"""Torch Module for GNNExplainer"""
# pylint: disable= no-member, arguments-differ, invalid-name
from math import sqrt
import torch
from torch import nn
from tqdm.auto import tqdm
from ....base import EID, NID
from ....subgraph import khop_in_subgraph
__all__ = ["GNNExplainer", "HeteroGNNExplainer"]
[docs]
class GNNExplainer(nn.Module):
r"""GNNExplainer model from `GNNExplainer: Generating Explanations for
Graph Neural Networks <https://arxiv.org/abs/1903.03894>`__
It identifies compact subgraph structures and small subsets of node features that play a
critical role in GNN-based node classification and graph classification.
To generate an explanation, it learns an edge mask :math:`M` and a feature mask :math:`F`
by optimizing the following objective function.
.. math::
l(y, \hat{y}) + \alpha_1 \|M\|_1 + \alpha_2 H(M) + \beta_1 \|F\|_1 + \beta_2 H(F)
where :math:`l` is the loss function, :math:`y` is the original model prediction,
:math:`\hat{y}` is the model prediction with the edge and feature mask applied, :math:`H` is
the entropy function.
Parameters
----------
model : nn.Module
The GNN model to explain.
* The required arguments of its forward function are graph and feat.
The latter one is for input node features.
* It should also optionally take an eweight argument for edge weights
and multiply the messages by it in message passing.
* The output of its forward function is the logits for the predicted
node/graph classes.
See also the example in :func:`explain_node` and :func:`explain_graph`.
num_hops : int
The number of hops for GNN information aggregation.
lr : float, optional
The learning rate to use, default to 0.01.
num_epochs : int, optional
The number of epochs to train.
alpha1 : float, optional
A higher value will make the explanation edge masks more sparse by decreasing
the sum of the edge mask.
alpha2 : float, optional
A higher value will make the explanation edge masks more sparse by decreasing
the entropy of the edge mask.
beta1 : float, optional
A higher value will make the explanation node feature masks more sparse by
decreasing the mean of the node feature mask.
beta2 : float, optional
A higher value will make the explanation node feature masks more sparse by
decreasing the entropy of the node feature mask.
log : bool, optional
If True, it will log the computation process, default to True.
"""
def __init__(
self,
model,
num_hops,
lr=0.01,
num_epochs=100,
*,
alpha1=0.005,
alpha2=1.0,
beta1=1.0,
beta2=0.1,
log=True,
):
super(GNNExplainer, self).__init__()
self.model = model
self.num_hops = num_hops
self.lr = lr
self.num_epochs = num_epochs
self.alpha1 = alpha1
self.alpha2 = alpha2
self.beta1 = beta1
self.beta2 = beta2
self.log = log
def _init_masks(self, graph, feat):
r"""Initialize learnable feature and edge mask.
Parameters
----------
graph : DGLGraph
Input graph.
feat : Tensor
Input node features.
Returns
-------
feat_mask : Tensor
Feature mask of shape :math:`(1, D)`, where :math:`D`
is the feature size.
edge_mask : Tensor
Edge mask of shape :math:`(E)`, where :math:`E` is the
number of edges.
"""
num_nodes, feat_size = feat.size()
num_edges = graph.num_edges()
device = feat.device
std = 0.1
feat_mask = nn.Parameter(torch.randn(1, feat_size, device=device) * std)
std = nn.init.calculate_gain("relu") * sqrt(2.0 / (2 * num_nodes))
edge_mask = nn.Parameter(torch.randn(num_edges, device=device) * std)
return feat_mask, edge_mask
def _loss_regularize(self, loss, feat_mask, edge_mask):
r"""Add regularization terms to the loss.
Parameters
----------
loss : Tensor
Loss value.
feat_mask : Tensor
Feature mask of shape :math:`(1, D)`, where :math:`D`
is the feature size.
edge_mask : Tensor
Edge mask of shape :math:`(E)`, where :math:`E`
is the number of edges.
Returns
-------
Tensor
Loss value with regularization terms added.
