def main():
args = get_arguments()
# Data loading
data_reader = read_split_load_data(3, args.allow_cold_users, args.seed)
URM_train, URM_test = data_reader.get_holdout_split()
ICM_categorical = data_reader.get_ICM_from_name("ICM_sub_class")
ICM_numerical, _ = get_ICM_numerical(data_reader.dataReader_object)
ICM_all, _ = get_ICM_train_new(data_reader)
similarity_type_list = None
if args.recommender_name == "item_cbf_numerical":
ICM = ICM_numerical
ICM_name = "ICM_numerical"
elif args.recommender_name == "item_cbf_categorical":
ICM = ICM_categorical
ICM_name = "ICM_categorical"
else:
ICM = ICM_all
ICM_name = "ICM_all"
# Setting evaluator
ignore_users = get_ignore_users(
URM_train,
data_reader.get_original_user_id_to_index_mapper(),
lower_threshold=args.lower_threshold,
upper_threshold=args.upper_threshold,
ignore_non_target_users=args.exclude_non_target)
evaluator = EvaluatorHoldout(URM_test,
cutoff_list=[10],
ignore_users=ignore_users)
# HP tuning
print("Start tuning...")
version_path = "../../report/hp_tuning/{}/".format(args.recommender_name)
now = datetime.now().strftime('%b%d_%H-%M-%S')
now = now + "_k_out_value_3/"
version_path = version_path + "/" + now
run_parameter_search_item_content(
URM_train=URM_train,
ICM_object=ICM,
ICM_name=ICM_name,
recommender_class=RECOMMENDER_CLASS_DICT[args.recommender_name],
evaluator_validation=evaluator,
metric_to_optimize="MAP",
output_folder_path=version_path,
similarity_type_list=similarity_type_list,
parallelizeKNN=True,
n_cases=args.n_cases,
n_random_starts=args.n_random_starts)
print("...tuning ended")
def setUp(self) -> None:
self.k_out = 3
self.cutoff = 5
self.path = "../../data/"
self.data_reader = read_split_load_data(self.k_out,
allow_cold_users=False,
seed=1000)
self.URM_train, self.URM_test = self.data_reader.get_holdout_split()
self.ICM_all, _ = get_ICM_train_new(self.data_reader)
self.UCM_all = get_UCM_train(self.data_reader)
self.main_rec = new_best_models.ItemCBF_CF.get_model(
URM_train=self.URM_train, ICM_train=self.ICM_all)
return model
if __name__ == '__main__':
set_env_variables()
seeds = get_seed_lists(N_FOLDS, get_split_seed())
# --------- DATA LOADING SECTION --------- #
URM_train_list = []
ICM_train_list = []
UCM_train_list = []
evaluator_list = []
model_list = []
for fold_idx in range(N_FOLDS):
# Read and split data
data_reader = read_split_load_data(K_OUT, ALLOW_COLD_USERS,
seeds[fold_idx])
URM_train, URM_test = data_reader.get_holdout_split()
ICM_train, item_feature2range = get_ICM_train_new(data_reader)
UCM_train, user_feature2range = get_UCM_train_new(data_reader)
# Ignore users and setting evaluator
ignore_users = get_ignore_users(
URM_train,
data_reader.get_original_user_id_to_index_mapper(),
LOWER_THRESHOLD,
UPPER_THRESHOLD,
ignore_non_target_users=IGNORE_NON_TARGET_USERS)
# Ignore users by age
# UCM_age = data_reader.get_UCM_from_name("UCM_age")
# age_feature_to_id_mapper = data_reader.dataReader_object.get_UCM_feature_to_index_mapper_from_name("UCM_age")
'epochs': 50,
'confidence_scaling': 'linear',
'alpha': 0.34370928029631664
}
recommender_class = ImplicitALSRecommender
model_name = "IALS_LT23"
if __name__ == '__main__':
# Set seed in order to have same splitting of data
seed_list = get_seed_list()
num_folds = len(seed_list)
URM_train_list = []
evaluator_list = []
for i in range(num_folds):
data_reader = read_split_load_data(K_OUT, ALLOW_COLD_USERS,
seed_list[i])
URM_train, URM_test = data_reader.get_holdout_split()
ignore_users = get_ignore_users(
URM_train,
data_reader.get_original_user_id_to_index_mapper(),
lower_threshold=LOWER_THRESHOLD,
upper_threshold=UPPER_THRESHOLD,
ignore_non_target_users=IGNORE_NON_TARGET_USERS)
evaluator = EvaluatorHoldout(URM_test,
cutoff_list=[CUTOFF],
ignore_users=ignore_users)
URM_train_list.append(URM_train)
evaluator_list.append(evaluator)
def main():
set_env_variables()
args = get_arguments()
seeds = get_seed_lists(args.n_folds, get_split_seed())
# --------- DATA LOADING SECTION --------- #
URM_train_list = []
ICM_train_list = []
UCM_train_list = []
evaluator_list = []
for fold_idx in range(args.n_folds):
# Read and split data
data_reader = read_split_load_data(K_OUT, args.allow_cold_users, seeds[fold_idx])
URM_train, URM_test = data_reader.get_holdout_split()
ICM_train, item_feature2range = get_ICM_train_new(data_reader)
UCM_train, user_feature2range = get_UCM_train_new(data_reader)
# Ignore users and setting evaluator
ignore_users = get_ignore_users(URM_train, data_reader.get_original_user_id_to_index_mapper(),
args.lower_threshold, args.upper_threshold,
ignore_non_target_users=args.exclude_non_target)
# Ignore users by age
# UCM_age = data_reader.get_UCM_from_name("UCM_age")
# age_feature_to_id_mapper = data_reader.dataReader_object.get_UCM_feature_to_index_mapper_from_name("UCM_age")
# age_demographic = get_user_demographic(UCM_age, age_feature_to_id_mapper, binned=True)
# ignore_users = np.unique(np.concatenate((ignore_users, get_ignore_users_age(age_demographic, AGE_TO_KEEP))))
URM_train_list.append(URM_train)
ICM_train_list.append(ICM_train)
UCM_train_list.append(UCM_train)
evaluator = EvaluatorHoldout(URM_test, cutoff_list=[CUTOFF], ignore_users=np.unique(ignore_users))
evaluator_list.append(evaluator)
# --------- HYPER PARAMETERS TUNING SECTION --------- #
print("Start tuning...")
