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qa_datasets.py
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qa_datasets.py
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from pathlib import Path
import pkg_resources
import pickle
from collections import defaultdict
from typing import Dict, Tuple, List
import json
import numpy as np
import torch
# from qa_models import QA_model
import utils
from tqdm import tqdm
from transformers import RobertaTokenizer
from transformers import DistilBertTokenizer
from transformers import BertTokenizer
import random
from torch.utils.data import Dataset, DataLoader
# from nltk import word_tokenize
# warning: padding id 0 is being used, can have issue like in Tucker
# however since so many entities (and timestamps?), it may not pose problem
import pdb
from copy import deepcopy
from collections import defaultdict
import random
from hard_supervision_functions import retrieve_times
class QA_Dataset(Dataset):
def __init__(self,
split,
dataset_name,
tokenization_needed=True):
filename = 'data/{dataset_name}/questions/{split}.pickle'.format(
dataset_name=dataset_name,
split=split
)
questions = pickle.load(open(filename, 'rb'))
#probably change for bert/roberta?
self.tokenizer_class = DistilBertTokenizer
self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
self.all_dicts = utils.getAllDicts(dataset_name)
print('Total questions = ', len(questions))
self.data = questions
self.tokenization_needed = tokenization_needed
def getEntitiesLocations(self, question):
question_text = question['question']
entities = question['entities']
ent2id = self.all_dicts['ent2id']
loc_ent = []
for e in entities:
e_id = ent2id[e]
location = question_text.find(e)
loc_ent.append((location, e_id))
return loc_ent
def getTimesLocations(self, question):
question_text = question['question']
times = question['times']
ts2id = self.all_dicts['ts2id']
loc_time = []
for t in times:
t_id = ts2id[(t,0,0)] + len(self.all_dicts['ent2id']) # add num entities
location = question_text.find(str(t))
loc_time.append((location, t_id))
return loc_time
def isTimeString(self, s):
# todo: cant do len == 4 since 3 digit times also there
if 'Q' not in s:
return True
else:
return False
def textToEntTimeId(self, text):
if self.isTimeString(text):
t = int(text)
ts2id = self.all_dicts['ts2id']
t_id = ts2id[(t,0,0)] + len(self.all_dicts['ent2id'])
return t_id
else:
ent2id = self.all_dicts['ent2id']
e_id = ent2id[text]
return e_id
def getOrderedEntityTimeIds(self, question):
loc_ent = self.getEntitiesLocations(question)
loc_time = self.getTimesLocations(question)
loc_all = loc_ent + loc_time
loc_all.sort()
ordered_ent_time = [x[1] for x in loc_all]
return ordered_ent_time
def entitiesToIds(self, entities):
output = []
ent2id = self.all_dicts['ent2id']
for e in entities:
output.append(ent2id[e])
return output
def getIdType(self, id):
if id < len(self.all_dicts['ent2id']):
return 'entity'
else:
return 'time'
def getEntityToText(self, entity_wd_id):
return self.all_dicts['wd_id_to_text'][entity_wd_id]
def getEntityIdToText(self, id):
ent = self.all_dicts['id2ent'][id]
return self.getEntityToText(ent)
def getEntityIdToWdId(self, id):
return self.all_dicts['id2ent'][id]
def timesToIds(self, times):
output = []
ts2id = self.all_dicts['ts2id']
for t in times:
output.append(ts2id[(t, 0, 0)])
return output
def getAnswersFromScores(self, scores, largest=True, k=10):
_, ind = torch.topk(scores, k, largest=largest)
predict = ind
answers = []
for a_id in predict:
a_id = a_id.item()
type = self.getIdType(a_id)
if type == 'entity':
# answers.append(self.getEntityIdToText(a_id))
answers.append(self.getEntityIdToWdId(a_id))
else:
time_id = a_id - len(self.all_dicts['ent2id'])
time = self.all_dicts['id2ts'][time_id]
answers.append(time[0])
return answers
def getAnswersFromScoresWithScores(self, scores, largest=True, k=10):
s, ind = torch.topk(scores, k, largest=largest)
predict = ind
answers = []
for a_id in predict:
a_id = a_id.item()
type = self.getIdType(a_id)
if type == 'entity':
# answers.append(self.getEntityIdToText(a_id))
answers.append(self.getEntityIdToWdId(a_id))
else:
time_id = a_id - len(self.all_dicts['ent2id'])
time = self.all_dicts['id2ts'][time_id]
answers.append(time[0])
