def __init__(self):
super(NetflixPrizeReader, self).__init__()
pre_splitted_path = "Data_manager_split_datasets/NetflixPrize/WWW/MultiVAE_our_interface/"
pre_splitted_filename = "splitted_data"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("NetflixPrizeReader: Attempting to load pre-splitted data")
for attrib_name, attrib_object in load_data_dict(pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print("NetflixPrizeReader: Pre-splitted data not found, building new one")
data_reader = NetflixPrizeReader_DataManager()
data_reader.load_data()
URM_all = data_reader.get_URM_all()
# binarize the data (only keep ratings >= 4)
URM_all.data = URM_all.data >= 4.0
URM_all.eliminate_zeros()
URM_all = sps.coo_matrix(URM_all)
dict_for_dataframe = {"userId": URM_all.row,
"movieId": URM_all.col,
"rating": URM_all.data
}
URM_all_dataframe = pd.DataFrame(data = dict_for_dataframe)
self.URM_train, self.URM_train_all, self.URM_validation, self.URM_test = split_train_validation_test_VAE_CF(URM_all_dataframe,
n_heldout_users = 40000)
n_rows = max(self.URM_train.shape[0], self.URM_train_all.shape[0], self.URM_validation.shape[0], self.URM_test.shape[0])
n_cols = max(self.URM_train.shape[1], self.URM_train_all.shape[1], self.URM_validation.shape[1], self.URM_test.shape[1])
newShape = (n_rows, n_cols)
self.URM_test = reshapeSparse(self.URM_test, newShape)
self.URM_train = reshapeSparse(self.URM_train, newShape)
self.URM_train_all = reshapeSparse(self.URM_train_all, newShape)
self.URM_test = reshapeSparse(self.URM_test, newShape)
data_dict = {
"URM_train": self.URM_train,
"URM_train_all": self.URM_train_all,
"URM_test": self.URM_test,
"URM_validation": self.URM_validation,
}
save_data_dict(data_dict, pre_splitted_path, pre_splitted_filename)
print("NetflixPrizeReader: Dataset loaded")
def __init__(self, split_type="cold_user"):
super(Movielens20MReader, self).__init__()
assert split_type in ["cold_user", "warm_user"]
pre_splitted_path = "Data_manager_split_datasets/Movielens20M/WWW/MultiVAE_our_interface/"
pre_splitted_filename = "splitted_data" + "_" + split_type
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Movielens20MReader: Attempting to load pre-splitted data")
for attrib_name, attrib_object in load_data_dict(pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print("Movielens20MReader: Pre-splitted data not found, building new one")
data_reader = Movielens20MReader_DataManager()
data_reader.load_data()
URM_all = data_reader.get_URM_all()
# binarize the data (only keep ratings >= 4)
URM_all.data = URM_all.data >= 4.0
URM_all.eliminate_zeros()
if split_type == "cold_user":
URM_all = sps.coo_matrix(URM_all)
dict_for_dataframe = {"userId": URM_all.row,
"movieId": URM_all.col,
"rating": URM_all.data
}
URM_all_dataframe = pd.DataFrame(data=dict_for_dataframe)
self.URM_train, self.URM_train_all, self.URM_validation, self.URM_test = split_train_validation_test_VAE_CF(
URM_all_dataframe,
n_heldout_users=10000)
n_rows = max(self.URM_train.shape[0], self.URM_train_all.shape[0], self.URM_validation.shape[0],
self.URM_test.shape[0])
n_cols = max(self.URM_train.shape[1], self.URM_train_all.shape[1], self.URM_validation.shape[1],
self.URM_test.shape[1])
newShape = (n_rows, n_cols)
self.URM_test = reshapeSparse(self.URM_test, newShape)
self.URM_train = reshapeSparse(self.URM_train, newShape)
self.URM_train_all = reshapeSparse(self.URM_train_all, newShape)
self.URM_test = reshapeSparse(self.URM_test, newShape)
data_dict = {
"URM_train": self.URM_train,
"URM_train_all": self.URM_train_all,
"URM_test": self.URM_test,
"URM_validation": self.URM_validation,
}
elif split_type == "warm_user":
URM_all = sps.csr_matrix(URM_all)
users_to_keep = np.ediff1d(URM_all.indptr) >= 4
URM_all = URM_all[users_to_keep, :]
URM_all = sps.csc_matrix(URM_all)
items_to_keep = np.ediff1d(URM_all.indptr) >= 1
URM_all = URM_all[:, items_to_keep]
URM_all = sps.csr_matrix(URM_all)
self.URM_train, self.URM_validation, self.URM_test, _ = split_train_validation_test_negative_leave_one_out_user_wise(
URM_all)
data_dict = {
"URM_train": self.URM_train,
"URM_test": self.URM_test,
"URM_validation": self.URM_validation
}
save_data_dict(data_dict, pre_splitted_path, pre_splitted_filename)
print("Movielens20MReader: Dataset loaded")