File size: 6,400 Bytes
d60afdd 71fcf55 d60afdd 555dc02 8d9d564 d60afdd 8d9d564 d60afdd 8d9d564 d60afdd 8d9d564 d60afdd 8d9d564 d60afdd 71fcf55 d60afdd 71fcf55 d60afdd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
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")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
# BUILDER_CONFIGS = [
# datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
# datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
# ]
#
# DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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")),
"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(set)
with open(path) as f:
for l in tqdm(f, desc="Pre-computing specs"):
try:
instance = json.loads(l)
rule_id = int(instance['id'])
rule = instance['question']
sent = instance['context']
specs[rule].add(sent)
id_to_rule[rule_id] = rule
except:
# TODO log
pass
return {rule_id:specs[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:
yield key, {
"rule_id": rule_id,
"parent": row[1],
"child": row[2],
"negative_child": row[3],
"spec": specs[rule_id],
"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) |