"""
# epsilon for numerical stability
eps = 1e-15
edge_mask = edge_mask.sigmoid()
# Edge mask sparsity regularization
loss = loss + self.alpha1 * torch.sum(edge_mask)
# Edge mask entropy regularization
ent = -edge_mask * torch.log(edge_mask + eps) - (
1 - edge_mask
) * torch.log(1 - edge_mask + eps)
loss = loss + self.alpha2 * ent.mean()
feat_mask = feat_mask.sigmoid()
# Feature mask sparsity regularization
loss = loss + self.beta1 * torch.mean(feat_mask)
# Feature mask entropy regularization
ent = -feat_mask * torch.log(feat_mask + eps) - (
1 - feat_mask
) * torch.log(1 - feat_mask + eps)
loss = loss + self.beta2 * ent.mean()
return loss
[docs]
def explain_node(self, node_id, graph, feat, **kwargs):
r"""Learn and return a node feature mask and subgraph that play a
crucial role to explain the prediction made by the GNN for node
:attr:`node_id`.
Parameters
----------
node_id : int
The node to explain.
graph : DGLGraph
A homogeneous graph.
feat : Tensor
The input feature of shape :math:`(N, D)`. :math:`N` is the
number of nodes, and :math:`D` is the feature size.
kwargs : dict
Additional arguments passed to the GNN model. Tensors whose
first dimension is the number of nodes or edges will be
assumed to be node/edge features.
Returns
-------
new_node_id : Tensor
The new ID of the input center node.
sg : DGLGraph
The subgraph induced on the k-hop in-neighborhood of the input center node.
feat_mask : Tensor
Learned node feature importance mask of shape :math:`(D)`, where :math:`D` is the
feature size. The values are within range :math:`(0, 1)`.
The higher, the more important.
edge_mask : Tensor
Learned importance mask of the edges in the subgraph, which is a tensor
of shape :math:`(E)`, where :math:`E` is the number of edges in the
subgraph. The values are within range :math:`(0, 1)`.
The higher, the more important.
Examples
--------
>>> import dgl
>>> import dgl.function as fn
>>> import torch
>>> import torch.nn as nn
>>> from dgl.data import CoraGraphDataset
>>> from dgl.nn import GNNExplainer
>>> # Load dataset
>>> data = CoraGraphDataset()
>>> g = data[0]
>>> features = g.ndata['feat']
>>> labels = g.ndata['label']
>>> train_mask = g.ndata['train_mask']
>>> # Define a model
>>> class Model(nn.Module):
... def __init__(self, in_feats, out_feats):
... super(Model, self).__init__()
... self.linear = nn.Linear(in_feats, out_feats)
...
... def forward(self, graph, feat, eweight=None):
... with graph.local_scope():
... feat = self.linear(feat)
... graph.ndata['h'] = feat
... if eweight is None:
... graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
... else:
... graph.edata['w'] = eweight
... graph.update_all(fn.u_mul_e('h', 'w', 'm'), fn.sum('m', 'h'))
... return graph.ndata['h']
>>> # Train the model
>>> model = Model(features.shape[1], data.num_classes)
>>> criterion = nn.CrossEntropyLoss()
>>> optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
>>> for epoch in range(10):
... logits = model(g, features)
... loss = criterion(logits[train_mask], labels[train_mask])
... optimizer.zero_grad()
... loss.backward()
... optimizer.step()
>>> # Explain the prediction for node 10
>>> explainer = GNNExplainer(model, num_hops=1)
>>> new_center, sg, feat_mask, edge_mask = explainer.explain_node(10, g, features)
>>> new_center
tensor([1])
>>> sg.num_edges()
12
>>> # Old IDs of the nodes in the subgraph
>>> sg.ndata[dgl.NID]
tensor([ 9, 10, 11, 12])
>>> # Old IDs of the edges in the subgraph
>>> sg.edata[dgl.EID]
tensor([51, 53, 56, 48, 52, 57, 47, 50, 55, 46, 49, 54])
>>> feat_mask
tensor([0.2638, 0.2738, 0.3039, ..., 0.2794, 0.2643, 0.2733])
>>> edge_mask
tensor([0.0937, 0.1496, 0.8287, 0.8132, 0.8825, 0.8515, 0.8146, 0.0915, 0.1145,
0.9011, 0.1311, 0.8437])
"""
self.model = self.model.to(graph.device)
self.model.eval()
num_nodes = graph.num_nodes()
num_edges = graph.num_edges()
