Decoder¶

class Decoder(*args, **kwargs)[source]¶

Bases: ABC, Module

Interface for all decoders.

A decoder takes as input latent coordinates and an encoded graph context, and produces either a new graph with predictions or a global output vector.

Equivariant Decoder¶

class EquivariantDecoder(*args, **kwargs)[source]¶

Bases: Decoder, ABC

Abstract base class for equivariant decoders that produce outputs per hyper-edges.

Equivariant decoders preserve the structure of the input graph and produce predictions for each hyper-edge in a permutation-equivariant manner.

class MLPEquivariantDecoder(*args, **kwargs)[source]¶

Equivariant decoder that applies class-specific MLPs over hyper-edge features and latent coordinates.

\[\forall c \in \mathcal{C}, \forall e \in \mathcal{E}^c_x, \hat{y}_e = \phi_\theta^c(x_e, h_e),\]

where \(\phi_\theta^c\) is a class specific MLP.

Parameters:

in_graph_structure – Input graph structure.
in_array_size – Size of the input coordinate arrays.
hidden_sizes – Hidden sizes of the MLPs \(\phi_\theta^c\).
activation – Activation of the MLP \(\phi_\theta^c\).
out_structure – Graph structure of the output.
use_bias – Whether to use bias in the MLPs \(\phi_\theta^c\).
kernel_init – Kernel initializer for the MLPs \(\phi_\theta^c\).
bias_init – Bias initializer for the MLPs \(\phi_\theta^c\).
final_activation – Activation of the final layer of the MLPs \(\phi_\theta^c\).
encoded_feature_size – None if the input data has not been encoded, otherwise the size of the encoded features.
seed – Seed for RNG streams for weight initialization.

Return type:

Any

class InvariantDecoder(*args, **kwargs)[source]¶

Bases: Decoder, ABC

Abstract base class for invariant decoders that produce global outputs.

Invariant decoders aggregate information from all addresses in a permutation-invariant manner to produce a single global output vector.

class SumInvariantDecoder(*args, **kwargs)[source]¶

Sum invariant decoder, that sums the information of all addresses.

\[\hat{y} = \phi_\theta \left( \sum_{a \in \mathcal{A}(x)} \psi_\theta(h_a)\right),\]

where \(\phi_\theta\) (outer) and \(\psi_\theta\) (inner) are both trainable MLPs.

Parameters:

Return type:

Any

class MeanInvariantDecoder(*args, **kwargs)[source]¶

Mean invariant decoder, that averages the information of all addresses.

\[\hat{y} = \phi_\theta \left( \frac{1}{\vert \mathcal{A}(x) \vert} \sum_{a \in \mathcal{A}(x)} \psi_\theta(h_a) \right),\]

where \(\phi_\theta\) (outer) and \(\psi_\theta\) (inner) are both trainable MLPs.

Parameters:

Return type:

Any