[CS224W] 9. Graph Neural Networks: Hands-on Session

.·2021년 3월 8일
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CS224W : GNN

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작성자 : 이재빈
Pytorch Geometric 을 이용하여 Graph Neural Networks 를 구현하고 학습하는 내용을 공부합니다.

PyTorch Geometric

torch-geometric : GNN implementation module


torch_geometric.data.Data : Graph Attributes

  • data.x : node feature matrix , [num_nodes, num_node_features]
  • data.edge_index : graph connectivity , [2, num_edges]
  • data.edge_attr : edge attribute matrix , [num_edges, num_edge_features]
  • data.y : Graph or node targets
    • graph level : [num_nodes, *]
    • node label : [1, *]

Setup

# install 

!pip install --verbose --no-cache-dir torch-scatter
!pip install --verbose --no-cache-dir torch-sparse
!pip install --verbose --no-cache-dir torch-cluster
!pip install torch-geometric 
!pip install tensorboardX
!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
!unzip ngrok-stable-linux-amd64.zip
import torch
import torch.nn as nn
import torch.nn.functional as F

import torch_geometric.nn as pyg_nn        # GNN module 
import torch_geometric.utils as pyg_utils  # GNN Utility Function
import torch_geometric.transforms as T


import time
from datetime import datetime

import networkx as nx                      # visualize Graph Structure 
import numpy as np
import torch
import torch.optim as optim

# dataset 
from torch_geometric.datasets import TUDataset
from torch_geometric.datasets import Planetoid
from torch_geometric.data import DataLoader

# visualize 
from tensorboardX import SummaryWriter     
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt

Define the Model

1. torch_geometric

class GNNStack(nn.Module):      # stacking of Graph Convolutions 
    def __init__(self, input_dim, hidden_dim, output_dim, task='node'):
        super(GNNStack, self).__init__()
        self.task = task
        # nn.ModuleList() : nn.Module()을 list로 정리! 각 layer를 list로 전달하고, layer의 iterator를 만듭니다. 
        self.convs = nn.ModuleList()    
        self.convs.append(self.build_conv_model(input_dim, hidden_dim))
        self.lns = nn.ModuleList()
        self.lns.append(nn.LayerNorm(hidden_dim))
        self.lns.append(nn.LayerNorm(hidden_dim))
        for l in range(2):
            self.convs.append(self.build_conv_model(hidden_dim, hidden_dim))

        # post-message-passing
        self.post_mp = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim), nn.Dropout(0.25), 
            nn.Linear(hidden_dim, output_dim))
        if not (self.task == 'node' or self.task == 'graph'):
            raise RuntimeError('Unknown task.')

        self.dropout = 0.25
        self.num_layers = 3

    # task에 따른 convolution layer를 만들어 줍니다. 
    def build_conv_model(self, input_dim, hidden_dim):
        # refer to pytorch geometric nn module for different implementation of GNNs.
        if self.task == 'node':  # node classification 
            return pyg_nn.GCNConv(input_dim, hidden_dim)
            # return CustomConv(input_dim, hidden_dim) : run my method 
        else:
            return pyg_nn.GINConv(nn.Sequential(nn.Linear(input_dim, hidden_dim),
                                  nn.ReLU(), nn.Linear(hidden_dim, hidden_dim)))

    def forward(self, data):
        '''
        x : feature matrix (# of nodes, # of node feature dim) 
        edge_index : sparse adj list, 연결된 edge에 대한 node 저장  
                     ex. node 1 : [1,4,6]
        batch : (array) batch마다 node 개수가 달라지므로 -> 어떤 node가 어떤 graph에 속하는지에 대한 정보 저장 
                ex. [1,1,1,1,1] : 5 nodes in graph 1 , [2,2,2] : 3 nodes in graph 2 
        '''

        x, edge_index, batch = data.x, data.edge_index, data.batch
        if data.num_node_features == 0:         # feature 없으면 -> constant 
          x = torch.ones(data.num_nodes, 1)

        # Neural Network 
        for i in range(self.num_layers):
            x = self.convs[i](x, edge_index)    # Conv Layer 
            emb = x
            x = F.relu(x)
            x = F.dropout(x, p=self.dropout, training=self.training)
            if not i == self.num_layers - 1:
                x = self.lns[i](x)

        if self.task == 'graph':                # mean pooling : average all the nodes 
            x = pyg_nn.global_mean_pool(x, batch)

        x = self.post_mp(x)

        return emb, F.log_softmax(x, dim=1)     

    def loss(self, pred, label):
        return F.nll_loss(pred, label)          # negative log-likelihood