# Extract node-centered k-hop subgraph and
# its associated node and edge features.
sg, inverse_indices = khop_in_subgraph(graph, node_id, self.num_hops)
sg_nodes = sg.ndata[NID].long()
sg_edges = sg.edata[EID].long()
feat = feat[sg_nodes]
for key, item in kwargs.items():
if torch.is_tensor(item) and item.size(0) == num_nodes:
item = item[sg_nodes]
elif torch.is_tensor(item) and item.size(0) == num_edges:
item = item[sg_edges]
kwargs[key] = item
# Get the initial prediction.
with torch.no_grad():
logits = self.model(graph=sg, feat=feat, **kwargs)
pred_label = logits.argmax(dim=-1)
feat_mask, edge_mask = self._init_masks(sg, feat)
params = [feat_mask, edge_mask]
optimizer = torch.optim.Adam(params, lr=self.lr)
if self.log:
pbar = tqdm(total=self.num_epochs)
pbar.set_description(f"Explain node {node_id}")
for _ in range(self.num_epochs):
optimizer.zero_grad()
h = feat * feat_mask.sigmoid()
logits = self.model(
graph=sg, feat=h, eweight=edge_mask.sigmoid(), **kwargs
)
log_probs = logits.log_softmax(dim=-1)
loss = -log_probs[inverse_indices, pred_label[inverse_indices]]
loss = self._loss_regularize(loss, feat_mask, edge_mask)
loss.backward()
optimizer.step()
if self.log:
pbar.update(1)
if self.log:
pbar.close()
feat_mask = feat_mask.detach().sigmoid().squeeze()
edge_mask = edge_mask.detach().sigmoid()
return inverse_indices, sg, feat_mask, edge_mask
[docs]
def explain_graph(self, graph, feat, **kwargs):
r"""Learn and return a node feature mask and an edge mask that play a
crucial role to explain the prediction made by the GNN for a graph.
Parameters
----------
graph : DGLGraph
A homogeneous graph.
feat : Tensor
The input feature of shape :math:`(N, D)`. :math:`N` is the
number of nodes, and :math:`D` is the feature size.
kwargs : dict
Additional arguments passed to the GNN model. Tensors whose
first dimension is the number of nodes or edges will be
assumed to be node/edge features.
Returns
-------
feat_mask : Tensor
Learned feature importance mask of shape :math:`(D)`, where :math:`D` is the
feature size. The values are within range :math:`(0, 1)`.
The higher, the more important.
edge_mask : Tensor
Learned importance mask of the edges in the graph, which is a tensor
of shape :math:`(E)`, where :math:`E` is the number of edges in the
graph. The values are within range :math:`(0, 1)`. The higher,
the more important.
Examples
--------
>>> import dgl.function as fn
>>> import torch
>>> import torch.nn as nn
>>> from dgl.data import GINDataset
>>> from dgl.dataloading import GraphDataLoader
>>> from dgl.nn import AvgPooling, GNNExplainer
>>> # Load dataset
>>> data = GINDataset('MUTAG', self_loop=True)
>>> dataloader = GraphDataLoader(data, batch_size=64, shuffle=True)
>>> # Define a model
>>> class Model(nn.Module):
... def __init__(self, in_feats, out_feats):
... super(Model, self).__init__()
... self.linear = nn.Linear(in_feats, out_feats)
... self.pool = AvgPooling()
...
