model.interaction_features
==========================

.. py:module:: model.interaction_features


Attributes
----------

.. autoapisummary::

   model.interaction_features.data


Classes
-------

.. autoapisummary::

   model.interaction_features.InteractingFeaturesNN


Module Contents
---------------

.. py:class:: InteractingFeaturesNN(n_features: int, weights: List[Union[float, Tuple]], interacting_features: List[Tuple[int, int]])

   Bases: :py:obj:`torch.nn.Sequential`


   Implements a neural network model designed to explicitly model interactions between specific pairs of features
   within the input data.

   This model is capable of emphasizing or de-emphasizing the impact of these interactions
   on the model's output through a specialized network architecture and custom weight assignments.

   The network consists of linear layers combined with ReLU activation, structured to manipulate the input
   # features based on the predefined interactions. The interactions are modelled such that the influence of one
   feature on another can be either enhanced or canceled, according to the specified weights and the interaction
   mechanism implemented within the network.

   Inherits from:
       torch.nn.Sequential: Parent class for implementing neural networks with modules defined in a
       sequential manner.

   Initializes the InteractingFeaturesNN model with specified dimensions, weights, and interactions.

   The architecture is designed to create a network that can process feature interactions by rearranging
   and weighting input features according to the specified interactions.

   :param n_features: The total number of features in the input data. This includes both interacting
                      and non-interacting features.
   :type n_features: int
   :param weights: A list of floats or tuples specifying the weights to be applied to the features of
                   the model. This list should have a len that matches the `n_features`, with each element
                   corresponding to a feature in the input data.
   :type weights: list
   :param interacting_features: A list where each tuple contains two integers representing
                                the indices of the interacting features. The first element in the tuple is considered the impacting
                                feature, and the second element is the feature being impacted.
   :type interacting_features: list[tuple]


   .. py:method:: _validate_inputs(weights: List[Union[float, Tuple]], interacting_features: List[Tuple[int, int]]) -> None

      Validates the inputs.

      :param weights: A list of floats or tuples specifying the weights to be applied to the features of
                      the model.
      :type weights: list
      :param interacting_features: A list where each tuple contains two integers representing
                                   the indices of the interacting features.
      :type interacting_features: list[tuple]

      :raises AssertionError: If the inputs are not in the valid datatypes.



   .. py:method:: _create_layer_weights(n_features: int, weights: List[Union[float, Tuple]], interacting_features: List[Tuple[int, int]]) -> Tuple

      Creates the weights for the layers in a InteractingFeaturesNN model.

      :param n_features: The total number of features in the input data.
      :type n_features: int
      :param weights: A list of floats or tuples specifying the weights to be applied to the features of
                      the model.
      :type weights: list
      :param interacting_features: A list where each tuple contains two integers representing
                                   the indices of the interacting features.
      :type interacting_features: list[tuple]

      :returns: Tuple containing the weights and activation functions for the
                neural network model.
      :rtype: tuple[list, NoneType, list]



.. py:data:: data