/
constrained_seq2seq_reader.py
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/
constrained_seq2seq_reader.py
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import re
import numpy as np
from typing import Dict, Optional, List, Set
from collections import defaultdict
import logging
import json
from overrides import overrides
from allennlp.data.tokenizers.word_tokenizer import SpacyWordSplitter
from allennlp.common.file_utils import cached_path
from allennlp.common.util import START_SYMBOL, END_SYMBOL
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import TextField, ListField, MetadataField, ArrayField
from allennlp.data.instance import Instance
from allennlp.data.tokenizers import Token, Tokenizer
from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer
from entity_linking.value_extractor import GrailQA_Value_Extractor
from utils.search_over_graphs import generate_all_logical_forms_alpha, generate_all_logical_forms_2, \
get_vocab_info_online, generate_all_logical_forms_for_literal
logger = logging.getLogger(__name__)
@DatasetReader.register("cons_seq2seq")
class Constrained_Seq2SeqDatasetReader(DatasetReader):
"""
Read a tsv file containing paired sequences, and create a dataset suitable for a
``ComposedSeq2Seq`` model, or any model with a matching API.
Expected format for each input line: <source_sequence_string>\t<target_sequence_string>
The output of ``read`` is a list of ``Instance`` s with the fields:
source_tokens : ``TextField`` and
target_tokens : ``TextField``
`START_SYMBOL` and `END_SYMBOL` tokens are added to the source and target sequences.
# Parameters
source_tokenizer : ``Tokenizer``, optional
Tokenizer to use to split the input sequences into words or other kinds of tokens. Defaults
to ``SpacyTokenizer()``.
target_tokenizer : ``Tokenizer``, optional
Tokenizer to use to split the output sequences (during training) into words or other kinds
of tokens. Defaults to ``source_tokenizer``.
source_token_indexers : ``Dict[str, TokenIndexer]``, optional
Indexers used to define input (source side) token representations. Defaults to
``{"tokens": SingleIdTokenIndexer()}``.
target_token_indexers : ``Dict[str, TokenIndexer]``, optional
Indexers used to define output (target side) token representations. Defaults to
``source_token_indexers``.
source_add_start_token : bool, (optional, default=True)
Whether or not to add `START_SYMBOL` to the beginning of the source sequence.
source_add_end_token : bool, (optional, default=True)
Whether or not to add `END_SYMBOL` to the end of the source sequence.
delimiter : str, (optional, default="\t")
Set delimiter for tsv/csv file.
"""
def __init__(
self,
source_tokenizer: Tokenizer = None,
target_tokenizer: Tokenizer = None,
source_token_indexers: Dict[str, TokenIndexer] = None,
target_token_indexers: Dict[str, TokenIndexer] = None,
source_add_start_token: bool = True,
source_add_end_token: bool = True,
delimiter: str = "\t",
source_max_tokens: Optional[int] = None,
target_max_tokens: Optional[int] = None,
lazy: bool = False,
offline: bool = True,
training: bool = True,
perfect_entity_linking: bool = True,
constrained_vocab=None,
ranking_mode: bool = False, # need to be consistent with the model
use_constrained_vocab: bool = False, # need to be consistent with the model
) -> None:
super().__init__(lazy)
self._source_tokenizer = source_tokenizer or SpacyWordSplitter()
self._target_tokenizer = target_tokenizer or (lambda x: x.replace('(', ' ( ').replace(')', ' ) ').split())
self._source_token_indexers = source_token_indexers or {"tokens": SingleIdTokenIndexer()}
self._target_token_indexers = target_token_indexers
self._source_add_start_token = source_add_start_token
self._source_add_end_token = source_add_end_token
self._delimiter = delimiter
self._source_max_tokens = source_max_tokens
self._target_max_tokens = target_max_tokens
