【机器学习】F1分数(F1 Score)详解及tensorflow、numpy实现

import tensorflow as tf
def f1(y_hat, y_true, model='multi'):
''' 输入张量y_hat是输出层经过sigmoid激活的张量 y_true是label{0,1}的集和 model指的是如果是多任务分类，single会返回每个分类的f1分数，multi会返回所有类的平均f1分数（Marco-F1） 如果只是单个二分类任务，则可以忽略model '''
epsilon = 1e-7
y_hat = tf.round(y_hat)#将经过sigmoid激活的张量四舍五入变为0，1输出
tp = tf.reduce_sum(tf.cast(y_hat*y_true, 'float'), axis=0)
#tn = tf.sum(tf.cast((1-y_hat)*(1-y_true), 'float'), axis=0)
fp = tf.reduce_sum(tf.cast(y_hat*(1-y_true), 'float'), axis=0)
fn = tf.reduce_sum(tf.cast((1-y_hat)*y_true, 'float'), axis=0)
p = tp/(tp+fp+epsilon)#epsilon的意义在于防止分母为0，否则当分母为0时python会报错
r = tp/(tp+fn+epsilon)
f1 = 2*p*r/(p+r+epsilon)
f1 = tf.where(tf.is_nan(f1), tf.zeros_like(f1), f1)
if model == 'single':
return f1
if model == 'multi':
return tf.reduce_mean(f1)

import tensorflow as tf
y_true = tf.constant([[1,1,0,0,1], [1,0,1,1,0], [0,1,1,0,0]])
y_hat = tf.constant([[0,1,1,1,1], [1,0,0,1,1], [1,0,1,0,0]])
with tf.Session() as sess:
f1 = f1(y_hat, y_true)
print('F1 score:', sess.run(f1))

F1 score: 0.5999999

import numpy as np
def f1(y_hat, y_true, THRESHOLD=0.5):
''' y_hat是未经过sigmoid函数激活的 输出的f1为Marco-F1 '''
epsilon = 1e-7
y_hat = y_hat>THRESHOLD
y_hat = np.int8(y_hat)
tp = np.sum(y_hat*y_true, axis=0)
fp = np.sum(y_hat*(1-y_true), axis=0)
fn = np.sum((1-y_hat)*y_true, axis=0)
p = tp/(tp+fp+epsilon)#epsilon的意义在于防止分母为0，否则当分母为0时python会报错
r = tp/(tp+fn+epsilon)
f1 = 2*p*r/(p+r+epsilon)
f1 = np.where(np.isnan(f1), np.zeros_like(f1), f1)
return np.mean(f1)