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项目中需要判断用户提交的多选题选项的正确率，比如正确答案应该为a, b, c，而用户选择的是a, d，那么如何判断他的正确率呢，这个场景就需要用到F1 score来计算。
From Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/F1_score
In statistical analysis of Binary classification, the F1 score (also F-score or F-measure) is a measure of a test’s accuracy.
It considers both the precision p and the recall r of the test to compute the score:
p is the number of correct results divided by the number of all returned results and r is the number of correct results divided by the number of results that should have been returned.
The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
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18#!/usr/bin/env python
#-*- coding:utf-8 -*-
def get_f1(standard_answer, user_answer):
s_user_answer = set(user_answer)
s_standard_answer = set(standard_answer)
correct_results_len = len(s_user_answer & s_standard_answer)
precision = (correct_results_len + 1e-8) / (len(user_answer) + 1e-8)
recall = (correct_results_len + 1e-8) / (len(standard_answer) + 1e-8)
f1 = 2 * precision * recall / (precision + recall)
return f1
if __name__ == ‘__main__’:
standard = [‘a’, ‘c’, ‘d’]
user = [‘a’]
print get_f1(standard, user)
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