文章目錄

• • @[toc]
  • • • EER值求取
      • ROC概念
      • 代碼示例

EER值求取

EER:等錯(cuò)誤概率是說話人識(shí)別中常用的評價(jià)標(biāo)準(zhǔn)，是錯(cuò)誤接受率（FA）和錯(cuò)誤拒絕率（FR）的一個(gè)相對平衡點(diǎn)的閾值點(diǎn)，這個(gè)閾值點(diǎn)可以作為實(shí)際使用階段的固定閾值。

def calculate_eer(y, y_score):# y denotes groundtruth scores,（真實(shí)標(biāo)簽）# y_score denotes the prediction scores.（經(jīng)過softmax得到的標(biāo)簽）from scipy.optimize import brentqfrom sklearn.metrics import roc_curvefrom scipy.interpolate import interp1dfpr, tpr, thresholds = roc_curve(y, y_score, pos_label=1)eer = brentq(lambda x : 1. - x - interp1d(fpr, tpr)(x), 0., 1.)thresh = interp1d(fpr, thresholds)(eer)return eer, thresh

ROC概念

ROC（Receiver Operating Characteristic）全稱受試者工作特征曲線。縱軸是真正例率（True Postitive Rate，TPR），橫軸是假正例率（False Positive Rate，FPR）

TPR = TP/(TP + FN)

FPR = FP/(FP + TN)

代碼示例

import numpy as np import matplotlib.pyplot as plt from itertools import cyclefrom 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# 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"])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()plt.legend(loc="lower right") plt.show()

引用了下面一篇博客：
P-R曲線與ROC曲線，python sklearn實(shí)現(xiàn)
僅作為學(xué)習(xí)記錄，供之后復(fù)習(xí)使用。

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