return s, answers
# from pytorch Transformer:
# If a BoolTensor is provided, the positions with the value of True will be ignored
# while the position with the value of False will be unchanged.
#
# so we want to pad with True
def padding_tensor(self, sequences, max_len = -1):
"""
:param sequences: list of tensors
:return:
"""
num = len(sequences)
if max_len == -1:
max_len = max([s.size(0) for s in sequences])
out_dims = (num, max_len)
out_tensor = sequences[0].data.new(*out_dims).fill_(0)
# mask = sequences[0].data.new(*out_dims).fill_(0)
mask = torch.ones((num, max_len), dtype=torch.bool) # fills with True
for i, tensor in enumerate(sequences):
length = tensor.size(0)
out_tensor[i, :length] = tensor
mask[i, :length] = False # fills good area with False
return out_tensor, mask
def toOneHot(self, indices, vec_len):
indices = torch.LongTensor(indices)
one_hot = torch.FloatTensor(vec_len)
one_hot.zero_()
one_hot.scatter_(0, indices, 1)
return one_hot
def prepare_data(self, data):
# we want to prepare answers lists for each question
# then at batch prep time, we just stack these
# and use scatter
question_text = []
entity_time_ids = []
num_total_entities = len(self.all_dicts['ent2id'])
answers_arr = []
for question in data:
# first pp is question text
# needs to be changed after making PD dataset
# to randomly sample from list
q_text = question['paraphrases'][0]
question_text.append(q_text)
et_id = self.getOrderedEntityTimeIds(question)
entity_time_ids.append(torch.tensor(et_id, dtype=torch.long))
if question['answer_type'] == 'entity':
answers = self.entitiesToIds(question['answers'])
else:
# adding num_total_entities to each time id
answers = [x + num_total_entities for x in self.timesToIds(question['answers'])]
answers_arr.append(answers)
# answers_arr = self.get_stacked_answers_long(answers_arr)
return {'question_text': question_text,
'entity_time_ids': entity_time_ids,
'answers_arr': answers_arr}
def is_template_keyword(self, word):
if '{' in word and '}' in word:
return True
else:
return False
def get_keyword_dict(self, template, nl_question):
template_tokenized = self.tokenize_template(template)
keywords = []
for word in template_tokenized:
if not self.is_template_keyword(word):
# replace only first occurence
nl_question = nl_question.replace(word, '*', 1)
else:
keywords.append(word[1:-1]) # no brackets
text_for_keywords = []
for word in nl_question.split('*'):
if word != '':
text_for_keywords.append(word)
keyword_dict = {}
for keyword, text in zip(keywords, text_for_keywords):
keyword_dict[keyword] = text
return keyword_dict
def addEntityAnnotation(self, data):
for i in range(len(data)):
question = data[i]
keyword_dicts = [] # we want for each paraphrase
template = question['template']
#for nl_question in question['paraphrases']:
nl_question = question['paraphrases'][0]
keyword_dict = self.get_keyword_dict(template, nl_question)
keyword_dicts.append(keyword_dict)
data[i]['keyword_dicts'] = keyword_dicts
#print(keyword_dicts)
#print(template, nl_question)
return data
def tokenize_template(self, template):
output = []
buffer = ''
i = 0
while i < len(template):
c = template[i]
if c == '{':
if buffer != '':
output.append(buffer)
buffer = ''
while template[i] != '}':
buffer += template[i]
i += 1
buffer += template[i]
output.append(buffer)
buffer = ''
else:
buffer += c
i += 1
if buffer != '':
output.append(buffer)
return output
class QA_Dataset_Baseline(QA_Dataset):
def __init__(self, split, dataset_name, tokenization_needed=True):
super().__init__(split, dataset_name, tokenization_needed)
print('Preparing data for split %s' % split)
# self.data = self.data[:30000]
# new_data = []
# # qn_type = 'simple_time'
# qn_type = 'simple_entity'
# print('Only {} questions'.format(qn_type))
# for qn in self.data:
# if qn['type'] == qn_type:
# new_data.append(qn)
# self.data = new_data
ents = self.all_dicts['ent2id'].keys()
self.all_dicts['tsstr2id'] = {str(k[0]):v for k,v in self.all_dicts['ts2id'].items()}
times = self.all_dicts['tsstr2id'].keys()
rels = self.all_dicts['rel2id'].keys()
self.prepared_data = self.prepare_data_(self.data)
self.num_total_entities = len(self.all_dicts['ent2id'])
