Let’s implement a regression example where the aim is to train a network to predict the value of a node given the value of all other nodes i.e. each node has a single feature (which is a scalar value). The aim of this example is to leverage the inherent relational information encoded in the graph to accurately predict numerical values for each node. The key thing to note is that we input the numerical value for all nodes except the target node (we mask the target node value with 0) then predict the target node’s value. For each data point, we repeat the process for all nodes. Perhaps this might come across as a bizarre task but lets see if we can predict the expected value of any node given the values of the other nodes. The data used is the corresponding simulation data to a series of sensors from industry and the graph structure I have chosen in the example below is based on the actual process structure. I have provided comments in the code to make it easy to follow. You can find a copy of the dataset here (Note: this is my own data, generated from simulations).
This code and training procedure is far from being optimised but it’s aim is to illustrate the implementation of GNNs and get an intuition for how they work. An issue with the currently way I have done that should definitely not be done this way beyond learning purposes is the masking of node feature value and predicting it from the neighbours feature. Currently you’d have to loop over each node (not very efficient), a much better way to do is the stop the model from include it’s own features in the aggregation step and hence you wouldn’t need to do one node at a time but I thought it is easier to build intuition for the model with the current method:)
Preprocessing Data
Importing the necessary libraries and Sensor data from CSV file. Normalise all data in the range of 0 to 1.
import pandas as pd
import torch
from torch_geometric.data import Data, Batch
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
import numpy as np
from torch_geometric.data import DataLoader# load and scale the dataset
df = pd.read_csv('SensorDataSynthetic.csv').dropna()
scaler = MinMaxScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
Defining the connectivity (edge index) between nodes in the graph using a PyTorch tensor — i.e. this provides the system’s graphical topology.
nodes_order = [
'Sensor1', 'Sensor2', 'Sensor3', 'Sensor4',
'Sensor5', 'Sensor6', 'Sensor7', 'Sensor8'
]# define the graph connectivity for the data
edges = torch.tensor([
[0, 1, 2, 2, 3, 3, 6, 2], # source nodes
[1, 2, 3, 4, 5, 6, 2, 7] # target nodes
], dtype=torch.long)
The Data imported from csv has a tabular structure but to use this in GNNs, it must be transformed to a graphical structure. Each row of data (one observation) is represented as one graph. Iterate through Each Row to Create Graphical representation of the data
A mask is created for each node/sensor to indicate the presence (1) or absence (0) of data, allowing for flexibility in handling missing data. In most systems, there may be items with no data available hence the need for flexibility in handling missing data. Split the data into training and testing sets
graphs = []# iterate through each row of data to create a graph for each observation
# some nodes will not have any data, not the case here but created a mask to allow us to deal with any nodes that do not have data available
for _, row in df_scaled.iterrows():
node_features = []
node_data_mask = []
for node in nodes_order:
if node in df_scaled.columns:
node_features.append([row[node]])
node_data_mask.append(1) # mask value of to indicate present of data
else:
# missing nodes feature if necessary
node_features.append(2)
node_data_mask.append(0) # data not present
node_features_tensor = torch.tensor(node_features, dtype=torch.float)
node_data_mask_tensor = torch.tensor(node_data_mask, dtype=torch.float)
# Create a Data object for this row/graph
graph_data = Data(x=node_features_tensor, edge_index=edges.t().contiguous(), mask = node_data_mask_tensor)
graphs.append(graph_data)
#### splitting the data into train, test observation
# Split indices
observation_indices = df_scaled.index.tolist()
train_indices, test_indices = train_test_split(observation_indices, test_size=0.05, random_state=42)
# Create training and testing graphs
train_graphs = [graphs[i] for i in train_indices]
test_graphs = [graphs[i] for i in test_indices]
Graph Visualisation
The graph structure created above using the edge indices can be visualised using networkx.
import networkx as nx
import matplotlib.pyplot as pltG = nx.Graph()
for src, dst in edges.t().numpy():
G.add_edge(nodes_order[src], nodes_order[dst])
plt.figure(figsize=(10, 8))
pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_color='lightblue', edge_color='gray', node_size=2000, font_weight='bold')
plt.title('Graph Visualization')
plt.show()
Model Definition
Let’s define the model. The model incorporates 2 GAT convolutional layers. The first layer transforms node features to an 8 dimensional space, and the second GAT layer further reduces this to an 8-dimensional representation.
GNNs are highly susceptible to overfitting, regularation (dropout) is applied after each GAT layer with a user defined probability to prevent over fitting. The dropout layer essentially randomly zeros some of the elements of the input tensor during training.
The GAT convolution layer output results are passed through a fully connected (linear) layer to map the 8-dimensional output to the final node feature which in this case is a scalar value per node.
