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),
'number_column:year': 2001.0,
'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',
'number_column:regular_season': 4.0,
'string_column:playoffs': 'quarterfinals',
'string_column:open_cup': 'did_not_qualify',
'number_column:open_cup': None,
'number_column:avg_attendance': 7169.0,
'string_column:avg_attendance': '7_169'},
{'date_column:year': Date(2005, -1, -1),
'number_column:year': 2005.0,
'string_column:year': '2005',
'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,
'number_column:avg_attendance': 6028.0,
'string_column:avg_attendance': '6_028'}]
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:division',
'string_column:league', 'string_column:regular_season',
'string_column:playoffs', 'string_column:open_cup',
'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'
}
assert neighbors['-1'] == ['date_column:year']
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 setUp(self):
super().setUp()
# Adding a bunch of random tokens in here so we get them as constants in the language.
question_tokens = [
Token(x)
for x in [
"what",
"was",
"the",
"last",
"year",
"2013",
"?",
"quarterfinals",
"a_league",
"2010",
"8000",
"did_not_qualify",
"2001",
"2",
"23",
"2005",
"1",
"2002",
"usl_a_league",
"usl_first_division",
]
]
self.table_file = self.FIXTURES_ROOT / "data" / "wikitables" / "sample_table.tagged"
self.table_context = TableQuestionContext.read_from_file(self.table_file, question_tokens)
self.language = WikiTablesLanguage(self.table_context)
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 setUp(self):
self.tokenizer = WordTokenizer(SpacyWordSplitter(pos_tags=True))
self.utterance = self.tokenizer.tokenize("where is mersin?")
self.token_indexers = {"tokens": SingleIdTokenIndexer("tokens")}
table_file = self.FIXTURES_ROOT / "data" / "wikitables" / "tables" / "341.tagged"
self.graph = TableQuestionContext.read_from_file(
table_file, self.utterance).get_table_knowledge_graph()
self.vocab = Vocabulary()
self.name_index = self.vocab.add_token_to_namespace("name",
namespace='tokens')
self.in_index = self.vocab.add_token_to_namespace("in",
namespace='tokens')
self.english_index = self.vocab.add_token_to_namespace(
"english", namespace='tokens')
self.location_index = self.vocab.add_token_to_namespace(
"location", namespace='tokens')
self.mersin_index = self.vocab.add_token_to_namespace(
"mersin", namespace='tokens')
self.oov_index = self.vocab.get_token_index('random OOV string',
namespace='tokens')
self.edirne_index = self.oov_index
self.field = KnowledgeGraphField(self.graph, self.utterance,
self.token_indexers, self.tokenizer)
super(KnowledgeGraphFieldTest, self).setUp()
def test_get_valid_actions_in_world_without_date_columns(self):
question_tokens = [Token(x) for x in ['what', 'was', 'the', 'first', 'title', '?']]
table_file = self.FIXTURES_ROOT / 'data' / 'corenlp_processed_tables' / 'TEST-4.table'
table_context = TableQuestionContext.read_from_file(table_file, question_tokens)
# The table does not have a date column.
assert "date" not in table_context.column_types.values()
world = WikiTablesVariableFreeWorld(table_context)
actions = world.get_valid_actions()
assert set(actions.keys()) == {
"<r,<g,s>>",
"<r,<f,<n,r>>>",
"<r,<c,r>>",
"<r,<g,r>>",
"<r,<r,<f,n>>>",
"<r,<t,<s,r>>>",
"<n,<n,<n,d>>>",
"<r,<f,n>>",
"<r,r>",
"<r,n>",
"d",
"n",
"s",
"t",
"f",
"r",
"@start@",
}
assert set([str(type_) for type_ in world.get_basic_types()]) == {'n', 'd', 's', 'r', 't',
'f', 'g', 'c'}
check_productions_match(actions['s'],
['[<r,<g,s>>, r, f]',
'[<r,<g,s>>, r, t]'])
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_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:position', 'string:m1',
'string_column:mnemonic', 'string_column:notation',
'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 neighbors == {'1': ['number_column:position'],
'string_column:mnemonic': [],
'string_column:short_name': [],
'string_column:swara': [],
'number_column:position': ['1'],
'string:m1': ['string_column:notation'],
'string_column:notation': ['string:m1']}
entity_text = knowledge_graph.entity_text
assert entity_text == {'1': '1',
'string:m1': 'm1',
'string_column:notation': 'notation',
'string_column:mnemonic': 'mnemonic',
'string_column:short_name': 'short name',
'string_column:swara': 'swara',
'number_column:position': 'position'}
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)
column_names = table_question_context.column_names
assert "date_column:first_elected" in column_names
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_get_nonterminal_productions_in_world_without_comparable_columns(
self):
question_tokens = [
Token(x) for x in ['what', 'was', 'the', 'first', 'title', '?']
