# -*- coding: utf-8 -*-
"""
GNN layers utils
"""
# Author: Sonia Mazelet <sonia.mazelet@ens-paris-saclay.fr>
# Rémi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import torch
from ..utils import dist
from ..gromov import fused_gromov_wasserstein2
from ..lp import emd2
from torch_geometric.utils import subgraph
def TFGW_template_initialization(
n_tplt, n_tplt_nodes, n_features, feature_init_mean=0.0, feature_init_std=1.0
):
"""
Initializes templates for the Template Fused Gromov Wasserstein layer.
Returns the adjacency matrices and the features of the nodes of the templates.
Adjacency matrices are initialized uniformly with values in :math:`[0,1]`.
Node features are initialized following a normal distribution.
Parameters
----------
n_tplt: int
Number of templates.
n_tplt_nodes: int
Number of nodes per template.
n_features: int
Number of features for the nodes.
feature_init_mean: float, optional
Mean of the random normal law to initialize the template features.
feature_init_std: float, optional
Standard deviation of the random normal law to initialize the template features.
Returns
----------
tplt_adjacencies: torch.Tensor, shape (n_templates, n_template_nodes, n_template_nodes)
Adjacency matrices for the templates.
tplt_features: torch.Tensor, shape (n_templates, n_template_nodes, n_features)
Node features for each template.
q: torch.Tensor, shape (n_templates, n_template_nodes)
weight on the template nodes.
"""
tplt_adjacencies = torch.rand((n_tplt, n_tplt_nodes, n_tplt_nodes))
tplt_features = torch.Tensor(n_tplt, n_tplt_nodes, n_features)
torch.nn.init.normal_(tplt_features, mean=feature_init_mean, std=feature_init_std)
q = torch.zeros(n_tplt, n_tplt_nodes)
tplt_adjacencies = 0.5 * (
tplt_adjacencies + torch.transpose(tplt_adjacencies, 1, 2)
)
return tplt_adjacencies, tplt_features, q
[docs]
def FGW_distance_to_templates(
G_edges,
tplt_adjacencies,
G_features,
tplt_features,
tplt_weights,
alpha=0.5,
multi_alpha=False,
batch=None,
):
"""
Computes the FGW distances between a graph and templates.
Parameters
----------
G_edges : torch.Tensor, shape (n_edges, 2)
Edge indices of the graph in the Pytorch Geometric format.
tplt_adjacencies : list of torch.Tensor, shape (n_templates, n_template_nodes, n_templates_nodes)
List of the adjacency matrices of the templates.
G_features : torch.Tensor, shape (n_nodes, n_features)
Graph node features.
tplt_features : list of torch.Tensor, shape (n_templates, n_template_nodes, n_features)
List of the node features of the templates.
weights : torch.Tensor, shape (n_templates, n_template_nodes)
Weights on the nodes of the templates.
alpha : float, optional
Trade-off parameter (0 < alpha < 1).
Weights features (alpha=0) and structure (alpha=1).
multi_alpha: bool, optional
If True, the alpha parameter is a vector of size n_templates.
batch: torch.Tensor, optional
Batch vector which assigns each node to its graph.
Returns
-------
distances : torch.Tensor, shape (n_templates) if batch=None, else shape (n_graphs, n_templates).
Vector of fused Gromov-Wasserstein distances between the graph and the templates.
"""
if batch is None:
n, n_feat = G_features.shape
n_T, _, n_feat_T = tplt_features.shape
weights_G = torch.ones(n) / n
C = torch.sparse_coo_tensor(
G_edges, torch.ones(len(G_edges[0])), size=(n, n)
).type(torch.float)
C = C.to_dense()
if not n_feat == n_feat_T:
raise ValueError(
"The templates and the graphs must have the same feature dimension."
