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context_and_negatives_pre.py
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context_and_negatives_pre.py
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# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name: context_and_negatives_pre.py
Description: this code generates sampled entity contexts and negative entities for training.
Author: Ruijie Wang (https://github.com/xjdwrj)
date: 14 Nov. 2019
-------------------------------------------------
"""
import torch
from tools.pickle_funcs import load_data, dump_data
from tools.uniform_sampling import sampled_id_generation
from tools.log_text import log_text
class ContextAndNegatives:
def __init__(self, names, dataset, head_context_size, tail_context_size, negative_batch_size):
self.print_results_for_validation = False
self.dataset = dataset
self.names = names
self.input_path = "./datasets/%s/input/" % self.dataset
self.output_path = "./datasets/%s/output/" % self.dataset
self.log_path = "./logs/context_and_negatives_pre_on_%s.log" % self.dataset
self.head_context_size = head_context_size
self.tail_context_size = tail_context_size
self.negative_batch_size = negative_batch_size
self.statistics = None
self.num_of_entities = None
self.train_head_relation_to_tail = {}
self.train_tail_relation_to_head = {}
self.train_head_context_statistics = {}
self.train_tail_context_statistics = {}
self.validate_head_relation_to_tail = {}
self.validate_tail_relation_to_head = {}
self.validate_head_context_statistics = {}
self.validate_tail_context_statistics = {}
self.test_head_relation_to_tail = {}
self.test_tail_relation_to_head = {}
self.test_head_context_statistics = {}
self.test_tail_context_statistics = {}
self.head_relation_to_tails = [self.train_head_relation_to_tail, self.validate_head_relation_to_tail, self.test_head_relation_to_tail]
self.tail_relation_to_heads = [self.train_tail_relation_to_head, self.validate_tail_relation_to_head, self.test_tail_relation_to_head]
self.head_context_statistics = [self.train_head_context_statistics, self.validate_head_context_statistics, self.test_head_context_statistics]
self.tail_context_statistics = [self.train_tail_context_statistics, self.validate_tail_context_statistics, self.test_tail_context_statistics]
self.train_head_context_head = {}
self.train_head_context_relation = {}
self.train_tail_context_relation = {}
self.train_tail_context_tail = {}
self.validate_head_context_head = {}
self.validate_head_context_relation = {}
self.validate_tail_context_relation = {}
self.validate_tail_context_tail = {}
self.test_head_context_head = {}
self.test_head_context_relation = {}
self.test_tail_context_relation = {}
self.test_tail_context_tail = {}
self.head_context_heads = [self.train_head_context_head, self.validate_head_context_head, self.test_head_context_head]
self.head_context_relations = [self.train_head_context_relation, self.validate_head_context_relation, self.test_head_context_relation]
self.tail_context_relations = [self.train_tail_context_relation, self.validate_tail_context_relation, self.test_tail_context_relation]
self.tail_context_tails = [self.train_tail_context_tail, self.validate_tail_context_tail, self.test_tail_context_tail]
self.train_entities = {} # entities included in train data
self.validate_entities = {} # entities included in validation data
self.test_entities = {} # entities included in test data
self.entity_dicts = [self.train_entities, self.validate_entities, self.test_entities]
self.train_head_entities = {} # train entities which have head context
self.train_tail_entities = {} # train entities which have tail context
self.train_both_entities = {} # train entities which have both context
self.validate_head_entities = {} # validation entities which have head context
self.validate_tail_entities = {} # validation entities which have head context
self.validate_both_entities = {} # validation entities which have head context
self.test_head_entities = {} # test entities which have head context
self.test_tail_entities = {} # test entities which have head context
self.test_both_entities = {} # test entities which have head context
self.train_entity_head = {}
self.train_entity_head_relation = {}
self.train_entity_tail_relation = {}
self.train_entity_tail = {}
self.validate_entity_head = {}
self.validate_entity_head_relation = {}
self.validate_entity_tail_relation = {}
self.validate_entity_tail = {}
self.test_entity_head = {}
self.test_entity_head_relation = {}
self.test_entity_tail_relation = {}
self.test_entity_tail = {}
self.entity_heads = [self.train_entity_head, self.validate_entity_head, self.test_entity_head]
self.entity_head_relations = [self.train_entity_head_relation, self.validate_entity_head_relation, self.test_entity_head_relation]
self.entity_tail_relations = [self.train_entity_tail_relation, self.validate_entity_tail_relation, self.test_entity_tail_relation]
self.entity_tails = [self.train_entity_tail, self.validate_entity_tail, self.test_entity_tail]
self.train_negatives = {}
self.validate_negatives = {}
self.test_negatives = {}
self.negatives = [self.train_negatives, self.validate_negatives, self.test_negatives]
self.run_funcs()
def run_funcs(self):
log_text(self.log_path, "...... Reading Data for Context and Negatives Sampling ......")
self.read_data()
log_text(self.log_path, "...... Entity Classification ......")
self.entity_classification()
log_text(self.log_path, "...... Context Sampling ......")
self.context_sampling()
log_text(self.log_path, "...... Negative Sampling ......")
