from graphgallery.gallery import GCN

# initialize a GNN trainer
trainer = GCN(graph)
# process your inputs, such as converting to tensors
trainer.process()
# build your GCN trainer with default hyper-parameters
trainer.build()
# train your trainer. here splits.train_nodes and splits.val_nodes are numpy arrays
# verbose takes 0, 1, 2, 3, 4
history = trainer.train(splits.train_nodes, splits.val_nodes, verbose=1, epochs=100)
# test your trainer
# verbose takes 0, 1, 2
results = trainer.test(splits.nodes, verbose=1)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')

>>> import graphgallery
>>> graphgallery.backend()
TensorFlow 2.1.2 Backend

>>> graphgallery.set_backend("pytorch")
PyTorch 1.6.0+cu101 Backend

# DGL PyTorch backend
>>> graphgallery.set_backend("dgl")

# DGL TensorFlow backend
>>> graphgallery.set_backend("dgl-tf")

from graphgallery.data import Graph

# Load the adjacency matrix A, attribute matrix X and labels vector y
# A - scipy.sparse.csr_matrix of shape [num_nodes, num_nodes]
# X - scipy.sparse.csr_matrix or np.ndarray of shape [num_nodes, num_attrs]
# y - np.ndarray of shape [num_nodes]

mydataset = Graph(adj_matrix=A, node_attr=X, node_label=y)
# save dataset
mydataset.to_npz('path/to/mydataset.npz')
# load dataset
mydataset = Graph.from_npz('path/to/mydataset.npz')