... def forward(self, graph, feat, eweight=None):
... with graph.local_scope():
... feat = self.linear(feat)
... graph.ndata['h'] = feat
... if eweight is None:
... graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
... else:
... graph.edata['w'] = eweight
... graph.update_all(fn.u_mul_e('h', 'w', 'm'), fn.sum('m', 'h'))
... return self.pool(graph, graph.ndata['h'])
>>> # Train the model
>>> feat_size = data[0][0].ndata['attr'].shape[1]
>>> model = Model(feat_size, data.gclasses)
>>> criterion = nn.CrossEntropyLoss()
>>> optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
>>> for bg, labels in dataloader:
... logits = model(bg, bg.ndata['attr'])
... loss = criterion(logits, labels)
... optimizer.zero_grad()
... loss.backward()
... optimizer.step()
>>> # Explain the prediction for graph 0
>>> explainer = GNNExplainer(model, num_hops=1)
>>> g, _ = data[0]
>>> features = g.ndata['attr']
>>> feat_mask, edge_mask = explainer.explain_graph(g, features)
>>> feat_mask
tensor([0.2362, 0.2497, 0.2622, 0.2675, 0.2649, 0.2962, 0.2533])
>>> edge_mask
tensor([0.2154, 0.2235, 0.8325, ..., 0.7787, 0.1735, 0.1847])
"""
self.model = self.model.to(graph.device)
self.model.eval()
# Get the initial prediction.
with torch.no_grad():
logits = self.model(graph=graph, feat=feat, **kwargs)
pred_label = logits.argmax(dim=-1)
feat_mask, edge_mask = self._init_masks(graph, feat)
params = [feat_mask, edge_mask]
optimizer = torch.optim.Adam(params, lr=self.lr)
if self.log:
pbar = tqdm(total=self.num_epochs)
pbar.set_description("Explain graph")
for _ in range(self.num_epochs):
optimizer.zero_grad()
h = feat * feat_mask.sigmoid()
logits = self.model(
graph=graph, feat=h, eweight=edge_mask.sigmoid(), **kwargs
)
log_probs = logits.log_softmax(dim=-1)
loss = -log_probs[0, pred_label[0]]
loss = self._loss_regularize(loss, feat_mask, edge_mask)
loss.backward()
optimizer.step()
if self.log:
pbar.update(1)
if self.log:
pbar.close()
feat_mask = feat_mask.detach().sigmoid().squeeze()
edge_mask = edge_mask.detach().sigmoid()
return feat_mask, edge_mask
[docs]
class HeteroGNNExplainer(nn.Module):
r"""GNNExplainer model from `GNNExplainer: Generating Explanations for
Graph Neural Networks <https://arxiv.org/abs/1903.03894>`__, adapted for heterogeneous graphs
It identifies compact subgraph structures and small subsets of node features that play a
critical role in GNN-based node classification and graph classification.
To generate an explanation, it learns an edge mask :math:`M` and a feature mask :math:`F`
by optimizing the following objective function.
.. math::
l(y, \hat{y}) + \alpha_1 \|M\|_1 + \alpha_2 H(M) + \beta_1 \|F\|_1 + \beta_2 H(F)
where :math:`l` is the loss function, :math:`y` is the original model prediction,
:math:`\hat{y}` is the model prediction with the edge and feature mask applied, :math:`H` is
the entropy function.
Parameters
----------
model : nn.Module
The GNN model to explain.
* The required arguments of its forward function are graph and feat.
The latter one is for input node features.
* It should also optionally take an eweight argument for edge weights
and multiply the messages by it in message passing.
* The output of its forward function is the logits for the predicted
node/graph classes.
See also the example in :func:`explain_node` and :func:`explain_graph`.
num_hops : int
The number of hops for GNN information aggregation.
lr : float, optional
The learning rate to use, default to 0.01.
num_epochs : int, optional
The number of epochs to train.
alpha1 : float, optional
A higher value will make the explanation edge masks more sparse by decreasing
the sum of the edge mask.
alpha2 : float, optional
A higher value will make the explanation edge masks more sparse by decreasing
the entropy of the edge mask.
beta1 : float, optional
A higher value will make the explanation node feature masks more sparse by
decreasing the mean of the node feature mask.
beta2 : float, optional
A higher value will make the explanation node feature masks more sparse by
decreasing the entropy of the node feature mask.
log : bool, optional
If True, it will log the computation process, default to True.
