4_link_predict.py

"""
Link Prediction using Graph Neural Networks
===========================================

In the :doc:`introduction <1_introduction>`, you have already learned
the basic workflow of using GNNs for node classification,
i.e. predicting the category of a node in a graph. This tutorial will
teach you how to train a GNN for link prediction, i.e. predicting the
existence of an edge between two arbitrary nodes in a graph.

By the end of this tutorial you will be able to

-  Build a GNN-based link prediction model.
-  Train and evaluate the model on a small DGL-provided dataset.

(Time estimate: 28 minutes)

"""

import dgl
import torch
import torch.nn as nn
import torch.nn.functional as F
import itertools
import numpy as np
import scipy.sparse as sp


######################################################################
# Overview of Link Prediction with GNN
# ------------------------------------
#
# Many applications such as social recommendation, item recommendation,
# knowledge graph completion, etc., can be formulated as link prediction,
# which predicts whether an edge exists between two particular nodes. This
# tutorial shows an example of predicting whether a citation relationship,
# either citing or being cited, between two papers exists in a citation
# network.
#
# This tutorial formulates the link prediction problem as a binary classification
# problem as follows:
#
# -  Treat the edges in the graph as *positive examples*.
# -  Sample a number of non-existent edges (i.e. node pairs with no edges
#    between them) as *negative* examples.
# -  Divide the positive examples and negative examples into a training
#    set and a test set.
# -  Evaluate the model with any binary classification metric such as Area
#    Under Curve (AUC).
#
# .. note::
#
#    The practice comes from
#    `SEAL <https://papers.nips.cc/paper/2018/file/53f0d7c537d99b3824f0f99d62ea2428-Paper.pdf>`__,
#    although the model here does not use their idea of node labeling.
#
# In some domains such as large-scale recommender systems or information
# retrieval, you may favor metrics that emphasize good performance of
# top-K predictions. In these cases you may want to consider other metrics
# such as mean average precision, and use other negative sampling methods,
# which are beyond the scope of this tutorial.
#
# Loading graph and features
# --------------------------
#
# Following the :doc:`introduction <1_introduction>`, this tutorial
# first loads the Cora dataset.
#

import dgl.data

dataset = dgl.data.CoraGraphDataset()
g = dataset[0]


######################################################################
# Prepare training and testing sets
# ---------------------------------
#
# This tutorial randomly picks 10% of the edges for positive examples in
# the test set, and leave the rest for the training set. It then samples
# the same number of edges for negative examples in both sets.
#

# Split edge set for training and testing
u, v = g.edges()

eids = np.arange(g.number_of_edges())
eids = np.random.permutation(eids)
test_size = int(len(eids) * 0.1)
train_size = g.number_of_edges() - test_size
test_pos_u, test_pos_v = u[eids[:test_size]], v[eids[:test_size]]
train_pos_u, train_pos_v = u[eids[test_size:]], v[eids[test_size:]]

# Find all negative edges and split them for training and testing
adj = sp.coo_matrix((np.ones(len(u)), (u.numpy(), v.numpy())))
adj_neg = 1 - adj.todense() - np.eye(g.number_of_nodes())
neg_u, neg_v = np.where(adj_neg != 0)

neg_eids = np.random.choice(len(neg_u), g.number_of_edges() // 2)
test_neg_u, test_neg_v = neg_u[neg_eids[:test_size]], neg_v[neg_eids[:test_size]]
train_neg_u, train_neg_v = neg_u[neg_eids[test_size:]], neg_v[neg_eids[test_size:]]


######################################################################
# When training, you will need to remove the edges in the test set from
# the original graph. You can do this via ``dgl.remove_edges``.
#
# .. note::
#
#    ``dgl.remove_edges`` works by creating a subgraph from the
#    original graph, resulting in a copy and therefore could be slow for
#    large graphs. If so, you could save the training and test graph to
#    disk, as you would do for preprocessing.
#

train_g = dgl.remove_edges(g, eids[:test_size])


######################################################################
# Define a GraphSAGE model
# ------------------------
#
# This tutorial builds a model consisting of two
# `GraphSAGE <https://arxiv.org/abs/1706.02216>`__ layers, each computes
# new node representations by averaging neighbor information. DGL provides
# ``dgl.nn.SAGEConv`` that conveniently creates a GraphSAGE layer.
#

from dgl.nn import SAGEConv

# ----------- 2. create model -------------- #
# build a two-layer GraphSAGE model
class GraphSAGE(nn.Module):
    def __init__(self, in_feats, h_feats):
        super(GraphSAGE, self).__init__()
        self.conv1 = SAGEConv(in_feats, h_feats, 'mean')
        self.conv2 = SAGEConv(h_feats, h_feats, 'mean')
    
    def forward(self, g, in_feat):
        h = self.conv1(g, in_feat)
        h = F.relu(h)
        h = self.conv2(g, h)
        return h


######################################################################
# The model then predicts the probability of existence of an edge by
# computing a score between the representations of both incident nodes
# with a function (e.g. an MLP or a dot product), which you will see in
# the next section.
#
# .. math::
#
#
#    \hat{y}_{u\sim v} = f(h_u, h_v)
#