hp_tuning_path = "../../../report/hp_tuning/" + args.recommender_name + "/"
date_string = datetime.now().strftime('%b%d_%H-%M-%S_k1_lt_{}/'.format(args.lower_threshold))
output_folder_path = hp_tuning_path + date_string
if args.recommender_name in COLLABORATIVE_RECOMMENDER_CLASS_DICT.keys():
run_cv_parameter_search(URM_train_list=URM_train_list,
recommender_class=RECOMMENDER_CLASS_DICT[args.recommender_name],
evaluator_validation_list=evaluator_list,
metric_to_optimize="MAP", output_folder_path=output_folder_path,
parallelize_search=args.parallelize, n_jobs=args.n_jobs,
n_cases=args.n_cases, n_random_starts=args.n_random_starts)
elif args.recommender_name in CONTENT_RECOMMENDER_CLASS_DICT.keys():
run_cv_parameter_search(URM_train_list=URM_train_list, ICM_train_list=ICM_train_list, ICM_name="ICM_all",
recommender_class=RECOMMENDER_CLASS_DICT[args.recommender_name],
evaluator_validation_list=evaluator_list,
metric_to_optimize="MAP", output_folder_path=output_folder_path,
parallelize_search=args.parallelize, n_jobs=args.n_jobs,
n_cases=args.n_cases, n_random_starts=args.n_random_starts)
elif args.recommender_name in DEMOGRAPHIC_RECOMMENDER_CLASS_DICT.keys():
run_cv_parameter_search(URM_train_list=URM_train_list, UCM_train_list=UCM_train_list, UCM_name="UCM_all",
recommender_class=RECOMMENDER_CLASS_DICT[args.recommender_name],
evaluator_validation_list=evaluator_list,
metric_to_optimize="MAP", output_folder_path=output_folder_path,
parallelize_search=args.parallelize, n_jobs=args.n_jobs,
n_cases=args.n_cases, n_random_starts=args.n_random_starts)
elif args.recommender_name in SIDE_INFO_CLASS_DICT:
temp_list = []
for i, URM in enumerate(URM_train_list):
temp = sps.vstack([URM, ICM_train_list[i].T], format="csr")
#temp = TF_IDF(temp).tocsr()
temp_list.append(temp)
run_cv_parameter_search(URM_train_list=temp_list,
recommender_class=RECOMMENDER_CLASS_DICT[args.recommender_name],
evaluator_validation_list=evaluator_list, metric_to_optimize="MAP",
output_folder_path=output_folder_path, parallelize_search=args.parallelize,
n_jobs=args.n_jobs, n_cases=args.n_cases, n_random_starts=args.n_random_starts)
print("...tuning ended")
all_models['RP3BETA_SIDE'] = new_best_models.RP3BetaSideInfo.get_model(
URM_train, ICM_all)
all_models['ITEM_CBF_FW'] = new_best_models.ItemCBF_all_FW.get_model(
URM_train, ICM_all)
all_models['PURE_SVD_SIDE'] = new_best_models.PureSVDSideInfo.get_model(
URM_train, ICM_all)
all_models['IALS_SIDE'] = new_best_models.IALSSideInfo.get_model(
URM_train, ICM_all)
#all_models['SSLIM_BPR'] = new_best_models.SSLIM_BPR.get_model(URM_train, ICM_all)
return all_models
if __name__ == '__main__':
# Data loading
data_reader = read_split_load_data(K_OUT, ALLOW_COLD_USERS,
get_split_seed())
URM_train, URM_test = data_reader.get_holdout_split()
# Build ICMs
ICM_all, _ = get_ICM_train_new(data_reader)
# Build UCMs
UCM_all = get_UCM_train(data_reader)
main_recommender = new_best_models.FusionMergeItem_CBF_CF.get_model(
URM_train, ICM_all)
model = HybridRerankingRecommender(URM_train, main_recommender)
all_models = _get_all_models(URM_train=URM_train,
UCM_all=UCM_all,
ICM_all=ICM_all)