dmlc--dgl
Smile
583aa76621
* add seal example * 1. add paper infomation in examples/README 2. adjust codes 3. option test * use latest `to_simple` to replace coalesce graph function * remove outdated codes * remove useless comment
124 行
3.8 KiB
Python
124 行
3.8 KiB
Python
import argparse
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from scipy.sparse.csgraph import shortest_path
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import numpy as np
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import pandas as pd
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import torch
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import dgl
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from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
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def parse_arguments():
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"""
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Parse arguments
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"""
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parser = argparse.ArgumentParser(description='SEAL')
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parser.add_argument('--dataset', type=str, default='ogbl-collab')
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parser.add_argument('--gpu_id', type=int, default=0)
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parser.add_argument('--hop', type=int, default=1)
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parser.add_argument('--model', type=str, default='dgcnn')
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parser.add_argument('--gcn_type', type=str, default='gcn')
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parser.add_argument('--num_layers', type=int, default=3)
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parser.add_argument('--hidden_units', type=int, default=32)
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parser.add_argument('--sort_k', type=int, default=30)
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parser.add_argument('--pooling', type=str, default='sum')
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parser.add_argument('--dropout', type=str, default=0.5)
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parser.add_argument('--hits_k', type=int, default=50)
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parser.add_argument('--lr', type=float, default=0.0001)
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parser.add_argument('--neg_samples', type=int, default=1)
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parser.add_argument('--subsample_ratio', type=float, default=0.1)
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parser.add_argument('--epochs', type=int, default=60)
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parser.add_argument('--batch_size', type=int, default=32)
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parser.add_argument('--eval_steps', type=int, default=5)
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parser.add_argument('--num_workers', type=int, default=32)
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parser.add_argument('--random_seed', type=int, default=2021)
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parser.add_argument('--save_dir', type=str, default='./processed')
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args = parser.parse_args()
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return args
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def load_ogb_dataset(dataset):
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"""
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Load OGB dataset
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Args:
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dataset(str): name of dataset (ogbl-collab, ogbl-ddi, ogbl-citation)
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Returns:
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graph(DGLGraph): graph
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split_edge(dict): split edge
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"""
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dataset = DglLinkPropPredDataset(name=dataset)
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split_edge = dataset.get_edge_split()
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graph = dataset[0]
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return graph, split_edge
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def drnl_node_labeling(subgraph, src, dst):
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"""
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Double Radius Node labeling
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d = r(i,u)+r(i,v)
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label = 1+ min(r(i,u),r(i,v))+ (d//2)*(d//2+d%2-1)
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Isolated nodes in subgraph will be set as zero.
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Extreme large graph may cause memory error.
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Args:
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subgraph(DGLGraph): The graph
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src(int): node id of one of src node in new subgraph
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dst(int): node id of one of dst node in new subgraph
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Returns:
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z(Tensor): node labeling tensor
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"""
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adj = subgraph.adj().to_dense().numpy()
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src, dst = (dst, src) if src > dst else (src, dst)
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idx = list(range(src)) + list(range(src + 1, adj.shape[0]))
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adj_wo_src = adj[idx, :][:, idx]
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idx = list(range(dst)) + list(range(dst + 1, adj.shape[0]))
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adj_wo_dst = adj[idx, :][:, idx]
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dist2src = shortest_path(adj_wo_dst, directed=False, unweighted=True, indices=src)
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dist2src = np.insert(dist2src, dst, 0, axis=0)
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dist2src = torch.from_numpy(dist2src)
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dist2dst = shortest_path(adj_wo_src, directed=False, unweighted=True, indices=dst - 1)
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dist2dst = np.insert(dist2dst, src, 0, axis=0)
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dist2dst = torch.from_numpy(dist2dst)
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dist = dist2src + dist2dst
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dist_over_2, dist_mod_2 = dist // 2, dist % 2
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z = 1 + torch.min(dist2src, dist2dst)
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z += dist_over_2 * (dist_over_2 + dist_mod_2 - 1)
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z[src] = 1.
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z[dst] = 1.
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z[torch.isnan(z)] = 0.
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return z.to(torch.long)
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def evaluate_hits(name, pos_pred, neg_pred, K):
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"""
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Compute hits
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Args:
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name(str): name of dataset
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pos_pred(Tensor): predict value of positive edges
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neg_pred(Tensor): predict value of negative edges
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K(int): num of hits
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Returns:
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hits(float): score of hits
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"""
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evaluator = Evaluator(name)
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evaluator.K = K
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hits = evaluator.eval({
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'y_pred_pos': pos_pred,
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'y_pred_neg': neg_pred,
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})[f'hits@{K}']
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return hits
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