def __init__(self, pre_splitted_path):
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_FilmTrust: 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_FilmTrust: Pre-splitted data not found, building new one"
)
data_reader = FilmTrustReader_DataManager()
loaded_dataset = data_reader.load_data()
URM_all = loaded_dataset.get_URM_all()
URM_all.eliminate_zeros()
URM_all.data = np.ones_like(URM_all.data)
URM_train, URM_test = split_train_validation_percentage_random_holdout(
URM_all, train_percentage=0.8)
URM_train, URM_validation = split_train_validation_percentage_random_holdout(
URM_train, train_percentage=0.9)
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("FilmTrust: Dataset loaded")
ut.print_stat_datareader(self)
def __init__(self):
super(PinterestICCVReader, self).__init__()
pre_splitted_path = "Data_manager_split_datasets/PinterestICCV/SIGIR/CMN_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("PinterestICCVReader: 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(
"PinterestICCVReader: Pre-splitted data not found, building new one"
)
print("PinterestICCVReader: loading URM")
# data_reader = PinterestICCVReader()
# data_reader.load_data()
#
# URM_all = data_reader.get_URM_all()
#
# self.URM_train, self.URM_validation, self.URM_test, self.URM_negative = split_train_validation_test_negative_leave_one_out_user_wise(URM_all, negative_items_per_positive=100)
dataset = Dataset_NeuralCollaborativeFiltering(
"Conferences/WWW/NeuMF_github/Data/pinterest-20")
self.URM_train_original, self.URM_test, self.URM_test_negative = dataset.URM_train, dataset.URM_test, dataset.URM_test_negative
self.URM_train, self.URM_validation = split_train_validation_percentage_random_holdout(
self.URM_train_original.copy(), train_percentage=0.8)
data_dict = {
"URM_train_original": self.URM_train_original,
"URM_train": self.URM_train,
"URM_test": self.URM_test,
"URM_test_negative": self.URM_test_negative,
"URM_validation": self.URM_validation,
}
save_data_dict(data_dict, pre_splitted_path, pre_splitted_filename)
print("PinterestICCVReader: loading complete")
def __init__(self,
pre_splitted_path,
dataset_variant="a",
train_interactions=1):
super(CiteulikeReader, self).__init__()
assert dataset_variant in [
"a", "t"
], "CiteulikeReader: dataset_variant must be either 'a' or 't'"
assert train_interactions in [
1, 10, "all"
], "CiteulikeReader: train_interactions must be: 1, 10 or 'all'"
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
original_data_path = "Conferences/KDD/CollaborativeVAE_github/data/citeulike-{}/".format(
dataset_variant)
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("CiteulikeReader: 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(
"CiteulikeReader: Pre-splitted data not found, building new one"
)
print("CiteulikeReader: loading URM")
if train_interactions == "all":
train_interactions_file_suffix = 10
else:
train_interactions_file_suffix = train_interactions
URM_train_builder = self._load_data_file(
original_data_path +
"cf-train-{}-users.dat".format(train_interactions_file_suffix))
URM_test_builder = self._load_data_file(
original_data_path +
"cf-test-{}-users.dat".format(train_interactions_file_suffix))
URM_test = URM_test_builder.get_SparseMatrix()
URM_train = URM_train_builder.get_SparseMatrix()
if dataset_variant == "a":
ICM_tokens_TFIDF = scipy.io.loadmat(original_data_path +
"mult_nor.mat")['X']
else:
# Variant "t" uses a different file format and is transposed
ICM_tokens_TFIDF = h5py.File(original_data_path +
"mult_nor.mat").get('X')
ICM_tokens_TFIDF = sps.csr_matrix(ICM_tokens_TFIDF).T
ICM_tokens_TFIDF = sps.csr_matrix(ICM_tokens_TFIDF)
ICM_tokens_bool = ICM_tokens_TFIDF.copy()
ICM_tokens_bool.data = np.ones_like(ICM_tokens_bool.data)
n_rows = max(URM_test.shape[0], URM_train.shape[0])
