文章目录

• • @[toc]
  • • • EER值求取
      • ROC概念
      • 代码示例

EER值求取

EER:等错误概率是说话人识别中常用的评价标准，是错误接受率（FA）和错误拒绝率（FR）的一个相对平衡点的阈值点，这个阈值点可以作为实际使用阶段的固定阈值。

def calculate_eer(y, y_score):
    # y denotes groundtruth scores,（真实标签）
    # y_score denotes the prediction scores.（经过softmax得到的标签）
    from scipy.optimize import brentq
    from sklearn.metrics import roc_curve
    from scipy.interpolate import interp1d

    fpr, 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）全称受试者工作特征曲线。纵轴是真正例率（True Postitive Rate，TPR），横轴是假正例率（False Positive Rate，FPR）

TPR = TP/(TP + FN)

FPR = FP/(FP + TN)

代码示例

import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
 
from 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曲线与ROC曲线，python sklearn实现
仅作为学习记录，供之后复习使用。