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FeaturePreprocessor

Constructor

__init__(self, name: str, use_dynamic: bool = False) -> None

Initializes a feature preprocessor.

Parameters- name (str): The name of the feature preprocessor.

use_dynamic (bool): Whether to use the dynamic preprocessing pipeline.

add_processing_step

add_processing_step(self, layer_creator: collections.abc.Callable[..., keras.src.layers.layer.Layer] = None, **layer_kwargs) -> None

Add a preprocessing layer to the feature preprocessor pipeline. If using the standard pipeline, a ProcessingStep is added. Otherwise, the layer is added to a list for dynamic handling.

Parameters- layer_creator (Callable[..., keras.layers.Layer]): A callable that creates a layer.

    If not provided, the default layer creator is used.
**layer_kwargs: Additional keyword arguments for the layer creator.

chain

chain(self, input_layer) -> keras.src.layers.layer.Layer

Chains the processing steps starting from the given input_layer.

For a static pipeline, this delegates to the internal Pipeline's chain() method. For the dynamic pipeline, it constructs the dynamic pipeline on the fly.

transform

transform(self, input_data: tensorflow.python.framework.tensor.Tensor) -> tensorflow.python.framework.tensor.Tensor

Process the input data through the pipeline. For the dynamic pipeline, wrap input in a dictionary and extract final output.

Parameters- input_data: The input data to process.

Returns

tf.Tensor: The processed data.