"""
def __init__(
self,
model,
num_hops,
lr=0.01,
num_epochs=100,
*,
alpha1=0.005,
alpha2=1.0,
beta1=1.0,
beta2=0.1,
log=True,
):
super(HeteroGNNExplainer, self).__init__()
self.model = model
self.num_hops = num_hops
self.lr = lr
self.num_epochs = num_epochs
self.alpha1 = alpha1
self.alpha2 = alpha2
self.beta1 = beta1
self.beta2 = beta2
self.log = log
def _init_masks(self, graph, feat):
r"""Initialize learnable feature and edge mask.
Parameters
----------
graph : DGLGraph
Input graph.
feat : dict[str, Tensor]
The dictionary that associates input node features (values) with
the respective node types (keys) present in the graph.
Returns
-------
feat_masks : dict[str, Tensor]
The dictionary that associates the node feature masks (values) with
the respective node types (keys). The feature masks are of shape :math:`(1, D_t)`,
where :math:`D_t` is the feature size for node type :math:`t`.
edge_masks : dict[tuple[str], Tensor]
The dictionary that associates the edge masks (values) with
the respective canonical edge types (keys). The edge masks are of shape :math:`(E_t)`,
where :math:`E_t` is the number of edges for canonical edge type :math:`t`.
"""
device = graph.device
feat_masks = {}
std = 0.1
for node_type, feature in feat.items():
_, feat_size = feature.size()
feat_masks[node_type] = nn.Parameter(
torch.randn(1, feat_size, device=device) * std
)
edge_masks = {}
for canonical_etype in graph.canonical_etypes:
src_num_nodes = graph.num_nodes(canonical_etype[0])
dst_num_nodes = graph.num_nodes(canonical_etype[-1])
num_nodes_sum = src_num_nodes + dst_num_nodes
num_edges = graph.num_edges(canonical_etype)
std = nn.init.calculate_gain("relu")
if num_nodes_sum > 0:
std *= sqrt(2.0 / num_nodes_sum)
edge_masks[canonical_etype] = nn.Parameter(
torch.randn(num_edges, device=device) * std
)
return feat_masks, edge_masks
def _loss_regularize(self, loss, feat_masks, edge_masks):
r"""Add regularization terms to the loss.
Parameters
----------
loss : Tensor
Loss value.
feat_masks : dict[str, Tensor]
The dictionary that associates the node feature masks (values) with
the respective node types (keys). The feature masks are of shape :math:`(1, D_t)`,
where :math:`D_t` is the feature size for node type :math:`t`.
edge_masks : dict[tuple[str], Tensor]
The dictionary that associates the edge masks (values) with
the respective canonical edge types (keys). The edge masks are of shape :math:`(E_t)`,
where :math:`E_t` is the number of edges for canonical edge type :math:`t`.
Returns
-------
Tensor
Loss value with regularization terms added.
"""
# epsilon for numerical stability
eps = 1e-15
for edge_mask in edge_masks.values():
edge_mask = edge_mask.sigmoid()
# Edge mask sparsity regularization
loss = loss + self.alpha1 * torch.sum(edge_mask)
# Edge mask entropy regularization
ent = -edge_mask * torch.log(edge_mask + eps) - (
1 - edge_mask
) * torch.log(1 - edge_mask + eps)
loss = loss + self.alpha2 * ent.mean()
for feat_mask in feat_masks.values():
feat_mask = feat_mask.sigmoid()
# Feature mask sparsity regularization
loss = loss + self.beta1 * torch.mean(feat_mask)
# Feature mask entropy regularization
ent = -feat_mask * torch.log(feat_mask + eps) - (
1 - feat_mask
) * torch.log(1 - feat_mask + eps)
loss = loss + self.beta2 * ent.mean()
return loss
[docs]
def explain_node(self, ntype, node_id, graph, feat, **kwargs):
r"""Learn and return node feature masks and a subgraph that play a
crucial role to explain the prediction made by the GNN for node
:attr:`node_id` of type :attr:`ntype`.
It requires :attr:`model` to return a dictionary mapping node types to type-specific
predictions.
Parameters
----------
ntype : str
The type of the node to explain. :attr:`model` must be trained to
make predictions for this particular node type.
node_id : int
The ID of the node to explain.
graph : DGLGraph
A heterogeneous graph.
feat : dict[str, Tensor]
The dictionary that associates input node features (values) with
the respective node types (keys) present in the graph.
