.. _guide-minibatch-link-classification-sampler:

6.3 Training GNN for Link Prediction with Neighborhood Sampling
--------------------------------------------------------------------

Define a neighborhood sampler and data loader with negative sampling
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can still use the same neighborhood sampler as the one in node/edge
classification.

.. code:: python

    sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)

:class:`~dgl.dataloading.pytorch.EdgeDataLoader` in DGL also
supports generating negative samples for link prediction. To do so, you
need to provide the negative sampling function.
:class:`~dgl.dataloading.negative_sampler.Uniform` is a
function that does uniform sampling. For each source node of an edge, it
samples ``k`` negative destination nodes.

The following data loader will pick 5 negative destination nodes
uniformly for each source node of an edge.

.. code:: python

    dataloader = dgl.dataloading.EdgeDataLoader(
        g, train_seeds, sampler,
        negative_sampler=dgl.dataloading.negative_sampler.Uniform(5),
        batch_size=args.batch_size,
        shuffle=True,
        drop_last=False,
        pin_memory=True,
        num_workers=args.num_workers)

For the builtin negative samplers please see :ref:`api-dataloading-negative-sampling`.

You can also give your own negative sampler function, as long as it
takes in the original graph ``g`` and the minibatch edge ID array
``eid``, and returns a pair of source ID arrays and destination ID
arrays.

The following gives an example of custom negative sampler that samples
negative destination nodes according to a probability distribution
proportional to a power of degrees.

.. code:: python

    class NegativeSampler(object):
        def __init__(self, g, k):
            # caches the probability distribution
            self.weights = g.in_degrees().float() ** 0.75
            self.k = k
    
        def __call__(self, g, eids):
            src, _ = g.find_edges(eids)
            src = src.repeat_interleave(self.k)
            dst = self.weights.multinomial(len(src), replacement=True)
            return src, dst
    
    dataloader = dgl.dataloading.EdgeDataLoader(
        g, train_seeds, sampler,
        negative_sampler=NegativeSampler(g, 5),
        batch_size=args.batch_size,
        shuffle=True,
        drop_last=False,
        pin_memory=True,
        num_workers=args.num_workers)

Adapt your model for minibatch training
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

As explained in :ref:`guide-training-link-prediction`, link prediction is trained
via comparing the score of an edge (positive example) against a
non-existent edge (negative example). To compute the scores of edges you
can reuse the node representation computation model you have seen in
edge classification/regression.

.. code:: python

    class StochasticTwoLayerGCN(nn.Module):
        def __init__(self, in_features, hidden_features, out_features):
            super().__init__()
            self.conv1 = dgl.nn.GraphConv(in_features, hidden_features)
            self.conv2 = dgl.nn.GraphConv(hidden_features, out_features)
    
        def forward(self, blocks, x):
            x = F.relu(self.conv1(blocks[0], x))
            x = F.relu(self.conv2(blocks[1], x))
            return x

For score prediction, since you only need to predict a scalar score for
each edge instead of a probability distribution, this example shows how
to compute a score with a dot product of incident node representations.

.. code:: python

    class ScorePredictor(nn.Module):
        def forward(self, edge_subgraph, x):
            with edge_subgraph.local_scope():
                edge_subgraph.ndata['x'] = x
                edge_subgraph.apply_edges(dgl.function.u_dot_v('x', 'x', 'score'))
                return edge_subgraph.edata['score']

When a negative sampler is provided, DGL’s data loader will generate
three items per minibatch:

-  A positive graph containing all the edges sampled in the minibatch.
-  A negative graph containing all the non-existent edges generated by
   the negative sampler.
-  A list of blocks generated by the neighborhood sampler.

So one can define the link prediction model as follows that takes in the
three items as well as the input features.

.. code:: python

    class Model(nn.Module):
        def __init__(self, in_features, hidden_features, out_features):
            super().__init__()
            self.gcn = StochasticTwoLayerGCN(
                in_features, hidden_features, out_features)
    
        def forward(self, positive_graph, negative_graph, blocks, x):
            x = self.gcn(blocks, x)
            pos_score = self.predictor(positive_graph, x)
            neg_score = self.predictor(negative_graph, x)
            return pos_score, neg_score

Training loop
~~~~~~~~~~~~~

The training loop simply involves iterating over the data loader and
feeding in the graphs as well as the input features to the model defined
above.

.. code:: python

    def compute_loss(pos_score, neg_score):
        # an example hinge loss
        n = pos_score.shape[0]
        return (neg_score.view(n, -1) - pos_score.view(n, -1) + 1).clamp(min=0).mean()

    model = Model(in_features, hidden_features, out_features)
    model = model.cuda()
    opt = torch.optim.Adam(model.parameters())
    
    for input_nodes, positive_graph, negative_graph, blocks in dataloader:
        blocks = [b.to(torch.device('cuda')) for b in blocks]
        positive_graph = positive_graph.to(torch.device('cuda'))
        negative_graph = negative_graph.to(torch.device('cuda'))
        input_features = blocks[0].srcdata['features']
        pos_score, neg_score = model(positive_graph, negative_graph, blocks, input_features)
        loss = compute_loss(pos_score, neg_score)
        opt.zero_grad()
        loss.backward()
        opt.step()

DGL provides the
`unsupervised learning GraphSAGE <https://github.com/dmlc/dgl/blob/master/examples/pytorch/graphsage/train_sampling_unsupervised.py>`__
that shows an example of link prediction on homogeneous graphs.

For heterogeneous graphs
~~~~~~~~~~~~~~~~~~~~~~~~
    
The models computing the node representations on heterogeneous graphs
can also be used for computing incident node representations for edge
classification/regression.