self.num_total_times = len(self.all_dicts['ts2id'])
self.answer_vec_size = self.num_total_entities + self.num_total_times
def prepare_data_(self, data):
# we want to prepare answers lists for each question
# then at batch prep time, we just stack these
# and use scatter
question_text = []
heads = []
tails = []
times = []
num_total_entities = len(self.all_dicts['ent2id'])
answers_arr = []
ent2id = self.all_dicts['ent2id']
self.data_ids_filtered=[]
# self.data=[]
for i,question in enumerate(data):
self.data_ids_filtered.append(i)
# first pp is question text
# needs to be changed after making PD dataset
# to randomly sample from list
q_text = question['paraphrases'][0]
entities_list_with_locations = self.getEntitiesLocations(question)
entities_list_with_locations.sort()
entities = [id for location, id in entities_list_with_locations] # ordering necessary otherwise set->list conversion causes randomness
head = entities[0] # take an entity
if len(entities) > 1:
tail = entities[1]
else:
tail = entities[0]
times_in_question = question['times']
if len(times_in_question) > 0:
time = self.timesToIds(times_in_question)[0] # take a time. if no time then 0
# exit(0)
else:
time = 0
time += num_total_entities
heads.append(head)
times.append(time)
tails.append(tail)
question_text.append(q_text)
if question['answer_type'] == 'entity':
answers = self.entitiesToIds(question['answers'])
else:
# adding num_total_entities to each time id
answers = [x + num_total_entities for x in self.timesToIds(question['answers'])]
answers_arr.append(answers)
# answers_arr = self.get_stacked_answers_long(answers_arr)
self.data=[self.data[idx] for idx in self.data_ids_filtered]
return {'question_text': question_text,
'head': heads,
'tail': tails,
'time': times,
'answers_arr': answers_arr}
def print_prepared_data(self):
for k, v in self.prepared_data.items():
print(k, v)
def __len__(self):
return len(self.data)
# return len(self.prepared_data['question_text'])
def __getitem__(self, index):
data = self.prepared_data
question_text = data['question_text'][index]
head = data['head'][index]
tail = data['tail'][index]
time = data['time'][index]
answers_arr = data['answers_arr'][index]
answers_single = random.choice(answers_arr)
return question_text, head, tail, time, answers_single #,answers_khot
def _collate_fn(self, items):
batch_sentences = [item[0] for item in items]
b = self.tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
heads = torch.from_numpy(np.array([item[1] for item in items]))
tails = torch.from_numpy(np.array([item[2] for item in items]))
times = torch.from_numpy(np.array([item[3] for item in items]))
answers_single = torch.from_numpy(np.array([item[4] for item in items]))
return b['input_ids'], b['attention_mask'], heads, tails, times, answers_single
def get_dataset_ques_info(self):
type2num={}
for question in self.data:
if question["type"] not in type2num: type2num[question["type"]]=0
type2num[question["type"]]+=1
return {"type2num":type2num, "total_num":len(self.data_ids_filtered)}.__str__()
# replace entity mention tokens
# rather than add + layernorm
class QA_Dataset_TempoQR(QA_Dataset):
def __init__(self, split, dataset_name, args, tokenization_needed=True):
super().__init__(split, dataset_name, tokenization_needed)
print('Preparing data for split %s' % split)
ents = self.all_dicts['ent2id'].keys()
self.all_dicts['tsstr2id'] = {str(k[0]):v for k,v in self.all_dicts['ts2id'].items()}
times = self.all_dicts['tsstr2id'].keys()
rels = self.all_dicts['rel2id'].keys()
self.split = split
#This is hard supervision
#args: given TKG, whether to corrupt hard, and how to use the reitrieved timestmaps
self.data = retrieve_times(args.tkg_file, args.dataset_name, self.data, args.corrupt_hard, args.fuse)
self.data = self.addEntityAnnotation(self.data)
self.num_total_entities = len(self.all_dicts['ent2id'])
self.num_total_times = len(self.all_dicts['ts2id'])
self.padding_idx = self.num_total_entities + self.num_total_times # padding id for embedding of ent/time
self.answer_vec_size = self.num_total_entities + self.num_total_times
self.prepared_data = self.prepare_data2(self.data)
def __len__(self):
