[Model][Pytorch] GraphSAGE (#403)

* simple implemention of GraphSAGE

* update the abstract method and `torch.nn` modules are more utilized

examples/pytorch/graphsage/README.md

+Inductive Representation Learning on Large Graphs (GraphSAGE)
+============
+
+- Paper link: [http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf](http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf)
+- Author's code repo: [https://github.com/williamleif/graphsage-simple](https://github.com/williamleif/graphsage-simple). Note that the original code is 
+simple reference implementation of GraphSAGE.
+
+Requirements
+------------
+- requests
+
+``bash
+pip install requests
+``
+
+
+Results
+-------
+
+Run with following (available dataset: "cora", "citeseer", "pubmed")
+```bash
+python graphsage.py --dataset cora --gpu 0
+```
+
+* cora: ~0.8470 
+* citeseer: ~0.6870
+* pubmed: ~0.7730

examples/pytorch/graphsage/graphsage.py

+"""
+Inductive Representation Learning on Large Graphs
+Paper: http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf
+Code: https://github.com/williamleif/graphsage-simple
+Simple reference implementation of GraphSAGE.
+"""
+import argparse
+import time
+import abc
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl import DGLGraph
+from dgl.data import register_data_args, load_data
+import dgl.function as fn
+
+
+class Aggregator(nn.Module):
+    def __init__(self, g, in_feats, out_feats, activation=None, bias=True):
+        super(Aggregator, self).__init__()
+        self.g = g
+        self.linear = nn.Linear(in_feats, out_feats, bias=bias)  # (F, EF) or (2F, EF)
+        self.activation = activation
+        nn.init.xavier_uniform_(self.linear.weight, gain=nn.init.calculate_gain('relu'))
+
+    def forward(self, node):
+        nei = node.mailbox['m']  # (B, N, F)
+        h = node.data['h']  # (B, F)
+        h = self.concat(h, nei, node)  # (B, F) or (B, 2F)
+        h = self.linear(h)   # (B, EF)
+        if self.activation:
+            h = self.activation(h)
+        norm = torch.pow(h, 2)
+        norm = torch.sum(norm, 1, keepdim=True)
+        norm = torch.pow(norm, -0.5)
+        norm[torch.isinf(norm)] = 0
+        # h = h * norm
+        return {'h': h}
+
+    @abc.abstractmethod
+    def concat(self, h, nei, nodes):
+        raise NotImplementedError
+
+
+class MeanAggregator(Aggregator):
+    def __init__(self, g, in_feats, out_feats, activation, bias):
+        super(MeanAggregator, self).__init__(g, in_feats, out_feats, activation, bias)
+
+    def concat(self, h, nei, nodes):
+        degs = self.g.in_degrees(nodes.nodes()).float()
+        if h.is_cuda:
+            degs = degs.cuda(h.device)
+        concatenate = torch.cat((nei, h.unsqueeze(1)), 1)
+        concatenate = torch.sum(concatenate, 1) / degs.unsqueeze(1)
+        return concatenate  # (B, F)
+
+
+class PoolingAggregator(Aggregator):
+    def __init__(self, g, in_feats, out_feats, activation, bias):  # (2F, F)
+        super(PoolingAggregator, self).__init__(g, in_feats*2, out_feats, activation, bias)
+        self.mlp = PoolingAggregator.MLP(in_feats, in_feats, F.relu, False, True)
+
+    def concat(self, h, nei, nodes):
+        nei = self.mlp(nei)  # (B, F)
+        concatenate = torch.cat((nei, h), 1)  # (B, 2F)
+        return concatenate
+
+    class MLP(nn.Module):
+        def __init__(self, in_feats, out_feats, activation, dropout, bias):  # (F, F)
+            super(PoolingAggregator.MLP, self).__init__()
+            self.linear = nn.Linear(in_feats, out_feats, bias=bias)  # (F, F)
+            self.dropout = nn.Dropout(p=dropout)
+            self.activation = activation
+            nn.init.xavier_uniform_(self.linear.weight, gain=nn.init.calculate_gain('relu'))
+
+        def forward(self, nei):
+            nei = self.dropout(nei)  # (B, N, F)
+            nei = self.linear(nei)
+            if self.activation:
+                nei = self.activation(nei)
+            max_value = torch.max(nei, dim=1)[0]  # (B, F)
+            return max_value
+
+
+class GraphSAGELayer(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 out_feats,
+                 activation,
+                 dropout,
+                 aggregator_type,
+                 bias=True,
+                 ):
+        super(GraphSAGELayer, self).__init__()
+        self.g = g
+        self.dropout = nn.Dropout(p=dropout)
+        if aggregator_type == "pooling":
+            self.aggregator = PoolingAggregator(g, in_feats, out_feats, activation, bias)
+        else:
+            self.aggregator = MeanAggregator(g, in_feats, out_feats, activation, bias)
+
+    def forward(self, h):
+        h = self.dropout(h)
+        self.g.ndata['h'] = h
+        self.g.update_all(fn.copy_src(src='h', out='m'), self.aggregator)
