def test_knowledge_graph_has_correct_neighbors(self):
question = "when was the attendance greater than 5000?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/wikitables/sample_table.tagged'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
knowledge_graph = table_question_context.get_table_knowledge_graph()
neighbors = knowledge_graph.neighbors
# '5000' is neighbors with number and date columns. '-1' is in entities because there is a
# date column, which is its only neighbor.
assert set(neighbors.keys()) == {
'date_column:year', 'number_column:year', 'string_column:year',
'number_column:division', 'string_column:division',
'string_column:league', 'string_column:regular_season',
'number_column:regular_season', 'string_column:playoffs',
'string_column:open_cup', 'number_column:open_cup',
'string_column:avg_attendance', 'number_column:avg_attendance',
'5000', '-1'
}
assert set(neighbors['date_column:year']) == {'5000', '-1'}
assert neighbors['number_column:division'] == ['5000']
assert neighbors['string_column:league'] == []
assert neighbors['string_column:regular_season'] == []
assert neighbors['string_column:playoffs'] == []
assert neighbors['string_column:open_cup'] == []
assert neighbors['number_column:avg_attendance'] == ['5000']
assert set(neighbors['5000']) == {
'date_column:year', 'number_column:division',
'number_column:avg_attendance', 'number_column:regular_season',
'number_column:year', 'number_column:open_cup'
}
assert neighbors['-1'] == ['date_column:year']
def test_rank_number_extraction(self):
question = "what was the first tamil-language film in 1943?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-1.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
_, numbers = table_question_context.get_entities_from_question()
assert numbers == [("1", 3), ('1943', 9)]
def test_entity_extraction_from_question_with_quotes(self):
question = "how many times does \"friendly\" appear in the competition column?"
question_tokens = self.tokenizer.tokenize(question)
test_file = 'fixtures/data/wikitables/tables/346.tagged'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
entities, _ = table_question_context.get_entities_from_question()
assert entities == [('string:friendly', ['string_column:competition'])]
def test_date_column_type_extraction_1(self):
question = "how many were elected?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-5.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
data = table_question_context.table_data[0]
assert "date_column:first_elected" in data
def test_multiword_entity_extraction(self):
question = "was the positioning better the year of the france venue or the year of the south korea venue?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-3.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
entities, _ = table_question_context.get_entities_from_question()
assert entities == [("string:france", ["string_column:venue"]),
("string:south_korea", ["string_column:venue"])]
def search(tables_directory: str, data: JsonDict, output_path: str,
max_path_length: int, max_num_logical_forms: int, use_agenda: bool,
output_separate_files: bool, conservative_agenda: bool) -> None:
print(f"Starting search with {len(data)} instances", file=sys.stderr)
executor_logger = logging.getLogger(
'weak_supervision.semparse.executors.wikitables_variable_free_executor'
)
executor_logger.setLevel(logging.ERROR)
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(conservative=conservative_agenda)
allow_partial_match = not conservative_agenda
all_logical_forms = walker.get_logical_forms_with_agenda(
agenda=agenda,
max_num_logical_forms=10000,
allow_partial_match=allow_partial_match)
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 setUp(self):
super().setUp()
question_tokens = [Token(x) for x in ['what', 'was', 'the', 'last', 'year', '2013', '?']]
self.table_file = self.FIXTURES_ROOT / 'data' / 'wikitables' / 'sample_table.tagged'
self.table_context = TableQuestionContext.read_from_file(self.table_file, question_tokens)
self.world_with_2013 = WikiTablesVariableFreeWorld(self.table_context)
usl_league_tokens = [Token(x) for x in ['what', 'was', 'the', 'last', 'year', 'with', 'usl',
'a', 'league', '?']]
self.world_with_usl_a_league = self._get_world_with_question_tokens(usl_league_tokens)
def test_number_comparison_works(self):
# TableQuestionContext normlaizes all strings according to some rules. We want to ensure
# that the original numerical values of number cells is being correctly processed here.
tokens = WordTokenizer().tokenize("when was the attendance the highest?")
tagged_file = self.FIXTURES_ROOT / "data" / "corenlp_processed_tables" / "TEST-2.table"
context = TableQuestionContext.read_from_file(tagged_file, tokens)
executor = WikiTablesVariableFreeExecutor(context.table_data)
result = executor.execute("(select_date (argmax all_rows number_column:attendance) date_column:date)")
assert result == Date(-1, 11, 10)
def test_date_extraction(self):
question = "how many laps did matt kenset complete on february 26, 2006."
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-8.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
_, number_entities = table_question_context.get_entities_from_question(
)
assert number_entities == [("2", 8), ("26", 9), ("2006", 11)]
def test_date_extraction_2(self):
question = """how many different players scored for the san jose earthquakes during their
1979 home opener against the timbers?"""
