aixd.mlmodel

This package contains the machine-learning model implementation.

Data Loading

DataModule

Data module for the ML model.

Architecture

Models

CondAEModel	Class representing a Conditional Autoencoder model.
CondVAEModel	Class representing a Conditional Variational Autoencoder model.
InverseModel	Wrapper class for taking a Cond(V)AEModel and fine-tuning the decoder using the encoder as surrogate model.
FreezeEncoder	A PyTorch Lightning callback class that extends the BaseFinetuning class and freezes the encoder of the CondAEModel before training.

Blocks

ResBlockFC	Module implementing a fully-connected residual block.
ResBlockConv	Module implementing a convolutional residual block.
ResBlock1D	Module implementing a 1D convolutional residual block.
ResBlock2D	Module implementing a 2D convolutional residual block.
ResBlock3D	Module implementing a 3D convolutional residual block.
SelfAttn	Module implementing a self-attention layer.
SelfAttn1D	Module implementing a 1D self-attention layer.
SelfAttn2D	Module implementing a 2D self-attention layer.
SelfAttn3D	Module implementing a 3D self-attention layer.
Activation	Module wrapping around an activation function.

Decoders/Encoders

Decoder	Decoder module of the conditional (variational) autoencoder.
Encoder	Encoder module of a conditional variational autoencoder.
VEncoder	Encoder module of a conditional variational autoencoder with additional parameters for the latent distribution.

Heads

InHeadFC	Fully-Connected Feed-Forward Network for encoding an unstrucured, 1D feature.
OutHeadFC	Fully-Connected Feed-Forward Network for decoding an unstrucured, 1D feature.
InHeadConv	Convolutional Network for encoding spatially strucured data in 1, 2 or 3 dimensions.
OutHeadConv	Convolutional Network for decoding spatially strucured data in 1, 2 or 3 dimensions.
InHeadConv1D	Convolutional Network for encoding spatially strucured (temporal) data in 1 dimension.
OutHeadConv1D	Convolutional Network for decoding spatially strucured (temporal) data in 1 dimension.
InHeadConv2D	Convolutional Network for encoding spatially strucured (image-like) data in 2 dimensions.
OutHeadConv2D	Convolutional Network for decoding spatially strucured (image-like) data in 2 dimensions.
InHeadConv3D	Convolutional Network for encoding spatially strucured (MRI- or video-like) data in 3 dimensions.
OutHeadConv3D	Convolutional Network for decoding spatially strucured (MRI- or video-like) data in 3 dimensions.

Losses

LossStd	Loss for the standard deviation of the values.
MGEloss	Mean gradient error.

Generation

Generator	Initialize a Generator instance.
GeneratorSampler	The Generator class needs to sampled from the outputML, in order to provide these values as entry to the decoder.

Sensitivity Analysis

LocalSensitivity	Sensitivity analysis class for the calculation and plotting of local sensitivities.
GlobalSensitivity	Sensitivity analysis class for the calculation and plotting of global sensitivities.