return len(self.data)
def getEntityTimeTextIds(self, question, pp_id=0):
keyword_dict = question['keyword_dicts'][pp_id]
keyword_id_dict = question['annotation'] # this does not depend on paraphrase
output_text = []
output_ids = []
entity_time_keywords = set(['head', 'tail', 'time', 'event_head', 'time1', 'time2'])
#print(keyword_dict, keyword_id_dict)
for keyword, value in keyword_dict.items():
if keyword in entity_time_keywords:
wd_id_or_time = keyword_id_dict[keyword]
output_text.append(value)
output_ids.append(wd_id_or_time)
#print(output_text, output_ids)
return output_text, output_ids
def get_entity_aware_tokenization(self, nl_question, ent_times, ent_times_ids):
# what we want finally is that after proper tokenization
# of nl question, we know which indices are beginning tokens
# of entities and times in the question
index_et_pairs = []
index_et_text_pairs = []
for e_text, e_id in zip(ent_times, ent_times_ids):
location = nl_question.find(e_text)
pair = (location, e_id)
index_et_pairs.append(pair)
pair = (location, e_text)
index_et_text_pairs.append(pair)
index_et_pairs.sort()
index_et_text_pairs.sort()
my_tokenized_question = []
start_index = 0
arr = []
for pair, pair_id in zip(index_et_text_pairs, index_et_pairs):
end_index = pair[0]
if nl_question[start_index: end_index] != '':
my_tokenized_question.append(nl_question[start_index: end_index])
arr.append(self.padding_idx)
start_index = end_index
end_index = start_index + len(pair[1])
# todo: assuming entity name can't be blank
# my_tokenized_question.append(nl_question[start_index: end_index])
my_tokenized_question.append(self.tokenizer.mask_token)
matrix_id = self.textToEntTimeId(pair_id[1]) # get id in embedding matrix
arr.append(matrix_id)
start_index = end_index
if nl_question[start_index:] != '':
my_tokenized_question.append(nl_question[start_index:])
arr.append(self.padding_idx)
tokenized, valid_ids = self.tokenize(my_tokenized_question)
entity_time_final = []
index = 0
for vid in valid_ids:
if vid == 0:
entity_time_final.append(self.padding_idx)
else:
entity_time_final.append(arr[index])
index += 1
entity_mask = [] # want 0 if entity, 1 if not, since will multiply this later with word embedding
for x in entity_time_final:
if x == self.padding_idx:
entity_mask.append(1.)
else:
entity_mask.append(0.)
#print(entity_time_final)
return tokenized, entity_time_final, entity_mask
def prepare_data2(self, data):
# we want to prepare answers lists for each question
# then at batch prep time, we just stack these
# and use scatter
heads = []
times = []
start_times = []
end_times = []
tails = []
tails2 = []
question_text = []
tokenized_question = []
entity_time_ids_tokenized_question = []
entity_mask_tokenized_question = []
pp_id = 0
num_total_entities = len(self.all_dicts['ent2id'])
answers_arr = []
for question in tqdm(data):
# randomly sample pp
# in test there is only 1 pp, so always pp_id=0
# TODO: this random is causing assertion bug later on
# pp_id = random.randint(0, len(question['paraphrases']) - 1)
pp_id = 0
nl_question = question['paraphrases'][pp_id]
q_text = nl_question
et_text, et_ids = self.getEntityTimeTextIds(question, pp_id)
entities_list_with_locations = self.getEntitiesLocations(question)
entities_list_with_locations.sort()
entities = [id for location, id in
entities_list_with_locations] # ordering necessary otherwise set->list conversion causes randomness
head = entities[0] # take an entity
if len(entities) > 1:
tail = entities[1]
if len(entities) > 2:
tail2 = entities[2]
else:
tail2 = tail
else:
tail = entities[0]
tail2 = tail
times_in_question = question['times']
if len(times_in_question) > 0:
time = self.timesToIds(times_in_question)[0] # take a time. if no time then 0
start_time = time
end_time = time
# exit(0)
else:
time = 0
#check for retrieved timestmaps
if len(question['fact']) > 0:
ts = []
#add all timestmaps and sort
for f in question['fact']:
for t in range(int(f[0]), int(f[1])+1):
ts.append(t)
ts = sorted(ts)
sorted_times = self.timesToIds(ts)
try:
start_time = sorted_times[0] # for random random.choice(sorted_times)
except:
start_time = 0
try:
end_time = sorted_times[-1]
except:
end_time = 0
else:
