def __init__(self, pre_splitted_path):
super(LastFMHetrec2011Reader, self).__init__()
pre_splitted_path += "lastfm_data_split/"
pre_splitted_filename = "splitted_data_"
original_data_path = "Conferences/AAAI/HERS_github/datasets/lastfm/"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print(
"LastFMHetrec2011Reader: Attempting to load pre-splitted data")
for attrib_name, attrib_object in load_data_dict_zip(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print(
"LastFMHetrec2011Reader: Pre-splitted data not found, building new one"
)
ratings_path = original_data_path + "lastfm_rating.txt"
users_net_path = original_data_path + "lastfm_userNet.txt"
items_net_path = original_data_path + "lastfm_itemNet.txt"
all_data = np.loadtxt(ratings_path, dtype=np.int32)
G_users = np.loadtxt(users_net_path, dtype=np.int32)
G_items = np.loadtxt(items_net_path, dtype=np.int32)
users_amount = max(G_users[:, 0])
items_amount = max(G_items[:, 0])
user_list_1 = np.asarray(list(G_users[:, 0])) - 1
user_list_2 = np.asarray(list(G_users[:, 1])) - 1
interactions = list(np.ones(len(user_list_2)))
UCM = sps.coo_matrix((interactions, (user_list_1, user_list_2)),
shape=(users_amount, users_amount),
dtype=np.int32)
UCM = UCM.tocsr()
item_list_1 = np.asarray(list(G_items[:, 0])) - 1
item_list_2 = np.asarray(list(G_items[:, 1])) - 1
interactions = list(np.ones(len(item_list_2)))
ICM = sps.coo_matrix((interactions, (item_list_1, item_list_2)),
shape=(items_amount, items_amount),
dtype=np.int32)
ICM = ICM.tocsr()
user_list = np.asarray(list(all_data[:, 0])) - 1
item_list = np.asarray(list(all_data[:, 1])) - 1
interactions = list(np.ones(len(item_list)))
URM_all = sps.coo_matrix((interactions, (user_list, item_list)),
shape=(users_amount, items_amount),
dtype=np.int32)
URM_all = URM_all.tocsr()
URM_train, URM_validation, URM_test, URM_negative = split_data_train_validation_test_negative_user_wise(
URM_all, negative_items_per_positive=10)
self.ICM_DICT = {
"ICM": ICM,
}
self.UCM_DICT = {
"UCM": UCM,
}
self.URM_DICT = {
"URM_train": URM_train,
"URM_validation": URM_validation,
"URM_test": URM_test,
"URM_negative": URM_negative,
}
save_data_dict_zip(self.URM_DICT, self.UCM_DICT, self.ICM_DICT,
pre_splitted_path, pre_splitted_filename)
print("LastFMHetrec2011Reader: loading complete")
def __init__(self, pre_splitted_path):
super(DeliciousHetrec2011Reader, self).__init__()
pre_splitted_path += "delicious_data_split/"
pre_splitted_filename = "splitted_data_"
original_data_path = "Conferences/AAAI/HERS_github/datasets/book/"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("DeliciousHetrec2011Reader: Attempting to load pre-splitted data")
for attrib_name, attrib_object in load_data_dict_zip(pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print("DeliciousHetrec2011Reader: Pre-splitted data not found, building new one")
users_net_path = original_data_path + "book_userNet.txt"
items_net_path = original_data_path + "book_itemNet.txt"
train_path = original_data_path + "book_rating_train_cold_user.txt"
test_path = original_data_path + "book_rating_test_cold_user.txt"
test_path_neg = original_data_path + "book_rating_test_cold_user_neg.txt"
users_data = np.loadtxt(users_net_path, dtype=np.int32)
items_data = np.loadtxt(items_net_path, dtype=np.int32)
train_data = np.loadtxt(train_path, dtype=np.int32)
test_data = np.loadtxt(test_path, dtype=np.int32)
users_amount = max(users_data[:, 0])
items_amount = max(items_data[:, 0])
user_list_1 = np.asarray(list(users_data[:, 0])) - 1
user_list_2 = np.asarray(list(users_data[:, 1])) - 1
interactions = list(np.ones(len(user_list_2)))
UCM = sps.coo_matrix((interactions, (user_list_1, user_list_2)), shape=(users_amount, users_amount), dtype=np.int32)
UCM = UCM.tocsr()
item_list_1 = np.asarray(list(items_data[:, 0])) - 1
item_list_2 = np.asarray(list(items_data[:, 1])) - 1
interactions = list(np.ones(len(item_list_2)))
ICM = sps.coo_matrix((interactions, (item_list_1, item_list_2)), shape=(items_amount, items_amount), dtype=np.int32)
ICM = ICM.tocsr()
user_list = np.asarray(list(train_data[:, 0])) - 1
item_list = np.asarray(list(train_data[:, 1])) - 1
interactions = list(np.ones(len(item_list)))
URM_train = sps.coo_matrix((interactions, (user_list, item_list)), shape=(users_amount, items_amount), dtype=np.int32)
URM_train = URM_train.tocsr()
user_list = np.asarray(list(test_data[:, 0])) - 1
item_list = np.asarray(list(test_data[:, 1])) - 1
interactions = list(np.ones(len(item_list)))
URM_test = sps.coo_matrix((interactions, (user_list, item_list)), shape=(users_amount, items_amount), dtype=np.int32)
URM_test = URM_test.tocsr()
with open(test_path_neg, "r") as f:
negative_nodes = np.asarray(list(csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)))
user_list = []
item_list = []
for j in range(len(negative_nodes)):
user_array = np.empty(len(negative_nodes[j][1:]))
user_array[:] = negative_nodes[j][0]
user_list = np.concatenate((user_list, user_array))
item_list = np.concatenate((item_list, negative_nodes[j][1:]))
user_list = user_list - 1
item_list = item_list - 1
interactions = list(np.ones(len(item_list)))
URM_negative = sps.coo_matrix((interactions, (user_list, item_list)), shape=(users_amount, items_amount), dtype=np.int32)
URM_negative = URM_negative.tocsr()
self.ICM_DICT = {
"ICM": ICM,
}
self.UCM_DICT = {
"UCM": UCM,
}
self.URM_DICT = {
"URM_train": URM_train,
"URM_validation": URM_test,
"URM_test": URM_test,
"URM_negative": URM_negative,
}
save_data_dict_zip(self.URM_DICT, self.UCM_DICT, self.ICM_DICT, pre_splitted_path, pre_splitted_filename)
print("DeliciousHetrec2011Reader: loading complete")