1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
|
from typing import Optional
import pytest
import torch
from torch_geometric.data import Data, HeteroData
from torch_geometric.explain import Explanation, HeteroExplanation
from torch_geometric.explain.config import (
MaskType,
ModelConfig,
ModelMode,
ModelReturnType,
ModelTaskLevel,
)
from torch_geometric.nn import (
HANConv,
HGTConv,
SAGEConv,
global_add_pool,
to_hetero,
)
from torch_geometric.nn.conv import GCNConv, HeteroConv
from torch_geometric.testing import get_random_edge_index
@pytest.fixture()
def data():
return Data(
x=torch.randn(4, 3),
edge_index=get_random_edge_index(4, 4, num_edges=6),
edge_attr=torch.randn(6, 3),
)
@pytest.fixture()
def hetero_data():
data = HeteroData()
data['paper'].x = torch.randn(8, 16)
data['author'].x = torch.randn(10, 8)
data['paper', 'paper'].edge_index = get_random_edge_index(8, 8, 10)
data['paper', 'paper'].edge_attr = torch.randn(10, 16)
data['paper', 'author'].edge_index = get_random_edge_index(8, 10, 10)
data['paper', 'author'].edge_attr = torch.randn(10, 8)
data['author', 'paper'].edge_index = get_random_edge_index(10, 8, 10)
data['author', 'paper'].edge_attr = torch.randn(10, 8)
return data
@pytest.fixture()
def hetero_model():
return HeteroSAGE
@pytest.fixture()
def hetero_model_custom():
return HeteroConvModel
class GraphSAGE(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = SAGEConv((-1, -1), 32)
self.conv2 = SAGEConv((-1, -1), 32)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
return self.conv2(x, edge_index)
class HeteroSAGE(torch.nn.Module):
def __init__(self, metadata, model_config: Optional[ModelConfig] = None):
super().__init__()
self.model_config = model_config
self.graph_sage = to_hetero(GraphSAGE(), metadata, debug=False)
# Determine output channels based on model_config
out_channels = 1
if (model_config
and model_config.mode == ModelMode.multiclass_classification):
out_channels = 7
self.lin = torch.nn.Linear(32, out_channels)
def forward(self, x_dict, edge_index_dict,
additonal_arg=None) -> torch.Tensor:
x = self.lin(self.graph_sage(x_dict, edge_index_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 == ModelReturnType.probs:
x = x.sigmoid()
elif self.model_config.mode == ModelMode.multiclass_classification:
if self.model_config.return_type == ModelReturnType.probs:
x = x.softmax(dim=-1)
elif (self.model_config.return_type ==
ModelReturnType.log_probs):
x = x.log_softmax(dim=-1)
return x
@pytest.fixture()
def check_explanation():
def _check_explanation(
explanation: Explanation,
node_mask_type: Optional[MaskType],
edge_mask_type: Optional[MaskType],
):
if node_mask_type == MaskType.attributes:
assert explanation.node_mask.size() == explanation.x.size()
assert explanation.node_mask.min() >= 0
assert explanation.node_mask.max() <= 1
elif node_mask_type == MaskType.object:
assert explanation.node_mask.size() == (explanation.num_nodes, 1)
assert explanation.node_mask.min() >= 0
assert explanation.node_mask.max() <= 1
elif node_mask_type == MaskType.common_attributes:
assert explanation.node_mask.size() == (1, explanation.x.size(-1))
assert explanation.node_mask.min() >= 0
assert explanation.node_mask.max() <= 1
elif node_mask_type is None:
assert 'node_mask' not in explanation
if edge_mask_type == MaskType.object:
assert explanation.edge_mask.size() == (explanation.num_edges, )
assert explanation.edge_mask.min() >= 0
assert explanation.edge_mask.max() <= 1
elif edge_mask_type is None:
assert 'edge_mask' not in explanation
return _check_explanation
@pytest.fixture()
def check_explanation_hetero():
def _check_explanation_hetero(
explanation: HeteroExplanation,
node_mask_type: Optional[MaskType],
edge_mask_type: Optional[MaskType],
hetero_data: HeteroData,
):
# Validate the explanation
explanation.validate(raise_on_error=True)
# Check node masks for different node types
if node_mask_type is not None:
for node_type in hetero_data.node_types:
assert explanation[node_type].get('node_mask') is not None
assert explanation[node_type].get('node_mask').min() >= 0