self._source_max_exceeded = 0
self._target_max_exceeded = 0
self._training = training
self._offline = offline
self._perfect_el = perfect_entity_linking
if not self._perfect_el:
self.el_results = json.load(open("entity_linking/grailqa_el.json"))
self.extractor = GrailQA_Value_Extractor()
self._constrained_vocab = constrained_vocab or '1_step'
# possible choices: {1_step, 2_step, cheating, domain, mix}
self._ranking_mode = ranking_mode
self._use_constrained_vocab = use_constrained_vocab
self._uncovered_count = defaultdict(lambda: 0)
@overrides
def _read(self, file_path: str):
# with open('cache/entity_names.json', 'r') as f:
# self._entity_names = json.load(f)
if self._ranking_mode:
with open('ontology/domain_info', 'r') as f:
self._constants_to_domain = defaultdict(lambda: None)
self._constants_to_domain.update(json.load(f))
if self._use_constrained_vocab:
if self._constrained_vocab == '1_step':
with open('cache/1hop_vocab', 'r') as f:
self._vocab_info = json.load(f)
if self._constrained_vocab == '2_step':
with open('cache/2hop_vocab', 'r') as f:
self._vocab_info = json.load(f)
if self._constrained_vocab == 'domain':
with open('ontology/domain_dict', 'r') as f:
self._domain_dict = json.load(f)
with open('ontology/domain_info', 'r') as f:
self._constants_to_domain = defaultdict(lambda: None)
self._constants_to_domain.update(json.load(f))
if self._constrained_vocab in ['mix1', 'mix2']:
if self._constrained_vocab == 'mix1':
with open('cache/1hop_vocab', 'r') as f:
self._vocab_info = json.load(f)
elif self._constrained_vocab == 'mix2':
with open('cache/2hop_vocab', 'r') as f:
self._vocab_info = json.load(f)
with open('ontology/domain_dict', 'r') as f:
self._domain_dict = json.load(f)
with open('ontology/domain_info', 'r') as f:
self._constants_to_domain = defaultdict(lambda: None)
self._constants_to_domain.update(json.load(f))
with open('ontology/domain_info', 'r') as f:
self._schema_constants = set(json.load(f).keys())
# Reset exceeded counts
self._source_max_exceeded = 0
self._target_max_exceeded = 0
with open(cached_path(file_path), 'r') as data_file:
logger.info("Reading instances from lines in file at: %s", file_path)
file_contents = json.load(data_file)
for item in file_contents:
if self._perfect_el:
entities = set()
entity_map = {}
for node in item['graph_query']['nodes']:
if node['node_type'] == 'entity':
entities.add(node['id'])
entity_map[node['id']] = ' '.join(
node['friendly_name'].split()[:5])
literals = set()
for node in item['graph_query']['nodes']:
if node['node_type'] == 'literal' and node['function'] not in ['argmin', 'argmax']:
literals.add(node['id'])
else:
entity_map = self.el_results[item['qid']]['entities']
entities = set(entity_map.keys())
for k in entity_map:
v = entity_map[k]['friendly_name']
entity_map[k] = ' '.join(v.split()[:5])
literals = set()
mentions = self.extractor.detect_mentions(item['question'])
for m in mentions:
literals.add(self.extractor.process_literal(m))
if self._ranking_mode:
logical_forms = []
if len(entities) > 0:
if self._perfect_el:
logical_forms.extend(generate_all_logical_forms_alpha(list(entities)[0],
offline=self._offline)) # use no domain info
logical_forms.extend(generate_all_logical_forms_2(list(entities)[0], offline=self._offline))
else:
for entity in entities:
logical_forms.extend(generate_all_logical_forms_alpha(entity, offline=self._offline))
logical_forms.extend(generate_all_logical_forms_2(entity, offline=self._offline))
for literal in literals:
logical_forms.extend(
generate_all_logical_forms_for_literal(literal))
if len(logical_forms) > 0:
yield self.text_to_instance(item, entity_map, literals, logical_forms)
else:
new_instance = self.text_to_instance(item, entity_map, literals)
if new_instance:
yield new_instance
if self._source_max_tokens and self._source_max_exceeded:
logger.info(