######################################################################
# Positive graph, negative graph, and ``apply_edges``
# ---------------------------------------------------
#
# In previous tutorials you have learned how to compute node
# representations with a GNN. However, link prediction requires you to
# compute representation of *pairs of nodes*.
#
# DGL recommends you to treat the pairs of nodes as another graph, since
# you can describe a pair of nodes with an edge. In link prediction, you
# will have a *positive graph* consisting of all the positive examples as
# edges, and a *negative graph* consisting of all the negative examples.
# The *positive graph* and the *negative graph* will contain the same set
# of nodes as the original graph.  This makes it easier to pass node
# features among multiple graphs for computation.  As you will see later,
# you can directly fed the node representations computed on the entire
# graph to the positive and the negative graphs for computing pair-wise
# scores.
#
# The following code constructs the positive graph and the negative graph
# for the training set and the test set respectively.
#

train_pos_g = dgl.graph((train_pos_u, train_pos_v), num_nodes=g.number_of_nodes())
train_neg_g = dgl.graph((train_neg_u, train_neg_v), num_nodes=g.number_of_nodes())

test_pos_g = dgl.graph((test_pos_u, test_pos_v), num_nodes=g.number_of_nodes())
test_neg_g = dgl.graph((test_neg_u, test_neg_v), num_nodes=g.number_of_nodes())


######################################################################
# The benefit of treating the pairs of nodes as a graph is that you can
# use the ``DGLGraph.apply_edges`` method, which conveniently computes new
# edge features based on the incident nodes’ features and the original
# edge features (if applicable).
#
# DGL provides a set of optimized builtin functions to compute new
# edge features based on the original node/edge features. For example,
# ``dgl.function.u_dot_v`` computes a dot product of the incident nodes’
# representations for each edge.
#

import dgl.function as fn

class DotPredictor(nn.Module):
    def forward(self, g, h):
        with g.local_scope():
            g.ndata['h'] = h
            # Compute a new edge feature named 'score' by a dot-product between the
            # source node feature 'h' and destination node feature 'h'.
            g.apply_edges(fn.u_dot_v('h', 'h', 'score'))
            # u_dot_v returns a 1-element vector for each edge so you need to squeeze it.
            return g.edata['score'][:, 0]


######################################################################
# You can also write your own function if it is complex.
# For instance, the following module produces a scalar score on each edge
# by concatenating the incident nodes’ features and passing it to an MLP.
#

class MLPPredictor(nn.Module):
    def __init__(self, h_feats):
        super().__init__()
        self.W1 = nn.Linear(h_feats * 2, h_feats)
        self.W2 = nn.Linear(h_feats, 1)

    def apply_edges(self, edges):
        """
        Computes a scalar score for each edge of the given graph.

        Parameters
        ----------
        edges :
            Has three members ``src``, ``dst`` and ``data``, each of
            which is a dictionary representing the features of the
            source nodes, the destination nodes, and the edges
            themselves.

        Returns
        -------
        dict
            A dictionary of new edge features.
        """
        h = torch.cat([edges.src['h'], edges.dst['h']], 1)
        return {'score': self.W2(F.relu(self.W1(h))).squeeze(1)}

    def forward(self, g, h):
        with g.local_scope():
            g.ndata['h'] = h
            g.apply_edges(self.apply_edges)
            return g.edata['score']


######################################################################
# .. note::
#
#    The builtin functions are optimized for both speed and memory.
#    We recommend using builtin functions whenever possible.
#
# .. note::
#
#    If you have read the :doc:`message passing
#    tutorial <3_message_passing>`, you will notice that the
#    argument ``apply_edges`` takes has exactly the same form as a message
#    function in ``update_all``.
#


######################################################################
# Training loop
# -------------
#
# After you defined the node representation computation and the edge score
# computation, you can go ahead and define the overall model, loss
# function, and evaluation metric.
#
# The loss function is simply binary cross entropy loss.
#
# .. math::
#
#
#    \mathcal{L} = -\sum_{u\sim v\in \mathcal{D}}\left( y_{u\sim v}\log(\hat{y}_{u\sim v}) + (1-y_{u\sim v})\log(1-\hat{y}_{u\sim v})) \right)
#
# The evaluation metric in this tutorial is AUC.
#

model = GraphSAGE(train_g.ndata['feat'].shape[1], 16)
# You can replace DotPredictor with MLPPredictor.
#pred = MLPPredictor(16)
pred = DotPredictor()

def compute_loss(pos_score, neg_score):
    scores = torch.cat([pos_score, neg_score])
    labels = torch.cat([torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0])])
    return F.binary_cross_entropy_with_logits(scores, labels)

def compute_auc(pos_score, neg_score):
    scores = torch.cat([pos_score, neg_score]).numpy()
    labels = torch.cat(
        [torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0])]).numpy()
    return roc_auc_score(labels, scores)


######################################################################
# The training loop goes as follows:
#
# .. note::
#
#    This tutorial does not include evaluation on a validation
#    set. In practice you should save and evaluate the best model based on
#    performance on the validation set.
#

# ----------- 3. set up loss and optimizer -------------- #
# in this case, loss will in training loop
optimizer = torch.optim.Adam(itertools.chain(model.parameters(), pred.parameters()), lr=0.01)

# ----------- 4. training -------------------------------- #
all_logits = []
for e in range(100):
    # forward
    h = model(train_g, train_g.ndata['feat'])
    pos_score = pred(train_pos_g, h)
    neg_score = pred(train_neg_g, h)
    loss = compute_loss(pos_score, neg_score)
    
    # backward
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    if e % 5 == 0:
        print('In epoch {}, loss: {}'.format(e, loss))

# ----------- 5. check results ------------------------ #
from sklearn.metrics import roc_auc_score
with torch.no_grad():
    pos_score = pred(test_pos_g, h)
    neg_score = pred(test_neg_g, h)
    print('AUC', compute_auc(pos_score, neg_score))