# coding: utf-8
import random
import math
import torch
import dgl
import graphgallery
from graphgallery.datasets import Planetoid
print("GraphGallery version: ", graphgallery.__version__)
print("PyTorch version: ", torch.__version__)
print("DGL version: ", dgl.__version__)
'''
Load Datasets
- cora/citeseer/pubmed
'''
data = Planetoid('cora', root="~/GraphData/datasets/", verbose=False)
graph = data.graph
device = torch.device('cuda') if torch.cuda.is_available() else torch.device(
'cpu')
# splits = data.split_nodes()
graphgallery.set_backend("dgl")
# experimental setup in
# `When Do GNNs Work: Understanding and Improving Neighborhood Aggregation
# <https://www.ijcai.org/Proceedings/2020/0181.pdf>`
random.seed(2020)
split = 0.01
n_nodes = graph.num_nodes
sample_size = math.ceil(n_nodes * split)
train_idx = random.sample(range(n_nodes - 1000), sample_size)
train_nodes = [idx if idx < 500 else idx + 1000 for idx in train_idx]
#!/usr/bin/env python
# coding: utf-8
import torch
import dgl
import graphgallery
print("GraphGallery version: ", graphgallery.__version__)
print("Torch version: ", torch.__version__)
print("DGL version: ", dgl.__version__)
'''
Load Datasets
- cora/citeseer/pubmed
'''
from graphgallery.datasets import Planetoid
data = Planetoid('cora', root="~/GraphData/datasets/", verbose=False)
graph = data.graph
splits = data.split_nodes()
graphgallery.set_backend("dgl")
from graphgallery.gallery import GCN
model = GCN(graph, attr_transform="normalize_attr", device="gpu", seed=123)
model.build()
his = model.train(splits.train_nodes, splits.val_nodes, verbose=1, epochs=100)
results = model.test(splits.test_nodes)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')
# coding: utf-8
import torch
import graphgallery
import torch_geometric
from graphgallery.gallery import callbacks
from graphgallery.datasets import Planetoid
print("GraphGallery version: ", graphgallery.__version__)
print("PyTorch version: ", torch.__version__)
print("Torch_Geometric version: ", torch_geometric.__version__)
'''
Load Datasets
- cora/citeseer/pubmed
'''
data = Planetoid('cora', root="~/GraphData/datasets/", verbose=False)
graph = data.graph
splits = data.split_edges(random_state=15)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
graphgallery.set_backend("pyg")
from graphgallery.gallery.linkpred import GAE, VGAE
trainer = GAE(device=device, seed=123).setup_graph(graph).build()
# trainer = VGAE(device=device, seed=123).setup_graph(graph).build()
cb = callbacks.ModelCheckpoint('model.pth', monitor='val_ap')
trainer.fit(splits.train_pos_edge_index,
val_data=(splits.val_pos_edge_index, splits.val_neg_edge_index), verbose=1, callbacks=[cb])
results = trainer.evaluate((splits.test_pos_edge_index, splits.test_neg_edge_index))
print(results)