"In %d instances, the source token length exceeded the max limit (%d) and were truncated.",
self._source_max_exceeded,
self._source_max_tokens,
)
if self._target_max_tokens and self._target_max_exceeded:
logger.info(
"In %d instances, the target token length exceeded the max limit (%d) and were truncated.",
self._target_max_exceeded,
self._target_max_tokens,
)
@overrides
def text_to_instance(self,
item: Dict,
entity_map: Dict,
literals: Set,
logical_forms: List = None) -> Instance: # type: ignore
qid = MetadataField(item['qid'])
source_string = item['question'].lower()
if 's_expression' in item:
target_string = item['s_expression']
else:
target_string = None
values = [] # entities and literals in a question. Entities and values are not supposed to be put in vocab
for e in entity_map:
values.append(e)
for k in entity_map:
entity_map[k] = entity_map[k].lower()
for l in literals:
values.append(l)
if len(values) == 0:
values.append('placeholder') # TODO: this is an expedient way to avoid the non-value bug
if self._ranking_mode:
lfs = []
candidates_value_indices = []
for lf in logical_forms:
# The correct answer will be put in the first position later for training
if lf == target_string and self._training:
continue
tokenized_lf = [Token(x) for x in self._target_tokenizer(lf)]
if self._target_max_tokens and len(tokenized_lf) > self._target_max_tokens:
self._target_max_exceeded += 1
tokenized_lf = tokenized_lf[: self._target_max_tokens]
tokenized_lf.insert(0, Token(START_SYMBOL))
tokenized_lf.append(Token(END_SYMBOL))
lf_field = TextField(tokenized_lf, self._target_token_indexers)
lfs.append(lf_field)
candidate_value_indices = []
for token in tokenized_lf:
try: # token is literal or entity
candidate_value_indices.append(values.index(token.text))
except ValueError:
candidate_value_indices.append(-1)
candidates_value_indices.append(ArrayField(np.array(candidate_value_indices), padding_value=-1))
if self._training:
tokenized_lf = [Token(x) for x in self._target_tokenizer(target_string)]
if self._target_max_tokens and len(tokenized_lf) > self._target_max_tokens:
self._target_max_exceeded += 1
tokenized_lf = tokenized_lf[: self._target_max_tokens]
tokenized_lf.insert(0, Token(START_SYMBOL))
tokenized_lf.append(Token(END_SYMBOL))
lf_field = TextField(tokenized_lf, self._target_token_indexers)
candidate_value_indices = []
lfs.insert(0, lf_field) # for training, always put the correct answer in the first position
for token in tokenized_lf:
try: # token is literal or entity
candidate_value_indices.append(values.index(token.text))
except ValueError:
candidate_value_indices.append(-1)
candidates_value_indices.insert(0, ArrayField(np.array(candidate_value_indices), padding_value=-1))
candidates = ListField(lfs)
candidates_value_indices = ListField(candidates_value_indices)
# tokenized_source = self._source_tokenizer.tokenize(source_string) # for bert
# tokenized_source = [Token(x) for x in self._source_tokenizer(source_string)]
tokenized_source = [x for x in self._source_tokenizer.split_words(source_string)]
if self._source_max_tokens and len(tokenized_source) > self._source_max_tokens:
self._source_max_exceeded += 1
tokenized_source = tokenized_source[: self._source_max_tokens]
if self._source_add_start_token:
tokenized_source.insert(0, Token(START_SYMBOL))
if self._source_add_end_token:
tokenized_source.append(Token(END_SYMBOL))
source_field = TextField(tokenized_source, self._source_token_indexers)
if self._use_constrained_vocab:
if self._training:
constrained_vocab = self._get_constrained_vocab(entity_map, item['s_expression'], item['domains'])
else:
constrained_vocab = self._get_constrained_vocab(entity_map)
else:
constrained_vocab = MetadataField(None)
instance_dict = {"source_tokens": source_field,
"constrained_vocab": constrained_vocab,
"values": MetadataField(values),
"ids": qid,
