def test_save_and_load(self):
recommender_class = NCRWrapper
recommender_instance_original, URM_train, URM_validation, URM_test, URM_negative = get_data_and_rec_instance(
recommender_class)
n_users, n_items = URM_train.shape
evaluator_test = EvaluatorNegativeItemSample(URM_test,
URM_negative, [50],
exclude_seen=True)
folder_path = "./temp_folder/"
file_name = "temp_file"
if not os.path.exists(folder_path):
os.makedirs(folder_path)
recommender_instance_original.fit()
recommender_instance_original.save_model(folder_path=folder_path,
file_name=file_name)
results_run_original, _ = evaluator_test.evaluateRecommender(
recommender_instance_original)
recommender_instance_loaded = recommender_class(URM_train)
recommender_instance_loaded.load_model(folder_path=folder_path,
file_name=file_name)
results_run_loaded, _ = evaluator_test.evaluateRecommender(
recommender_instance_loaded)
print("Result original: {}\n".format(results_run_original))
print("Result loaded: {}\n".format(results_run_loaded))
users_to_evaluate_mask = np.zeros(n_users, dtype=np.bool)
rows = URM_test.indptr
num_ratings = np.ediff1d(rows)
new_mask = num_ratings > 0
users_to_evaluate_mask = np.logical_or(users_to_evaluate_mask,
new_mask)
user_id_list = np.arange(n_users, dtype=np.int)[users_to_evaluate_mask]
URM_items_to_rank = sps.csr_matrix(URM_test.copy().astype(
np.bool)) + sps.csr_matrix(URM_negative.copy().astype(np.bool))
URM_items_to_rank.eliminate_zeros()
URM_items_to_rank.data = np.ones_like(URM_items_to_rank.data)
for test_user in user_id_list:
start_pos = URM_items_to_rank.indptr[test_user]
end_pos = URM_items_to_rank.indptr[test_user + 1]
items_to_compute = URM_items_to_rank.indices[start_pos:end_pos]
item_scores_original = recommender_instance_original._compute_item_score(
user_id_array=np.atleast_1d(test_user),
items_to_compute=items_to_compute)
item_scores_loaded = recommender_instance_loaded._compute_item_score(
user_id_array=np.atleast_1d(test_user),
items_to_compute=items_to_compute)
self.assertTrue(
np.allclose(item_scores_original, item_scores_loaded),
"item_scores of the fitted model and of the loaded model are different"
)
shutil.rmtree(folder_path, ignore_errors=True)
cutoff = 5
print_statistics(URM_train)
CMN_wrapper_train = CMN_RecommenderWrapper(URM_train)
user_KNNCF_Recommender = UserKNNCFRecommender(URM_train)
item_KNNCF_Recommender = ItemKNNCFRecommender(URM_train)
rp3_beta_Recommender = RP3betaRecommender(URM_train)
evaluator_negative_item_sample = EvaluatorNegativeItemSample(
URM_test, URM_test_negative, cutoff_list=[5, 10])
CMN_wrapper_train.loadModel('result_experiments/SIGIR/CMN_pinterest/',
'CMN_RecommenderWrapper_best_model')
d, s = evaluator_negative_item_sample.evaluateRecommender(CMN_wrapper_train)
print('CMN_wrapper_train')
print(s)
user_KNNCF_Recommender.loadModel('result_experiments/SIGIR/CMN_pinterest/',
'UserKNNCFRecommender_cosine_best_model')
d, s = evaluator_negative_item_sample.evaluateRecommender(
user_KNNCF_Recommender)
print('user_KNNCF_Recommender')
print(s)
item_KNNCF_Recommender.loadModel('result_experiments/SIGIR/CMN_pinterest/',
'ItemKNNCFRecommender_cosine_best_model')
d, s = evaluator_negative_item_sample.evaluateRecommender(
item_KNNCF_Recommender)
print('item_KNNCF_Recommender')