def test(self, testset):
"""
Test the recommendation system by recommending scores to all users in testset.
:param testset: test dataset
:return:
"""
if not self.n_rec_movie or not self.trainset or not self.movie_popular \
or not self.movie_count or not self.movie_popular_sort:
raise ValueError(
'UserCF has not init or fit method has not called yet.')
self.testset = testset
print('Test recommendation system start...')
N = self.n_rec_movie
# varables for precision and recall
hit = 0
rec_count = 0
test_count = 0
# varables for coverage
all_rec_movies = set()
# varables for popularity
popular_sum = 0
# record the calculate time has spent.
test_time = utils.LogTime(print_step=1000)
for i, user in enumerate(self.trainset):
test_movies = self.testset.get(user, {})
rec_movies = self.recommend(user) # type:list
for movie in rec_movies:
if movie in test_movies:
hit += 1
all_rec_movies.add(movie)
popular_sum += math.log(1 + self.movie_popular[movie])
# log steps and times.
rec_count += N
test_count += len(test_movies)
# print time per 500 times.
test_time.count_time()
precision = hit / (1.0 * rec_count)
recall = hit / (1.0 * test_count)
coverage = len(all_rec_movies) / (1.0 * self.movie_count)
popularity = popular_sum / (1.0 * rec_count)
print('Test recommendation system success.')
test_time.finish()
print('precision=%.4f\trecall=%.4f\tcoverage=%.4f\tpopularity=%.4f\n' %
(precision, recall, coverage, popularity))
summary = []
summary.append(precision)
summary.append(recall)
summary.append(coverage)
summary.append(popularity)
def predict(self, testset):
"""
Recommend movies to all users in testset.
:param testset: test dataset
:return: `dict` : recommend list for each user.
"""
movies_recommend = defaultdict(list)
print('Predict scores start...')
# record the calculate time has spent.
predict_time = utils.LogTime(print_step=500)
for i, user in enumerate(testset):
rec_movies = self.recommend(user) # type:list
movies_recommend[user].append(rec_movies)
# log steps and times.
predict_time.count_time()
print('Predict scores success.')
predict_time.finish()
return movies_recommend