def _json_blob_to_instance(self, json_obj: JsonDict) -> Instance:
question_tokens = self._read_tokens_from_json_list(
json_obj['question_tokens'])
question_field = TextField(question_tokens,
self._question_token_indexers)
question_metadata = MetadataField(
{"question_tokens": [x.text for x in question_tokens]})
table_knowledge_graph = TableQuestionKnowledgeGraph.read_from_lines(
json_obj['table_lines'], question_tokens)
entity_tokens = [
self._read_tokens_from_json_list(token_list)
for token_list in json_obj['entity_texts']
]
table_field = KnowledgeGraphField(
table_knowledge_graph,
question_tokens,
tokenizer=None,
token_indexers=self._table_token_indexers,
entity_tokens=entity_tokens,
linking_features=json_obj['linking_features'],
include_in_vocab=self._use_table_for_vocab,
max_table_tokens=self._max_table_tokens)
world = WikiTablesWorld(table_knowledge_graph)
world_field = MetadataField(world)
production_rule_fields: List[Field] = []
for production_rule in world.all_possible_actions():
_, rule_right_side = production_rule.split(' -> ')
is_global_rule = not world.is_table_entity(rule_right_side)
field = ProductionRuleField(production_rule, is_global_rule)
production_rule_fields.append(field)
action_field = ListField(production_rule_fields)
example_string_field = MetadataField(json_obj['example_lisp_string'])
fields = {
'question': question_field,
'metadata': question_metadata,
'table': table_field,
'world': world_field,
'actions': action_field,
'example_lisp_string': example_string_field
}
if 'target_action_sequences' in json_obj or 'agenda' in json_obj:
action_map = {
action.rule: i
for i, action in enumerate(action_field.field_list)
} # type: ignore
if 'target_action_sequences' in json_obj:
action_sequence_fields: List[Field] = []
for sequence in json_obj['target_action_sequences']:
index_fields: List[Field] = []
for production_rule in sequence:
index_fields.append(
IndexField(action_map[production_rule], action_field))
action_sequence_fields.append(ListField(index_fields))
fields['target_action_sequences'] = ListField(
action_sequence_fields)
if 'agenda' in json_obj:
agenda_index_fields: List[Field] = []
for agenda_action in json_obj['agenda']:
agenda_index_fields.append(
IndexField(action_map[agenda_action], action_field))
fields['agenda'] = ListField(agenda_index_fields)
return Instance(fields)
def text_to_instance(
self, # type: ignore
question: str,
table_lines: List[str],
example_lisp_string: str = None,
dpd_output: List[str] = None,
tokenized_question: List[Token] = None) -> Instance:
"""
Reads text inputs and makes an instance. WikitableQuestions dataset provides tables as TSV
files, which we use for training.
Parameters
----------
question : ``str``
Input question
table_lines : ``List[str]``
The table content itself, as a list of rows. See
``TableQuestionKnowledgeGraph.read_from_lines`` for the expected format.
example_lisp_string : ``str``, optional
The original (lisp-formatted) example string in the WikiTableQuestions dataset. This
comes directly from the ``.examples`` file provided with the dataset. We pass this to
SEMPRE for evaluating logical forms during training. It isn't otherwise used for
anything.
dpd_output : List[str], optional
List of logical forms, produced by dynamic programming on denotations. Not required
during test.
tokenized_question : ``List[Token]``, optional
If you have already tokenized the question, you can pass that in here, so we don't
duplicate that work. You might, for example, do batch processing on the questions in
the whole dataset, then pass the result in here.
"""
# pylint: disable=arguments-differ
tokenized_question = tokenized_question or self._tokenizer.tokenize(
question.lower())
question_field = TextField(tokenized_question,
self._question_token_indexers)
metadata: Dict[str, Any] = {
"question_tokens": [x.text for x in tokenized_question]
}
metadata["original_table"] = "".join(table_lines)
table_knowledge_graph = TableQuestionKnowledgeGraph.read_from_lines(
table_lines, tokenized_question)
table_metadata = MetadataField(table_lines)
table_field = KnowledgeGraphField(
table_knowledge_graph,
tokenized_question,
self._table_token_indexers,
tokenizer=self._tokenizer,
feature_extractors=self._linking_feature_extractors,
include_in_vocab=self._use_table_for_vocab,
max_table_tokens=self._max_table_tokens)
world = WikiTablesWorld(table_knowledge_graph)
world_field = MetadataField(world)
production_rule_fields: List[Field] = []
for production_rule in world.all_possible_actions():
_, rule_right_side = production_rule.split(' -> ')
is_global_rule = not world.is_table_entity(rule_right_side)
field = ProductionRuleField(production_rule, is_global_rule)
production_rule_fields.append(field)
action_field = ListField(production_rule_fields)
fields = {
'question': question_field,
'metadata': MetadataField(metadata),
'table': table_field,
'world': world_field,
'actions': action_field
}
if self._include_table_metadata:
fields['table_metadata'] = table_metadata
if example_lisp_string:
fields['example_lisp_string'] = MetadataField(example_lisp_string)
# We'll make each target action sequence a List[IndexField], where the index is into
# the action list we made above. We need to ignore the type here because mypy doesn't
# like `action.rule` - it's hard to tell mypy that the ListField is made up of
# ProductionRuleFields.
action_map = {
action.rule: i
for i, action in enumerate(action_field.field_list)
} # type: ignore
if dpd_output:
action_sequence_fields: List[Field] = []
for logical_form in dpd_output:
if not self._should_keep_logical_form(logical_form):
logger.debug(f'Question was: {question}')
logger.debug(f'Table info was: {table_lines}')
continue
try:
expression = world.parse_logical_form(logical_form)
except ParsingError as error:
logger.debug(
f'Parsing error: {error.message}, skipping logical form'
)
logger.debug(f'Question was: {question}')
logger.debug(f'Logical form was: {logical_form}')
logger.debug(f'Table info was: {table_lines}')
continue
except:
logger.error(logical_form)
raise
action_sequence = world.get_action_sequence(expression)
try:
index_fields: List[Field] = []
for production_rule in action_sequence:
index_fields.append(
IndexField(action_map[production_rule],
action_field))
action_sequence_fields.append(ListField(index_fields))
except KeyError as error:
logger.debug(
f'Missing production rule: {error.args}, skipping logical form'
)
logger.debug(f'Question was: {question}')
logger.debug(f'Table info was: {table_lines}')
logger.debug(f'Logical form was: {logical_form}')
continue
if len(action_sequence_fields) >= self._max_dpd_logical_forms:
break
if not action_sequence_fields:
# This is not great, but we're only doing it when we're passed logical form
# supervision, so we're expecting labeled logical forms, but we can't actually
# produce the logical forms. We should skip this instance. Note that this affects
# _dev_ and _test_ instances, too, so your metrics could be over-estimates on the
# full test data.
return None
fields['target_action_sequences'] = ListField(
action_sequence_fields)
if self._output_agendas:
agenda_index_fields: List[Field] = []
for agenda_string in world.get_agenda():
agenda_index_fields.append(
IndexField(action_map[agenda_string], action_field))
if not agenda_index_fields:
agenda_index_fields = [IndexField(-1, action_field)]
fields['agenda'] = ListField(agenda_index_fields)
return Instance(fields)