Training a GNN on the Mantra Dataset

In this tutorial, we provide an example use-case for the mantra dataset. We show how to train a GNN to predict the orientability based on random node features.

The torch-geometric interface for the MANTRA dataset can be installed with pip via the command {python} pip install mantra

As a preprocessing step we apply three transforms to the base dataset. Since the dataset does not have intrinsic coordinates attached to the vertices, we first have to create a transform that generates random node features. Each manifold in MANTRA comes as a list of triples, where the integers in each triple are vertex id’s. The starting id in each manifold is \(1\) and has to be converted to a torch-geometric compliant \(0\)-based index. GNN’s are typically trained on graphs and the FaceToEdge transform converts our manifold to a graph.

For each of the transforms we use a single class and are succesively applied to form the final transformed dataset.

# Load all required packages.
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.data import random_split

from torchvision.transforms import Compose

from torch_geometric.loader import DataLoader
from torch_geometric.transforms import Compose, FaceToEdge

from torch_geometric.nn import GCNConv, global_mean_pool

# Load the mantra dataset
from mantra.datasets import ManifoldTriangulations
from mantra.transforms import NodeIndex, RandomNodeFeatures

# Instantiate the dataset. Following the `torch-geometric` API, we download the
# dataset into the root directory.
dataset = ManifoldTriangulations(root="./data", manifold="2", version="latest",
                                 transform=Compose([
                                        NodeIndex(),
                                        RandomNodeFeatures(),
                                        FaceToEdge(remove_faces=True),
                                        ]
                                    )
                                )

Downloading https://github.com/aidos-lab/MANTRADataset/releases/latest/download/2_manifolds.json.gz
Extracting data/simplicial/raw/2_manifolds.json.gz
Processing...
Done!

train_dataset, test_dataset = random_split(
            dataset,
            [0.8,0.2
            ],
        )  # type: ignore

train_dataloader = DataLoader(train_dataset,batch_size=32)
test_dataloader = DataLoader(test_dataset,batch_size=32)

class GCN(nn.Module):
    '''
    A standard Graph Convolutional Neural Network with three layers for
    predicting the orientability of the manifold.
    Note that since our model only uses the edge information, it is not
    able to learn the orientability. It will therefore only serve as
    an example.
    '''
    def __init__(self):
        super().__init__()

        self.conv_input = GCNConv(
           8, 16
        )
        self.final_linear = nn.Linear(
            16, 1
        )

    def forward(self, batch):
        x, edge_index, batch = batch.x, batch.edge_index, batch.batch

        # 1. Obtain node embeddings
        x = self.conv_input(x, edge_index)
        # 2. Readout layer
        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]
        # 3. Apply a final classifier
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.final_linear(x)
        return x

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GCN().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
loss_fn = nn.BCEWithLogitsLoss()

model.train()
for epoch in range(10):
    for batch in train_dataloader:
        batch.orientable = batch.orientable.to(torch.float)
        batch.to(device)
        optimizer.zero_grad()
        out = model(batch)
        loss = loss_fn(out.squeeze(), batch.orientable)
        loss.backward()
        optimizer.step()
    print(f"Epoch {epoch}, {loss.item()}")

Epoch 0, 0.10134144872426987
Epoch 1, 0.09631027281284332
Epoch 2, 0.08791007101535797
Epoch 3, 0.08849794417619705
Epoch 4, 0.08793244510889053
Epoch 5, 0.08987350761890411
Epoch 6, 0.08849993348121643
Epoch 7, 0.08913585543632507
Epoch 8, 0.08902224153280258
Epoch 9, 0.09069301933050156

correct = 0
total = 0
model.eval()
for testbatch in test_dataloader:
    testbatch.to(device)
    pred = model(testbatch)
    correct += ((pred.squeeze() < 0) == testbatch.orientable).sum()
    total += len(testbatch)

acc = int(correct) / int(total)
print(f'Accuracy: {acc:.4f}')

Accuracy: 0.0800