Source code for cogdl.models.nn.unsup_graphsage

import numpy as np
import tqdm

import torch
import torch.nn as nn
import torch.nn.functional as F

from .. import register_model, BaseModel
from cogdl.models.nn.graphsage import sage_sampler, GraphSAGELayer
from cogdl.trainers.self_supervised_trainer import SelfSupervisedTrainer
from cogdl.utils import RandomWalker

[docs]@register_model("unsup_graphsage") class SAGE(BaseModel): """ Implementation of unsupervised GraphSAGE in paper `"Inductive Representation Learning on Large Graphs"` <> Parameters ---------- num_features : int Size of each input sample hidden_size : int num_layers : int The number of GNN layers. samples_size : list The number sampled neighbors of different orders dropout : float walk_length : int The length of random walk negative_samples : int """
[docs] @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" # fmt: off parser.add_argument("--num-features", type=int) parser.add_argument("--hidden-size", type=int, default=128) parser.add_argument("--num-layers", type=int, default=2) parser.add_argument("--sample-size", type=int, nargs='+', default=[10, 10]) parser.add_argument("--dropout", type=float, default=0.5) parser.add_argument("--walk-length", type=int, default=10) parser.add_argument("--negative-samples", type=int, default=30) parser.add_argument("--lr", type=float, default=0.001) parser.add_argument("--max-epochs", type=int, default=3000)
# fmt: on
[docs] @classmethod def build_model_from_args(cls, args): return cls( num_features=args.num_features, hidden_size=args.hidden_size, num_layers=args.num_layers, sample_size=args.sample_size, dropout=args.dropout, walk_length=args.walk_length, negative_samples=args.negative_samples, )
def __init__(self, num_features, hidden_size, num_layers, sample_size, dropout, walk_length, negative_samples): super(SAGE, self).__init__() self.adjlist = {} self.num_features = num_features self.hidden_size = hidden_size self.num_layers = num_layers self.sample_size = sample_size self.dropout = dropout self.walk_length = walk_length self.num_negative_samples = negative_samples self.walk_res = None self.num_nodes = 0 self.negative_samples = 1 shapes = [num_features] + [hidden_size] * num_layers self.convs = nn.ModuleList([GraphSAGELayer(shapes[layer], shapes[layer + 1]) for layer in range(num_layers)]) self.random_walker = RandomWalker()
[docs] def forward(self, x, edge_index): for i in range(self.num_layers): edge_index_sp = self.sampling(edge_index, self.sample_size[i]).to(x.device) x = self.convs[i](x, edge_index_sp) if i != self.num_layers - 1: x = F.relu(x) x = F.dropout(x, p=self.dropout, return x
[docs] def node_classification_loss(self, data): return self.loss(data)
[docs] def loss(self, data): x = self.forward(data.x, data.edge_index) device = x.device self.random_walker.build_up(data.edge_index, data.x.shape[0]) walk_res = self.random_walker.walk( start=torch.arange(0, x.shape[0]).to(device), walk_length=self.walk_length + 1 ) self.walk_res = torch.as_tensor(walk_res)[:, 1:] if not self.num_nodes: self.num_nodes = torch.max(data.edge_index).item() + 1 # if self.negative_samples is None: self.negative_samples = torch.from_numpy( np.random.choice(self.num_nodes, (self.num_nodes, self.num_negative_samples)) ).to(device) pos_loss = -torch.log( torch.sigmoid(torch.sum(x.unsqueeze(1).repeat(1, self.walk_length, 1) * x[self.walk_res], dim=-1)) ).mean() neg_loss = -torch.log( torch.sigmoid( -torch.sum(x.unsqueeze(1).repeat(1, self.num_negative_samples, 1) * x[self.negative_samples], dim=-1) ) ).mean() return (pos_loss + neg_loss) / 2
[docs] def embed(self, data): emb = self.forward(data.x, data.edge_index) return emb
[docs] def sampling(self, edge_index, num_sample): return sage_sampler(self.adjlist, edge_index, num_sample)
[docs] @staticmethod def get_trainer(taskType, args): return SelfSupervisedTrainer