def perform_evaluation_slim(recommender, test_data: dict):
"""Takes an already fitted recommender and evaluates on test data.
If test_mode is false writes the submission"""
if not test_data:
print("Missing test data! Exiting...")
exit(-1)
print("Performing evaluation on test set...")
MAP_final = 0.0
evaluator = Evaluator()
for user in tqdm(test_data.keys()):
recommended_items = recommender.recommend(int(user),
exclude_seen=True)[:10]
relevant_item = test_data[int(user)]
MAP_final += evaluator.MAP(recommended_items, relevant_item)
MAP_final /= len(test_data.keys())
MAP_final *= 0.665
print("MAP-10 score:", MAP_final)
return MAP_final
def perform_evaluation(recommender):
"""Takes an already fitted recommender and evaluates on test data.
If test_mode is false writes the submission"""
print("Performing evaluation on test set...")
MAP_final = 0.0
evaluator, helper = Evaluator(), Helper()
URM_train, eval_data = helper.URM_train_validation, helper.validation_data
recommender.fit(URM_train)
for user in tqdm(eval_data.keys()):
recommended_items = recommender.recommend(int(user),
exclude_seen=True)
relevant_item = eval_data[int(user)]
MAP_final += evaluator.MAP(recommended_items, relevant_item)
MAP_final /= len(eval_data.keys())
print("MAP-10 score:", MAP_final)
MAP_final *= 0.665
print("MAP-10 public approx score:", MAP_final)
return MAP_final
# relevant_item = URM_all[int(user), :].toarray().squeeze()
#print(recommended_items)
# items = np.argwhere(relevant_item > 0)
#
# np.random.shuffle(items)
# relevant_item = items[:10].squeeze()
# print(relevant_item)
#relevant_item = helper.convert_list_of_string_into_int(relevant_item)
# print(recommended_items)
MAP_final += evaluator.MAP(recommended_items, relevant_item)
MAP_final /= len(target_users_test)
print(MAP_final)
if __name__ == '__main__':
helper = Helper()
# Train and test data are now loaded by the helper
URM_train, test_data = helper.get_train_evaluation_data()
weights_hybrid = {"cbf": 0.3, "cb": 0.7}
recommender = SLIMRecommender(URM_train)
recommender.fit(epochs=20)