2. Custom Model

class CustomConv(pyg_nn.MessagePassing):    # inherenting from MessagePassing 
    def __init__(self, in_channels, out_channels):
        super(CustomConv, self).__init__(aggr='add')  # Neighborhood Aggregation : Mean, Max, Add, ... 
        self.lin = nn.Linear(in_channels, out_channels)
        self.lin_self = nn.Linear(in_channels, out_channels)

    def forward(self, x, edge_index):
        '''
        x : feature matrix 
        edge_index : connectivity, Adj list in the edge index  
        '''

        # x has shape [N, in_channels]
        # edge_index has shape [2, E]

        # original code 
        # Add self-loops to the adjacency matrix : neighbor + self 
        # pyg_utils.add_self_loops(edge_index, num_nodes = x.size(0))   # A + I 

        # 여기에서는 remove self-loops : skip layer on top of that 
        edge_index, _ = pyg_utils.remove_self_loops(edge_index)    

        # Transform node feature matrix.
        self_x = self.lin_self(x)   # B 
        # x = self.lin(x)           # W 

        return self_x + self.propagate(edge_index, size=(x.size(0), x.size(0)), x=self.lin(x))

    def message(self, x_i, x_j, edge_index, size):
        '''
        GCN : D^(-1/2)*A*D(1/2)*W*X
        x_i : self  
        x_j : neighborhood 
        '''

        # Compute messages
        # x_j has shape [E, out_channels]

        row, col = edge_index
        deg = pyg_utils.degree(row, size[0], dtype=x_j.dtype)
        deg_inv_sqrt = deg.pow(-0.5)
        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]

        return x_j

    def update(self, aggr_out):
        # aggr_out has shape [N, out_channels] : add additional layer after message passing 

        # GraphSAGE : L2 Normalization 
        # F.normalize(aggr_out, p=2, dim=-1) 

        return aggr_out
# Custom Model 을 사용하는 경우, GNNStack class 내의 build_conv_model return 값을 다음과 같이 수정해 주면 됩니다. 

    def build_conv_model(self, input_dim, hidden_dim):
        # refer to pytorch geometric nn module for different implementation of GNNs.
        if self.task == 'node':
            # return pyg_nn.GCNConv(input_dim, hidden_dim)
            return CustomConv(input_dim, hidden_dim) # run my method 
        else:
            return pyg_nn.GINConv(nn.Sequential(nn.Linear(input_dim, hidden_dim),
                                  nn.ReLU(), nn.Linear(hidden_dim, hidden_dim)))

Model

Training Setup

Train

def train(dataset, task, writer):
    if task == 'graph':
        data_size = len(dataset)
        loader = DataLoader(dataset[:int(data_size * 0.8)], batch_size=64, shuffle=True)
        test_loader = DataLoader(dataset[int(data_size * 0.8):], batch_size=64, shuffle=True)
    else:
        test_loader = loader = DataLoader(dataset, batch_size=64, shuffle=True)

    # build model
    model = GNNStack(max(dataset.num_node_features, 1), 32, dataset.num_classes, task=task)
    opt = optim.Adam(model.parameters(), lr=0.01)
    
    # train
    for epoch in range(200):
        total_loss = 0
        model.train()
        for batch in loader:
            #print(batch.train_mask, '----')
            opt.zero_grad()
            embedding, pred = model(batch)
            label = batch.y
            if task == 'node':
                pred = pred[batch.train_mask]
                label = label[batch.train_mask]
            loss = model.loss(pred, label)
            loss.backward()
            opt.step()
            total_loss += loss.item() * batch.num_graphs
        total_loss /= len(loader.dataset)
        writer.add_scalar("loss", total_loss, epoch)

        if epoch % 10 == 0:
            test_acc = test(test_loader, model)
            print("Epoch {}. Loss: {:.4f}. Test accuracy: {:.4f}".format(
                epoch, total_loss, test_acc))
            writer.add_scalar("test accuracy", test_acc, epoch)

    return model

Validation / Test

def test(loader, model, is_validation=False):
    model.eval()

    correct = 0
    for data in loader:
        with torch.no_grad():
            emb, pred = model(data)
            pred = pred.argmax(dim=1)
            label = data.y

