tasks.md

Predictive Tasks

There are two predictive tasks currently supported: node classification and link prediction. Predictive tasks are decoupled from the GNN architecture they model and are passed to the GNN's train method to define the training and validation objectives.

1. Node Classification
2. Link Prediction

Node Classification

The following code demonstrates an example of how to use pass a NodeClassification predictive task to the GNN to let it know what to train towards.

from experiments.experiment_setup import dgl_setup
import gnntf

gnntf.set_seed(0)
G, labels, features, train, valid, test = dgl_setup("cora")
num_classes = len(set(labels))
gnn = gnntf.APPNP(G, features, num_classes=num_classes)

gnn.train(train=gnntf.NodeClassification(train, labels[train]),
          valid=gnntf.NodeClassification(valid, labels[valid]))

prediction = gnn.predict(gnntf.NodeClassification(test))
accuracy = gnntf.acc(prediction, labels[test])
print(accuracy)

Link Prediction

from experiments.experiment_setup import dgl_setup
import gnntf
import random

gnntf.set_seed(0)
G, _, features = dgl_setup("cora")[:3]
adj = gnntf.graph2adj(G)
edges = adj.indices.numpy()
train = random.sample(range(len(edges)), int(len(edges) * 0.8))
valid = random.sample(list(set(range(len(edges))) - set(train)), (len(edges)-len(train))//4)
test = list(set(range(len(edges))) - set(valid) - set(train))

training_graph = gnntf.create_nx_graph(G, edges[train])

gnn = gnntf.APPNP(gnntf.graph2adj(training_graph), features, num_classes=16, positional_dims=16)
gnn.train(train=gnntf.LinkPrediction(*gnntf.negative_sampling(edges[train], G)),
          valid=gnntf.LinkPrediction(*gnntf.negative_sampling(edges[valid], G)),
          test=gnntf.LinkPrediction(*gnntf.negative_sampling(edges[test], G)),
          patience=50, verbose=True)

edges, labels = gnntf.negative_sampling(edges[test], G)
prediction = gnn.predict(gnntf.LinkPrediction(edges))
print(gnntf.auc(labels, prediction))

import numpy as np
from experiments.experiment_setup import dgl_setup
G = dgl_setup("cora")[0]
features = np.zeros((len(G),1))