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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""SQUALL: Lexical-level Supervised Table Question Answering Dataset."""
import json
import re
import datasets
from datasets.tasks import QuestionAnsweringExtractive
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{Shi:Zhao:Boyd-Graber:Daume-III:Lee-2020,
Title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries},
Author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee},
Booktitle = {Findings of EMNLP},
Year = {2020},
}
"""
_DESCRIPTION = """\
To explore the utility of fine-grained, lexical-level supervision, authors \
introduce SQUALL, a dataset that enriches 11,276 WikiTableQuestions \
English-language questions with manually created SQL equivalents plus \
alignments between SQL and question fragments.
"""
_URL = "https://raw.githubusercontent.com/tzshi/squall/main/data/"
# _URLS = {
# "squall": _URL + "squall.json",
# "wtq-test": _URL + "wtq-test.json",
# "dev-0": _URL + "dev-0.ids",
# "dev-1": _URL + "dev-1.ids",
# "dev-2": _URL + "dev-2.ids",
# "dev-3": _URL + "dev-3.ids",
# "dev-4": _URL + "dev-4.ids",
# }
_URLS = {
"squall": _URL,
"wtq-test": _URL,
"dev-0": _URL,
"dev-1": _URL,
"dev-2": _URL,
"dev-3": _URL,
"dev-4": _URL,
}
class SquallConfig(datasets.BuilderConfig):
"""BuilderConfig for Squall."""
def __init__(self, **kwargs):
"""BuilderConfig for Squall.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SquallConfig, self).__init__(**kwargs)
class Squall(datasets.GeneratorBasedBuilder):
"""SQUALL: Lexical-level Supervised Table Question Answering Dataset."""
BUILDER_CONFIGS = [
SquallConfig(name = '0'),
SquallConfig(name = '1'),
SquallConfig(name = '2'),
SquallConfig(name = '3'),
SquallConfig(name = '4')
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"nt": datasets.Value("string"),
"tbl": datasets.Value("string"),
"columns":
{
"raw_header": datasets.features.Sequence(datasets.Value("string")),
"tokenized_header": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
"column_suffixes": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
"column_dtype": datasets.features.Sequence(datasets.Value("string")),
"example": datasets.features.Sequence(datasets.Value("string"))
},
"nl": datasets.features.Sequence(datasets.Value("string")),
"nl_pos": datasets.features.Sequence(datasets.Value("string")),
"nl_ner": datasets.features.Sequence(datasets.Value("string")),
"nl_incolumns": datasets.features.Sequence(datasets.Value("bool_")),
"nl_incells": datasets.features.Sequence(datasets.Value("bool_")),
"columns_innl": datasets.features.Sequence(datasets.Value("bool_")),
"tgt": datasets.Value("string"),
"sql": datasets.features.Sequence(datasets.Value("string"))
# "align" is not implemented
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://github.com/tzshi/squall/",
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="nl", context_column="columns", answers_column="tgt"
)
],
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"split_key": "train", "filepath": downloaded_files}),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"split_key": "dev", "filepath": downloaded_files}),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"split_key": "test", "filepath": downloaded_files}),
]
def _generate_examples(self, split_key, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
squall_full = filepath["squall"] + '/squall.json'
dev_ids = filepath["dev-" + self.config.name] + "/dev-" + self.config.name + ".ids"
test = filepath["wtq-test"] + "/wtq-test.json"
if split_key != 'test':
with open(squall_full, encoding="utf-8") as f:
squall_full_data = json.load(f)
NUM_MAPPING = {
'half': 0.5,
'one': 1,
'two': 2,
'three': 3,
'four': 4,
'five': 5,
'six': 6,
'seven': 7,
'eight': 8,
'nine': 9,
'ten': 10,
'eleven': 11,
'twelve': 12,
'twenty': 20,
'thirty': 30,
'once': 1,
'twice': 2,
'first': 1,
'second': 2,
'third': 3,
'fourth': 4,
'fifth': 5,
'sixth': 6,
'seventh': 7,
'eighth': 8,
'ninth': 9,
'tenth': 10,
'hundred': 100,
'thousand': 1000,
'million': 1000000,
'jan': 1,
'feb': 2,
'mar': 3,
'apr': 4,
'may': 5,
'jun': 6,
'jul': 7,
'aug': 8,
'sep': 9,
'oct': 10,
'nov': 11,
'dec': 12,
'january': 1,
'february': 2,
'march': 3,
'april': 4,
'june': 6,
'july': 7,
'august': 8,
'september': 9,
'october': 10,
'november': 11,
'december': 12,
}
def parse_number(s):
if s in NUM_MAPPING:
return NUM_MAPPING[s]
s = s.replace(',', '')
# https://stackoverflow.com/questions/4289331/python-extract-numbers-from-a-string
ret = re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", s)
if len(ret) > 0:
return ret[0]
return None
for instance in squall_full_data:
has_number = False
numbers = []
for x in instance["nl"]:
numbers.append(parse_number(x))
if numbers[-1] is not None:
has_number = True
instance["numbers"] = numbers
instance["has_number"] = has_number
with open(dev_ids) as f:
dev_ids = json.load(f)
if split_key == "train":
set = [x for x in squall_full_data if x["tbl"] not in dev_ids]
else:
set = [x for x in squall_full_data if x["tbl"] in dev_ids]
idx = 0
for sample in set:
cols = {}
keys = ["raw_header", "tokenized_header", "column_suffixes", "column_dtype", "example"]
n_col = len(sample["columns"])
for k in range(5):
tmp = []
for j in range(n_col):
tmp.append(sample["columns"][j][k])
cols[keys[k]] = tmp
sql = [x[1] for x in sample["sql"]]
yield idx, {
"nt": sample["nt"],
"tbl": sample["tbl"],
"columns": cols,
"nl": sample["nl"],
"nl_pos": sample["nl_pos"],
"nl_ner": sample["nl_ner"],
# "nl_ralign": sample["nl_ralign"],
"nl_incolumns": sample["nl_incolumns"],
"nl_incells": sample["nl_incells"],
"columns_innl": sample["columns_innl"],
"tgt": sample["tgt"],
"sql": sql,
# "align": sample["align"]
}
idx += 1
else:
with open(test, encoding="utf-8") as f:
test_data = json.load(f)
idx = 0
for sample in test_data:
cols = {}
keys = ["raw_header", "tokenized_header", "column_suffixes", "column_dtype", "example"]
n_col = len(sample["columns"])
for k in range(5):
tmp = []
for j in range(n_col):
tmp.append(sample["columns"][j][k])
cols[keys[k]] = tmp
yield idx, {
"nt": sample["nt"],
"tbl": sample["tbl"],
"columns": cols,
"nl": sample["nl"],
"nl_pos": sample["nl_pos"],
"nl_ner": sample["nl_ner"],
# "nl_ralign": sample["nl_ralign"],
"nl_incolumns": sample["nl_incolumns"],
"nl_incells": sample["nl_incells"],
"columns_innl": sample["columns_innl"],
# "tgt": '',
# "sql": [],
# "align": sample["align"]
}
idx += 1
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