recommender.py

import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
from lightfm.evaluation import auc_score

#fetch data and format it
data = fetch_movielens()


#create warp model
model = LightFM(loss='warp')

#train model
model.fit(data['train'], epochs=30, num_threads=2)


#function to find recommendation
def sample_recommendation(model, data, user_ids):

	#number of users and movies in training data
	n_users, n_items = data['train'].shape

	#generate recommendations for each user we input 
	for user_id in user_ids:

		#movies they already like
		known_positives = data['item_labels'][data['train'].tocsr()[user_id].indices]

		#movies predicted by our model
		scores= model.predict(user_id, np.arange(n_items))
		#rank them in order of most liked to least
        top_items = data['item_labels'][np.argsort(-scores)]

        #print the result
        print("User %s" % user_id)
        print("    known_positives:")

        for x in known_positives[:3]:
        	print("       %s" % x)

        print("    Recommended:")

        for x in top_items[:3]:
        	print("       %s" % x)

sample_recommendation(model,data,[3,25,4])