self.negative_sampling()
if self.print_results_for_validation:
log_text(self.log_path, "...... Result Validation ......")
self.result_validation()
def re_sampling(self):
for index in range(3):
self.entity_heads[index].clear()
self.entity_head_relations[index].clear()
self.entity_tail_relations[index].clear()
self.entity_tails[index].clear()
self.negatives[index].clear()
log_text(self.log_path, "...... Context Sampling ......")
self.context_sampling()
log_text(self.log_path, "...... Negative Sampling ......")
self.negative_sampling()
def read_data(self):
for index in range(len(self.names)):
name = self.names[index]
self.read_dict(self.head_relation_to_tails[index], load_data(self.output_path + "%s_head_relation_to_tail.pickle" % name, self.log_path, "self.%s_head_relation_to_tail" % name))
self.read_dict(self.tail_relation_to_heads[index], load_data(self.output_path + "%s_tail_relation_to_head.pickle" % name, self.log_path, "self.%s_tail_relation_to_head" % name))
self.read_dict(self.head_context_statistics[index], load_data(self.output_path + "%s_head_context_statistics.pickle" % name, self.log_path, "self.%s_head_context_statistics" % name))
self.read_dict(self.tail_context_statistics[index], load_data(self.output_path + "%s_tail_context_statistics.pickle" % name, self.log_path, "self.%s_tail_context_statistics" % name))
self.read_dict(self.head_context_heads[index], load_data(self.output_path + "%s_head_context_head.pickle" % name, self.log_path, "self.%s_head_context_head" % name))
self.read_dict(self.head_context_relations[index], load_data(self.output_path + "%s_head_context_relation.pickle" % name, self.log_path, "self.%s_head_context_relation" % name))
self.read_dict(self.tail_context_relations[index], load_data(self.output_path + "%s_tail_context_relation.pickle" % name, self.log_path, "self.%s_tail_context_relation" % name))
self.read_dict(self.tail_context_tails[index], load_data(self.output_path + "%s_tail_context_tail.pickle" % name, self.log_path, "self.%s_tail_context_tail" % name))
self.statistics = load_data(self.output_path + "statistics.pickle", self.log_path, "self.statistics")
self.num_of_entities = [self.statistics["num_of_train_entities"], self.statistics["num_of_validate_entities"], self.statistics["num_of_test_entities"]]
@staticmethod
def read_dict(dict1, dict2):
for key in dict2:
dict1[key] = dict2[key]
def entity_classification(self):
counts = [0, 0, 0]
dataset_classifications = [self.train_entities, self.validate_entities, self.test_entities]
context_head_classifications = [self.train_head_entities, self.validate_head_entities, self.test_head_entities]
context_tail_classifications = [self.train_tail_entities, self.validate_tail_entities, self.test_tail_entities]
context_both_classifications = [self.train_both_entities, self.validate_both_entities, self.test_both_entities]
for entity in range(self.statistics["num_of_entities"]):
for index in range(len(self.names)):
if entity in self.head_relation_to_tails[index] or entity in self.tail_relation_to_heads[index]:
dataset_classifications[index][counts[index]] = entity
counts[index] += 1
if self.head_context_statistics[index][entity] > 0 and self.tail_context_statistics[index][entity] == 0:
context_head_classifications[index][entity] = None
if self.head_context_statistics[index][entity] == 0 and self.tail_context_statistics[index][entity] > 0:
context_tail_classifications[index][entity] = None
if self.head_context_statistics[index][entity] > 0 and self.tail_context_statistics[index][entity] > 0:
context_both_classifications[index][entity] = None
self.statistics["num_of_train_entities"] = counts[0]
self.statistics["num_of_validate_entities"] = counts[1]
self.statistics["num_of_test_entities"] = counts[2]
for index in range(len(self.names)):
dump_data(dataset_classifications[index], self.output_path + "%s_entities.pickle" % self.names[index], self.log_path, "")
dump_data(context_head_classifications[index], self.output_path + "%s_head_entities.pickle" % self.names[index], self.log_path, "")
dump_data(context_tail_classifications[index], self.output_path + "%s_tail_entities.pickle" % self.names[index], self.log_path, "")
dump_data(context_both_classifications[index], self.output_path + "%s_both_entities.pickle" % self.names[index], self.log_path, "")
dump_data(self.statistics, self.output_path + "statistics.pickle", self.log_path, "")
def context_sampling(self):
for index in range(len(self.names)):
num_of_entity = self.num_of_entities[index]
entity_dict = self.entity_dicts[index]
head_context_statistic = self.head_context_statistics[index]
tail_context_statistic = self.tail_context_statistics[index]
head_context_head = self.head_context_heads[index]
head_context_relation = self.head_context_relations[index]
tail_context_relation = self.tail_context_relations[index]
tail_context_tail = self.tail_context_tails[index]