start_time = 0
end_time = 0
# print('No time in qn!')
time += num_total_entities
heads.append(head)
times.append(time)
start_times.append(start_time)
end_times.append(end_time)
tails.append(tail)
tails2.append(tail2)
tokenized, entity_time_final, entity_mask = self.get_entity_aware_tokenization(nl_question, et_text, et_ids)
assert len(tokenized) == len(entity_time_final)
question_text.append(nl_question)
tokenized_question.append(self.tokenizer.convert_tokens_to_ids(tokenized))
entity_mask_tokenized_question.append(entity_mask)
entity_time_ids_tokenized_question.append(entity_time_final)
if question['answer_type'] == 'entity':
answers = self.entitiesToIds(question['answers'])
else:
# adding num_total_entities to each time id
answers = [x + num_total_entities for x in self.timesToIds(question['answers'])]
answers_arr.append(answers)
return {'question_text': question_text,
'tokenized_question': tokenized_question,
'entity_time_ids': entity_time_ids_tokenized_question,
'entity_mask': entity_mask_tokenized_question,
'head': heads,
'tail': tails,
'time': times,
'start_time': start_times,
'end_time': end_times,
'tail2': tails2,
'answers_arr': answers_arr}
# tokenization function taken from NER code
def tokenize(self, words):
""" tokenize input"""
tokens = []
valid_positions = []
tokens.append(self.tokenizer.cls_token)
valid_positions.append(0)
for i, word in enumerate(words):
token = self.tokenizer.tokenize(word)
tokens.extend(token)
for i in range(len(token)):
if i == 0:
valid_positions.append(1)
else:
valid_positions.append(0)
tokens.append(self.tokenizer.sep_token)
valid_positions.append(0)
return tokens, valid_positions
def __getitem__(self, index):
data = self.prepared_data
question_text = data['question_text'][index]
entity_time_ids = np.array(data['entity_time_ids'][index], dtype=np.long)
answers_arr = data['answers_arr'][index]
answers_single = random.choice(answers_arr)
# answers_khot = self.toOneHot(answers_arr, self.answer_vec_size)
tokenized_question = data['tokenized_question'][index]
entity_mask = data['entity_mask'][index]
head = data['head'][index]
tail = data['tail'][index]
tail2 = data['tail2'][index]
time = data['time'][index]
start_time = data['start_time'][index]
end_time = data['end_time'][index]
return question_text, tokenized_question, entity_time_ids, entity_mask, head, tail, time, start_time, end_time, tail2, answers_single
def pad_for_batch(self, to_pad, padding_val, dtype=np.long):
padded = np.ones([len(to_pad), len(max(to_pad, key=lambda x: len(x)))], dtype=dtype) * padding_val
for i, j in enumerate(to_pad):
padded[i][0:len(j)] = j
return padded
# do this before padding for batch
def get_attention_mask(self, tokenized):
# first make zeros array of appropriate size
mask = np.zeros([len(tokenized), len(max(tokenized, key=lambda x: len(x)))], dtype=np.long)
# now set ones everywhere needed
for i, j in enumerate(tokenized):
mask[i][0:len(j)] = np.ones(len(j), dtype=np.long)
return mask
def _collate_fn(self, items):
# please don't tokenize again
# b = self.tokenizer(batch_sentences, padding=True, truncation=False, return_tensors="pt")
tokenized_questions = [item[1] for item in items]
attention_mask = torch.from_numpy(self.get_attention_mask(tokenized_questions))
input_ids = torch.from_numpy(self.pad_for_batch(tokenized_questions, self.tokenizer.pad_token_id, np.long))
entity_time_ids_list = [item[2] for item in items]
entity_time_ids_padded = self.pad_for_batch(entity_time_ids_list, self.padding_idx, np.long)
entity_time_ids_padded = torch.from_numpy(entity_time_ids_padded)
entity_mask = [item[3] for item in items] # 0 if entity, 1 if not
entity_mask_padded = self.pad_for_batch(entity_mask, 1.0,
np.float32) # doesnt matter probably cuz attention mask will be used. maybe pad with 1?
entity_mask_padded = torch.from_numpy(entity_mask_padded)
# can make foll mask in forward function using attention mask
# entity_time_ids_padded_mask = ~(attention_mask.bool())
heads = torch.from_numpy(np.array([item[4] for item in items]))
tails = torch.from_numpy(np.array([item[5] for item in items]))
times = torch.from_numpy(np.array([item[6] for item in items]))
start_times = torch.from_numpy(np.array([item[7] for item in items]))
end_times = torch.from_numpy(np.array([item[8] for item in items]))
tails2 = torch.from_numpy(np.array([item[9] for item in items]))
# answers_khot = torch.stack([item[4] for item in items])
answers_single = torch.from_numpy(np.array([item[10] for item in items]))
return input_ids, attention_mask, entity_time_ids_padded, entity_mask_padded, heads, tails, times, start_times, end_times, tails2, answers_single