Masking the value of the target Node; as mentioned earlier, the aim of this of task is to regress the value of the target node based on the value of it’s neighbours. This is the reason behind masking/replacing the target node’s value with zero.
from torch_geometric.nn import GATConv
import torch.nn.functional as F
import torch.nn as nnclass GNNModel(nn.Module):
def __init__(self, num_node_features):
super(GNNModel, self).__init__()
self.conv1 = GATConv(num_node_features, 16)
self.conv2 = GATConv(16, 8)
self.fc = nn.Linear(8, 1) # Outputting a single value per node
def forward(self, data, target_node_idx=None):
x, edge_index = data.x, data.edge_index
edge_index = edge_index.T
x = x.clone()
# Mask the target node's feature with a value of zero!
# Aim is to predict this value from the features of the neighbours
if target_node_idx is not None:
x[target_node_idx] = torch.zeros_like(x[target_node_idx])
x = F.relu(self.conv1(x, edge_index))
x = F.dropout(x, p=0.05, training=self.training)
x = F.relu(self.conv2(x, edge_index))
x = F.relu(self.conv3(x, edge_index))
x = F.dropout(x, p=0.05, training=self.training)
x = self.fc(x)
return x
Training the model
Initialising the model and defining the optimiser, loss function and the hyper parameters including learning rate, weight decay (for regularisation), batch_size and number of epochs.
model = GNNModel(num_node_features=1)
batch_size = 8
optimizer = torch.optim.Adam(model.parameters(), lr=0.0002, weight_decay=1e-6)
criterion = torch.nn.MSELoss()
num_epochs = 200
train_loader = DataLoader(train_graphs, batch_size=1, shuffle=True)
model.train()
The training process is fairly standard, each graph (one data point) of data is passed through the forward pass of the model (iterating over each node and predicting the target node. The loss from the prediction is accumulated over the defined batch size before updating the GNN through backpropagation.
for epoch in range(num_epochs):
accumulated_loss = 0
optimizer.zero_grad()
loss = 0
for batch_idx, data in enumerate(train_loader):
mask = data.mask
for i in range(1,data.num_nodes):
if mask[i] == 1: # Only train on nodes with data
output = model(data, i) # get predictions with the target node masked
# check the feed forward part of the model
target = data.x[i]
prediction = output[i].view(1)
loss += criterion(prediction, target)
#Update parameters at the end of each set of batches
if (batch_idx+1) % batch_size == 0 or (batch_idx +1 ) == len(train_loader):
loss.backward()
optimizer.step()
optimizer.zero_grad()
accumulated_loss += loss.item()
loss = 0average_loss = accumulated_loss / len(train_loader)
print(f'Epoch {epoch+1}, Average Loss: {average_loss}')
Testing the trained model
Using the test dataset, pass each graph through the forward pass of the trained model and predict each node’s value based on it’s neighbours value.
test_loader = DataLoader(test_graphs, batch_size=1, shuffle=True)
model.eval()actual = []
pred = []
for data in test_loader:
mask = data.mask
for i in range(1,data.num_nodes):
output = model(data, i)
prediction = output[i].view(1)
target = data.x[i]
actual.append(target)
pred.append(prediction)
Visualising the test results
Using iplot we can visualise the predicted values of nodes against the ground truth values.
import plotly.graph_objects as go
from plotly.offline import iplotactual_values_float = [value.item() for value in actual]
pred_values_float = [value.item() for value in pred]
scatter_trace = go.Scatter(
x=actual_values_float,
y=pred_values_float,
mode='markers',
marker=dict(
size=10,
opacity=0.5,
color='rgba(255,255,255,0)',
line=dict(
width=2,
color='rgba(152, 0, 0, .8)',
)
),
name='Actual vs Predicted'
)
line_trace = go.Scatter(
x=[min(actual_values_float), max(actual_values_float)],
y=[min(actual_values_float), max(actual_values_float)],
mode='lines',
marker=dict(color='blue'),
name='Perfect Prediction'
)
data = [scatter_trace, line_trace]
layout = dict(
title='Actual vs Predicted Values',
xaxis=dict(title='Actual Values'),
yaxis=dict(title='Predicted Values'),
autosize=False,
width=800,
height=600
)
fig = dict(data=data, layout=layout)
iplot(fig)
Despite a lack of fine tuning the model architecture or hyperparameters, it has done a decent job actually, we could tune the model further to get improved accuracy.
This brings us to the end of this article. GNNs are relatively newer than other branches of machine learning, it will be very exciting to see the developments of this field but also it’s application to different problems. Finally, thank you for taking the time to read this article, I hope you found it useful in your understanding of GNNs or their mathematical background.
Unless otherwise noted, all images are by the author
