#!/usr/bin/env python
# coding: utf-8
import torch
import graphgallery
import torch_geometric
print("GraphGallery version: ", graphgallery.__version__)
print("Torch version: ", torch.__version__)
print("Torch_Geometric version: ", torch_geometric.__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("pyg")
from graphgallery.gallery.nodeclas import GCN
trainer = GCN(device="gpu",
seed=123).make_data(graph,
adj_transform="GDC",
attr_transform="normalize_attr").build()
trainer.build()
his = trainer.fit(splits.train_nodes, splits.val_nodes, verbose=1, epochs=100)
results = trainer.evaluate(splits.test_nodes)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')
#!/usr/bin/env python
# coding: utf-8
import torch
import graphgallery
from graphgallery import functional as gf
print("GraphGallery version: ", graphgallery.__version__)
print("Torch version: ", torch.__version__)
'''
Load Datasets
- cora/citeseer/pubmed/dblp/polblogs/cora_ml, etc...
'''
from graphgallery.datasets import Planetoid, NPZDataset
data = NPZDataset('cora', root="~/GraphData/datasets/", transform=gf.Standardize(), verbose=False)
graph = data.graph
splits = data.split_nodes(random_state=15)
graphgallery.set_backend("pytorch")
from graphgallery.gallery.nodeclas import GCN
trainer = GCN(device="gpu", seed=123).make_data(graph, graph_transform="SVD").build()
history = trainer.fit(splits.train_nodes, splits.val_nodes, verbose=1, epochs=100)
results = trainer.evaluate(splits.test_nodes)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')
#!/usr/bin/env python
# coding: utf-8
import torch
import graphgallery
from graphgallery.datasets import Planetoid
from graphgallery.gallery import callbacks
print("GraphGallery version: ", graphgallery.__version__)
print("PyTorch version: ", torch.__version__)
'''
Load Datasets
- cora/citeseer/pubmed
'''
data = Planetoid('cora', root="~/GraphData/datasets/", verbose=False)
graph = data.graph
splits = data.split_nodes()
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
graphgallery.set_backend("pyg")
from graphgallery.gallery.nodeclas import GCN
trainer = GCN(device=device, seed=123).setup_graph(graph, feat_transform="normalize_feat").build()
cb = callbacks.ModelCheckpoint('model.pth', monitor='val_accuracy')
trainer.fit(splits.train_nodes, splits.val_nodes, verbose=1, callbacks=[cb])
results = trainer.evaluate(splits.test_nodes)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')