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import pytest
import torch
from torch_geometric.explain import (
AttentionExplainer,
Explainer,
HeteroExplanation,
)
from torch_geometric.explain.config import (
ExplanationType,
MaskType,
ModelConfig,
ModelMode,
)
from torch_geometric.nn import (
AttentiveFP,
GATConv,
GATv2Conv,
TransformerConv,
to_hetero,
)
from torch_geometric.nn.conv import HeteroConv
class AttentionGNN(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = GATConv(3, 16, heads=4)
self.conv2 = GATv2Conv(4 * 16, 16, heads=2)
self.conv3 = TransformerConv(2 * 16, 7, heads=1)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index)
x = self.conv3(x, edge_index)
return x
class HeteroAttentionGNN(torch.nn.Module):
def __init__(self, metadata, model_config=None):
super().__init__()
self.model_config = model_config
# Create a single BaseGNN that uses all three attention mechanisms
class BaseGNN(torch.nn.Module):
def __init__(self):
super().__init__()
# Use different attention mechanisms in sequence
self.conv1 = GATConv((-1, -1), 16, heads=2,
add_self_loops=False)
self.conv2 = GATv2Conv((-1, -1), 16, heads=2,
add_self_loops=False)
self.conv3 = TransformerConv((-1, -1), 32, heads=1)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index).relu()
x = self.conv3(x, edge_index)
return x
# Convert to heterogeneous model with a single to_hetero call
self.gnn = to_hetero(BaseGNN(), metadata, debug=False)
# Output dimension based on model config
out_channels = 7 if (model_config and model_config.mode
== ModelMode.multiclass_classification) else 1
self.lin = torch.nn.Linear(32, out_channels)
def forward(self, x_dict, edge_index_dict, **kwargs):
# Process through the heterogeneous GNN
out_dict = self.gnn(x_dict, edge_index_dict)
# Project paper node embeddings for classification/regression
x = self.lin(out_dict['paper'])
# Apply appropriate output transformation based on model config
if self.model_config:
if self.model_config.mode == ModelMode.binary_classification:
if self.model_config.return_type == 'probs':
x = x.sigmoid()
elif self.model_config.mode == ModelMode.multiclass_classification:
if self.model_config.return_type == 'probs':
x = x.softmax(dim=-1)
elif self.model_config.return_type == 'log_probs':
x = x.log_softmax(dim=-1)
return x
class HeteroConvAttentionGNN(torch.nn.Module):
def __init__(self, metadata, model_config=None):
super().__init__()
self.model_config = model_config
# Determine output channels based on model_config
self.out_channels = 1
if (model_config
and model_config.mode == ModelMode.multiclass_classification):
self.out_channels = 7
# Initialize node type-specific layers
self.lin_dict = torch.nn.ModuleDict()
self.initialized = False
# Create a dictionary of attention-based convolutions for each edge
# type
conv_dict = {}
for edge_type in metadata[1]: # metadata[1] contains edge types
src_type, _, dst_type = edge_type
if src_type == dst_type:
# For same node type, use GATConv with add_self_loops=False
# Use concat=False to avoid dimension issues
conv_dict[edge_type] = GATConv(
(-1, -1), 32, heads=2, add_self_loops=False, concat=False)
else:
# For different node types, use GATv2Conv with
# add_self_loops=False Use concat=False to avoid dimension
# issues
conv_dict[edge_type] = GATv2Conv(
(-1, -1), 32, heads=2, add_self_loops=False, concat=False)
# Create the HeteroConv layer
self.conv = HeteroConv(conv_dict, aggr='sum')
# Output layer will be initialized in forward pass
self.out_lin = None
def _initialize_layers(self, x_dict):
"""Initialize layers with correct dimensions when we first see the
data.
