import graphgallery
import tensorflow as tf
graphgallery.set_memory_growth()
print("GraphGallery version: ", graphgallery.__version__)
print("TensorFlow version: ", tf.__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()
from graphgallery.gallery import Node2vec
trainer = Node2vec(graph).process().build()
his = trainer.train(splits.train_nodes)
results = trainer.test(splits.test_nodes)
print(f'Test accuracy {results.accuracy:.2%}')
import graphgallery as gg
from graphgallery import functional as gf
gg.set_memory_growth()
from graphgallery.datasets import Planetoid, NPZDataset
data = NPZDataset('cora',
root="~/GraphData/datasets/",
verbose=False,
transform='standardize')
graph = data.graph
splits = data.split_nodes(random_state=15)
for backend in ['th', 'dgl', 'pyg', 'tf']:
gg.set_backend(backend)
for device in ['cpu', 'cuda', 'gpu']:
for name, m in gg.gallery.nodeclas.models():
if name in ['LGCN', 'GraphMLP', 'PDN', 'ClusterGCN']:
continue
print(backend, device, name)
trainer = m(device=device)
trainer.setup_graph(graph, feat_transform=None)
trainer.build()
trainer.fit(splits.train_nodes,
splits.val_nodes,
verbose=0,
epochs=2)
