import os.path as osp
import torch
from torch.nn import Linear
import torch.nn.functional as F
from sklearn.metrics import f1_score
from torch_geometric.datasets import PPI
import torch_geometric.transforms as T
from torch_geometric.nn import GCN2Conv
from torch_geometric.loader import DataLoader
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'GCN2_PPI')
pre_transform = T.Compose([T.GCNNorm(), T.ToSparseTensor()])
train_dataset = PPI(path, split='train', pre_transform=pre_transform)
val_dataset = PPI(path, split='val', pre_transform=pre_transform)
test_dataset = PPI(path, split='test', pre_transform=pre_transform)
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False)
class Net(torch.nn.Module):
def __init__(self, hidden_channels, num_layers, alpha, theta,
shared_weights=True, dropout=0.0):
super(Net, self).__init__()
self.lins = torch.nn.ModuleList()
self.lins.append(Linear(train_dataset.num_features, hidden_channels))
self.lins.append(Linear(hidden_channels, train_dataset.num_classes))
self.convs = torch.nn.ModuleList()
from tqdm import tqdm
import torch
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from torch_geometric.datasets import BAShapes
from torch_geometric.nn import GCN, GNNExplainer
from torch_geometric.utils import k_hop_subgraph
import torch_geometric.transforms as T
dataset = BAShapes(transform=T.GCNNorm())
data = dataset[0]
idx = torch.arange(data.num_nodes)
train_idx, test_idx = train_test_split(idx, train_size=0.8, stratify=data.y)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
data = data.to(device)
model = GCN(data.num_node_features,
hidden_channels=20,
num_layers=3,
out_channels=dataset.num_classes,
normalize=False).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=0.005)
def train():
model.train()
optimizer.zero_grad()
import os.path as osp
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
from torch_geometric.datasets import Planetoid
import torch_geometric.transforms as T
from torch_geometric.nn import GCNConv, GNNExplainer
dataset = 'Cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Planetoid')
transform = T.Compose([T.GCNNorm(), T.NormalizeFeatures()])
dataset = Planetoid(path, dataset, transform=transform)
data = dataset[0]
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = GCNConv(dataset.num_features, 16, normalize=False)
self.conv2 = GCNConv(16, dataset.num_classes, normalize=False)
def forward(self, x, edge_index, edge_weight):
x = F.relu(self.conv1(x, edge_index, edge_weight))
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index, edge_weight)
return F.log_softmax(x, dim=1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)