class Source_Maker_With_K_Fold_Validation(Source_Maker):
def __init__(self):
super(Source_Maker_With_K_Fold_Validation, self).__init__()
self.file_path_all_data = None
self.validation_percentage = None
def read_data_from_csv(self):
self.all_data = Dataset()
self.all_data.read_csv(self.file_path_all_data, index_col=0)
def apply_parameters(self):
if self.moving_average_window is not None:
self.all_data.rolling_average_with_window(
self.moving_average_window)
def remove_validation_data(self):
self.calculate_train_test_validation_sizes()
idxs_validation = range(0, self._sizes['validation'])
idxs_all = list(range(self._sizes['all']))
idxs_train_test = [i for i in idxs_all if i not in idxs_validation]
self.validation = self.create_df_subset_with_idxs(idxs_validation)
self.train_test = self.create_df_subset_with_idxs(idxs_train_test)
def calculate_train_test_validation_sizes(self):
size_all = self.all_data.get_number_rows()
size_validation = int(size_all * (self.validation_percentage / 100))
self._sizes = {'validation': size_validation, 'all': size_all}
def create_df_subset_with_idxs(self, row_range):
d = Dataset()
d.df = pd.DataFrame(self.all_data.df.iloc[row_range, :])
return d
def get_all_sensors(self):
return self.all_data.get_column_names()
class Source_Maker_With_Single_Input_File(Source_Maker):
def __init__(self):
super(Source_Maker_With_Single_Input_File, self).__init__()
self.file_path_all_data = None
def read_data_from_csv(self):
self.all_data = Dataset()
self.all_data.read_csv(self.file_path_all_data, index_col=0)
def apply_parameters(self):
if self.moving_average_window is not None:
self.all_data.rolling_average_with_window(
self.moving_average_window)
def get_all_sensors(self):
return self.all_data.get_column_names()