.. code:: python

    class StochasticTwoLayerRGCN(nn.Module):
        def __init__(self, in_feat, hidden_feat, out_feat, rel_names):
            super().__init__()
            self.conv1 = dglnn.HeteroGraphConv({
                    rel : dglnn.GraphConv(in_feat, hidden_feat, norm='right')
                    for rel in rel_names
                })
            self.conv2 = dglnn.HeteroGraphConv({
                    rel : dglnn.GraphConv(hidden_feat, out_feat, norm='right')
                    for rel in rel_names
                })
    
        def forward(self, blocks, x):
            x = self.conv1(blocks[0], x)
            x = self.conv2(blocks[1], x)
            return x

For score prediction, the only implementation difference between the
homogeneous graph and the heterogeneous graph is that we are looping
over the edge types for :meth:`dgl.DGLHeteroGraph.apply_edges`.

.. code:: python

    class ScorePredictor(nn.Module):
        def forward(self, edge_subgraph, x):
            with edge_subgraph.local_scope():
                edge_subgraph.ndata['x'] = x
                for etype in edge_subgraph.canonical_etypes:
                    edge_subgraph.apply_edges(
                        dgl.function.u_dot_v('x', 'x', 'score'), etype=etype)
                return edge_subgraph.edata['score']

    class Model(nn.Module):
        def __init__(self, in_features, hidden_features, out_features, num_classes,
                     etypes):
            super().__init__()
            self.rgcn = StochasticTwoLayerRGCN(
                in_features, hidden_features, out_features, etypes)
            self.pred = ScorePredictor()

        def forward(self, positive_graph, negative_graph, blocks, x):
            x = self.rgcn(blocks, x)
            pos_score = self.pred(positive_graph, x)
            neg_score = self.pred(negative_graph, x)
            return pos_score, neg_score

Data loader definition is also very similar to that of edge
classification/regression. The only difference is that you need to give
the negative sampler and you will be supplying a dictionary of edge
types and edge ID tensors instead of a dictionary of node types and node
ID tensors.

.. code:: python

    sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
    dataloader = dgl.dataloading.EdgeDataLoader(
        g, train_eid_dict, sampler,
        negative_sampler=dgl.dataloading.negative_sampler.Uniform(5),
        batch_size=1024,
        shuffle=True,
        drop_last=False,
        num_workers=4)

If you want to give your own negative sampling function, the function
should take in the original graph and the dictionary of edge types and
edge ID tensors. It should return a dictionary of edge types and
source-destination array pairs. An example is given as follows:

.. code:: python

   class NegativeSampler(object):
       def __init__(self, g, k):
           # caches the probability distribution
           self.weights = {
               etype: g.in_degrees(etype=etype).float() ** 0.75
               for _, etype, _ in g.canonical_etypes
           }
           self.k = k

       def __call__(self, g, eids_dict):
           result_dict = {}
           for etype, eids in eids_dict.items():
               src, _ = g.find_edges(eids, etype=etype)
               src = src.repeat_interleave(self.k)
               dst = self.weights[etype].multinomial(len(src), replacement=True)
               result_dict[etype] = (src, dst)
           return result_dict

Then you can give the dataloader a dictionary of edge types and edge IDs as well as the negative
sampler.  For instance, the following iterates over all edges of the heterogeneous graph.

.. code:: python
    train_eid_dict = {
        g.edges(etype=etype, form='eid')
        for etype in g.etypes}

    dataloader = dgl.dataloading.EdgeDataLoader(
        g, train_eid_dict, sampler,
        negative_sampler=NegativeSampler(g, 5),
        batch_size=1024,
        shuffle=True,
        drop_last=False,
        num_workers=4)

The training loop is again almost the same as that on homogeneous graph,
except for the implementation of ``compute_loss`` that will take in two
dictionaries of node types and predictions here.

.. code:: python

    model = Model(in_features, hidden_features, out_features, num_classes, etypes)
    model = model.cuda()
    opt = torch.optim.Adam(model.parameters())
    
    for input_nodes, positive_graph, negative_graph, blocks in dataloader:
        blocks = [b.to(torch.device('cuda')) for b in blocks]
        positive_graph = positive_graph.to(torch.device('cuda'))
        negative_graph = negative_graph.to(torch.device('cuda'))
        input_features = blocks[0].srcdata['features']
        pos_score, neg_score = model(positive_graph, negative_graph, blocks, input_features)
        loss = compute_loss(pos_score, neg_score)
        opt.zero_grad()
        loss.backward()
        opt.step()



