def test_prepare_encoders(self):
df, config = self.df, self.config
ds = DataSource(df, config)
assert ds.enable_cache
encoders = ds.encoders
for col in ['x1', 'x2']:
assert isinstance(encoders[col], NumericEncoder)
assert encoders[col]._prepared is True
assert encoders[col].is_target is False
assert encoders[col]._type == 'int'
assert isinstance(encoders['y'], CategoricalAutoEncoder)
assert encoders['y']._prepared is True
assert encoders['y'].is_target is True
assert encoders['y'].onehot_encoder._prepared is True
assert encoders['y'].onehot_encoder.is_target is True
assert encoders['y'].use_autoencoder is False
encoded_column_x1 = ds.get_encoded_column_data('x1')
assert isinstance(encoded_column_x1, Tensor)
assert encoded_column_x1.shape[0] == len(df)
encoded_column_x2 = ds.get_encoded_column_data('x2')
assert isinstance(encoded_column_x2, Tensor)
assert encoded_column_x2.shape[0] == len(df)
encoded_column_y = ds.get_encoded_column_data('y')
assert isinstance(encoded_column_y, Tensor)
assert encoded_column_y.shape[0] == len(df)
def test_encoded_cache(self):
df, config = self.df, self.config
ds = DataSource(df, config)
assert ds.enable_cache
for column in ['x1', 'x2', 'y']:
assert not column in ds.encoded_cache
encoded_column = ds.get_encoded_column_data(column)
assert (ds.encoded_cache[column] == encoded_column).all()