+        h = self.g.ndata.pop('h')
+        return h
+
+
+class GraphSAGE(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation,
+                 dropout,
+                 aggregator_type):
+        super(GraphSAGE, self).__init__()
+        self.layers = nn.ModuleList()
+
+        # input layer
+        self.layers.append(GraphSAGELayer(g, in_feats, n_hidden, activation, dropout, aggregator_type))
+        # hidden layers
+        for i in range(n_layers - 1):
+            self.layers.append(GraphSAGELayer(g, n_hidden, n_hidden, activation, dropout, aggregator_type))
+        # output layer
+        self.layers.append(GraphSAGELayer(g, n_hidden, n_classes, None, dropout, aggregator_type))
+
+    def forward(self, features):
+        h = features
+        for layer in self.layers:
+            h = layer(h)
+        return h
+
+
+def evaluate(model, features, labels, mask):
+    model.eval()
+    with torch.no_grad():
+        logits = model(features)
+        logits = logits[mask]
+        labels = labels[mask]
+        _, indices = torch.max(logits, dim=1)
+        correct = torch.sum(indices == labels)
+        return correct.item() * 1.0 / len(labels)
+
+def main(args):
+    # load and preprocess dataset
+    data = load_data(args)
+    features = torch.FloatTensor(data.features)
+    labels = torch.LongTensor(data.labels)
+    train_mask = torch.ByteTensor(data.train_mask)
+    val_mask = torch.ByteTensor(data.val_mask)
+    test_mask = torch.ByteTensor(data.test_mask)
+    in_feats = features.shape[1]
+    n_classes = data.num_labels
+    n_edges = data.graph.number_of_edges()
+    print("""----Data statistics------'
+      #Edges %d
+      #Classes %d
+      #Train samples %d
+      #Val samples %d
+      #Test samples %d""" %
+          (n_edges, n_classes,
+           train_mask.sum().item(),
+           val_mask.sum().item(),
+           test_mask.sum().item()))
+
+    if args.gpu < 0:
+        cuda = False
+    else:
+        cuda = True
+        torch.cuda.set_device(args.gpu)
+        features = features.cuda()
+        labels = labels.cuda()
+        train_mask = train_mask.cuda()
+        val_mask = val_mask.cuda()
+        test_mask = test_mask.cuda()
+        print("use cuda:", args.gpu)
+
+    # graph preprocess and calculate normalization factor
+    g = DGLGraph(data.graph)
+    n_edges = g.number_of_edges()
+
+    # create GraphSAGE model
+    model = GraphSAGE(g,
+                      in_feats,
+                      args.n_hidden,
+                      n_classes,
+                      args.n_layers,
+                      F.relu,
+                      args.dropout,
+                      args.aggregator_type
+                      )
+
+    if cuda:
+        model.cuda()
+    loss_fcn = torch.nn.CrossEntropyLoss()
+
+    # use optimizer
+    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
+
+    # initialize graph
+    dur = []
+    for epoch in range(args.n_epochs):
+        model.train()
+        if epoch >= 3:
+            t0 = time.time()
+        # forward
+        logits = model(features)
+        loss = loss_fcn(logits[train_mask], labels[train_mask])
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if epoch >= 3:
+            dur.append(time.time() - t0)
+
+        acc = evaluate(model, features, labels, val_mask)
+        print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
+              "ETputs(KTEPS) {:.2f}".format(epoch, np.mean(dur), loss.item(),
+                                            acc, n_edges / np.mean(dur) / 1000))
+
+    print()
+    acc = evaluate(model, features, labels, test_mask)
+    print("Test Accuracy {:.4f}".format(acc))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='GCN')
+    register_data_args(parser)
+    parser.add_argument("--dropout", type=float, default=0.5,
+                        help="dropout probability")
+    parser.add_argument("--gpu", type=int, default=-1,
+                        help="gpu")
+    parser.add_argument("--lr", type=float, default=1e-2,
+                        help="learning rate")
+    parser.add_argument("--n-epochs", type=int, default=200,
+                        help="number of training epochs")
+    parser.add_argument("--n-hidden", type=int, default=16,
+                        help="number of hidden gcn units")
+    parser.add_argument("--n-layers", type=int, default=1,
+                        help="number of hidden gcn layers")
+    parser.add_argument("--weight-decay", type=float, default=5e-4,
+                        help="Weight for L2 loss")
+    parser.add_argument("--aggregator-type", type=str, default="mean",
+                        help="Weight for L2 loss")
+    args = parser.parse_args()
+    print(args)
+
+    main(args)