def train_test_split_matrix(self, split=0.2, use_saved=True):
"""
if use_saved is true, the pickle files of train and test are loaded. If not, we split the data into train and
test datasets.
:param split: the test proportion of the data; has to be a float between 0 and 1
:param use_saved: if true, we use the local pickled copies of the data
"""
if use_saved:
self.train = Util.load_pickle_object(
self.config.get_train_data_loc())
self.test = Util.load_pickle_object(
self.config.get_test_data_loc())
else:
matrix_copy = self.matrix.copy(deep=True)
split_point = int(matrix_copy.shape[1] * split)
# here we shuffle the columns to prevent overfitting
shuffled_columns = matrix_copy.columns.tolist()
shuffle(shuffled_columns)
matrix_copy = matrix_copy[shuffled_columns]
train_matrix = matrix_copy.copy(deep=True)
train_matrix.iloc[:, split_point:] = 0
test_matrix = matrix_copy.copy(deep=True)
test_matrix.iloc[:, :split_point] = 0
Util.pickle_object(self.config.get_train_data_loc(), train_matrix)
Util.pickle_object(self.config.get_test_data_loc(), test_matrix)
self.train = train_matrix
self.test = test_matrix
def save_matrix(self):
Util.pickle_object(self.config.get_matrix_loc(), self.matrix)
def save_data_dict(self, data_dict):
Util.pickle_object(self.config.get_data_dict_loc(), data_dict)
