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)
# Data loading
root_data_path = "../../data/"
data_reader = RecSys2019Reader(root_data_path)
data_reader = New_DataSplitter_leave_k_out(data_reader,
k_out_value=K_OUT,
use_validation_set=False,
force_new_split=True,
seed=get_split_seed())
data_reader.load_data()
URM_train, URM_test = data_reader.get_holdout_split()
# Build ICMs
ICM_all = get_ICM_train(data_reader)
# Build UCMs
UCM_all = get_UCM_train(data_reader)
model = HybridWeightedAverageRecommender(URM_train, normalize=NORMALIZE)
all_models = _get_all_models(URM_train=URM_train,
UCM_all=UCM_all,
ICM_all=ICM_all)
for model_name, model_object in all_models.items():
model.add_fitted_model(model_name, model_object)
print("The models added in the hybrid are: {}".format(
list(all_models.keys())))
# Setting evaluator
ignore_users = get_ignore_users(
URM_train,
data_reader.get_original_user_id_to_index_mapper(),
