pyopmnearwell.ml.scaler_layers module

Provide MinMax scaler layers for tensorflow.keras.

Warning: Tensorflow 2.17 and Keras 3.0 introduce many pylint errors, hence we disable linting completely. It is possible that the module is not functional at the moment.

class pyopmnearwell.ml.scaler_layers.MinMaxScalerLayer(*args, **kwargs)

Bases: ScalerLayer, Layer

Scales the input according to MinMaxScaling.

See https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html for an explanation of the transform.

call(inputs: Tensor) → Tensor
compute_output_shape(input_shape)

Calculate the output shape.

classmethod from_config(config)

Reconstruct the layer from its config.

get_config()

Return the config for serialization.

class pyopmnearwell.ml.scaler_layers.MinMaxUnScalerLayer(*args, **kwargs)

Bases: ScalerLayer, Layer

Unscales the input by applying the inverse transform of MinMaxScalerLayer.

See https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html for an explanation of the transformation.

call(inputs: Tensor) → Tensor
compute_output_shape(input_shape)

Calculate the output shape.

classmethod from_config(config)

Reconstruct the layer from its config.

get_config()

Return the config for serialization.

class pyopmnearwell.ml.scaler_layers.ScalerLayer(*args, **kwargs)

Bases: Layer

MixIn to provide functionality for the Scaler Layer.

_adapt() → None
adapt(data: ArrayLike) → None

Fit the layer to the min and max of the data. This is done individually for each input feature.

Note

So far, this is only tested for 1 dimensional input and output. For higher dimensional input and output some functionality might need to be added.

Parameters:

data – _description_

build(input_shape: tuple[int, ...]) → None

Initialize data_min and data_max with the default values if they have not been initialized yet.

Parameters:

input_shape (tuple[int, ...]) – _description_

property data_max
property data_min
property feature_range
get_weights() → list[ArrayLike]

Return parameters of the scaling.

Returns:

List with three elements in the following order: self.data_min_, self.data_max_, self.feature_range_

Return type:

list[ArrayLike]

property is_adapted
min: Tensor
scalar: Tensor
set_weights(weights: list[ArrayLike]) → None

Set parameters of the scaling.

Parameters:

  • weights (list[ArrayLike]) – List with three elements in the following order:

  • data_min

  • data_max

  • feature_range

Raises:

ValueError – If feature_range[0] >= feature_range[1].