The input features are of shape :math:`(N_t, D_t)`. :math:`N_t` is the
number of nodes for node type :math:`t`, and :math:`D_t` is the feature size for
node type :math:`t`
kwargs : dict
Additional arguments passed to the GNN model.
Returns
-------
new_node_id : Tensor
The new ID of the input center node.
sg : DGLGraph
The subgraph induced on the k-hop in-neighborhood of the input center node.
feat_mask : dict[str, Tensor]
The dictionary that associates the learned node feature importance masks (values) with
the respective node types (keys). The masks are of shape :math:`(D_t)`, where
:math:`D_t` is the node feature size for node type :attr:`t`. The values are within
range :math:`(0, 1)`. The higher, the more important.
edge_mask : dict[Tuple[str], Tensor]
The dictionary that associates the learned edge importance masks (values) with
the respective canonical edge types (keys). The masks are of shape :math:`(E_t)`,
where :math:`E_t` is the number of edges for canonical edge type :math:`t` in the
subgraph. The values are within range :math:`(0, 1)`.
The higher, the more important.
Examples
--------
>>> import dgl
>>> import dgl.function as fn
>>> import torch as th
>>> import torch.nn as nn
>>> import torch.nn.functional as F
>>> from dgl.nn import HeteroGNNExplainer
>>> class Model(nn.Module):
... def __init__(self, in_dim, num_classes, canonical_etypes):
... super(Model, self).__init__()
... self.etype_weights = nn.ModuleDict({
... '_'.join(c_etype): nn.Linear(in_dim, num_classes)
... for c_etype in canonical_etypes
... })
...
... def forward(self, graph, feat, eweight=None):
... with graph.local_scope():
... c_etype_func_dict = {}
... for c_etype in graph.canonical_etypes:
... src_type, etype, dst_type = c_etype
... wh = self.etype_weights['_'.join(c_etype)](feat[src_type])
... graph.nodes[src_type].data[f'h_{c_etype}'] = wh
... if eweight is None:
... c_etype_func_dict[c_etype] = (fn.copy_u(f'h_{c_etype}', 'm'),
... fn.mean('m', 'h'))
... else:
... graph.edges[c_etype].data['w'] = eweight[c_etype]
... c_etype_func_dict[c_etype] = (
... fn.u_mul_e(f'h_{c_etype}', 'w', 'm'), fn.mean('m', 'h'))
... graph.multi_update_all(c_etype_func_dict, 'sum')
... return graph.ndata['h']
>>> input_dim = 5
>>> num_classes = 2
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1])})
>>> g.nodes['user'].data['h'] = th.randn(g.num_nodes('user'), input_dim)
>>> g.nodes['game'].data['h'] = th.randn(g.num_nodes('game'), input_dim)
>>> transform = dgl.transforms.AddReverse()
>>> g = transform(g)
>>> # define and train the model
>>> model = Model(input_dim, num_classes, g.canonical_etypes)
>>> feat = g.ndata['h']
>>> optimizer = th.optim.Adam(model.parameters())
>>> for epoch in range(10):
... logits = model(g, feat)['user']
... loss = F.cross_entropy(logits, th.tensor([1, 1, 1]))
... optimizer.zero_grad()
... loss.backward()
... optimizer.step()
>>> # Explain the prediction for node 0 of type 'user'
>>> explainer = HeteroGNNExplainer(model, num_hops=1)
>>> new_center, sg, feat_mask, edge_mask = explainer.explain_node('user', 0, g, feat)
>>> new_center
tensor([0])
>>> sg
Graph(num_nodes={'game': 1, 'user': 1},
num_edges={('game', 'rev_plays', 'user'): 1, ('user', 'plays', 'game'): 1,
('user', 'rev_rev_plays', 'game'): 1},
metagraph=[('game', 'user', 'rev_plays'), ('user', 'game', 'plays'),
('user', 'game', 'rev_rev_plays')])
>>> feat_mask
{'game': tensor([0.2348, 0.2780, 0.2611, 0.2513, 0.2823]),
'user': tensor([0.2716, 0.2450, 0.2658, 0.2876, 0.2738])}
>>> edge_mask
{('game', 'rev_plays', 'user'): tensor([0.0630]),
('user', 'plays', 'game'): tensor([0.1939]),
('user', 'rev_rev_plays', 'game'): tensor([0.9166])}
"""
self.model = self.model.to(graph.device)
self.model.eval()
