import torch
import torch.nn.functional as F
from torch_geometric.nn import GATConv
from . import register_model
from .base import BaseAutoModel, activate_func
from ....utils import get_logger
LOGGER = get_logger("GATModel")
def set_default(args, d):
for k, v in d.items():
if k not in args:
args[k] = v
return args
class GAT(torch.nn.Module):
def __init__(self, args):
super(GAT, self).__init__()
self.args = args
self.num_layer = int(self.args["num_layers"])
missing_keys = list(
set(
[
"features_num",
"num_class",
"num_layers",
"hidden",
"heads",
"dropout",
"act",
]
)
- set(self.args.keys())
)
if len(missing_keys) > 0:
raise Exception("Missing keys: %s." % ",".join(missing_keys))
if not self.num_layer == len(self.args["hidden"]) + 1:
LOGGER.warn("Warning: layer size does not match the length of hidden units")
self.convs = torch.nn.ModuleList()
self.convs.append(
GATConv(
self.args["features_num"],
self.args["hidden"][0],
heads=self.args["heads"],
dropout=self.args["dropout"],
)
)
last_dim = self.args["hidden"][0] * self.args["heads"]
for i in range(self.num_layer - 2):
self.convs.append(
GATConv(
last_dim,
self.args["hidden"][i + 1],
heads=self.args["heads"],
dropout=self.args["dropout"],
)
)
last_dim = self.args["hidden"][i + 1] * self.args["heads"]
self.convs.append(
GATConv(
last_dim,
self.args["num_class"],
heads=1,
concat=False,
dropout=self.args["dropout"],
)
)
def forward(self, data):
try:
x = data.x
except:
print("no x")
pass
try:
edge_index = data.edge_index
except:
print("no index")
pass
try:
edge_weight = data.edge_weight
except:
edge_weight = None
pass
for i in range(self.num_layer):
x = F.dropout(x, p=self.args["dropout"], training=self.training)
x = self.convs[i](x, edge_index, edge_weight)
if i != self.num_layer - 1:
x = activate_func(x, self.args["act"])
return F.log_softmax(x, dim=1)
def lp_encode(self, data):
x = data.x
for i in range(self.num_layer - 1):
x = self.convs[i](x, data.edge_index) # Jie
if i != self.num_layer - 2:
x = activate_func(x, self.args["act"])
# x = F.dropout(x, p=self.args["dropout"], training=self.training)
return x
def lp_decode(self, z, pos_edge_index, neg_edge_index):
edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1)
logits = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1)
return logits
def lp_decode_all(self, z):
prob_adj = z @ z.t()
return (prob_adj > 0).nonzero(as_tuple=False).t()
[docs]@register_model("gat-model")
class AutoGAT(BaseAutoModel):
r"""
AutoGAT. The model used in this automodel is GAT, i.e., the graph attentional network from the `"Graph Attention Networks"
<https://arxiv.org/abs/1710.10903>`_ paper. The layer is
.. math::
\mathbf{x}^{\prime}_i = \alpha_{i,i}\mathbf{\Theta}\mathbf{x}_{i} +
\sum_{j \in \mathcal{N}(i)} \alpha_{i,j}\mathbf{\Theta}\mathbf{x}_{j}
where the attention coefficients :math:`\alpha_{i,j}` are computed as
.. math::
\alpha_{i,j} =
\frac{
\exp\left(\mathrm{LeakyReLU}\left(\mathbf{a}^{\top}
[\mathbf{\Theta}\mathbf{x}_i \, \Vert \, \mathbf{\Theta}\mathbf{x}_j]
\right)\right)}
{\sum_{k \in \mathcal{N}(i) \cup \{ i \}}
\exp\left(\mathrm{LeakyReLU}\left(\mathbf{a}^{\top}
[\mathbf{\Theta}\mathbf{x}_i \, \Vert \, \mathbf{\Theta}\mathbf{x}_k]
\right)\right)}.
Parameters
----------
num_features: `int`.
The dimension of features.
num_classes: `int`.
The number of classes.
device: `torch.device` or `str`
The device where model will be running on.
init: `bool`.
If True(False), the model will (not) be initialized.
args: Other parameters.
"""
def __init__(
self, num_features=None, num_classes=None, device=None, **args
):
super().__init__(num_features, num_classes, device, **args)
self.hyper_parameter_space = [
{
"parameterName": "num_layers",
"type": "DISCRETE",
"feasiblePoints": "2,3,4",
},
{
"parameterName": "hidden",
"type": "NUMERICAL_LIST",
"numericalType": "INTEGER",
"length": 3,
"minValue": [8, 8, 8],
"maxValue": [64, 64, 64],
"scalingType": "LOG",
"cutPara": ("num_layers",),
"cutFunc": lambda x: x[0] - 1,
},
{
"parameterName": "dropout",
"type": "DOUBLE",
"maxValue": 0.8,
"minValue": 0.2,
"scalingType": "LINEAR",
},
{
"parameterName": "heads",
"type": "DISCRETE",
"feasiblePoints": "2,4,8,16",
},
{
"parameterName": "act",
"type": "CATEGORICAL",
"feasiblePoints": ["leaky_relu", "relu", "elu", "tanh"],
},
]
self.hyper_parameters = {
"num_layers": 2,
"hidden": [32],
"heads": 4,
"dropout": 0.2,
"act": "leaky_relu",
}
def _initialize(self):
# """Initialize model."""
self._model = GAT({
"features_num": self.input_dimension,
"num_class": self.output_dimension,
**self.hyper_parameters
}).to(self.device)