|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""CFQ (Compositional Freebase Questions) dataset.""" |
|
|
|
|
|
import json |
|
import re |
|
|
|
import datasets |
|
|
|
|
|
logger = datasets.logging.get_logger(__name__) |
|
|
|
|
|
_HOMEPAGE = "https://github.com/google-research/google-research/tree/master/cfq" |
|
|
|
_LICENSE = "CC BY 4.0" |
|
|
|
_CITATION = """ |
|
@inproceedings{Keysers2020, |
|
title={Measuring Compositional Generalization: A Comprehensive Method on |
|
Realistic Data}, |
|
author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and |
|
Hylke Buisman and Daniel Furrer and Sergii Kashubin and |
|
Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and |
|
Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and |
|
Olivier Bousquet}, |
|
booktitle={ICLR}, |
|
year={2020}, |
|
url={https://arxiv.org/abs/1912.09713.pdf}, |
|
} |
|
""" |
|
|
|
_DESCRIPTION = """ |
|
The CFQ dataset (and it's splits) for measuring compositional generalization. |
|
|
|
See https://arxiv.org/abs/1912.09713.pdf for background. |
|
|
|
Example usage: |
|
data = datasets.load_dataset('cfq/mcd1') |
|
""" |
|
|
|
_DATA_URL = "https://storage.googleapis.com/cfq_dataset/cfq.tar.gz" |
|
|
|
|
|
class CfqConfig(datasets.BuilderConfig): |
|
"""BuilderConfig for CFQ splits.""" |
|
|
|
def __init__(self, name, directory="splits", **kwargs): |
|
"""BuilderConfig for CFQ. |
|
|
|
Args: |
|
name: Unique name of the split. |
|
directory: Which subdirectory to read the split from. |
|
**kwargs: keyword arguments forwarded to super. |
|
""" |
|
|
|
super(CfqConfig, self).__init__( |
|
name=name, version=datasets.Version("1.0.1"), description=_DESCRIPTION, **kwargs |
|
) |
|
self.splits_path = f"cfq/{directory}/{name}.json" |
|
|
|
|
|
_QUESTION = "question" |
|
_QUERY = "query" |
|
_QUESTION_FIELD = "questionPatternModEntities" |
|
_QUERY_FIELD = "sparqlPatternModEntities" |
|
|
|
|
|
class Cfq(datasets.GeneratorBasedBuilder): |
|
"""CFQ task / splits.""" |
|
|
|
BUILDER_CONFIGS = [ |
|
CfqConfig(name="mcd1"), |
|
CfqConfig(name="mcd2"), |
|
CfqConfig(name="mcd3"), |
|
CfqConfig(name="question_complexity_split"), |
|
CfqConfig(name="question_pattern_split"), |
|
CfqConfig(name="query_complexity_split"), |
|
CfqConfig(name="query_pattern_split"), |
|
CfqConfig(name="random_split"), |
|
] |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
_QUESTION: datasets.Value("string"), |
|
_QUERY: datasets.Value("string"), |
|
} |
|
), |
|
supervised_keys=(_QUESTION, _QUERY), |
|
homepage=_HOMEPAGE, |
|
license=_LICENSE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
archive_path = dl_manager.download(_DATA_URL) |
|
return [ |
|
datasets.SplitGenerator( |
|
name=split, |
|
gen_kwargs={ |
|
"data_files": dl_manager.iter_archive(archive_path), |
|
"split_id": f"{split}Idxs", |
|
}, |
|
) |
|
for split in [datasets.Split.TRAIN, datasets.Split.TEST] |
|
] |
|
|
|
def _scrub_json(self, content): |
|
"""Reduce JSON by filtering out only the fields of interest.""" |
|
|
|
|
|
|
|
|
|
|
|
question_regex = re.compile(r'("%s":\s*"[^"]*")' % _QUESTION_FIELD) |
|
query_regex = re.compile(r'("%s":\s*"[^"]*")' % _QUERY_FIELD) |
|
question_match = None |
|
for line in content: |
|
line = line.decode("utf-8") |
|
if not question_match: |
|
question_match = question_regex.match(line) |
|
else: |
|
query_match = query_regex.match(line) |
|
if query_match: |
|
yield json.loads("{" + question_match.group(1) + "," + query_match.group(1) + "}") |
|
question_match = None |
|
|
|
def _generate_examples(self, data_files, split_id): |
|
"""Yields examples.""" |
|
samples_path = "cfq/dataset.json" |
|
for path, file in data_files: |
|
if path == self.config.splits_path: |
|
splits = json.load(file)[split_id] |
|
elif path == samples_path: |
|
|
|
generator = enumerate(self._scrub_json(file)) |
|
samples = {} |
|
splits_set = set(splits) |
|
for split_idx in splits: |
|
if split_idx in samples: |
|
sample = samples.pop(split_idx) |
|
else: |
|
for sample_idx, sample in generator: |
|
if sample_idx == split_idx: |
|
break |
|
elif sample_idx in splits_set: |
|
samples[sample_idx] = sample |
|
yield split_idx, {_QUESTION: sample[_QUESTION_FIELD], _QUERY: sample[_QUERY_FIELD]} |
|
|