# Extract node-centered k-hop subgraph and
# its associated node and edge features.
sg, inverse_indices = khop_in_subgraph(
graph, {ntype: node_id}, self.num_hops
)
inverse_indices = inverse_indices[ntype]
sg_nodes = sg.ndata[NID]
sg_feat = {}
for node_type in sg_nodes.keys():
sg_feat[node_type] = feat[node_type][sg_nodes[node_type].long()]
# Get the initial prediction.
with torch.no_grad():
logits = self.model(graph=sg, feat=sg_feat, **kwargs)[ntype]
pred_label = logits.argmax(dim=-1)
feat_mask, edge_mask = self._init_masks(sg, sg_feat)
params = [*feat_mask.values(), *edge_mask.values()]
optimizer = torch.optim.Adam(params, lr=self.lr)
if self.log:
pbar = tqdm(total=self.num_epochs)
pbar.set_description(f"Explain node {node_id} with type {ntype}")
for _ in range(self.num_epochs):
optimizer.zero_grad()
h = {}
for node_type, sg_node_feat in sg_feat.items():
h[node_type] = sg_node_feat * feat_mask[node_type].sigmoid()
eweight = {}
for canonical_etype, canonical_etype_mask in edge_mask.items():
eweight[canonical_etype] = canonical_etype_mask.sigmoid()
logits = self.model(graph=sg, feat=h, eweight=eweight, **kwargs)[
ntype
]
log_probs = logits.log_softmax(dim=-1)
loss = -log_probs[inverse_indices, pred_label[inverse_indices]]
loss = self._loss_regularize(loss, feat_mask, edge_mask)
loss.backward()
optimizer.step()
if self.log:
pbar.update(1)
if self.log:
pbar.close()
for node_type in feat_mask:
feat_mask[node_type] = (
feat_mask[node_type].detach().sigmoid().squeeze()
)
for canonical_etype in edge_mask:
edge_mask[canonical_etype] = (
edge_mask[canonical_etype].detach().sigmoid()
)
return inverse_indices, sg, feat_mask, edge_mask
[docs]
def explain_graph(self, graph, feat, **kwargs):
r"""Learn and return node feature masks and edge masks that play a
crucial role to explain the prediction made by the GNN for a graph.
Parameters
----------
graph : DGLGraph
A heterogeneous graph that will be explained.
feat : dict[str, Tensor]
The dictionary that associates input node features (values) with
the respective node types (keys) present in the graph.
The input features are of shape :math:`(N_t, D_t)`. :math:`N_t` is the
number of nodes for node type :math:`t`, and :math:`D_t` is the feature size for
node type :math:`t`
kwargs : dict
Additional arguments passed to the GNN model.
Returns
-------
feat_mask : dict[str, Tensor]
The dictionary that associates the learned node feature importance masks (values) with
the respective node types (keys). The masks are of shape :math:`(D_t)`, where
:math:`D_t` is the node feature size for node type :attr:`t`. The values are within
range :math:`(0, 1)`. The higher, the more important.
edge_mask : dict[Tuple[str], Tensor]
The dictionary that associates the learned edge importance masks (values) with
the respective canonical edge types (keys). The masks are of shape :math:`(E_t)`,
where :math:`E_t` is the number of edges for canonical edge type :math:`t` in the
graph. The values are within range :math:`(0, 1)`. The higher, the more important.
Examples
--------
>>> import dgl
>>> import dgl.function as fn
>>> import torch as th
>>> import torch.nn as nn
>>> import torch.nn.functional as F
>>> from dgl.nn import HeteroGNNExplainer
>>> class Model(nn.Module):
... def __init__(self, in_dim, num_classes, canonical_etypes):
... super(Model, self).__init__()
... self.etype_weights = nn.ModuleDict({
... '_'.join(c_etype): nn.Linear(in_dim, num_classes)
... for c_etype in canonical_etypes
... })
...
