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_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('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_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('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 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 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分類.png")

plt.show()

print("--- %s seconds ---" % (time.time() - start_time))

ROC圖如下所示：

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