如何计算python中2个列表的精度和召回率

Question

I write a movie recommendation system. I have list of 20 films that I recommend to the user and list of 150 movies that the user really saw at last. How can I calculate in python with sklearn the precision and recall in these 2 lists?

For example I have 10 movies that I recommnded to user that the user realy saw, the calculation of reacall is: 10/150, the calculation of precision is: 10/20

Answer 1

From what I read, the simplest way would be to use the intersection between two sets.

I imagine you use some kind of identifier for the movies so your lists must not have duplicates (you probably don't recommend the same movie twice for instance) meaning you can use sets and their built-in intersection .

recommendations={"movie1", "movie2", "movie3"}
saw={"movie1", "movie2", "movie4", "movie5", "movie6"}

"recommended movies saw by the user"
recommendations.intersection(saw)
>>> {"movie1", "movie2"}

# To get the "number of recommended movie that the user saw":
movie_intersect = len(recommendations.intersection(saw))
movie_intersect
>>> 2

# Precision is just:
movie_intersect/len(recommendations)
>>> 0.666666666666666667

# Recall:
movie_intersect/len(saw)
>>> 0.4