model.pertinent_negative

Attributes

data

Classes

PertinentNN

Implements a neural network model specifically designed to handle pertinent negatives in input features.

Module Contents

class model.pertinent_negative.PertinentNN(n_features: int, weights: torch.Tensor, pn_features: torch.Tensor, pn_weight_factor: float)

Bases: torch.nn.Sequential

Implements a neural network model specifically designed to handle pertinent negatives in input features.

This model modifies input features based on their relevance and the presence of pertinent negatives, employing a specialized network architecture with custom weights and biases to emphasize or suppress certain features according to their pertinence.

The network consists of linear layers combined with ReLU activation functions, structured to manipulate the input features dynamically. It uses the provided weights and a ‘pertinence’ tensor to adjust the impact of each feature on the model’s output, effectively highlighting the role of pertinent negatives in the prediction process.

Inherits from:

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

Initializes the PertinentNN model with specified dimensions, weights, pertinent negatives, and a multiplier.

The architecture is designed to first adjust the input features based on their pertinence, then to process these adjusted features through a series of layers that further manipulate and combine them based on the specified weights and the multiplier for pertinent negatives. The final output is a single value obtained through a linear combination of the processed features.

Parameters:

  • n_features (int) – The total number of features in the input data.

  • weights (torch.Tensor) – A tensor specifying the weights to be applied to the features of the model. This tensor should have a shape that matches the n_features, with each weight corresponding to a feature in the input data.

  • pn_features (torch.Tensor) – A tensor indicating the presence (1) or absence (0) of pertinent negatives for each feature. The length of this tensor can be equal to or less than n_features. If it is less, the missing values are assumed to be 0 (no pertinent negative).

  • pn_weight_factor (float) – A multiplier used to adjust the weights of the features identified as pertinent negatives.

_reformat_pn_weight(n_features: int, pn_features: torch.Tensor) → torch.Tensor

Reformats pn_features into tensors.

Parameters:

  • n_features (int) – The total number of features in the input data.

  • pn_features (torch.Tensor) – A torch.Tensor object indicating which feature is a pertinent negative.

Returns:

Reformatted tensor representing the features.

Return type:

torch.Tensor

_create_layer_weights(n_features: int, weights: torch.Tensor, pn_features: torch.Tensor, pn_weight_factor: float) → Tuple

Creates the weights for the layers in a PertinentNN model.

Parameters:

  • n_features (int) – The total number of features in the input data.

  • weights (torch.Tensor) – A tensor specifying the weights to be applied to the features of the model.

  • pn_features (torch.Tensor) – A tensor indicating the presence (1) or absence (0) of pertinent negatives for each feature.

  • pn_weight_factor (float) – A multiplier used to adjust the weights of the features identified as pertinent negatives.

Returns:

Tuple containing the weights and activation functions for the neural network model.

Return type:

tuple[list, list, list]

model.pertinent_negative.data