... def forward(self, graph, feat, eweight=None):
... with graph.local_scope():
... c_etype_func_dict = {}
... for c_etype in graph.canonical_etypes:
... src_type, etype, dst_type = c_etype
... wh = self.etype_weights['_'.join(c_etype)](feat[src_type])
... graph.nodes[src_type].data[f'h_{c_etype}'] = wh
... if eweight is None:
... c_etype_func_dict[c_etype] = (fn.copy_u(f'h_{c_etype}', 'm'),
... fn.mean('m', 'h'))
... else:
... graph.edges[c_etype].data['w'] = eweight[c_etype]
... c_etype_func_dict[c_etype] = (
... fn.u_mul_e(f'h_{c_etype}', 'w', 'm'), fn.mean('m', 'h'))
... graph.multi_update_all(c_etype_func_dict, 'sum')
... hg = 0
... for ntype in graph.ntypes:
... if graph.num_nodes(ntype):
... hg = hg + dgl.mean_nodes(graph, 'h', ntype=ntype)
... return hg
>>> input_dim = 5
>>> num_classes = 2
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1])})
>>> g.nodes['user'].data['h'] = th.randn(g.num_nodes('user'), input_dim)
>>> g.nodes['game'].data['h'] = th.randn(g.num_nodes('game'), input_dim)
>>> transform = dgl.transforms.AddReverse()
>>> g = transform(g)
>>> # define and train the model
>>> model = Model(input_dim, num_classes, g.canonical_etypes)
>>> feat = g.ndata['h']
>>> optimizer = th.optim.Adam(model.parameters())
>>> for epoch in range(10):
... logits = model(g, feat)
... loss = F.cross_entropy(logits, th.tensor([1]))
... optimizer.zero_grad()
... loss.backward()
... optimizer.step()
>>> # Explain for the graph
>>> explainer = HeteroGNNExplainer(model, num_hops=1)
>>> feat_mask, edge_mask = explainer.explain_graph(g, feat)
>>> feat_mask
{'game': tensor([0.2684, 0.2597, 0.3135, 0.2976, 0.2607]),
'user': tensor([0.2216, 0.2908, 0.2644, 0.2738, 0.2663])}
>>> edge_mask
{('game', 'rev_plays', 'user'): tensor([0.8922, 0.1966, 0.8371, 0.1330]),
('user', 'plays', 'game'): tensor([0.1785, 0.1696, 0.8065, 0.2167])}
"""
self.model = self.model.to(graph.device)
self.model.eval()
# Get the initial prediction.
with torch.no_grad():
logits = self.model(graph=graph, feat=feat, **kwargs)
pred_label = logits.argmax(dim=-1)
feat_mask, edge_mask = self._init_masks(graph, feat)
params = [*feat_mask.values(), *edge_mask.values()]
optimizer = torch.optim.Adam(params, lr=self.lr)
if self.log:
pbar = tqdm(total=self.num_epochs)
pbar.set_description("Explain graph")
for _ in range(self.num_epochs):
optimizer.zero_grad()
h = {}
for node_type, node_feat in feat.items():
h[node_type] = node_feat * feat_mask[node_type].sigmoid()
eweight = {}
for canonical_etype, canonical_etype_mask in edge_mask.items():
eweight[canonical_etype] = canonical_etype_mask.sigmoid()
logits = self.model(graph=graph, feat=h, eweight=eweight, **kwargs)
log_probs = logits.log_softmax(dim=-1)
loss = -log_probs[0, pred_label[0]]
loss = self._loss_regularize(loss, feat_mask, edge_mask)
loss.backward()
optimizer.step()
if self.log:
pbar.update(1)
if self.log:
pbar.close()
for node_type in feat_mask:
feat_mask[node_type] = (
feat_mask[node_type].detach().sigmoid().squeeze()
)
for canonical_etype in edge_mask:
edge_mask[canonical_etype] = (
edge_mask[canonical_etype].detach().sigmoid()
)
return feat_mask, edge_mask