Exploing Graph neural networks

pytorch

deep learning

…

Author

Temi

Published

October 11, 2023

Note

This post is still under construction; I am adding sutff as I get the time to.

My foray into graph neural networks

What is a graph?

what does a graphical data look like?

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# %%capture
# import os
# import torch
# os.environ['TORCH'] = torch.__version__
# os.environ['PYTHONWARNINGS'] = "ignore"
# !pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html
# !pip install torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html
# !pip install git+https://github.com/pyg-team/pytorch_geometric.git
# !pip install torch_geometric
# !pip install tabulate
# !pip install torchsummary

Collecting torchsummary
  Downloading torchsummary-1.5.1-py3-none-any.whl (2.8 kB)
Installing collected packages: torchsummary
Successfully installed torchsummary-1.5.1

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import torch
import torch_geometric as tg

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edge_index = torch.tensor([[0, 1, 1, 2],
                           [1, 0, 2, 1]], dtype=torch.long)
x = torch.tensor([[-1], [0], [1]], dtype=torch.float)

data = tg.data.Data(x=x, edge_index=edge_index)
data

Data(x=[3, 1], edge_index=[2, 4])

The nodes define what is connected. This is a graph with 3 nodes -1, 0 and 1

The edges define the connections too: 0 -> 1, 1 -> 0, 1 -> 2, and 2 -> 1

Graphs shold have a few things

x: the data, node feature matrix with shape [num_nodes, num_node_features]
edge_index: Graph connectivity in coordinate format with shape [2, num_edges] and type torch.long
edge_attr: Edge feature matrix with shape [num_edges, num_edge_features] (note that edges can have features too)
y: Target to train against (may have arbitrary shape), e.g., node-level targets of shape [num_nodes, *] or graph-level targets of shape [1, *]
pos: Node position matrix with shape [num_nodes, num_dimensions]

given a graph data type in torch, you can do a few things:

validate the graph

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data.validate(raise_on_error=True)

True

Count how many nodes and edges

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data.num_nodes

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data.edge_index.shape

torch.Size([2, 4])

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data.num_edge_features # no features for the edges

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data.num_node_features # just one feature for each node

is the graph directed?

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data.is_directed()

False

Let’s look at a real-life dataset

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from torch_geometric.datasets import TUDataset

dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES')
dataset

ENZYMES(600)

There are 600 graphs in this dataset. Each graph is a protein structure; 600 protein structures. We can look at one of them…

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dataset[3].num_nodes

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dataset[3]

Data(edge_index=[2, 90], x=[24, 3], y=[1])

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dataset[0]

Data(edge_index=[2, 168], x=[37, 3], y=[1])

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a_protein_graph = dataset[1]
a_protein_graph

Data(edge_index=[2, 102], x=[23, 3], y=[1])

This protein graph has 23 nodes and 3 node-level features. There are no edge attributes. y refers to the class of protein, and there are 102 connections.

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b_protein_graph = dataset[0]
b_protein_graph

Data(edge_index=[2, 168], x=[37, 3], y=[1])

Another one.

We can split all the 600 graphs into 2, so that we train on some, and test on another.

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train_graphs = dataset[:500]
test_graphs = dataset[500:]

train_graphs, test_graphs

(ENZYMES(500), ENZYMES(100))

Training a graph neural net

Let’s use another dataset, Cora, a less complex dataset:

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from torch_geometric.datasets import Planetoid
import torchsummary

dataset = Planetoid(root='/tmp/Cora', name='Cora')
dataset

Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.x
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.tx
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.allx
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.y
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ty
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ally
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.graph
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.test.index
Processing...
Done!

Cora()

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len(dataset)

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data = dataset[0]
data

Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708])

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dataset.num_classes, data.num_node_features

(7, 1433)

So, this dataset has 2708 nodes, and 1433 node-level features. It has 10556 connectivities.

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import torch.nn.functional as F
from torch_geometric.loader import DataLoader

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class SimpleGNN(torch.nn.Module):
    def __init__(self, nnode_features, num_classes, num_layers=3):
        super().__init__()
        self.conv1 = tg.nn.GCNConv(in_channels=nnode_features, out_channels=32)
        #self.mlp = tg.nn.models.MLP(in_channels=16, out_channels=16, hidden_channels=4, num_layers=3, dropout=0.4, act='relu')
        
        # self.layers = torch.nn.ModuleList()
        # self.layers.append(torch.nn.Linear(in_features=8, out_features=4))
        # self.layers.append(torch.nn.ReLU())
        # for _ in range(num_layers - 2):
        #     self.layers.append(torch.nn.Linear(in_features=4, out_features=4))
        #     self.layers.append(torch.nn.ReLU())
        # self.layers.append(torch.nn.Linear(in_features=4, out_features=4))

        self.conv2 = tg.nn.GCNConv(in_channels=32, out_channels=num_classes)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        # x = self.mlp(x)
        # x = F.relu(x)
        # for layer in self.layers:
        #     x = layer(x)
        # x = F.relu(x)
        x = self.conv2(x, edge_index)
        
        return(F.log_softmax(x, dim=1))