"entity_name": MetadataField(entity_map)}
if 'answer' in item:
answer = []
for a in item['answer']:
answer.append(a['answer_argument'])
instance_dict['answer'] = MetadataField(answer)
if target_string is not None:
tokenized_target = [Token(x) for x in self._target_tokenizer(target_string)]
if self._target_max_tokens and len(tokenized_target) > self._target_max_tokens:
self._target_max_exceeded += 1
tokenized_target = tokenized_target[: self._target_max_tokens]
tokenized_target.insert(0, Token(START_SYMBOL))
tokenized_target.append(Token(END_SYMBOL))
# print(len(tokenized_target), len(set(tokenized_target).intersection(constrained_vocab)))
# print(set(tokenized_target).difference(constrained_vocab))
target_field = TextField(tokenized_target, self._target_token_indexers)
value_indices = []
for token in target_field:
try: # token is literal or entity
value_indices.append(values.index(token.text))
except ValueError:
value_indices.append(-1)
instance_dict["value_indices"] = ArrayField(np.array(value_indices), padding_value=-1)
def get_target_words(schema_item: str) -> List[str]:
if schema_item in entity_map:
schema_item = entity_map[schema_item]
return re.split('[._ ]', schema_item)[:5]
return re.split('[._ ]', schema_item)
target_words = [Token(word) for x in self._target_tokenizer(target_string)
for word in get_target_words(x)]
target_words.append(Token(START_SYMBOL))
target_words.append(Token(END_SYMBOL))
# The target words field is only used to construct the vocabulary for words in schema items
target_words_field = TextField(target_words,
{"tokens": SingleIdTokenIndexer(namespace='tgt_words')})
instance_dict['target_tokens'] = target_field
instance_dict['target_words'] = target_words_field
if self._ranking_mode:
instance_dict['candidates'] = candidates
instance_dict['candidates_value_indices'] = candidates_value_indices
else:
if self._ranking_mode:
instance_dict['candidates'] = candidates
instance_dict['candidates_value_indices'] = candidates_value_indices
return Instance(instance_dict)
def _get_vocab_info(self, entity):
if self._offline:
if entity in self._vocab_info:
return self._vocab_info[entity]
else:
return get_vocab_info_online(entity)
else:
return get_vocab_info_online(entity)
def _get_constrained_vocab(self,
entity_map,
s_expression=None,
domains=None):
if self._constrained_vocab in ['1_step', '2_step']:
vocab = {'(', ')', 'JOIN', 'AND', 'R', 'ARGMAX', 'ARGMIN', 'COUNT', 'ge', 'gt', 'le', 'lt'}
flag = False
for e in entity_map:
# vocab.update(self._get_vocab_info(e))
vocab.update(set(self._get_vocab_info(e)).intersection(self._schema_constants))
flag = True
if not flag: # no entity:
vocab.update(self._schema_constants)
vocab = [Token(x) for x in vocab]
vocab.append(Token(END_SYMBOL))
if self._training:
vocab.extend([Token(x) for x in self._target_tokenizer(s_expression)])
vocab = list(set(vocab))
return TextField(vocab, self._target_token_indexers)
elif self._constrained_vocab in ['mix1', 'mix2']: # only used for ideal experiments
assert self._training
vocab = set()
for domain in domains:
vocab.update(self._domain_dict[domain])
vocab_hop = set()
flag = False
for e in entity_map:
# vocab_hop.update(self._get_vocab_info(e))
vocab_hop.update(set(self._get_vocab_info(e)).intersection(self._schema_constants))
flag = True
if not flag: # no entity:
vocab.update(self._schema_constants)
vocab = vocab.intersection(vocab_hop)
vocab.update({'(', ')', 'JOIN', 'AND', 'R', 'ARGMAX', 'ARGMIN', 'COUNT', 'ge', 'gt', 'le', 'lt'})
vocab = [Token(x) for x in vocab]
vocab.append(Token(END_SYMBOL))
if self._training:
vocab.extend([Token(x) for x in self._target_tokenizer(s_expression)])
vocab = list(set(vocab))
return TextField(vocab, self._target_token_indexers)
else:
raise Exception('_constrained_vocab must be one of 1_step, 2_step, cheating, '
'but received {}'.format(self._constrained_vocab))