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-6.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
_, number_entities = table_question_context.get_entities_from_question(
)
assert number_entities == [("1979", 12)]
def test_number_extraction(self):
question = """how many players on the 191617 illinois fighting illini men's basketball team
had more than 100 points scored?"""
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-7.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
_, number_entities = table_question_context.get_entities_from_question(
)
assert number_entities == [("191617", 5), ("100", 16)]
def test_null_extraction(self):
question = "on what date did the eagles score the least points?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-2.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
entities, numbers = table_question_context.get_entities_from_question()
# "Eagles" does not appear in the table.
assert entities == []
assert numbers == []
def test_string_column_types_extraction(self):
question = "how many were elected?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-10.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
data = table_question_context.table_data[0]
assert "string_column:birthplace" in data
assert "string_column:advocate" in data
assert "string_column:notability" in data
assert "string_column:name" in data
def test_number_and_entity_extraction(self):
question = "other than m1 how many notations have 1 in them?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f"{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-11.table"
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
string_entities, number_entities = table_question_context.get_entities_from_question(
)
assert string_entities == [("string:m1", ["string_column:notation"]),
("string:1", ["string_column:position"])]
assert number_entities == [("1", 2), ("1", 7)]
def test_numerical_column_type_extraction(self):
question = """how many players on the 191617 illinois fighting illini men's basketball team
had more than 100 points scored?"""
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-7.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
data = table_question_context.table_data[0]
assert "number_column:games_played" in data
assert "number_column:field_goals" in data
assert "number_column:free_throws" in data
assert "number_column:points" in data
def test_get_knowledge_graph(self):
question = "other than m1 how many notations have 1 in them?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f"{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-11.table"
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
knowledge_graph = table_question_context.get_table_knowledge_graph()
entities = knowledge_graph.entities
# -1 is not in entities because there are no date columns in the table.
assert sorted(entities) == [
'1', 'number_column:notation', 'number_column:position',
'string:1', 'string:m1', 'string_column:mnemonic',
'string_column:notation', 'string_column:position',
'string_column:short_name', 'string_column:swara'
]
neighbors = knowledge_graph.neighbors
# Each number extracted from the question will have all number and date columns as
# neighbors. Each string entity extracted from the question will only have the corresponding
# column as the neighbor.
assert set(neighbors['1']) == {
'number_column:notation', 'number_column:position'
}
assert neighbors['string_column:mnemonic'] == []
assert neighbors['string_column:short_name'] == []
assert neighbors['string_column:swara'] == []
assert neighbors['number_column:position'] == ['1']
assert neighbors['number_column:notation'] == ['1']
assert neighbors['string_column:position'] == ['string:1']
assert neighbors['string:1'] == ['string_column:position']
assert neighbors['string:m1'] == ['string_column:notation']
assert neighbors['string_column:notation'] == ['string:m1']
entity_text = knowledge_graph.entity_text
assert entity_text == {
'1': '1',
'string:m1': 'm1',
'string:1': '1',
'number_column:notation': 'notation',
'string_column:notation': 'notation',
'string_column:mnemonic': 'mnemonic',
'string_column:short_name': 'short name',
'string_column:swara': 'swara',
'string_column:position': 'position',
'number_column:position': 'position'
}
def evaluate_logical_form(self, logical_form: str, target_list: List[str]) -> bool:
"""
Takes a logical form, and the list of target values as strings from the original lisp
string, and returns True iff the logical form executes to the target list.
"""
normalized_target_list = [TableQuestionContext.normalize_string(value) for value in
target_list]
target_value_list = evaluator.to_value_list(normalized_target_list)
try:
denotation = self.execute(logical_form)
except ExecutionError:
logger.warning(f'Failed to execute: {logical_form}')
return False
if isinstance(denotation, list):
denotation_list = [str(denotation_item) for denotation_item in denotation]
else:
if isinstance(denotation, Date):
target_list = [str(self._make_date(target)) for target in target_list]
denotation_list = [str(denotation)]
denotation_value_list = evaluator.to_value_list(denotation_list)
return evaluator.check_denotation(target_value_list, denotation_value_list)
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(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)
def __init__(self, table_context: TableQuestionContext) -> None:
super().__init__(constant_type_prefixes={
"string": types.STRING_TYPE,
"num": types.NUMBER_TYPE
},
global_type_signatures=types.COMMON_TYPE_SIGNATURE,
global_name_mapping=types.COMMON_NAME_MAPPING)
