super_eurlex / super_eurlex.py
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Simplified super_eurlex.py by outsourcing constants
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
import numpy as np
import pandas as pd
import datasets
from tqdm.auto import tqdm
from constants import _CITATION, _HOMEPAGE, _LICENSE, _DESCRIPTION
from constants import SECTOR_DESCRIPTIONS, AVAILABLE_FEATURES, AVAILABLE_LANGUAGES, SECTORS
class SuperEurlexConfig(datasets.BuilderConfig):
"""BuilderConfig for SuperGLUE."""
def __init__(self, sector, language, features, citation, url, **kwargs):
"""BuilderConfig for SuperGLUE.
Args:
sector: sector of the wanted data
language: the language code for the language in which the text shall
be written in
features: *list[string]*, list of the features that will appear in the
feature dict.
citation: *string*, citation for the data set.
url: *string*, url for information about the data set.
**kwargs: keyword arguments forwarded to super.
"""
name=sector+'.'+language
super().__init__(name=name, version=datasets.Version("0.1.0"), **kwargs)
self.features = features
self.language = language
self.sector = sector
self.text_data_url = f"text_data/{language}/{sector}.parquet"
self.meta_data_url = f"meta_data/{sector}.parquet"
self.citation = citation
self.url = url
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class SuperEurlex(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
# 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 = [
SuperEurlexConfig(#version=VERSION,
sector=sect,
language=lang,
description=SECTOR_DESCRIPTIONS[sect],
features=AVAILABLE_FEATURES[sect],
citation=_CITATION,
url=_HOMEPAGE)
for lang in AVAILABLE_LANGUAGES for sect in SECTORS
]
DEFAULT_CONFIG_NAME = "3.DE" # 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 = AVAILABLE_FEATURES[self.config.sector]
info = 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,
)
return info
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
urls = {'text': self.config.text_data_url,
'meta': self.config.meta_data_url} #_URLS[self.config.name]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"text": data_dir['text'],
"meta": data_dir['meta'],
"language": self.config.language,
"sector": self.config.sector,
'split': 'train'
},
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, text, meta, sector, language, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
text_data = pd.read_parquet(text)
text_data['celex_id'] = text_data['celex_id'].apply(lambda x: str(x.tolist()[0]) if isinstance(x,list) else x)
meta_data = pd.read_parquet(meta)
meta_data['celex_id'] = meta_data['celex_id'].apply(lambda x: str(x.tolist()[0]) if isinstance(x, np.ndarray) else x)
combined_data = pd.merge(text_data, meta_data, on='celex_id')
dataset = datasets.Dataset.from_pandas(combined_data)
dataset = dataset.remove_columns('__index_level_0__')
for i, sample in enumerate(dataset):
yield i, sample
if __name__ == '__main__':
import datasets as ds
import sys
print(sys.argv[0])
for sector in SECTORS:
for lang in AVAILABLE_LANGUAGES:
print(f'{sector}.{lang}')
dataset = ds.load_dataset(sys.argv[0],f'{sector}.{lang}')
print(dataset)
print('\n')