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分类指标计算 Precision、Recall、F

【机器学习】一文读懂分类算法常用评价指标

诊断试验评价——真实性评价以及医疗图像这些指标的含义功能

import numpy as np
from sklearn import metrics
from sklearn.metrics import roc_auc_score
# import precisionpltdef calculate_TP(y, y_pred):tp = 0for i, j in zip(y, y_pred):if i == j == 1:tp += 1return tp
def calculate_TN(y, y_pred):tn = 0for i, j in zip(y, y_pred):if i == j == 0:tn += 1return tn
def calculate_FP(y, y_pred):fp = 0for i, j in zip(y, y_pred):if i == 0 and j == 1:fp += 1return fp
def calculate_FN(y, y_pred):fn = 0for i, j in zip(y, y_pred):if i == 1 and j == 0:fn += 1return fn# acc 这两个值是一样的
def calculate_accuracy_sklearn(y, y_pred):return metrics.accuracy_score(y, y_pred)
# accu
def calculate_accuracy(y, y_pred):tp = calculate_TP(y, y_pred)tn = calculate_TN(y, y_pred)fp = calculate_FP(y, y_pred)fn = calculate_FN(y, y_pred)return (tp+tn) / (tp+tn+fp+fn)# 精度 Precision
def calculate_precision(y, y_pred):tp = calculate_TP(y, y_pred)fp = calculate_FP(y, y_pred)return tp / (tp + fp)
# 召回率 Recall  也是 TPR  有多少被预测成正类（正类预测正确）
def calculate_recall(y, y_pred):tp = calculate_TP(y, y_pred)fn = calculate_FN(y, y_pred)return tp / (tp + fn)
def precision_recall_curve(y, y_pred):y_pred_class,precision,recall = [],[],[]thresholds = [0.1, 0.2, 0.3, 0.6, 0.65]for thresh in thresholds:for i in y_pred:     #y_pred holds prob value for class 1if i>=thresh: y_pred_class.append(1)else: y_pred_class.append(0)precision.append(calculate_precision(y, y_pred_class))recall.append(calculate_recall(y, y_pred_class))return recall, precisionplt.plot(recall, precision)# F1分数 F1结合了Precision和Recall得分，得到一个单一的数字，可以帮助直接比较不同的模型。 可以将其视为P和R的谐波均值
def calculate_F1(y, y_pred):p = calculate_precision(y, y_pred)r = calculate_recall(y, y_pred)return 2*p*r / (p+r)# AUC-ROC是用于二分类问题的非常常见的评估指标之一。 这是一条曲线，绘制在y轴的TPR（正确率）和x轴的FPR（错误率）之间，
# ROC曲线下的AUC（曲线下的面积）值越接近1，模型越好
def roc_auc(y, y_pred):return roc_auc_score(y, y_pred)# 所有反类中，有多少被预测成正类（正类预测错误）
def FPR(y, y_pred):fp= calculate_FP(y, y_pred)tn= calculate_TN(y, y_pred)tp = calculate_TP(y, y_pred)fn = calculate_FN(y, y_pred)return fp / (fp + tn)# 所有正类中，有多少被预测成反类（反类预测错误）
def FNR(y, y_pred):#tp = calculate_TP(y, y_pred)fn = calculate_FN(y, y_pred)return fn / (fn + tp)
# TNR= TN / (FP + TN)   , return tp / (tp + fp)
def TNR(y,y_pred):tn = calculate_TN(y, y_pred)fp = calculate_FP(y, y_pred)return tn / (fp + tn)#TPR=TP/ (TP+ FN)  TPR即为敏感度（sensitivity）  也是recall
def TPR(y,y_pred):tp = calculate_TP(y, y_pred)fn = calculate_FN(y, y_pred)return tp / (fn + tp)# Recall	F1_Score	precision	FPR假阳性率	FNR假阴性率
# AUC	AUC910%CI	ACC准确,TPR敏感,TNR特异度(TPR即为敏感度（sensitivity），TNR即为特异度（specificity）)
y=[]
y_pred=[]a="/home/syy/code/PaddleClas/school_pre/pred3.txt"
f=open(a)
for line in f.readlines():line = line.strip().split()y.append(int(line[0]))y_pred.append(int(line[1]))print(y)
print(y_pred)Recall=calculate_recall(y, y_pred)
precision=calculate_precision(y, y_pred)
F1_Score=calculate_F1(y, y_pred)
FPR=FPR(y, y_pred)
FNR=FNR(y, y_pred)auc = roc_auc(y, y_pred)
accuracy=calculate_accuracy_sklearn(y, y_pred)
TPR = TPR(y,y_pred)
TNR = TNR(y,y_pred)print("Recall",round(Recall,4))
print("precision",round(precision,4))
print("F1_Score",round(F1_Score,4))
print("FPR",round(FPR,4))
print("FNR",round(FNR,4))print("auc",round(auc,4))
print("accuracy",round(accuracy,4))
print("TPR",round(TPR,4))
print("TNR",round(TNR,4))

本文标签： 分类指标计算 PrecisionRecallF

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