC曲線

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport timestart_time = time.time()import matplotlib.pyplot as pltfrom sklearn.metrics import roc_curvefrom sklearn.metrics import aucimport numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import recall_score,accuracy_scorefrom sklearn.metrics import precision_score,f1_scorefrom keras.optimizers import Adam,SGD,sgdfrom keras.models import load_modelprint(’讀取數據’)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(’獲取測試數據和驗證數據’)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(’獲取模型’)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中元素最大值所對應的索引Y_valid = [np.argmax(y) for y in Y_valid]# micro：多分類# weighted：不均衡數量的類來說，計算二分類metrics的平均# macro：計算二分類metrics的均值，為每個類給出相同權重的分值。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)# 二分類　ＲＯＣ曲線# roc_curve:真正率（True Positive Rate , TPR）或靈敏度（sensitivity）# 橫坐標：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分類.png')plt.show()print('--- %s seconds ---' % (time.time() - start_time))

ROC圖如下所示：

多分類問題：ROC曲線

ROC曲線通常用于二分類以研究分類器的輸出。為了將ROC曲線和ROC區域擴展到多類或多標簽分類，有必要對輸出進行二值化。⑴可以每個標簽繪制一條ROC曲線。⑵也可以通過將標簽指示符矩陣的每個元素視為二元預測（微平均）來繪制ROC曲線。⑶另一種用于多類別分類的評估方法是宏觀平均，它對每個標簽的分類給予相同的權重。

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport timestart_time = time.time()import matplotlib.pyplot as pltfrom sklearn.metrics import roc_curvefrom sklearn.metrics import aucimport numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import recall_score,accuracy_scorefrom sklearn.metrics import precision_score,f1_scorefrom keras.optimizers import Adam,SGD,sgdfrom keras.models import load_modelfrom itertools import cyclefrom scipy import interpfrom sklearn.preprocessing import label_binarizenb_classes = 5print(’讀取數據’)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(’獲取測試數據和驗證數據’)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(’獲取模型’)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中元素最大值所對應的索引Y_valid = [np.argmax(y) for y in Y_valid]# Binarize the outputY_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：多分類# weighted：不均衡數量的類來說，計算二分類metrics的平均# macro：計算二分類metrics的均值，為每個類給出相同權重的分值。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）或靈敏度（sensitivity）# 橫坐標：假正率（False Positive Rate , FPR）# Compute ROC curve and ROC area for each classfpr = 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 areafpr['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 ratesall_fpr = np.unique(np.concatenate([fpr[i] for i in range(nb_classes)]))# Then interpolate all ROC curves at this pointsmean_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 AUCmean_tpr /= nb_classesfpr['macro'] = all_fprtpr['macro'] = mean_tprroc_auc['macro'] = auc(fpr['macro'], tpr['macro'])# Plot all ROC curveslw = 2plt.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分類.png')plt.show()print('--- %s seconds ---' % (time.time() - start_time))

ROC圖如下所示：

以上這篇python實現二分類和多分類的ROC曲線教程就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持好吧啦網。