"""
if not self.initialized:
# Initialize input projections
for node_type, x in x_dict.items():
in_channels = x.size(-1)
self.lin_dict[node_type] = torch.nn.Linear(in_channels,
32).to(x.device)
# Initialize output projection
self.out_lin = torch.nn.Linear(32, self.out_channels).to(
x_dict['paper'].device)
self.initialized = True
def forward(self, x_dict, edge_index_dict):
# Initialize layers if not done yet
self._initialize_layers(x_dict)
# Apply node type-specific transformations
h_dict = {}
for node_type, x in x_dict.items():
h_dict[node_type] = self.lin_dict[node_type](x).relu_()
# Apply heterogeneous convolution
out_dict = self.conv(h_dict, edge_index_dict)
# Final transformation for paper nodes
out = self.out_lin(out_dict['paper'])
# Apply transformations based on model_config if available
if self.model_config:
if self.model_config.mode == ModelMode.binary_classification:
if self.model_config.return_type == 'probs':
out = out.sigmoid()
elif self.model_config.mode == ModelMode.multiclass_classification:
if self.model_config.return_type == 'probs':
out = out.softmax(dim=-1)
elif self.model_config.return_type == 'log_probs':
out = out.log_softmax(dim=-1)
return out
x = torch.randn(8, 3)
edge_index = torch.tensor([
[0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7],
[1, 0, 2, 1, 3, 2, 4, 3, 5, 4, 6, 5, 7, 6],
])
edge_attr = torch.randn(edge_index.size(1), 5)
batch = torch.tensor([0, 0, 0, 1, 1, 2, 2, 2])
@pytest.mark.parametrize('index', [None, 2, torch.arange(3)])
def test_attention_explainer(index, check_explanation):
explainer = Explainer(
model=AttentionGNN(),
algorithm=AttentionExplainer(),
explanation_type='model',
edge_mask_type='object',
model_config=dict(
mode='multiclass_classification',
task_level='node',
return_type='raw',
),
)
explanation = explainer(x, edge_index, index=index)
check_explanation(explanation, None, explainer.edge_mask_type)
@pytest.mark.parametrize('explanation_type', [e for e in ExplanationType])
@pytest.mark.parametrize('node_mask_type', [m for m in MaskType])
def test_attention_explainer_supports(explanation_type, node_mask_type):
with pytest.raises(ValueError, match="not support the given explanation"):
Explainer(
model=AttentionGNN(),
algorithm=AttentionExplainer(),
explanation_type=explanation_type,
node_mask_type=node_mask_type,
edge_mask_type='object',
model_config=dict(
mode='multiclass_classification',
task_level='node',
return_type='raw',
),
)
def test_attention_explainer_attentive_fp(check_explanation):
model = AttentiveFP(3, 16, 1, edge_dim=5, num_layers=2, num_timesteps=2)
explainer = Explainer(
model=model,
algorithm=AttentionExplainer(),
explanation_type='model',
edge_mask_type='object',
model_config=dict(
mode='binary_classification',
task_level='node',
return_type='raw',
),
)
explanation = explainer(x, edge_index, edge_attr=edge_attr, batch=batch)
check_explanation(explanation, None, explainer.edge_mask_type)
@pytest.mark.parametrize('index', [None, 2, torch.arange(3)])
def test_attention_explainer_hetero(index, hetero_data,
check_explanation_hetero):
# Create model configuration
model_config = ModelConfig(
mode='multiclass_classification',
task_level='node',
return_type='raw',
)
# Get metadata from hetero_data
metadata = hetero_data.metadata()
# Create the hetero attention model
model = HeteroAttentionGNN(metadata, model_config)
# Create the explainer
explainer = Explainer(
model=model,
algorithm=AttentionExplainer(),
explanation_type='model',
edge_mask_type='object',
model_config=model_config,
)
# Generate the explanation
explanation = explainer(
hetero_data.x_dict,
hetero_data.edge_index_dict,
index=index,
)
# Check that the explanation is correct
assert isinstance(explanation, HeteroExplanation)
check_explanation_hetero(explanation, None, explainer.edge_mask_type,
hetero_data)
@pytest.mark.parametrize('index', [None, 2, torch.arange(3)])
def test_attention_explainer_hetero_conv(index, hetero_data,
check_explanation_hetero):
"""Test AttentionExplainer with HeteroConv using attention-based layers."""
# Create model configuration
model_config = ModelConfig(
mode='multiclass_classification',
task_level='node',
return_type='raw',
)
# Get metadata from hetero_data
metadata = hetero_data.metadata()
# Create the hetero conv attention model
model = HeteroConvAttentionGNN(metadata, model_config)
# Create the explainer
explainer = Explainer(
model=model,
algorithm=AttentionExplainer(),
explanation_type='model',
edge_mask_type='object',
model_config=model_config,
)
# Generate the explanation
explanation = explainer(
hetero_data.x_dict,
hetero_data.edge_index_dict,
index=index,
)
# Check that the explanation is correct
assert isinstance(explanation, HeteroExplanation)
check_explanation_hetero(explanation, None, explainer.edge_mask_type,
hetero_data)
|
