topobench.nn.backbones.simplicial package

Submodules

topobench.nn.backbones.simplicial.sccnn module

Implementation of the Simplicial Complex Convolutional Neural Network (SCCNN) for complex classification.

class topobench.nn.backbones.simplicial.sccnn.SCCNNCustom(in_channels_all, hidden_channels_all, conv_order, sc_order, aggr_norm=False, update_func=None, n_layers=2)

Bases: Module

SCCNN implementation for complex classification.

Note: In this task, we can consider the output on any order of simplices for the classification task, which of course can be amended by a readout layer.

Parameters:

in_channels_alltuple of int

Dimension of input features on (nodes, edges, faces).

hidden_channels_alltuple of int

Dimension of features of hidden layers on (nodes, edges, faces).

conv_orderint

Order of convolutions, we consider the same order for all convolutions.

sc_orderint

Order of simplicial complex.

aggr_normbool, optional

Whether to normalize the aggregation (default: False).

update_funcstr, optional

Update function for the simplicial complex convolution (default: None).

n_layersint, optional

Number of layers (default: 2).

forward(x_all, laplacian_all, incidence_all)

Forward computation.

Parameters:

x_alltuple(tensors)

Tuple of feature tensors (node, edge, face).

laplacian_alltuple(tensors)

Tuple of Laplacian tensors (graph laplacian L0, down edge laplacian L1_d, upper edge laplacian L1_u, face laplacian L2).

incidence_alltuple(tensors)

Tuple of order 1 and 2 incidence matrices.

Returns:

tuple(tensors)

Tuple of final hidden state tensors (node, edge, face).

class topobench.nn.backbones.simplicial.sccnn.SCCNNLayer(in_channels, out_channels, conv_order, sc_order, aggr_norm: bool = False, update_func=None, initialization: str = 'xavier_normal')

Bases: Module

Layer of a Simplicial Complex Convolutional Neural Network.

Parameters:

in_channelstuple of int

Dimensions of input features on nodes, edges, and faces.

out_channelstuple of int

Dimensions of output features on nodes, edges, and faces.

conv_orderint

Convolution order of the simplicial filters.

sc_orderint

SC order.

aggr_normbool, optional

Whether to normalize the aggregated message by the neighborhood size (default: False).

update_funcstr, optional

Activation function used in aggregation layers (default: None).

initializationstr, optional

Initialization method for the weights (default: “xavier_normal”).

aggr_norm_func(conv_operator, x)

Perform aggregation normalization.

Parameters:

conv_operatortorch.sparse

Convolution operator.

xtorch.Tensor

Feature tensor.

Returns:

torch.Tensor

Normalized feature tensor.

chebyshev_conv(conv_operator, conv_order, x)

Perform Chebyshev convolution.

Parameters:

conv_operatortorch.sparse

Convolution operator.

conv_orderint

Order of the convolution.

xtorch.Tensor

Feature tensor.

Returns:

torch.Tensor

Output tensor.

forward(x_all, laplacian_all, incidence_all)

Forward computation.

Parameters:

x_alltuple of tensors

Tuple of input feature tensors (node, edge, face).

laplacian_alltuple of tensors

Tuple of Laplacian tensors (graph laplacian L0, down edge laplacian L1_d, upper edge laplacian L1_u, face laplacian L2).

incidence_alltuple of tensors

Tuple of order 1 and 2 incidence matrices.

Returns:

torch.Tensor

Output tensor for each 0-cell.

torch.Tensor

Output tensor for each 1-cell.

torch.Tensor

Output tensor for each 2-cell.

reset_parameters(gain: float = 1.414)

Reset learnable parameters.

Parameters:

gainfloat

Gain for the weight initialization.

update(x)

Update embeddings on each cell (step 4).

Parameters:

xtorch.Tensor

Input tensor.

Returns:

torch.Tensor

Updated tensor.

Module contents

Simplicial backbones with automated exports.

class topobench.nn.backbones.simplicial.SCCNNCustom(in_channels_all, hidden_channels_all, conv_order, sc_order, aggr_norm=False, update_func=None, n_layers=2)

Bases: Module

SCCNN implementation for complex classification.

Note: In this task, we can consider the output on any order of simplices for the classification task, which of course can be amended by a readout layer.

Parameters:

in_channels_alltuple of int

Dimension of input features on (nodes, edges, faces).

hidden_channels_alltuple of int

Dimension of features of hidden layers on (nodes, edges, faces).

conv_orderint

Order of convolutions, we consider the same order for all convolutions.

sc_orderint

Order of simplicial complex.

aggr_normbool, optional

Whether to normalize the aggregation (default: False).

update_funcstr, optional

Update function for the simplicial complex convolution (default: None).

n_layersint, optional

Number of layers (default: 2).

forward(x_all, laplacian_all, incidence_all)

Forward computation.

Parameters:

x_alltuple(tensors)

Tuple of feature tensors (node, edge, face).

laplacian_alltuple(tensors)

Tuple of Laplacian tensors (graph laplacian L0, down edge laplacian L1_d, upper edge laplacian L1_u, face laplacian L2).

incidence_alltuple(tensors)

Tuple of order 1 and 2 incidence matrices.

Returns:

tuple(tensors)

Tuple of final hidden state tensors (node, edge, face).

class topobench.nn.backbones.simplicial.SCCNNLayer(in_channels, out_channels, conv_order, sc_order, aggr_norm: bool = False, update_func=None, initialization: str = 'xavier_normal')

Bases: Module

Layer of a Simplicial Complex Convolutional Neural Network.

Parameters:

in_channelstuple of int

Dimensions of input features on nodes, edges, and faces.

out_channelstuple of int

Dimensions of output features on nodes, edges, and faces.

conv_orderint

Convolution order of the simplicial filters.

sc_orderint

SC order.

aggr_normbool, optional

Whether to normalize the aggregated message by the neighborhood size (default: False).

update_funcstr, optional

Activation function used in aggregation layers (default: None).

initializationstr, optional

Initialization method for the weights (default: “xavier_normal”).

aggr_norm_func(conv_operator, x)

Perform aggregation normalization.

Parameters:

conv_operatortorch.sparse

Convolution operator.

xtorch.Tensor

Feature tensor.

Returns:

torch.Tensor

Normalized feature tensor.

chebyshev_conv(conv_operator, conv_order, x)

Perform Chebyshev convolution.

Parameters:

conv_operatortorch.sparse

Convolution operator.

conv_orderint

Order of the convolution.

xtorch.Tensor

Feature tensor.

Returns:

torch.Tensor

Output tensor.

forward(x_all, laplacian_all, incidence_all)

Forward computation.

Parameters:

x_alltuple of tensors

Tuple of input feature tensors (node, edge, face).

laplacian_alltuple of tensors

Tuple of Laplacian tensors (graph laplacian L0, down edge laplacian L1_d, upper edge laplacian L1_u, face laplacian L2).

incidence_alltuple of tensors

Tuple of order 1 and 2 incidence matrices.

Returns:

torch.Tensor

Output tensor for each 0-cell.

torch.Tensor

Output tensor for each 1-cell.

torch.Tensor

Output tensor for each 2-cell.

reset_parameters(gain: float = 1.414)

Reset learnable parameters.

Parameters:

gainfloat

Gain for the weight initialization.

update(x)

Update embeddings on each cell (step 4).

Parameters:

xtorch.Tensor

Input tensor.

Returns:

torch.Tensor

Updated tensor.