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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = SimpleGNN(nnode_features=dataset.num_node_features, num_classes=dataset.num_classes, num_layers=1).to(device)
print(tg.nn.summary(model, dataset[0].to(device)))

+------------------+--------------------------+----------------+----------+
| Layer            | Input Shape              | Output Shape   | #Param   |
|------------------+--------------------------+----------------+----------|
| SimpleGNN        | [2708, 2708]             | [2708, 7]      | 46,119   |
| ├─(conv1)GCNConv | [2708, 1433], [2, 10556] | [2708, 32]     | 45,888   |
| ├─(conv2)GCNConv | [2708, 32], [2, 10556]   | [2708, 7]      | 231      |
+------------------+--------------------------+----------------+----------+

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lls = ([], [], [], [])
train_loss_tally, train_acc_tally, test_loss_tally, test_acc_tally = lls

data = dataset[0].to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=5e-3)

model.train()
for epoch in range(5000):
    optimizer.zero_grad()
    out = model(data)
    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    train_loss_tally.append(loss.item())

    pred = out.argmax(dim=1)
    correct = (pred[data.train_mask] == data.y[data.train_mask]).sum()
    acc = int(correct) / int(data.train_mask.sum())
    train_acc_tally.append(acc)
    
    optimizer.step()

    model.eval()
    loss = F.nll_loss(out[data.test_mask], data.y[data.test_mask])
    test_loss_tally.append(loss.item())

    pred = out.argmax(dim=1)
    correct = (pred[data.test_mask] == data.y[data.test_mask]).sum()
    acc = int(correct) / int(data.test_mask.sum())
    test_acc_tally.append(acc)

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import matplotlib.pyplot as plt

plt.plot(train_acc_tally, label = 'train')
plt.plot(test_acc_tally, label = 'test')
plt.legend(title="acc",loc=4, fontsize='small', fancybox=True)

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plt.plot(train_loss_tally, label = 'train')
plt.plot(test_loss_tally, label = 'test')
plt.legend(title="loss",loc=4, fontsize='small', fancybox=True)

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model.eval()
pred = model(data).argmax(dim=1)
correct = (pred[data.test_mask] == data.y[data.test_mask]).sum()
acc = int(correct) / int(data.test_mask.sum())
print(f'Accuracy: {acc:.4f}')

Accuracy: 0.8110

Graph attention network

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class SimpleGAT(torch.nn.Module):
    def __init__(self, nnode_features, num_classes, num_layers=3):
        super().__init__()
        self.conv1 = tg.nn.conv.GATConv(in_channels=nnode_features, out_channels=32, heads=12, concat=False, dropout=0.2, add_self_loops=True)
        self.conv2 = tg.nn.conv.GATConv(in_channels=32, out_channels=num_classes, heads=12, concat=False, add_self_loops=True)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = self.conv2(x, edge_index)
        
        return(F.log_softmax(x, dim=1))

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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = SimpleGAT(nnode_features=dataset.num_node_features, num_classes=dataset.num_classes, num_layers=1).to(device)
print(tg.nn.summary(model, dataset[0].to(device)))

+------------------+--------------------------+----------------+----------+
| Layer            | Input Shape              | Output Shape   | #Param   |
|------------------+--------------------------+----------------+----------|
| SimpleGAT        | [2708, 2708]             | [2708, 7]      | 553,935  |
| ├─(conv1)GATConv | [2708, 1433], [2, 10556] | [2708, 32]     | 551,072  |
| ├─(conv2)GATConv | [2708, 32], [2, 10556]   | [2708, 7]      | 2,863    |
+------------------+--------------------------+----------------+----------+

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lls = ([], [], [], [])
train_loss_tally, train_acc_tally, test_loss_tally, test_acc_tally = lls

data = dataset[0].to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.00001, weight_decay=5e-3)

model.train()
for epoch in range(5000):
    optimizer.zero_grad()
    out = model(data)
    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    train_loss_tally.append(loss.item())

    pred = out.argmax(dim=1)
    correct = (pred[data.train_mask] == data.y[data.train_mask]).sum()
    acc = int(correct) / int(data.train_mask.sum())
    train_acc_tally.append(acc)
    
    optimizer.step()

    model.eval()
    loss = F.nll_loss(out[data.test_mask], data.y[data.test_mask])
    test_loss_tally.append(loss.item())

    pred = out.argmax(dim=1)
    correct = (pred[data.test_mask] == data.y[data.test_mask]).sum()
    acc = int(correct) / int(data.test_mask.sum())
    test_acc_tally.append(acc)

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import matplotlib.pyplot as plt

plt.plot(train_acc_tally, label = 'train')
plt.plot(test_acc_tally, label = 'test')
plt.legend(title="acc",loc=4, fontsize='small', fancybox=True)

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plt.plot(train_loss_tally, label = 'train')
plt.plot(test_loss_tally, label = 'test')
plt.legend(title="loss",loc=4, fontsize='small', fancybox=True)

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model.eval()
pred = model(data).argmax(dim=1)
correct = (pred[data.test_mask] == data.y[data.test_mask]).sum()
acc = int(correct) / int(data.test_mask.sum())
print(f'Accuracy: {acc:.4f}')

Accuracy: 0.7270