def test_env(self, ranking, test_file):
ranking_dict = dict()
test = ReadFile(test_file, space_type=self.space_type)
test.main_information_item_recommendation()
for sample in ranking:
ranking_dict.setdefault(sample[0], list()).append(sample[1])
return self.default_evaluation(ranking_dict, test)
def all_but_one_evaluation(self, file_result, file_test, n_ranks=list([1, 3, 5, 10])):
check_error_file(file_result)
check_error_file(file_test)
predict = ReadFile(file_result, space_type=self.space_type)
predict.main_information_item_recommendation()
test = ReadFile(file_test, space_type=self.space_type)
test.main_information_item_recommendation()
num_user = len(test.list_users)
final_values = list()
for user in test.list_users:
test.dict_users[user] = [test.dict_users[user][0]]
for i, n in enumerate(n_ranks):
if n < 1:
print('Error: N must >= 1.')
sys.exit()
partial_precision = list()
partial_recall = list()
avg_prec_total = list()
for user in test.list_users:
num_user_interactions = len(test.dict_users[user])
hit_cont = 0
avg_prec_sum = 0
try:
# Generate user intersection list between the recommended items and test.
intersection = list(set(predict.dict_users[user][:n]).intersection(
set(test.dict_users[user])))
if len(intersection) > 0:
partial_precision.append((float(len(intersection)) / float(n)))
partial_recall.append((float(len(intersection)) / float(num_user_interactions)))
for item in intersection:
hit_cont += 1
avg_prec_sum += (float(hit_cont) / float(test.dict_users[user].index(item) + 1))
avg_prec_total.append(float(avg_prec_sum) / float(num_user_interactions))
except KeyError:
pass
if not self.only_map:
final_precision = sum(partial_precision) / float(num_user)
final_values.append(final_precision)
final_recall = sum(partial_recall) / float(num_user)
final_values.append(final_recall)
final_map = sum(avg_prec_total) / float(num_user)
final_values.append(final_map)
return final_values
def simple_evaluation(self, file_result, file_test):
# Verify that the files are valid
check_error_file(file_result)
check_error_file(file_test)
predict = ReadFile(file_result, space_type=self.space_type)
predict.main_information_item_recommendation()
test = ReadFile(file_test, space_type=self.space_type)
test.main_information_item_recommendation()
return self.default_evaluation(predict.dict_users, test)
