def search(tables_directory: str,
input_examples_file: str,
output_file: str,
max_path_length: int,
max_num_logical_forms: int,
use_agenda: bool) -> None:
data = [wikitables_util.parse_example_line(example_line) for example_line in
open(input_examples_file)]
tokenizer = WordTokenizer()
with open(output_file, "w") as output_file_pointer:
for instance_data in data:
utterance = instance_data["question"]
question_id = instance_data["id"]
if utterance.startswith('"') and utterance.endswith('"'):
utterance = utterance[1:-1]
# For example: csv/200-csv/47.csv -> tagged/200-tagged/47.tagged
table_file = instance_data["table_filename"].replace("csv", "tagged")
# pylint: disable=protected-access
target_list = [TableQuestionContext._normalize_string(value) for value in
instance_data["target_values"]]
try:
target_value_list = evaluator.to_value_list(target_list)
except:
print(target_list)
target_value_list = evaluator.to_value_list(target_list)
tokenized_question = tokenizer.tokenize(utterance)
table_file = f"{tables_directory}/{table_file}"
context = TableQuestionContext.read_from_file(table_file, tokenized_question)
world = WikiTablesVariableFreeWorld(context)
walker = ActionSpaceWalker(world, max_path_length=max_path_length)
correct_logical_forms = []
print(f"{question_id} {utterance}", file=output_file_pointer)
if use_agenda:
agenda = world.get_agenda()
print(f"Agenda: {agenda}", file=output_file_pointer)
all_logical_forms = walker.get_logical_forms_with_agenda(agenda=agenda,
max_num_logical_forms=10000)
else:
all_logical_forms = walker.get_all_logical_forms(max_num_logical_forms=10000)
for logical_form in all_logical_forms:
try:
denotation = world.execute(logical_form)
except ExecutionError:
print(f"Failed to execute: {logical_form}", file=sys.stderr)
continue
if isinstance(denotation, list):
denotation_list = [str(denotation_item) for denotation_item in denotation]
else:
# For numbers and dates
denotation_list = [str(denotation)]
denotation_value_list = evaluator.to_value_list(denotation_list)
if evaluator.check_denotation(target_value_list, denotation_value_list):
correct_logical_forms.append(logical_form)
if not correct_logical_forms:
print("NO LOGICAL FORMS FOUND!", file=output_file_pointer)
for logical_form in correct_logical_forms[:max_num_logical_forms]:
print(logical_form, file=output_file_pointer)
print(file=output_file_pointer)
def search(tables_directory: str, input_examples_file: str, output_path: str,
max_path_length: int, max_num_logical_forms: int, use_agenda: bool,
output_separate_files: bool) -> None:
data = [
wikitables_util.parse_example_line(example_line)
for example_line in open(input_examples_file)
]
tokenizer = WordTokenizer()
if output_separate_files and not os.path.exists(output_path):
os.makedirs(output_path)
if not output_separate_files:
output_file_pointer = open(output_path, "w")
for instance_data in data:
utterance = instance_data["question"]
question_id = instance_data["id"]
if utterance.startswith('"') and utterance.endswith('"'):
utterance = utterance[1:-1]
# For example: csv/200-csv/47.csv -> tagged/200-tagged/47.tagged
table_file = instance_data["table_filename"].replace("csv", "tagged")
target_list = instance_data["target_values"]
tokenized_question = tokenizer.tokenize(utterance)
table_file = f"{tables_directory}/{table_file}"
context = TableQuestionContext.read_from_file(table_file,
tokenized_question)
world = WikiTablesVariableFreeWorld(context)
walker = ActionSpaceWalker(world, max_path_length=max_path_length)
correct_logical_forms = []
if use_agenda:
agenda = world.get_agenda()
all_logical_forms = walker.get_logical_forms_with_agenda(
agenda=agenda,
max_num_logical_forms=10000,
allow_partial_match=True)
else:
all_logical_forms = walker.get_all_logical_forms(
max_num_logical_forms=10000)
for logical_form in all_logical_forms:
if world.evaluate_logical_form(logical_form, target_list):
correct_logical_forms.append(logical_form)
if output_separate_files and correct_logical_forms:
with gzip.open(f"{output_path}/{question_id}.gz",
"wt") as output_file_pointer:
for logical_form in correct_logical_forms:
print(logical_form, file=output_file_pointer)
elif not output_separate_files:
print(f"{question_id} {utterance}", file=output_file_pointer)
if use_agenda:
print(f"Agenda: {agenda}", file=output_file_pointer)
if not correct_logical_forms:
print("NO LOGICAL FORMS FOUND!", file=output_file_pointer)
for logical_form in correct_logical_forms[:max_num_logical_forms]:
print(logical_form, file=output_file_pointer)
