import os
from collections import Counter
import gzip
import pandas as pd
import numpy as np
import torch
import torch.nn.functional as F
from torch_scatter import scatter_add
from torch_geometric.data import (InMemoryDataset, Data, download_url,
extract_tar)
[docs]class Entities(InMemoryDataset):
r"""The relational entities networks "AIFB", "MUTAG", "BGS" and "AM" from
the `"Modeling Relational Data with Graph Convolutional Networks"
<https://arxiv.org/abs/1703.06103>`_ paper.
Training and test splits are given by node indices.
Args:
root (string): Root directory where the dataset should be saved.
name (string): The name of the dataset (:obj:`"AIFB"`,
:obj:`"MUTAG"`, :obj:`"BGS"`, :obj:`"AM"`).
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
"""
url = 'https://s3.us-east-2.amazonaws.com/dgl.ai/dataset/{}.tgz'
def __init__(self, root, name, transform=None, pre_transform=None):
assert name in ['AIFB', 'AM', 'MUTAG', 'BGS']
self.name = name.lower()
super(Entities, self).__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def num_relations(self):
return self.data.edge_type.max().item() + 1
@property
def num_classes(self):
return self.data.train_y.max().item() + 1
@property
def raw_file_names(self):
return [
'{}_stripped.nt.gz'.format(self.name),
'completeDataset.tsv',
'trainingSet.tsv',
'testSet.tsv',
]
@property
def processed_file_names(self):
return 'data.pt'
def download(self):
path = download_url(self.url.format(self.name), self.root)
extract_tar(path, self.raw_dir)
os.unlink(path)
def triples(self, graph, relation=None):
for s, p, o in graph.triples((None, relation, None)):
yield s, p, o
def process(self):
import rdflib as rdf
graph_file, task_file, train_file, test_file = self.raw_paths
g = rdf.Graph()
with gzip.open(graph_file, 'rb') as f:
g.parse(file=f, format='nt')
freq_ = Counter(g.predicates())
def freq(rel):
return freq_[rel] if rel in freq_ else 0
relations = sorted(set(g.predicates()), key=lambda rel: -freq(rel))
subjects = set(g.subjects())
objects = set(g.objects())
nodes = list(subjects.union(objects))
relations_dict = {rel: i for i, rel in enumerate(list(relations))}
nodes_dict = {node: i for i, node in enumerate(nodes)}
edge_list = []
for s, p, o in g.triples((None, None, None)):
src, dst, rel = nodes_dict[s], nodes_dict[o], relations_dict[p]
edge_list.append([src, dst, 2 * rel])
edge_list.append([dst, src, 2 * rel + 1])
edge_list = sorted(edge_list, key=lambda x: (x[0], x[1], x[2]))
edge = torch.tensor(edge_list, dtype=torch.long).t().contiguous()
edge_index, edge_type = edge[:2], edge[2]
oh = F.one_hot(edge_type,
num_classes=2 * len(relations)).to(torch.float)
deg = scatter_add(oh, edge_index[0], dim=0, dim_size=len(nodes))
index = edge_type + torch.arange(len(edge_list)) * 2 * len(relations)
edge_norm = 1 / deg[edge_index[0]].view(-1)[index]
if self.name == 'am':
label_header = 'label_cateogory'
nodes_header = 'proxy'
elif self.name == 'aifb':
label_header = 'label_affiliation'
nodes_header = 'person'
elif self.name == 'mutag':
label_header = 'label_mutagenic'
nodes_header = 'bond'
elif self.name == 'bgs':
label_header = 'label_lithogenesis'
nodes_header = 'rock'
labels_df = pd.read_csv(task_file, sep='\t')
labels_set = set(labels_df[label_header].values.tolist())
labels_dict = {lab: i for i, lab in enumerate(list(labels_set))}
nodes_dict = {np.unicode(key): val for key, val in nodes_dict.items()}
train_labels_df = pd.read_csv(train_file, sep='\t')
train_indices, train_labels = [], []
for nod, lab in zip(train_labels_df[nodes_header].values,
train_labels_df[label_header].values):
train_indices.append(nodes_dict[nod])
train_labels.append(labels_dict[lab])
train_idx = torch.tensor(train_indices, dtype=torch.long)
train_y = torch.tensor(train_labels, dtype=torch.long)
test_labels_df = pd.read_csv(test_file, sep='\t')
test_indices, test_labels = [], []
for nod, lab in zip(test_labels_df[nodes_header].values,
test_labels_df[label_header].values):
test_indices.append(nodes_dict[nod])
test_labels.append(labels_dict[lab])
test_idx = torch.tensor(test_indices, dtype=torch.long)
test_y = torch.tensor(test_labels, dtype=torch.long)
data = Data(edge_index=edge_index)
data.edge_type = edge_type
data.edge_norm = edge_norm
data.train_idx = train_idx
data.train_y = train_y
data.test_idx = test_idx
data.test_y = test_y
data.num_nodes = edge_index.max().item() + 1
data, slices = self.collate([data])
torch.save((data, slices), self.processed_paths[0])
def __repr__(self):
return '{}{}()'.format(self.name.upper(), self.__class__.__name__)