wehub-resource-sync
241 lines
7.0 KiB
Python
241 lines
7.0 KiB
Python
import os
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import pickle
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import random
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import time
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import dgl
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import numpy as np
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import scipy.sparse as sp
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import torch
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from dgl.data.utils import (
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_get_dgl_url,
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download,
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extract_archive,
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get_download_dir,
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)
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from torch.utils.data import DataLoader
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def ReadTxtNet(file_path="", undirected=True):
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"""Read the txt network file.
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Notations: The network is unweighted.
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Parameters
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----------
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file_path str : path of network file
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undirected bool : whether the edges are undirected
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Return
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------
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net dict : a dict recording the connections in the graph
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node2id dict : a dict mapping the nodes to their embedding indices
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id2node dict : a dict mapping nodes embedding indices to the nodes
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"""
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if file_path == "youtube" or file_path == "blog":
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name = file_path
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dir = get_download_dir()
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zip_file_path = "{}/{}.zip".format(dir, name)
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download(
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_get_dgl_url(
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os.path.join("dataset/DeepWalk/", "{}.zip".format(file_path))
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),
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path=zip_file_path,
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)
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extract_archive(zip_file_path, "{}/{}".format(dir, name))
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file_path = "{}/{}/{}-net.txt".format(dir, name, name)
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node2id = {}
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id2node = {}
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cid = 0
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src = []
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dst = []
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weight = []
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net = {}
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with open(file_path, "r") as f:
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for line in f.readlines():
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tup = list(map(int, line.strip().split(" ")))
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assert len(tup) in [
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2,
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3,
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], "The format of network file is unrecognizable."
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if len(tup) == 3:
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n1, n2, w = tup
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elif len(tup) == 2:
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n1, n2 = tup
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w = 1
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if n1 not in node2id:
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node2id[n1] = cid
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id2node[cid] = n1
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cid += 1
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if n2 not in node2id:
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node2id[n2] = cid
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id2node[cid] = n2
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cid += 1
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n1 = node2id[n1]
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n2 = node2id[n2]
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if n1 not in net:
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net[n1] = {n2: w}
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src.append(n1)
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dst.append(n2)
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weight.append(w)
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elif n2 not in net[n1]:
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net[n1][n2] = w
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src.append(n1)
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dst.append(n2)
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weight.append(w)
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if undirected:
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if n2 not in net:
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net[n2] = {n1: w}
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src.append(n2)
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dst.append(n1)
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weight.append(w)
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elif n1 not in net[n2]:
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net[n2][n1] = w
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src.append(n2)
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dst.append(n1)
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weight.append(w)
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print("node num: %d" % len(net))
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print("edge num: %d" % len(src))
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assert max(net.keys()) == len(net) - 1, "error reading net, quit"
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sm = sp.coo_matrix((np.array(weight), (src, dst)), dtype=np.float32)
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return net, node2id, id2node, sm
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def net2graph(net_sm):
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"""Transform the network to DGL graph
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Return
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------
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G DGLGraph : graph by DGL
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"""
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start = time.time()
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G = dgl.DGLGraph(net_sm)
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end = time.time()
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t = end - start
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print("Building DGLGraph in %.2fs" % t)
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return G
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def make_undirected(G):
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G.add_edges(G.edges()[1], G.edges()[0])
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return G
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def find_connected_nodes(G):
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nodes = torch.nonzero(G.out_degrees(), as_tuple=False).squeeze(-1)
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return nodes
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class LineDataset:
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def __init__(
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self,
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net_file,
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batch_size,
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num_samples,
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negative=5,
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gpus=[0],
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fast_neg=True,
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ogbl_name="",
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load_from_ogbl=False,
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ogbn_name="",
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load_from_ogbn=False,
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):
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"""This class has the following functions:
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1. Transform the txt network file into DGL graph;
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2. Generate random walk sequences for the trainer;
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3. Provide the negative table if the user hopes to sample negative
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nodes according to nodes' degrees;
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Parameter
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---------
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net_file str : path of the dgl network file
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walk_length int : number of nodes in a sequence
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window_size int : context window size
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num_walks int : number of walks for each node
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batch_size int : number of node sequences in each batch
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negative int : negative samples for each positve node pair
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fast_neg bool : whether do negative sampling inside a batch
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"""
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self.batch_size = batch_size
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self.negative = negative
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self.num_samples = num_samples
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self.num_procs = len(gpus)
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self.fast_neg = fast_neg
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if load_from_ogbl:
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assert (
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len(gpus) == 1
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), "ogb.linkproppred is not compatible with multi-gpu training."
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from load_dataset import load_from_ogbl_with_name
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self.G = load_from_ogbl_with_name(ogbl_name)
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elif load_from_ogbn:
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assert (
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len(gpus) == 1
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), "ogb.linkproppred is not compatible with multi-gpu training."
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from load_dataset import load_from_ogbn_with_name
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self.G = load_from_ogbn_with_name(ogbn_name)
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else:
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self.G = dgl.load_graphs(net_file)[0][0]
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self.G = make_undirected(self.G)
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print("Finish reading graph")
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self.num_nodes = self.G.num_nodes()
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start = time.time()
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seeds = np.random.choice(
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np.arange(self.G.num_edges()), self.num_samples, replace=True
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) # edge index
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self.seeds = torch.split(
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torch.LongTensor(seeds),
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int(np.ceil(self.num_samples / self.num_procs)),
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0,
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)
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end = time.time()
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t = end - start
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print("generate %d samples in %.2fs" % (len(seeds), t))
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# negative table for true negative sampling
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self.valid_nodes = find_connected_nodes(self.G)
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if not fast_neg:
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node_degree = self.G.out_degrees(self.valid_nodes).numpy()
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node_degree = np.power(node_degree, 0.75)
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node_degree /= np.sum(node_degree)
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node_degree = np.array(node_degree * 1e8, dtype=int)
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self.neg_table = []
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for idx, node in enumerate(self.valid_nodes):
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self.neg_table += [node] * node_degree[idx]
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self.neg_table_size = len(self.neg_table)
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self.neg_table = np.array(self.neg_table, dtype=int)
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del node_degree
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def create_sampler(self, i):
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"""create random walk sampler"""
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return EdgeSampler(self.G, self.seeds[i])
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def save_mapping(self, map_file):
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with open(map_file, "wb") as f:
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pickle.dump(self.node2id, f)
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class EdgeSampler(object):
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def __init__(self, G, seeds):
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self.G = G
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self.seeds = seeds
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self.edges = torch.cat(
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(self.G.edges()[0].unsqueeze(0), self.G.edges()[1].unsqueeze(0)), 0
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).t()
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def sample(self, seeds):
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"""seeds torch.LongTensor : a batch of indices of edges"""
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return self.edges[torch.LongTensor(seeds)]
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