]
table_file = self.FIXTURES_ROOT / 'data' / 'corenlp_processed_tables' / 'TEST-1.table'
table_context = TableQuestionContext.read_from_file(
table_file, question_tokens)
# The table does not have date or number columns.
assert "date" not in table_context.column_types.values()
assert "number" not in table_context.column_types.values()
world = WikiTablesLanguage(table_context)
actions = world.get_nonterminal_productions()
assert set(actions.keys()) == {
"<List[Row],Column:List[str]>",
"<List[Row],Column:List[Row]>",
"<List[Row],StringColumn,str:List[Row]>",
"<Number,Number,Number:Date>",
"<List[Row]:List[Row]>",
"<List[Row]:Number>",
"Date",
"Number",
"List[str]",
"Column",
"StringColumn",
"List[Row]",
"@start@",
}
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)
predicted_types = table_question_context.column_types
assert predicted_types["first_elected"] == "date"
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_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 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_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 == ["france", "south_korea"]
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")]
assert number_entities == [("1", 2), ("1", 7)]
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 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)
language_logger = logging.getLogger("allennlp.semparse.domain_languages.wikitables_language")
language_logger.setLevel(logging.ERROR)
tokenizer = SpacyTokenizer()
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 = WikiTablesLanguage(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 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 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 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_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_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 (argmax all_rows number_column:attendance) date_column:date)")
assert result == ["november_10"]
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_date_column_type_extraction_2(self):
question = "how many were elected?"
question_tokens = self.tokenizer.tokenize(question)
test_file = f'{self.FIXTURES_ROOT}/data/corenlp_processed_tables/TEST-9.table'
table_question_context = TableQuestionContext.read_from_file(
test_file, question_tokens)
predicted_types = table_question_context.column_types
assert predicted_types["date_of_appointment"] == "date"
assert predicted_types["date_of_election"] == "date"
def setUp(self):
super().setUp()
# Adding a bunch of random tokens in here so we get them as constants in the language.
question_tokens = [Token(x) for x in ['what', 'was', 'the', 'last', 'year', '2013', '?',
'quarterfinals', 'a_league', '2010', '8000',
'did_not_qualify', '2001', '2', '23', '2005', '1',
'2002', 'usl_a_league', 'usl_first_division']]
self.table_file = self.FIXTURES_ROOT / 'data' / 'wikitables' / 'sample_table.tagged'
self.table_context = TableQuestionContext.read_from_file(self.table_file, question_tokens)
self.language = WikiTablesLanguage(self.table_context)
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)
column_names = table_question_context.column_names
assert "string_column:birthplace" in column_names
assert "string_column:advocate" in column_names
assert "string_column:notability" in column_names
assert "string_column:name" in column_names
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)
predicted_types = table_question_context.column_types
assert predicted_types["birthplace"] == "string"
assert predicted_types["advocate"] == "string"
assert predicted_types["notability"] == "string"
assert predicted_types["name"] == "string"
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 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.
