def test_table_data_from_untagged_file(self):
question = "what was the attendance when usl a league played?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f"{self.FIXTURES_ROOT}/data/wikitables/sample_table.tsv"
table_lines = [line.strip() for line in open(test_file).readlines()]
table_question_context = TableQuestionContext.read_from_lines(
table_lines, question_tokens)
# The content in the table represented by the untagged file we are reading here is the same as the one we
# had in the tagged file above, except that we have a "Score" column instead of "Avg. Attendance" column,
# which is changed to test the num2 extraction logic. I've shown the values not being extracted here as
# well and commented them out.
assert table_question_context.table_data == [
{
"number_column:year": 2001.0,
# The value extraction logic we have for untagged lines does
# not extract this value as a date.
# 'date_column:year': Date(2001, -1, -1),
"string_column:year": "2001",
"number_column:division": 2.0,
"string_column:division": "2",
"string_column:league": "usl_a_league",
"string_column:regular_season": "4th_western",
# We only check for strings that are entirely numbers. So 4.0
# will not be extracted.
# 'number_column:regular_season': 4.0,
"string_column:playoffs": "quarterfinals",
"string_column:open_cup": "did_not_qualify",
# 'number_column:open_cup': None,
"number_column:score": 20.0,
"num2_column:score": 30.0,
"string_column:score": "20_30",
},
{
"number_column:year": 2005.0,
# 'date_column:year': Date(2005, -1, -1),
"string_column:year": "2005",
"number_column:division": 2.0,
"string_column:division": "2",
"string_column:league": "usl_first_division",
"string_column:regular_season": "5th",
# Same here as in the "division" column for the first row.
# 5.0 will not be extracted from "5th".
# 'number_column:regular_season': 5.0,
"string_column:playoffs": "quarterfinals",
"string_column:open_cup": "4th_round",
# 'number_column:open_cup': 4.0,
"number_column:score": 50.0,
"num2_column:score": 40.0,
"string_column:score": "50_40",
},
]
def text_to_instance(
self, # type: ignore
question: str,
table_lines: List[List[str]],
target_values: List[str] = None,
offline_search_output: List[str] = None,
) -> Instance:
"""
Reads text inputs and makes an instance. We pass the ``table_lines`` to ``TableQuestionContext``, and that
method accepts this field either as lines from CoreNLP processed tagged files that come with the dataset,
or simply in a tsv format where each line corresponds to a row and the cells are tab-separated.
Parameters
----------
question : ``str``
Input question
table_lines : ``List[List[str]]``
The table content optionally preprocessed by CoreNLP. See ``TableQuestionContext.read_from_lines``
for the expected format.
target_values : ``List[str]``, optional
Target values for the denotations the logical forms should execute to. Not required for testing.
offline_search_output : ``List[str]``, optional
List of logical forms, produced by offline search. Not required during test.
"""
tokenized_question = 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]
}
table_context = TableQuestionContext.read_from_lines(
table_lines, tokenized_question)
world = WikiTablesLanguage(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_productions():
_, 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,
"metadata": MetadataField(metadata),
"table": table_field,
"world": world_field,
"actions": action_field,
}
if target_values is not None:
target_values_field = MetadataField(target_values)
fields["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:
action_sequence = world.logical_form_to_action_sequence(
logical_form)
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 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 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
except: # noqa
logger.error(logical_form)
raise
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(conservative=True):
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)
