def __init__(self, pre_splitted_path):
test_percentage = 0.2
validation_percentage = 0.2
pre_splitted_path += "data_split/"
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(
"Dataset_MovielensHetrec2011: 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(
"Dataset_MovielensHetrec2011: Pre-splitted data not found, building new one"
)
data_reader = MovielensHetrec2011Reader_DataManager()
loaded_dataset = data_reader.load_data()
URM_all = loaded_dataset.get_URM_all()
# keep only ratings 5
URM_all.data = URM_all.data == 5
URM_all.eliminate_zeros()
# create train - test - validation
URM_train_original, URM_test = split_train_validation_percentage_user_wise(
URM_all, train_percentage=1 - test_percentage, verbose=False)
URM_train, URM_validation = split_train_validation_percentage_user_wise(
URM_train_original,
train_percentage=1 - validation_percentage,
verbose=False)
self.URM_DICT = {
"URM_train": URM_train,
"URM_test": URM_test,
"URM_validation": URM_validation,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("Dataset_MovielensHetrec2011: Dataset loaded")
ut.print_stat_datareader(self)
def __init__(self):
super(Movielens100KReader, self).__init__()
pre_splitted_path = "Data_manager_split_datasets/Movielens100K/KDD/MCRec_our_interface/"
pre_splitted_filename = "splitted_data"
original_data_path = "Conferences/KDD/MCRec_github/data/"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Movielens100KReader: 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("Movielens100KReader: Pre-splitted data not found, building new one")
print("Movielens100KReader: loading URM")
from Conferences.KDD.MCRec_github.code.Dataset import Dataset
dataset = 'ml-100k'
dataset = Dataset(original_data_path + dataset)
URM_train, testRatings, testNegatives = dataset.trainMatrix, dataset.testRatings, dataset.testNegatives
# Dataset adds 1 to user and item id, removing it to restore 0 indexing
URM_train = sps.coo_matrix(URM_train)
URM_train.row -= 1
URM_train.col -= 1
self.URM_train = sps.csr_matrix((np.ones_like(URM_train.data), (URM_train.row, URM_train.col)))
num_users, num_items = self.URM_train.shape
# Build sparse matrices from lists
URM_test_builder = IncrementalSparseMatrix(n_rows=num_users, n_cols=num_items)
URM_test_negative_builder = IncrementalSparseMatrix(n_rows=num_users, n_cols=num_items)
for user_index in range(len(testRatings)):
user_id = testRatings[user_index][0]
current_user_test_items = testRatings[user_index][1:]
current_user_test_negative_items = testNegatives[user_index]
current_user_test_items = np.array(current_user_test_items) -1
current_user_test_negative_items = np.array(current_user_test_negative_items) -1
URM_test_builder.add_single_row(user_id -1, current_user_test_items, 1.0)
URM_test_negative_builder.add_single_row(user_id -1, current_user_test_negative_items, 1.0)
# the test data has repeated data, apparently
self.URM_test = URM_test_builder.get_SparseMatrix()
self.URM_test_negative = URM_test_negative_builder.get_SparseMatrix()
# Split validation from train as 10%
from Data_manager.split_functions.split_train_validation import split_train_validation_percentage_user_wise
self.URM_train, self.URM_validation = split_train_validation_percentage_user_wise(self.URM_train, train_percentage=0.9)
# Load features
data_reader = Movielens100KReader_DataManager()
data_reader.load_data()
zipFile_path = data_reader.DATASET_SPLIT_ROOT_FOLDER + data_reader.DATASET_SUBFOLDER
dataFile = zipfile.ZipFile(zipFile_path + "ml-100k.zip")
ICM_path = dataFile.extract("ml-100k/u.item", path=zipFile_path + "decompressed/")
ICM_genre = self._loadICM(ICM_path)
ICM_genre = ICM_genre.get_SparseMatrix()
shutil.rmtree(zipFile_path + "decompressed", ignore_errors=True)
self.ICM_dict = {"ICM_genre": ICM_genre}
data_dict = {
"URM_train": self.URM_train,
"URM_test": self.URM_test,
"URM_validation": self.URM_validation,
"URM_test_negative": self.URM_test_negative,
"ICM_dict": self.ICM_dict,
}
save_data_dict(data_dict, pre_splitted_path, pre_splitted_filename)
print("Movielens100KReader: loading complete")
def __init__(self):
test_percentage = 0.2
validation_percentage = 0.2
pre_splitted_path = "Data_manager_split_datasets/AmazonInstantVideo/RecSys/SpectralCF_our_interface/"
pre_splitted_filename = "splitted_data"