entity_head = self.entity_heads[index]
entity_head_relation = self.entity_head_relations[index]
entity_tail_relation = self.entity_tail_relations[index]
entity_tail = self.entity_tails[index]
for entity_id in range(num_of_entity):
entity = entity_dict[entity_id]
num_of_head_context = head_context_statistic[entity]
num_of_tail_context = tail_context_statistic[entity]
if num_of_head_context > 0:
heads = head_context_head[entity]
relations = head_context_relation[entity]
sampled_ids = sampled_id_generation(0, num_of_head_context, self.head_context_size)
entity_head[entity] = torch.unsqueeze(torch.LongTensor([heads[_] for _ in sampled_ids]), 0)
entity_head_relation[entity] = torch.unsqueeze(torch.LongTensor([relations[_] for _ in sampled_ids]), 0)
if num_of_tail_context > 0:
relations = tail_context_relation[entity]
tails = tail_context_tail[entity]
sampled_ids = sampled_id_generation(0, num_of_tail_context, self.tail_context_size)
entity_tail_relation[entity] = torch.unsqueeze(torch.LongTensor([relations[_] for _ in sampled_ids]), 0)
entity_tail[entity] = torch.unsqueeze(torch.LongTensor([tails[_] for _ in sampled_ids]), 0)
name = self.names[index]
dump_data(entity_head, self.output_path + name + "_context_head.pickle", self.log_path, "")
dump_data(entity_head_relation, self.output_path + name + "_context_head_relation.pickle", self.log_path, "")
dump_data(entity_tail_relation, self.output_path + name + "_context_tail_relation.pickle", self.log_path, "")
dump_data(entity_tail, self.output_path + name + "_context_tail.pickle", self.log_path, "")
def negative_sampling(self):
for index in range(len(self.names)):
name = self.names[index]
num_of_entity = self.num_of_entities[index]
entity_dict = self.entity_dicts[index]
negative = self.negatives[index]
for entity_id in range(num_of_entity):
entity = entity_dict[entity_id]
negative_entities = []
sampled_entities = {}
sampled_entity_count = 0
while len(negative_entities) < self.negative_batch_size and sampled_entity_count < num_of_entity:
sampled_entity = entity_dict[sampled_id_generation(0, num_of_entity, 1)[0]]
while sampled_entity in sampled_entities:
sampled_entity = entity_dict[sampled_id_generation(0, num_of_entity, 1)[0]]
sampled_entities[sampled_entity] = None
sampled_entity_count += 1
if self.negative_or_not(entity, sampled_entity):
negative_entities.append(sampled_entity)
if len(negative_entities) == 0:
sampled_ids = sampled_id_generation(0, num_of_entity, self.negative_batch_size)
for sampled_id in sampled_ids:
negative_entities.append(entity_dict[sampled_id])
if len(negative_entities) < self.negative_batch_size:
sampled_ids = sampled_id_generation(0, len(negative_entities), self.negative_batch_size - len(negative_entities))
for sampled_id in sampled_ids:
negative_entities.append(negative_entities[sampled_id])
negative[entity] = torch.unsqueeze(torch.LongTensor(negative_entities), 0)
dump_data(negative, self.output_path + "%s_negatives.pickle" % name, self.log_path, "")
def negative_or_not(self, entity, sampled_entity):
neighbors = [self.train_head_relation_to_tail, self.train_tail_relation_to_head]
for neighbor in neighbors:
if entity in neighbor and sampled_entity in neighbor:
for tmp_relation in neighbor[entity]:
if tmp_relation in neighbor[sampled_entity]:
for tmp_entity in neighbor[entity][tmp_relation]:
if tmp_entity in neighbor[sampled_entity][tmp_relation]:
return False
return True
def result_validation(self):
log_text(self.log_path, "...... Result of Entity Classification ......")
for name in self.names:
log_text(self.log_path, load_data(self.output_path + "%s_entities.pickle" % name, self.log_path, ""))
log_text(self.log_path, load_data(self.output_path + "%s_head_entities.pickle" % name, self.log_path, ""))
log_text(self.log_path, load_data(self.output_path + "%s_tail_entities.pickle" % name, self.log_path, ""))
log_text(self.log_path, load_data(self.output_path + "%s_both_entities.pickle" % name, self.log_path, ""))
log_text(self.log_path, "...... Result of Context Sampling ......")
for name in self.names:
log_text(self.log_path, load_data(self.output_path + "%s_context_head.pickle" % name, self.log_path, ""))
log_text(self.log_path, load_data(self.output_path + "%s_context_head_relation.pickle" % name, self.log_path, ""))
log_text(self.log_path, load_data(self.output_path + "%s_context_tail_relation.pickle" % name, self.log_path, ""))
log_text(self.log_path, load_data(self.output_path + "%s_context_tail.pickle" % name, self.log_path, ""))
log_text(self.log_path, "...... Result of Negative Sampling ......")
for name in self.names:
log_text(self.log_path, load_data(self.output_path + "%s_negatives.pickle" % name, self.output_path, ""))
log_text(self.log_path, "...... Other Results ......")
log_text(self.log_path, load_data(self.output_path + "statistics.pickle", self.log_path, "statistics"))
if __name__ == "__main__":
contextAndNegatives = ContextAndNegatives()