        # mask 를 통해 validation, test 결정 
        if model.task == 'node':
            mask = data.val_mask if is_validation else data.test_mask
            # node classification: only evaluate on nodes in test set
            pred = pred[mask]
            label = data.y[mask]
            
        correct += pred.eq(label).sum().item()
    
    if model.task == 'graph':
        total = len(loader.dataset) 
    else:
        total = 0
        for data in loader.dataset:
            total += torch.sum(data.test_mask).item()
    return correct / total

Training the Model

# Setting TensorboardX in Colab 
get_ipython().system_raw(
    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'
    .format("./log")
)
get_ipython().system_raw('./ngrok http 6006 &')
!curl -s http://localhost:4040/api/tunnels | python3 -c \
    "import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])"

Visualize Node Embeddings

color_list = ["crimson", "orange", "green", "royalblue", "purple", "dimgrey", "gold"]

loader = DataLoader(dataset, batch_size=64, shuffle=True)
embs = []
colors = []
for batch in loader:
    emb, pred = model(batch)
    embs.append(emb)
    colors += [color_list[y] for y in batch.y]
embs = torch.cat(embs, dim=0)

xs, ys = zip(*TSNE().fit_transform(embs.detach().numpy()))

plt.figure(figsize=(10, 8))
plt.scatter(xs, ys, color=colors, alpha=0.5)

Learning Unsupervised Embeddings with Graph AutoEncoders

# VGAE : variational graph auto-encoder
# Knowledge Graph, Graph Reasoning 

class Encoder(torch.nn.Module):
    '''
    Encoder : Graph Conv to get embeddings 
    Decoder : inner product -> 2개 node 사이의 값이 크면, there's a likely link between them 
    '''

    def __init__(self, in_channels, out_channels):
        super(Encoder, self).__init__()
        self.conv1 = pyg_nn.GCNConv(in_channels, 2 * out_channels, cached=True)
        self.conv2 = pyg_nn.GCNConv(2 * out_channels, out_channels, cached=True)

    def forward(self, x, edge_index):
        x = F.relu(self.conv1(x, edge_index))
        return self.conv2(x, edge_index)

def train(epoch):
    model.train()
    optimizer.zero_grad()
    z = model.encode(x, train_pos_edge_index)
    loss = model.recon_loss(z, train_pos_edge_index)    # reconstruction loss 
    loss.backward()
    optimizer.step()
    
    writer.add_scalar("loss", loss.item(), epoch)

def test(pos_edge_index, neg_edge_index):
    model.eval()
    with torch.no_grad():
        z = model.encode(x, train_pos_edge_index)
    return model.test(z, pos_edge_index, neg_edge_index)
writer = SummaryWriter("./log/" + datetime.now().strftime("%Y%m%d-%H%M%S"))

dataset = Planetoid("/tmp/citeseer", "Citeseer", transform = T.NormalizeFeatures())
data = dataset[0]

channels = 16
dev = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('CUDA availability:', torch.cuda.is_available())
# encoder: written by us; decoder: default (inner product)
model = pyg_nn.GAE(Encoder(dataset.num_features, channels)).to(dev)
labels = data.y
data.train_mask = data.val_mask = data.test_mask = data.y = None

# data = model.split_edges(data) # split_edges 안 돌아가서 변경!  
data = pyg_utils.train_test_split_edges(data)   # construct positive/negative edges (for negative sampling!)
x, train_pos_edge_index = data.x.to(dev), data.train_pos_edge_index.to(dev)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

for epoch in range(1, 201):
    train(epoch)
    auc, ap = test(data.test_pos_edge_index, data.test_neg_edge_index)
    writer.add_scalar("AUC", auc, epoch)
    writer.add_scalar("AP", ap, epoch)
    if epoch % 10 == 0:
        print('Epoch: {:03d}, AUC: {:.4f}, AP: {:.4f}'.format(epoch, auc, ap))

model.eval()
z = model.encode(x, train_pos_edge_index)
colors = [color_list[y] for y in labels]

xs, ys = zip(*TSNE().fit_transform(z.cpu().detach().numpy()))

plt.figure(figsize=(10, 8))
plt.scatter(xs, ys, color=colors, alpha=0.5)
plt.show()

Reference

  1. Tobigs Graph Study : Chapter9. Graph Neural Networks:Hands-on Session
  2. 예제를 통해 알아보는 PyTorch Geometric 5 Basic Concepts
  3. CS224W Winter 2021 Lecture 7. Graph Neural Networks - 2: Design Space
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