ratings_file_name = "ratings_Amazon_Instant_Video.csv"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print(
"Dataset_AmazonInstantVideo: 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(
"Dataset_AmazonInstantVideo: Pre-splitted data not found, building new one"
)
folder_path = self.DATASET_SPLIT_ROOT_FOLDER + self.DATASET_SUBFOLDER
downloadFromURL(self.DATASET_URL, folder_path, ratings_file_name)
# read Amazon Instant Video
df = pd.read_csv(folder_path + ratings_file_name,
sep=',',
header=None,
names=['user', 'item', 'rating',
'timestamp'])[['user', 'item', 'rating']]
# keep only ratings = 5
URM_train_builder = IncrementalSparseMatrix(
auto_create_col_mapper=True, auto_create_row_mapper=True)
URM_train_builder.add_data_lists(df['user'].values,
df['item'].values,
df['rating'].values)
URM_all = URM_train_builder.get_SparseMatrix()
URM_all.data = URM_all.data == 5
URM_all.eliminate_zeros()
# keep only users with at least 5 ratings
URM_all = ut.filter_urm(URM_all,
user_min_number_ratings=5,
item_min_number_ratings=1)
# create train - test - validation
URM_train_original, self.URM_test = split_train_validation_percentage_user_wise(
URM_all, train_percentage=1 - test_percentage, verbose=False)
self.URM_train, self.URM_validation = split_train_validation_percentage_user_wise(
URM_train_original,
train_percentage=1 - validation_percentage,
verbose=False)
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("Dataset_AmazonInstantVideo: Dataset loaded")
ut.print_stat_datareader(self)
def __init__(self,
pre_splitted_path,
type="original",
cold_start=False,
cold_items=None):
assert type in ["original", "ours"]
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
# their mode in cold start
mode = 1
# path for pre existed movielens1M split
movielens_splitted_path = "Conferences/RecSys/SpectralCF_github/data/ml-1m/"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Dataset_Movielens1M: 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(
"Dataset_Movielens1M: Pre-splitted data not found, building new one"
)
if type == "original":
assert (cold_start is False)
# use the SpectralCF class to read data
data_generator = Data(
train_file=movielens_splitted_path + 'train_users.dat',
test_file=movielens_splitted_path + 'test_users.dat',
batch_size=BATCH_SIZE)
# convert train into csr
full_train_matrix = sps.csr_matrix(data_generator.R)
URM_train_original = full_train_matrix
# convert test into csr
test_set = data_generator.test_set
uids, items = [], []
for uid in test_set.keys():
uids += np.full(len(test_set[uid]), uid).tolist()
items += test_set[uid]
test_matrix = sps.csr_matrix(
(np.ones(len(items)), (uids, items)),
shape=(full_train_matrix.shape))
if not cold_start:
URM_test = test_matrix
# create validation
URM_train, URM_validation = split_train_validation_percentage_user_wise(
URM_train_original,
train_percentage=0.9,
verbose=False)
else:
print('nothing')
elif type == "ours":
data_reader = Movielens1MReader_DataManager()
loaded_dataset = data_reader.load_data()
URM_all = loaded_dataset.get_URM_all()
URM_all.data = URM_all.data == 5
URM_all.eliminate_zeros()
if not cold_start:
URM_train, URM_test = split_train_validation_percentage_user_wise(
URM_all, train_percentage=0.8, verbose=False)
URM_train, URM_validation = split_train_validation_percentage_user_wise(
URM_train, train_percentage=0.9, verbose=False)
else:
if mode == 1: # their mode, cold start for full dataset
URM_train, URM_test = split_train_validation_cold_start_user_wise(
URM_all,
full_train_percentage=0.0,
cold_items=cold_items,
verbose=False)
URM_test, URM_validation = split_train_validation_percentage_user_wise(
URM_test, train_percentage=0.9, verbose=False)
if mode == 2: # cold start only for some users
URM_train, URM_test = split_train_validation_cold_start_user_wise(
URM_all,
full_train_percentage=0.8,
cold_items=cold_items,
verbose=False)
URM_train, URM_validation = split_train_validation_cold_start_user_wise(
URM_train,
full_train_percentage=0.9,
cold_items=cold_items,
verbose=False)
self.URM_DICT = {
"URM_train": URM_train,
"URM_test": URM_test,
"URM_validation": URM_validation,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("Dataset_Movielens1M: Dataset loaded")
ut.print_stat_datareader(self)