n_cols = max(URM_test.shape[1], URM_train.shape[1],
ICM_tokens_TFIDF.shape[0])
newShape = (n_rows, n_cols)
URM_test = reshapeSparse(URM_test, newShape)
URM_train = reshapeSparse(URM_train, newShape)
if train_interactions == "all":
URM_train += URM_test
URM_train, URM_test = split_train_validation_percentage_random_holdout(
URM_train, train_percentage=0.8)
URM_train, URM_validation = split_train_validation_percentage_random_holdout(
URM_train.copy(), train_percentage=0.8)
elif train_interactions == 10:
# If train interactions == 10 the train will NOT contain the validation data
URM_train, URM_validation = split_train_validation_percentage_random_holdout(
URM_train.copy(), train_percentage=0.8)
else:
# If train interactions == 10 the train WILL contain the validation data
_, URM_validation = split_train_validation_percentage_random_holdout(
URM_train.copy(), train_percentage=0.8)
self.ICM_DICT = {
"ICM_tokens_TFIDF": ICM_tokens_TFIDF,
"ICM_tokens_bool": ICM_tokens_bool,
}
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("CiteulikeReader: loading complete")
def __init__(self, dataset_variant="a", train_interactions=1):
super(CiteulikeReader, self).__init__()
assert dataset_variant in [
"a", "t"
], "CiteulikeReader: dataset_variant must be either 'a' or 't'"
assert train_interactions in [
1, 10, "all"
], "CiteulikeReader: train_interactions must be: 1, 10 or 'all'"
pre_splitted_path = "Data_manager_split_datasets/CiteULike/KDD/CollaborativeVAE_our_interface/"
pre_splitted_filename = "splitted_data_citeulike-{}-{}-items".format(
dataset_variant, train_interactions)
original_data_path = "Conferences/KDD/CollaborativeVAE_github/data/citeulike-{}/".format(
dataset_variant)
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("CiteulikeReader: 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(
"CiteulikeReader: Pre-splitted data not found, building new one"
)
print("CiteulikeReader: loading URM")
if train_interactions == "all":
train_interactions_file_suffix = 10
else:
train_interactions_file_suffix = train_interactions
URM_train_builder = self._load_data_file(
original_data_path +
"cf-train-{}-users.dat".format(train_interactions_file_suffix))
URM_test_builder = self._load_data_file(
original_data_path +
"cf-test-{}-users.dat".format(train_interactions_file_suffix))
self.URM_test = URM_test_builder.get_SparseMatrix()
self.URM_train = URM_train_builder.get_SparseMatrix()
if dataset_variant == "a":
self.ICM_title_abstract = scipy.io.loadmat(original_data_path +
"mult_nor.mat")['X']
else:
# Variant "t" uses a different file format and is transposed
self.ICM_title_abstract = h5py.File(original_data_path +
"mult_nor.mat").get('X')
self.ICM_title_abstract = sps.csr_matrix(
self.ICM_title_abstract).T
self.ICM_title_abstract = sps.csr_matrix(self.ICM_title_abstract)
n_rows = max(self.URM_test.shape[0], self.URM_train.shape[0])
n_cols = max(self.URM_test.shape[1], self.URM_train.shape[1],
self.ICM_title_abstract.shape[0])
newShape = (n_rows, n_cols)
self.URM_test = reshapeSparse(self.URM_test, newShape)
self.URM_train = reshapeSparse(self.URM_train, newShape)
if train_interactions == "all":
self.URM_train += self.URM_test
self.URM_train, self.URM_test = split_train_validation_percentage_random_holdout(
self.URM_train, train_percentage=0.8)
self.URM_train, self.URM_validation = split_train_validation_percentage_random_holdout(
self.URM_train, train_percentage=0.8)
else:
self.URM_train, self.URM_validation = split_train_validation_percentage_random_holdout(
self.URM_train, train_percentage=0.8)
data_dict = {
"URM_train": self.URM_train,
"URM_test": self.URM_test,
"URM_validation": self.URM_validation,
"ICM_title_abstract": self.ICM_title_abstract
}
save_data_dict(data_dict, pre_splitted_path, pre_splitted_filename)
print("CiteulikeReader: loading complete")