neighbors_with_sets = {
key: set(value)
for key, value in neighbors.items()
}
assert neighbors_with_sets == {
"1": {"number_column:position", "number_column:notation"},
"string_column:mnemonic": set(),
"string_column:short_name": set(),
"string_column:swara": set(),
"number_column:position": {"1"},
"number_column:notation": {"1"},
"string:m1": {"string_column:notation"},
"string:1": {"string_column:position"},
"string_column:notation": {"string:m1"},
"string_column:position": {"string:1"},
}
entity_text = knowledge_graph.entity_text
assert entity_text == {
"1": "1",
"string:m1": "m1",
"string:1": "1",
"string_column:notation": "notation",
"number_column:notation": "notation",
"string_column:mnemonic": "mnemonic",
"string_column:short_name": "short name",
"string_column:swara": "swara",
"number_column:position": "position",
"string_column:position": "position",
}
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)
column_names = table_question_context.column_names
assert "number_column:games_played" in column_names
assert "number_column:field_goals" in column_names
assert "number_column:free_throws" in column_names
assert "number_column:points" in column_names
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)
predicted_types = table_question_context.column_types
assert predicted_types["games_played"] == "number"
assert predicted_types["field_goals"] == "number"
assert predicted_types["free_throws"] == "number"
assert predicted_types["points"] == "number"
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 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",
"number_column:avg_attendance",
"string_column:avg_attendance",
"5000",
"-1",
}
assert set(neighbors["date_column:year"]) == {"5000", "-1"}
assert neighbors["number_column:year"] == ["5000"]
assert neighbors["string_column:year"] == []
assert neighbors["number_column:division"] == ["5000"]
assert neighbors["string_column:division"] == []
assert neighbors["string_column:league"] == []
assert neighbors["string_column:regular_season"] == []
assert neighbors["number_column:regular_season"] == ["5000"]
assert neighbors["string_column:playoffs"] == []
assert neighbors["string_column:open_cup"] == []
assert neighbors["number_column:open_cup"] == ["5000"]
assert neighbors["number_column:avg_attendance"] == ["5000"]
assert neighbors["string_column:avg_attendance"] == []
assert set(neighbors["5000"]) == {
"date_column:year",
"number_column:year",
"number_column:division",
"number_column:avg_attendance",
"number_column:regular_season",
"number_column:open_cup",
}
assert neighbors["-1"] == ["date_column:year"]
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': '2001',
'number_column:division': '2',
'string_column:league': 'usl_a_league',
'string_column:regular_season': '4th_western',
'string_column:playoffs': 'quarterfinals',
'string_column:open_cup': 'did_not_qualify',
'number_column:avg_attendance': '7_169'},
{'date_column:year': '2005',
'number_column:division': '2',
'string_column:league': 'usl_first_division',
'string_column:regular_season': '5th',
'string_column:playoffs': 'quarterfinals',
'string_column:open_cup': '4th_round',
'number_column:avg_attendance': '6_028'}]
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),
"number_column:year": 2001.0,
"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",
"number_column:regular_season": 4.0,
"string_column:playoffs": "quarterfinals",
"string_column:open_cup": "did_not_qualify",
"number_column:open_cup": None,
"number_column:avg_attendance": 7169.0,
"string_column:avg_attendance": "7_169",
},
{
"date_column:year": Date(2005, -1, -1),
"number_column:year": 2005.0,
"string_column:year": "2005",
"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,
"number_column:avg_attendance": 6028.0,
"string_column:avg_attendance": "6_028",
},
]
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:division',
'string_column:league', 'string_column:regular_season',
'string_column:playoffs', 'string_column:open_cup',
'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'}
assert neighbors['-1'] == ['date_column:year']
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:position', 'string:m1',
'string_column:mnemonic', 'string_column:notation',
'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 neighbors == {
'1': ['number_column:position'],
'string_column:mnemonic': [],
'string_column:short_name': [],
'string_column:swara': [],
'number_column:position': ['1'],
'string:m1': ['string_column:notation'],
'string_column:notation': ['string:m1']
}
entity_text = knowledge_graph.entity_text
assert entity_text == {
'1': '1',
'string:m1': 'm1',
'string_column:notation': 'notation',
'string_column:mnemonic': 'mnemonic',
'string_column:short_name': 'short name',
'string_column:swara': 'swara',
'number_column:position': 'position'
}
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 _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 _get_world_with_question_tokens_and_table_file(
self, tokens: List[Token], table_file: str) -> WikiTablesLanguage:
table_context = TableQuestionContext.read_from_file(table_file, tokens)
world = WikiTablesLanguage(table_context)
return world