)
distances = torch.zeros(n_T)
for j in range(n_T):
template_features = tplt_features[j].reshape(
len(tplt_features[j]), n_feat_T
)
M = dist(G_features, template_features).type(torch.float)
# if alpha is zero the emd distance is used
if multi_alpha and torch.any(alpha > 0):
embedding = fused_gromov_wasserstein2(
M,
C,
tplt_adjacencies[j],
weights_G,
tplt_weights[j],
alpha=alpha[j],
symmetric=True,
max_iter=50,
)
elif not multi_alpha and torch.all(alpha == 0):
embedding = emd2(weights_G, tplt_weights[j], M, numItermax=50)
elif not multi_alpha and alpha > 0:
embedding = fused_gromov_wasserstein2(
M,
C,
tplt_adjacencies[j],
weights_G,
tplt_weights[j],
alpha=alpha,
symmetric=True,
max_iter=50,
)
else:
embedding = emd2(weights_G, tplt_weights[j], M, numItermax=50)
distances[j] = embedding
else:
n_T, _, n_feat_T = tplt_features.shape
num_graphs = torch.max(batch) + 1
distances = torch.zeros(num_graphs, n_T)
# iterate over the graphs in the batch
for i in range(num_graphs):
nodes = torch.where(batch == i)[0]
G_edges_i, _ = subgraph(nodes, edge_index=G_edges, relabel_nodes=True)
G_features_i = G_features[nodes]
n, n_feat = G_features_i.shape
weights_G = torch.ones(n) / n
n_edges = len(G_edges_i[0])
C = torch.sparse_coo_tensor(
G_edges_i, torch.ones(n_edges), size=(n, n)
).type(torch.float)
C = C.to_dense()
if not n_feat == n_feat_T:
raise ValueError(
"The templates and the graphs must have the same feature dimension."
)
for j in range(n_T):
template_features = tplt_features[j].reshape(
len(tplt_features[j]), n_feat_T
)
M = dist(G_features_i, template_features).type(torch.float)
# if alpha is zero the emd distance is used
if multi_alpha and torch.any(alpha > 0):
embedding = fused_gromov_wasserstein2(
M,
C,
tplt_adjacencies[j],
weights_G,
tplt_weights[j],
alpha=alpha[j],
symmetric=True,
max_iter=50,
)
elif not multi_alpha and torch.all(alpha == 0):
embedding = emd2(weights_G, tplt_weights[j], M, numItermax=50)
elif not multi_alpha and alpha > 0:
embedding = fused_gromov_wasserstein2(
M,
C,
tplt_adjacencies[j],
weights_G,
tplt_weights[j],
alpha=alpha,
symmetric=True,
max_iter=50,
)
else:
embedding = emd2(weights_G, tplt_weights[j], M, numItermax=50)
distances[i, j] = embedding
return distances
[docs]
def wasserstein_distance_to_templates(
G_features, tplt_features, tplt_weights, batch=None
):
"""
Computes the Wasserstein distances between a graph and graph templates.
Parameters
----------
G_features : torch.Tensor, shape (n_nodes, n_features)
Node features of the graph.
tplt_features : list of torch.Tensor, shape (n_templates, n_template_nodes, n_features)
List of the node features of the templates.
weights : torch.Tensor, shape (n_templates, n_template_nodes)
Weights on the nodes of the templates.
batch: torch.Tensor, optional
Batch vector which assigns each node to its graph.
Returns
-------
distances : torch.Tensor, shape (n_templates) if batch=None, else shape (n_graphs, n_templates)
Vector of Wasserstein distances between the graph and the templates.
"""
if batch is None:
n, n_feat = G_features.shape
n_T, _, n_feat_T = tplt_features.shape
weights_G = torch.ones(n) / n
if not n_feat == n_feat_T:
raise ValueError(
"The templates and the graphs must have the same feature dimension."
)
distances = torch.zeros(n_T)
for j in range(n_T):
template_features = tplt_features[j].reshape(
len(tplt_features[j]), n_feat_T
)
M = dist(G_features, template_features).type(torch.float)
distances[j] = emd2(weights_G, tplt_weights[j], M, numItermax=50)
else:
n_T, _, n_feat_T = tplt_features.shape
num_graphs = torch.max(batch) + 1
distances = torch.zeros(num_graphs, n_T)
# iterate over the graphs in the batch
for i in range(num_graphs):
nodes = torch.where(batch == i)[0]
G_features_i = G_features[nodes]
n, n_feat = G_features_i.shape
weights_G = torch.ones(n) / n
if not n_feat == n_feat_T:
raise ValueError(
"The templates and the graphs must have the same feature dimension."
)
for j in range(n_T):
template_features = tplt_features[j].reshape(
len(tplt_features[j]), n_feat_T
)
M = dist(G_features_i, template_features).type(torch.float)
distances[i, j] = emd2(weights_G, tplt_weights[j], M, numItermax=50)
return distances
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