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dataset.py
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dataset.py
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from utils import *
import numpy as np
import random
import igraph
import copy
import time
import os
class TrainData():
def __init__(self, path):
self.path = path
self.rel_info = {}
self.pair_info = {}
self.spanning = []
self.remaining = []
self.ent2id = None
self.rel2id = None
self.id2ent, self.id2rel, self.triplets = self.read_triplet(path + 'train.txt')
self.num_triplets = len(self.triplets)
self.num_ent, self.num_rel = len(self.id2ent), len(self.id2rel)
def read_triplet(self, path):
id2ent, id2rel, triplets = [], [], []
with open(path, 'r') as f:
for line in f.readlines():
h, r, t = line.strip().split('\t')
id2ent.append(h)
id2ent.append(t)
id2rel.append(r)
triplets.append((h, r, t))
id2ent = remove_duplicate(id2ent)
id2rel = remove_duplicate(id2rel)
self.ent2id = {ent: idx for idx, ent in enumerate(id2ent)}
self.rel2id = {rel: idx for idx, rel in enumerate(id2rel)}
triplets = [(self.ent2id[h], self.rel2id[r], self.ent2id[t]) for h, r, t in triplets]
for (h,r,t) in triplets:
if (h,t) in self.rel_info:
self.rel_info[(h,t)].append(r)
else:
self.rel_info[(h,t)] = [r]
if r in self.pair_info:
self.pair_info[r].append((h,t))
else:
self.pair_info[r] = [(h,t)]
G = igraph.Graph.TupleList(np.array(triplets)[:, 0::2])
G_ent = igraph.Graph.TupleList(np.array(triplets)[:, 0::2], directed = True)
spanning = G_ent.spanning_tree()
G_ent.delete_edges(spanning.get_edgelist())
for e in spanning.es:
e1,e2 = e.tuple
e1 = spanning.vs[e1]["name"]
e2 = spanning.vs[e2]["name"]
self.spanning.append((e1,e2))
spanning_set = set(self.spanning)
print("-----Train Data Statistics-----")
print(f"{len(self.ent2id)} entities, {len(self.rel2id)} relations")
print(f"{len(triplets)} triplets")
self.triplet2idx = {triplet:idx for idx, triplet in enumerate(triplets)}
self.triplets_with_inv = np.array([(t, r + len(id2rel), h) for h,r,t in triplets] + triplets)
return id2ent, id2rel, triplets
def split_transductive(self, p):
msg, sup = [], []
rels_encountered = np.zeros(self.num_rel)
remaining_triplet_indexes = np.ones(self.num_triplets)
for h,t in self.spanning:
r = random.choice(self.rel_info[(h,t)])
msg.append((h, r, t))
remaining_triplet_indexes[self.triplet2idx[(h,r,t)]] = 0
rels_encountered[r] = 1
for r in (1-rels_encountered).nonzero()[0].tolist():
h,t = random.choice(self.pair_info[int(r)])
msg.append((h, r, t))
remaining_triplet_indexes[self.triplet2idx[(h,r,t)]] = 0
start = time.time()
sup = [self.triplets[idx] for idx, tf in enumerate(remaining_triplet_indexes) if tf]
msg = np.array(msg)
random.shuffle(sup)
sup = np.array(sup)
add_num = max(int(self.num_triplets * p) - len(msg), 0)
msg = np.concatenate([msg, sup[:add_num]])
sup = sup[add_num:]
msg_inv = np.fliplr(msg).copy()
msg_inv[:,1] += self.num_rel
msg = np.concatenate([msg, msg_inv])
return msg, sup
class TestNewData():
def __init__(self, path, data_type = "valid"):
self.path = path
self.data_type = data_type
self.ent2id = None
self.rel2id = None
self.id2ent, self.id2rel, self.msg_triplets, self.sup_triplets, self.filter_dict = self.read_triplet()
self.num_ent, self.num_rel = len(self.id2ent), len(self.id2rel)
def read_triplet(self):
id2ent, id2rel, msg_triplets, sup_triplets = [], [], [], []
total_triplets = []
with open(self.path + "msg.txt", 'r') as f:
for line in f.readlines():
h, r, t = line.strip().split('\t')
id2ent.append(h)
id2ent.append(t)
id2rel.append(r)
msg_triplets.append((h, r, t))
total_triplets.append((h, r, t))
id2ent = remove_duplicate(id2ent)
id2rel = remove_duplicate(id2rel)
self.ent2id = {ent: idx for idx, ent in enumerate(id2ent)}
self.rel2id = {rel: idx for idx, rel in enumerate(id2rel)}
num_rel = len(self.rel2id)
msg_triplets = [(self.ent2id[h], self.rel2id[r], self.ent2id[t]) for h, r, t in msg_triplets]
msg_inv_triplets = [(t, r+num_rel, h) for h,r,t in msg_triplets]
with open(self.path + self.data_type + ".txt", 'r') as f:
for line in f.readlines():
h, r, t = line.strip().split('\t')
sup_triplets.append((self.ent2id[h], self.rel2id[r], self.ent2id[t]))
assert (self.ent2id[h], self.rel2id[r], self.ent2id[t]) not in msg_triplets, \
(self.ent2id[h], self.rel2id[r], self.ent2id[t])
total_triplets.append((h,r,t))
for data_type in ['valid', 'test']:
if data_type == self.data_type:
continue
with open(self.path + data_type + ".txt", 'r') as f:
for line in f.readlines():
h, r, t = line.strip().split('\t')
assert (self.ent2id[h], self.rel2id[r], self.ent2id[t]) not in msg_triplets, \
(self.ent2id[h], self.rel2id[r], self.ent2id[t])
total_triplets.append((h,r,t))
filter_dict = {}
for triplet in total_triplets:
h,r,t = triplet
if ('_', self.rel2id[r], self.ent2id[t]) not in filter_dict:
filter_dict[('_', self.rel2id[r], self.ent2id[t])] = [self.ent2id[h]]
else:
filter_dict[('_', self.rel2id[r], self.ent2id[t])].append(self.ent2id[h])
if (self.ent2id[h], '_', self.ent2id[t]) not in filter_dict:
filter_dict[(self.ent2id[h], '_', self.ent2id[t])] = [self.rel2id[r]]
else:
filter_dict[(self.ent2id[h], '_', self.ent2id[t])].append(self.rel2id[r])
if (self.ent2id[h], self.rel2id[r], '_') not in filter_dict:
filter_dict[(self.ent2id[h], self.rel2id[r], '_')] = [self.ent2id[t]]
else:
filter_dict[(self.ent2id[h], self.rel2id[r], '_')].append(self.ent2id[t])
print(f"-----{self.data_type.capitalize()} Data Statistics-----")
print(f"Message set has {len(msg_triplets)} triplets")
print(f"Supervision set has {len(sup_triplets)} triplets")
print(f"{len(self.ent2id)} entities, " + \
f"{len(self.rel2id)} relations, "+ \
f"{len(total_triplets)} triplets")
msg_triplets = msg_triplets + msg_inv_triplets
return id2ent, id2rel, np.array(msg_triplets), np.array(sup_triplets), filter_dict