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dbp15k dataset

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rusty1s committed Aug 6, 2019
1 parent f244906 commit ea593ee8e3794aae2219f06eeb3e11ebfd29b078
Showing with 125 additions and 2 deletions.
  1. +1 −0 setup.py
  2. +2 −0 torch_geometric/datasets/__init__.py
  3. +121 −0 torch_geometric/datasets/dbp15k.py
  4. +1 −2 torch_geometric/datasets/planetoid.py
@@ -12,6 +12,7 @@
'pandas',
'rdflib',
'h5py',
'googledrivedownloader',
]
setup_requires = ['pytest-runner']
tests_require = ['pytest', 'pytest-cov', 'mock']
@@ -25,6 +25,7 @@
from .icews import ICEWS18
from .gdelt import GDELT
from .willow_object_class import WILLOWObjectClass
from .dbp15k import DBP15K

__all__ = [
'KarateClub',
@@ -54,4 +55,5 @@
'ICEWS18',
'GDELT',
'WILLOWObjectClass',
'DBP15K',
]
@@ -0,0 +1,121 @@
import os
import os.path as osp
import shutil

import torch
from google_drive_downloader import GoogleDriveDownloader as gdd
from torch_geometric.data import Data, InMemoryDataset, extract_zip
from torch_geometric.read import read_txt_array


class DBP15K(InMemoryDataset):
r"""The DBP15K dataset from the
`"Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding"
<https://arxiv.org/abs/1708.05045>`_ paper, where Chinese, Japanese and
French versions of DBpedia were linked to its English version.
Node features are given by pre-trained and aligned monolingual word
embeddings.
Args:
root (string): Root directory where the dataset should be saved.
pair (string): The pair of languages (:obj:`"en_zh"`, :obj:`"en_fr"`,
:obj:`"en_ja"`, :obj:`"zh_en"`, :obj:`"fr_en"`, :obj:`"ja_en"`).
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`)
"""

file_id = '1dYJtj1_J4nYJdrDY95ucGLCuZXDXI7PL'

def __init__(self, root, pair, transform=None, pre_transform=None):
assert pair in ['en_zh', 'en_fr', 'en_ja', 'zh_en', 'fr_en', 'ja_en']
self.pair = pair
super(DBP15K, self).__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])

@property
def raw_file_names(self):
return ['en_zh', 'en_fr', 'en_ja', 'zh_en', 'fr_en', 'ja_en']

@property
def processed_file_names(self):
return '{}.pt'.format(self.pair)

def download(self):
path = osp.join(self.root, 'raw.zip')
gdd.download_file_from_google_drive(self.file_id, path)
extract_zip(path, self.root)
os.unlink(path)
shutil.rmtree(self.raw_dir)
os.rename(osp.join(self.root, 'DBP15K'), self.raw_dir)

def process(self):
embs = {}
with open(osp.join(self.raw_dir, 'sub.glove.300d'), 'r') as f:
for i, line in enumerate(f):
info = line.strip().split(' ')
if len(info) > 300:
embs[info[0]] = torch.tensor([float(x) for x in info[1:]])
else:
embs['**UNK**'] = torch.tensor([float(x) for x in info])

g1_path = osp.join(self.raw_dir, self.pair, 'triples_1')
x1_path = osp.join(self.raw_dir, self.pair, 'id_features_1')
g2_path = osp.join(self.raw_dir, self.pair, 'triples_2')
x2_path = osp.join(self.raw_dir, self.pair, 'id_features_2')

x1, edge_index1, rel1, assoc1 = self.process_graph(
g1_path, x1_path, embs)
x2, edge_index2, rel2, assoc2 = self.process_graph(
g2_path, x2_path, embs)

train_path = osp.join(self.raw_dir, self.pair, 'train.examples.20')
train_y = self.process_y(train_path, assoc1, assoc2)

test_path = osp.join(self.raw_dir, self.pair, 'test.examples.1000')
test_y = self.process_y(test_path, assoc1, assoc2)

data = Data(x1=x1, edge_index1=edge_index1, rel1=rel1, x2=x2,
edge_index2=edge_index2, rel2=rel2, train_y=train_y,
test_y=test_y)
torch.save(self.collate([data]), self.processed_paths[0])

def process_graph(self, triple_path, feature_path, embeddings):
g1 = read_txt_array(triple_path, sep='\t', dtype=torch.long)
subj, rel, obj = g1.t()

x_dict = {}
with open(feature_path, 'r') as f:
for line in f:
info = line.strip().split('\t')
info = info if len(info) == 2 else info + ['**UNK**']
seq = info[1].lower().split()
hs = [embeddings.get(w, embeddings['**UNK**']) for w in seq]
x_dict[int(info[0])] = torch.stack(hs, dim=0)

idx = torch.tensor(list(x_dict.keys()))
assoc = torch.full((idx.max().item() + 1, ), -1, dtype=torch.long)
assoc[idx] = torch.arange(idx.size(0))

subj, obj = assoc[subj], assoc[obj]
edge_index = torch.stack([subj, obj], dim=0)

xs = [None for _ in range(idx.size(0))]
for i in x_dict.keys():
xs[assoc[i]] = x_dict[i]
x = torch.nn.utils.rnn.pad_sequence(xs, batch_first=True)

return x, edge_index, rel, assoc

def process_y(self, path, assoc1, assoc2):
row, col, mask = read_txt_array(path, sep='\t', dtype=torch.long).t()
mask = mask.to(torch.uint8)
return torch.stack([assoc1[row[mask]], assoc2[col[mask]]], dim=0)

def __repr__(self):
return '{}({})'.format(self.__class__.__name__, self.pair)
@@ -47,8 +47,7 @@ def download(self):
def process(self):
data = read_planetoid_data(self.raw_dir, self.name)
data = data if self.pre_transform is None else self.pre_transform(data)
data, slices = self.collate([data])
torch.save((data, slices), self.processed_paths[0])
torch.save(self.collate([data]), self.processed_paths[0])

def __repr__(self):
return '{}()'.format(self.name)

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