self.table_context = table_context
# We add name mapping and signatures corresponding to specific column types to the local
# name mapping based on the table content here.
column_types = table_context.column_types
self._table_has_string_columns = False
self._table_has_date_columns = False
self._table_has_number_columns = False
if "string" in column_types:
for name, translated_name in types.STRING_COLUMN_NAME_MAPPING.items(
):
signature = types.STRING_COLUMN_TYPE_SIGNATURE[translated_name]
self._add_name_mapping(name, translated_name, signature)
self._table_has_string_columns = True
if "date" in column_types:
for name, translated_name in types.DATE_COLUMN_NAME_MAPPING.items(
):
signature = types.DATE_COLUMN_TYPE_SIGNATURE[translated_name]
self._add_name_mapping(name, translated_name, signature)
# Adding -1 to mapping because we need it for dates where not all three fields are
# specified. We want to do this only when the table has a date column. This is because
# the knowledge graph is also constructed in such a way that -1 is an entity with date
# columns as the neighbors only if any date columns exist in the table.
self._map_name(f"num:-1", keep_mapping=True)
self._table_has_date_columns = True
if "number" in column_types or "num2" in column_types:
for name, translated_name in types.NUMBER_COLUMN_NAME_MAPPING.items(
):
signature = types.NUMBER_COLUMN_TYPE_SIGNATURE[translated_name]
self._add_name_mapping(name, translated_name, signature)
self._table_has_number_columns = True
if "date" in column_types or "number" in column_types or "num2" in column_types:
for name, translated_name in types.COMPARABLE_COLUMN_NAME_MAPPING.items(
):
signature = types.COMPARABLE_COLUMN_TYPE_SIGNATURE[
translated_name]
self._add_name_mapping(name, translated_name, signature)
self.table_graph = table_context.get_table_knowledge_graph()
self._executor = WikiTablesVariableFreeExecutor(
self.table_context.table_data)
# TODO (pradeep): Use a NameMapper for mapping entity names too.
# For every new column name seen, we update this counter to map it to a new NLTK name.
self._column_counter = 0
# Adding entities and numbers seen in questions to the mapping.
question_entities, question_numbers = table_context.get_entities_from_question(
)
self._question_entities = [entity for entity, _ in question_entities]
self._question_numbers = [number for number, _ in question_numbers]
for entity in self._question_entities:
# These entities all have prefix "string:"
self._map_name(entity, keep_mapping=True)
for number_in_question in self._question_numbers:
self._map_name(f"num:{number_in_question}", keep_mapping=True)
# Keeps track of column name productions so that we can add them to the agenda.
self._column_productions_for_agenda: Dict[str, str] = {}
# Adding column names to the local name mapping.
for column_name in table_context.table_data[0].keys():
self._map_name(column_name, keep_mapping=True)
self.terminal_productions: Dict[str, str] = {}
name_mapping = [(name, mapping)
for name, mapping in self.global_name_mapping.items()]
name_mapping += [(name, mapping)
for name, mapping in self.local_name_mapping.items()]
signatures = self.global_type_signatures.copy()
signatures.update(self.local_type_signatures)
for predicate, mapped_name in name_mapping:
if mapped_name in signatures:
signature = signatures[mapped_name]
self.terminal_productions[
predicate] = f"{signature} -> {predicate}"
# We don't need to recompute this ever; let's just compute it once and cache it.
self._valid_actions: Dict[str, List[str]] = None
def _get_world_with_question_tokens(self, tokens: List[Token]) -> WikiTablesVariableFreeWorld:
table_context = TableQuestionContext.read_from_file(self.table_file, tokens)
world = WikiTablesVariableFreeWorld(table_context)
return world
def test_table_data(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.tagged'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
assert table_question_context.table_data == [{
'date_column:year':
Date(2001, -1, -1),
'string_column:year':
'2001',
'number_column:year':
2001.0,
'number_column:division':
2.0,
'string_column:division':
'2',
'string_column:league':
'usl_a_league',
'string_column:regular_season':
'4th_western',
'number_column:regular_season':
4.0,
'string_column:playoffs':
'quarterfinals',
'string_column:open_cup':
'did_not_qualify',
'number_column:open_cup':
None,
'string_column:avg_attendance':
'7_169',
'number_column:avg_attendance':
7169.0
}, {
'date_column:year':
Date(2005, -1, -1),
'string_column:year':
'2005',
'number_column:year':
2005.0,
'number_column:division':
2.0,
'string_column:division':
'2',
'string_column:league':
'usl_first_division',
'string_column:regular_season':
'5th',
'number_column:regular_season':
5.0,
'string_column:playoffs':
'quarterfinals',
'string_column:open_cup':
'4th_round',
'number_column:open_cup':
4.0,
'string_column:avg_attendance':
'6_028',
'number_column:avg_attendance':
6028.0
}]
