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load_data.py
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import os
import torch
from scipy.sparse import csr_matrix
import numpy as np
from collections import defaultdict
import pickle as pkl
import networkx as nx
import time
from tqdm import tqdm
from torch.utils.data import Dataset
class DataLoader(Dataset):
def __init__(self, args, mode='train'):
self.args = args
self.mode = mode
self.task_dir = task_dir = args.data_path
with open(os.path.join(task_dir, 'entities.txt')) as f:
self.entity2id = dict()
n_ent = 0
for line in f:
entity = line.strip()
self.entity2id[entity] = n_ent
n_ent += 1
with open(os.path.join(task_dir, 'relations.txt')) as f:
self.relation2id = dict()
n_rel = 0
for line in f:
relation = line.strip()
self.relation2id[relation] = n_rel
n_rel += 1
self.n_ent = n_ent
self.n_rel = n_rel
self.filters = defaultdict(lambda:set())
self.fact_triple = self.read_triples('facts.txt')
self.train_triple = self.read_triples('train.txt')
self.valid_triple = self.read_triples('valid.txt')
self.test_triple = self.read_triples('test.txt')
self.all_triple = np.concatenate([np.array(self.fact_triple), np.array(self.train_triple)], axis=0)
# add inverse
self.fact_data = self.double_triple(self.fact_triple)
self.train_data = np.array(self.double_triple(self.train_triple))
self.valid_data = self.double_triple(self.valid_triple)
self.test_data = self.double_triple(self.test_triple)
self.idd_data = np.concatenate([np.expand_dims(np.arange(self.n_ent),1), 2*self.n_rel*np.ones((self.n_ent, 1)), np.expand_dims(np.arange(self.n_ent),1)], 1)
self.shuffle_train()
self.valid_q, self.valid_a = self.load_query(self.valid_data)
self.test_q, self.test_a = self.load_query(self.test_data)
self.n_train = len(self.train_data)
self.n_valid = len(self.valid_q)
self.n_test = len(self.test_q)
for filt in self.filters:
self.filters[filt] = list(self.filters[filt])
if mode == 'train':
self.len = len(self.train_data)
elif mode == 'valid':
self.len = len(self.valid_q)
else:
self.len = len(self.test_q)
def addSampler(self, sampler):
self.sampler = sampler
self.getOneSubgraph = self.sampler.getOneSubgraph
self.getBatchSubgraph = self.sampler.getBatchSubgraph
def __len__(self):
return self.len
def __getitem__(self, idx):
# indexing
if self.mode == 'train':
sub, rel, obj = self.train_data[idx]
sub = torch.LongTensor([sub]).unsqueeze(0)
rel = torch.LongTensor([rel]).unsqueeze(0)
obj = torch.LongTensor([obj]).unsqueeze(0)
else:
if self.mode == 'valid':
query, answer = self.valid_q, self.valid_a
elif self.mode == 'test':
query, answer = self.test_q, self.test_a
sub, rel = query[idx]
sub, rel = torch.LongTensor([sub]), torch.LongTensor([rel])
obj = torch.zeros((self.n_ent)).long()
obj[answer[idx]] = 1
# subgraph sampling
subgraph = self.getOneSubgraph(int(sub))
return sub, rel, obj, subgraph
def collate_fn(self, data):
subs = torch.stack([_[0] for _ in data], dim=0)
rels = torch.stack([_[1] for _ in data], dim=0)
objs = torch.stack([_[2] for _ in data], dim=0)
subgraph_list = [_[3] for _ in data]
batch_subgraph = self.getBatchSubgraph(subgraph_list)
# NOTE: we can not return sparse tensor here
# thus, we return its indices and values which are dense tensor.
batch_idxs, abs_idxs, query_sub_idxs, edge_batch_idxs, batch_sampled_edges = batch_subgraph
return subs, rels, objs, batch_idxs, abs_idxs, query_sub_idxs, edge_batch_idxs, batch_sampled_edges
def read_triples(self, filename):
triples = []
with open(os.path.join(self.task_dir, filename)) as f:
for line in f:
h, r, t = line.strip().split()
h, r, t = self.entity2id[h], self.relation2id[r], self.entity2id[t]
triples.append([h,r,t])
self.filters[(h,r)].add(t)
self.filters[(t,r+self.n_rel)].add(h)
return triples
def double_triple(self, triples):
new_triples = []
for triple in triples:
h, r, t = triple
new_triples.append([t, r+self.n_rel, h])
return list(triples) + new_triples
def load_query(self, triples):
triples.sort(key=lambda x:(x[0], x[1]))
trip_hr = defaultdict(lambda:list())
for trip in triples:
h, r, t = trip
trip_hr[(h,r)].append(t)
queries = []
answers = []
for key in trip_hr:
queries.append(key)
answers.append(np.array(trip_hr[key]))
return queries, answers
def shuffle_train(self):
# print('shuffle training set...')
all_triple = self.all_triple
n_all = len(all_triple)
rand_idx = np.random.permutation(n_all)
all_triple = all_triple[rand_idx]
bar = int(n_all * self.args.fact_ratio)
self.fact_data = np.array(self.double_triple(all_triple[:bar].tolist()))
self.train_data = np.array(self.double_triple(all_triple[bar:].tolist()))
if self.args.remove_1hop_edges:
print('==> removing 1-hop links...')
tmp_index = np.ones((self.n_ent, self.n_ent))
tmp_index[self.train_data[:, 0], self.train_data[:, 2]] = 0
save_facts = tmp_index[self.fact_data[:, 0], self.fact_data[:, 2]].astype(bool)
self.fact_data = self.fact_data[save_facts]
print('==> done')
# update
self.n_train = len(self.train_data)
self.len = len(self.train_data)
# shuffle training data
n_all = len(self.train_data)
rand_idx = np.random.permutation(n_all)
self.train_data = self.train_data[rand_idx]