Datasets:

enoriega
/

odinsynth_dataset

	import csv
	import json
	import os
	from collections import defaultdict

	import datasets
	from tqdm import tqdm

	_DESCRIPTION = """\
	Supervised training data for odinsynth
	"""



	class OdinsynthDatasetBuilder(datasets.GeneratorBasedBuilder):

	VERSION = datasets.Version("1.0.0")

	def _info(self):

	features = datasets.Features(
	{
	"rule_id": datasets.Value("int32"),
	"parent": datasets.Value("string"),
	"child": datasets.Value("string"),
	"negative_child": datasets.Value("string"),
	"spec": datasets.Sequence(datasets.Value("string")),
	"matches": datasets.Sequence(datasets.Sequence(datasets.Value("int16"))),
	"step": datasets.Value("int8"),
	"length": datasets.Value("int8")
	}
	)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	# homepage=_HOMEPAGE,
	# # License for the dataset if available
	# license=_LICENSE,
	# # Citation for the dataset
	# citation=_CITATION,
	)

	def _build_specs(self, path:str):
	id_to_rule = {}
	specs = defaultdict(list)
	matches = defaultdict(list)
	with open(path) as f:
	for l in tqdm(f, desc="Pre-computing specs"):
	try:
	instance = json.loads(l)
	if instance['match']:
	rule_id = int(instance['id'])
	rule = instance['question']
	sent = instance['context']
	if sent not in specs[rule]:
	specs[rule].append(sent)
	matches[rule].append([instance['match_start'], instance['match_end']])
	id_to_rule[rule_id] = rule
	except:
	# TODO log
	pass

	return {rule_id:(specs[rule], matches[rule]) for rule_id, rule in id_to_rule.items()}



	def _split_generators(self, dl_manager):
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	JSON_PATH = dl_manager.download_and_extract('merged_train_split_train.jsonl.gz')
	TRAIN_ARCHIVE_PATH = dl_manager.download('train.tar.bz2')
	VAL_ARCHIVE_PATH = dl_manager.download('val.tar.bz2')
	TEST_ARCHIVE_PATH = dl_manager.download('test.tar.bz2')

	specs = self._build_specs(JSON_PATH)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"archive_iter": dl_manager.iter_archive(TRAIN_ARCHIVE_PATH),
	"specs": specs,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"archive_iter": dl_manager.iter_archive(TEST_ARCHIVE_PATH),
	"specs": specs,
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"archive_iter": dl_manager.iter_archive(VAL_ARCHIVE_PATH),
	"specs": specs,
	"split": "val",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, archive_iter, specs, split):
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.

	key = 0
	for tsv_path, file in archive_iter:
	if tsv_path.endswith(".tsv"):
	# Read the lines
	reader = csv.reader((l.decode() for l in file), delimiter='\t')
	for row in reader:
	rule_id = int(row[0])
	if rule_id in specs:
	spec, matches = specs[rule_id]
	assert len(spec) == len(matches), f"Rule id {id} has different number of sentences and matches"

	yield key, {
	"rule_id": rule_id,
	"parent": row[1],
	"child": row[2],
	"negative_child": row[3],
	"spec": spec,
	"matches": matches,
	"step": int(row[4]),
	"length": int(row[5]),
	}
	# Increase the key after yielding the instacne
	key += 1


	if __name__ == "__main__":
	ds = OdinsynthDatasetBuilder()
	ds.download_and_prepare()
	print(ds.cache_dir)