print(file=output_file_pointer)
if not output_separate_files:
output_file_pointer.close()
def search(tables_directory: str,
input_examples_file: str,
output_path: str,
max_path_length: int,
max_num_logical_forms: int,
use_agenda: bool,
output_separate_files: bool) -> None:
data = [wikitables_util.parse_example_line(example_line) for example_line in
open(input_examples_file)]
tokenizer = WordTokenizer()
if output_separate_files and not os.path.exists(output_path):
os.makedirs(output_path)
if not output_separate_files:
output_file_pointer = open(output_path, "w")
for instance_data in data:
utterance = instance_data["question"]
question_id = instance_data["id"]
if utterance.startswith('"') and utterance.endswith('"'):
utterance = utterance[1:-1]
# For example: csv/200-csv/47.csv -> tagged/200-tagged/47.tagged
table_file = instance_data["table_filename"].replace("csv", "tagged")
target_list = instance_data["target_values"]
tokenized_question = tokenizer.tokenize(utterance)
table_file = f"{tables_directory}/{table_file}"
context = TableQuestionContext.read_from_file(table_file, tokenized_question)
world = WikiTablesVariableFreeWorld(context)
walker = ActionSpaceWalker(world, max_path_length=max_path_length)
correct_logical_forms = []
if use_agenda:
agenda = world.get_agenda()
all_logical_forms = walker.get_logical_forms_with_agenda(agenda=agenda,
max_num_logical_forms=10000)
else:
all_logical_forms = walker.get_all_logical_forms(max_num_logical_forms=10000)
for logical_form in all_logical_forms:
if world.evaluate_logical_form(logical_form, target_list):
correct_logical_forms.append(logical_form)
if output_separate_files and correct_logical_forms:
with gzip.open(f"{output_path}/{question_id}.gz", "wt") as output_file_pointer:
for logical_form in correct_logical_forms:
print(logical_form, file=output_file_pointer)
elif not output_separate_files:
print(f"{question_id} {utterance}", file=output_file_pointer)
if use_agenda:
print(f"Agenda: {agenda}", file=output_file_pointer)
if not correct_logical_forms:
print("NO LOGICAL FORMS FOUND!", file=output_file_pointer)
for logical_form in correct_logical_forms[:max_num_logical_forms]:
print(logical_form, file=output_file_pointer)
print(file=output_file_pointer)
if not output_separate_files:
output_file_pointer.close()
def text_to_instance(
self, # type: ignore
question: str,
table_lines: List[List[str]],
target_values: List[str],
offline_search_output: List[str] = None) -> Instance:
"""
Reads text inputs and makes an instance. WikitableQuestions dataset provides tables as
TSV files pre-tagged using CoreNLP, which we use for training.
Parameters
----------
question : ``str``
Input question
table_lines : ``List[List[str]]``
The table content preprocessed by CoreNLP. See ``TableQuestionContext.read_from_lines``
for the expected format.
target_values : ``List[str]``
offline_search_output : List[str], optional
List of logical forms, produced by offline search. Not required during test.
"""
# pylint: disable=arguments-differ
tokenized_question = self._tokenizer.tokenize(question.lower())
question_field = TextField(tokenized_question,
self._question_token_indexers)
# TODO(pradeep): We'll need a better way to input CoreNLP processed lines.
table_context = TableQuestionContext.read_from_lines(
table_lines, tokenized_question)
target_values_field = MetadataField(target_values)
world = WikiTablesVariableFreeWorld(table_context)
world_field = MetadataField(world)
# Note: Not passing any featre extractors when instantiating the field below. This will make
# it use all the available extractors.
table_field = KnowledgeGraphField(
table_context.get_table_knowledge_graph(),
tokenized_question,
self._table_token_indexers,
tokenizer=self._tokenizer,
include_in_vocab=self._use_table_for_vocab,
max_table_tokens=self._max_table_tokens)
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_instance_specific_entity(
rule_right_side)
field = ProductionRuleField(production_rule,
is_global_rule=is_global_rule)
production_rule_fields.append(field)
action_field = ListField(production_rule_fields)
fields = {
'question': question_field,
'table': table_field,
'world': world_field,
'actions': action_field,
'target_values': target_values_field
}
# 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 offline_search_output:
action_sequence_fields: List[Field] = []
for logical_form in offline_search_output:
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_offline_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)
