def load_random_split(self,
test_rate=0.1,
random=False,
n_negative=100,
by_user=False,
n_test=10):
"""load split date generated by random_split.
Load split data generated by random_split from Onedrive, with test_rate = 0.1 and by_user = False.
Args:
test_rate: percentage of the test data. Note that percentage of the validation data will be the same as
test data.
random: bool. Whether randomly leave one basket as testing.
n_negative: Number of negative samples for testing and validation data.
by_user: bool. Default False.
- Ture: user-based split,
- False: global split,
n_test: int. Default 10. The number of testing and validation copies.
If n_test==0, will load the original (no negative items) valid and test datasets.
Returns:
train_data (DataFrame): Interaction for training.
valid_data list(DataFrame): List of interactions for validation
test_data list(DataFrame): List of interactions for testing
"""
processed_random_split_path = os.path.join(self.processed_path,
"random")
if not os.path.exists(processed_random_split_path):
os.mkdir(processed_random_split_path)
parameterized_path = generate_parameterized_path(test_rate=test_rate,
random=random,
n_negative=n_negative,
by_user=by_user)
download_path = processed_random_split_path
processed_random_split_path = os.path.join(processed_random_split_path,
parameterized_path)
if not os.path.exists(processed_random_split_path):
if (test_rate == 0.1 and random is False and n_negative == 100
and by_user is False):
# default parameters, can be downloaded from Onedrive
folder = OneDrive(url=self.processed_random_split_url,
path=download_path)
folder.download()
un_zip(processed_random_split_path + ".zip", download_path)
else:
# make
self.make_random_split(
test_rate=test_rate,
random=random,
n_negative=n_negative,
by_user=by_user,
n_test=n_test,
)
# load data from local storage
return load_split_data(processed_random_split_path, n_test=n_test)
def load_leave_one_out(self,
random=False,
n_negative=100,
n_test=10,
download=False):
"""Load split data generated by leave_out_out without random select.
Load split data generated by leave_out_out without random select from Onedrive.
Args:
random: bool. Whether randomly leave one item as testing.
n_negative: Number of negative samples for testing and validation data.
n_test: int. Default 10. The number of testing and validation copies.
If n_test==0, will load the original (no negative items) valid and test datasets.
Returns:
train_data (DataFrame): Interaction for training.
valid_data list(DataFrame): List of interactions for validation
test_data list(DataFrame): List of interactions for testing
"""
processed_leave_one_out_path = os.path.join(self.processed_path,
"leave_one_out")
if not os.path.exists(processed_leave_one_out_path):
os.mkdir(processed_leave_one_out_path)
parameterized_path = generate_parameterized_path(test_rate=0,
random=random,
n_negative=n_negative,
by_user=False)
download_path = processed_leave_one_out_path
processed_leave_one_out_path = os.path.join(
processed_leave_one_out_path, parameterized_path)
if not os.path.exists(processed_leave_one_out_path):
if download and random is False and n_negative == 100:
# default parameters, can be downloaded from Onedrive
folder = OneDrive(url=self.processed_leave_one_out_url,
path=download_path)
folder.download()
un_zip(processed_leave_one_out_path + ".zip", download_path)
else:
# make
self.make_leave_one_out(random=random,
n_negative=n_negative,
n_test=n_test)
# load data from local storage
return load_split_data(processed_leave_one_out_path, n_test=n_test)
