def balance_data(self, data_model: DataModel,
target_column_name: str) -> DataModel:
weights_by_label = self.map_weights_by_cat_label(
data_model, target_column_name)
df = data_model.get_dataframe()
weights = self.get_weights_list(df, target_column_name,
weights_by_label)
weights_column_warning = 'note: adding weights column ({}), make sure it is passed to the estimator- and ' \
'data builder!'.format(WEIGHTS_COLUMN)
warnings.warn(weights_column_warning)
df[WEIGHTS_COLUMN] = weights
data_model.set_dataframe(df)
return data_model
def balance_data(self, data_model: DataModel,
target_column_name: str) -> DataModel:
long_stack, short_stack = self.prepare_data(
data_model=data_model, target_column_name=target_column_name)
length_to_have = len(long_stack)
duplicate_short_stack = short_stack.copy()
while len(short_stack) < length_to_have:
short_stack = pd.concat([short_stack, duplicate_short_stack])
short_stack = self.cut_df_to_length(short_stack, length_to_have)
self.validate_result(long_stack, short_stack)
new_df = self.merge_stacks(long_stack, short_stack)
data_model.set_dataframe(new_df)
return data_model
def randomize_data(data: DataModel, seed: int):
df = data.get_dataframe()
df = df.sample(frac=1, random_state=seed)
data.set_dataframe(df)