assert explanation[node_type].get('node_mask').max() <= 1
# Check dimensions based on mask type
if node_mask_type == MaskType.attributes:
mask = explanation[node_type].get('node_mask')
assert mask.size() == hetero_data.x_dict[node_type].size()
elif node_mask_type == MaskType.object:
mask = explanation[node_type].get('node_mask')
assert mask.size() == (
hetero_data.x_dict[node_type].size(0), 1)
elif node_mask_type == MaskType.common_attributes:
mask = explanation[node_type].get('node_mask')
assert mask.size() == (
1, hetero_data.x_dict[node_type].size(1))
# Check edge masks for different edge types
if edge_mask_type is not None:
for edge_type in hetero_data.edge_types:
assert explanation[edge_type].get('edge_mask') is not None
assert explanation[edge_type].get('edge_mask').min() >= 0
assert explanation[edge_type].get('edge_mask').max() <= 1
return _check_explanation_hetero
class NativeHeteroGNN(torch.nn.Module):
def __init__(self, metadata, model_config: Optional[ModelConfig] = None,
conv_type: str = 'HGTConv', hidden_channels: int = 32):
super().__init__()
self.model_config = model_config
self.conv_type = conv_type
self.hidden_channels = hidden_channels
self.metadata = metadata
# Determine output size based on model_config
self.out_channels = 1
if (model_config
and model_config.mode == ModelMode.multiclass_classification):
self.out_channels = 7
# Initialize dictionaries to store the layers
self.lin_dict = torch.nn.ModuleDict()
self.initialized = False
# Heterogeneous convolution layer
if conv_type == 'HGTConv':
self.conv = HGTConv(hidden_channels, hidden_channels, metadata,
heads=2)
elif conv_type == 'HANConv':
self.conv = HANConv(hidden_channels, hidden_channels, metadata,
heads=2)
else:
raise ValueError(f"Unsupported conv_type: {conv_type}")
# Output projection 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, self.hidden_channels).to(x.device)
# Initialize output projection
self.out_lin = torch.nn.Linear(self.hidden_channels,
self.out_channels).to(x.device)
self.initialized = True
def forward(self, x_dict, edge_index_dict):
# Initialize layers if not done yet
self._initialize_layers(x_dict)
# Apply input projections
x_dict = {
node_type: self.lin_dict[node_type](x).relu_()
for node_type, x in x_dict.items()
}
# Apply heterogeneous convolution
x_dict = self.conv(x_dict, edge_index_dict)
# Get paper node features for prediction
x = x_dict['paper']
# Apply output projection
out = self.out_lin(x)
# For graph-level tasks, perform global pooling
if (self.model_config
and self.model_config.task_level == ModelTaskLevel.graph):
# Since we don't have batch information in the fixture,
# we'll treat the whole graph as a single graph
batch_size = x.size(0)
batch = torch.zeros(batch_size, dtype=torch.long, device=x.device)
out = global_add_pool(out, batch)
return out
@pytest.fixture()
def hetero_model_native():
return NativeHeteroGNN
class HeteroConvModel(torch.nn.Module):
def __init__(self, metadata, model_config: Optional[ModelConfig] = None):
super().__init__()
self.model_config = model_config
# Create a HeteroConv model
conv_dict = {}
for edge_type in metadata[1]: # metadata[1] contains edge types
src_type, _, dst_type = edge_type
if src_type == dst_type:
conv_dict[edge_type] = GCNConv(-1, 32)
else:
# For different node types, use SAGEConv
conv_dict[edge_type] = SAGEConv((-1, -1), 32)
self.conv = HeteroConv(conv_dict, aggr='sum')
# Determine output channels based on model_config
out_channels = 1
if (model_config
and model_config.mode == ModelMode.multiclass_classification):
out_channels = 7
# Output layer
self.out_lin = torch.nn.Linear(32, out_channels)
def forward(self, x_dict, edge_index_dict):
# Apply heterogeneous convolution
out_dict = self.conv(x_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 == ModelReturnType.probs:
out = out.sigmoid()
elif self.model_config.mode == ModelMode.multiclass_classification:
if self.model_config.return_type == ModelReturnType.probs:
out = out.softmax(dim=-1)
elif (self.model_config.return_type ==
ModelReturnType.log_probs):
out = out.log_softmax(dim=-1)
return out
|
