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Scotch / Scotch.py

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	# coding=utf-8
	# 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: Add a description here."""


	import csv
	import json
	import os

	import datasets
	import pickle
	from pathlib import Path

	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """
	@inproceedings{
	dahal2022scotch,
	title={Scotch: A Semantic Code Search Engine for {IDE}s},
	author={Samip Dahal and Adyasha Maharana and Mohit Bansal},
	booktitle={Deep Learning for Code Workshop},
	year={2022},
	url={https://openreview.net/forum?id=rSxfCiOZk-c}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	Scotch is a dataset of about 19 million functions collected from open-source repositiories from GitHub with permissive licenses. Each function has its corresponding code context and about 4 million functions have corresponding docstrings. The dataset includes functions written in programming languages Python, Java, Javascript, and Go."""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://github.com/sdpmas/Scotch"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "The MIT License"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	languages=['python','javascript','java','go']
	language_map={'python':'py','javascript':'js','go':'go','java':'java'}
	_URLs = {lang:f'https://scotchdata.s3.amazonaws.com/{lang}.tar.gz' for lang in languages}
	_URLs['all']=_URLs.copy()


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class ScotchDataset(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="all", version=VERSION, description="All available data with docstrings"),
	datasets.BuilderConfig(name="python", version=VERSION, description="Python data"),
	datasets.BuilderConfig(name="javascript", version=VERSION, description="Javascript data"),
	datasets.BuilderConfig(name="java", version=VERSION, description="Java data"),
	datasets.BuilderConfig(name="go", version=VERSION, description="Go data"),
	]

	DEFAULT_CONFIG_NAME = "all"

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset

	features = datasets.Features(
	{
	"repository_name": datasets.Value("string"),
	"function_path": datasets.Value("string"),
	"function_identifier": datasets.Value("string"),
	"language": datasets.Value("string"),
	"function": datasets.Value("string"),
	"docstring": datasets.Value("string"),
	"function_url": datasets.Value("string"),
	"context":datasets.Value("string"),
	"license":datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features, # Here we define them above because they are different between the two configurations
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	my_urls = _URLs[self.config.name]
	if isinstance(my_urls, str):
	my_urls = {self.config.name:my_urls}
	data_dir = [os.path.join(lang_dir,lang) for lang,lang_dir in dl_manager.download_and_extract(my_urls).items()]

	# splitpaths={split:[os.path.join(lang_dir,f'{split}.bin') for lang_dir in data_dir] for split in ['train','valid','test']}
	splitpaths={}
	for split in ['train','valid','test']:
	for lang_dir in data_dir:
	# Path glob .bin files
	lang_split_files=sorted(Path(os.path.join(lang_dir,split)).glob('*.bin'))
	if not split in splitpaths:
	splitpaths[split]=lang_split_files
	else:
	splitpaths[split].extend(lang_split_files)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": splitpaths['train'],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": splitpaths['test'],
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": splitpaths['valid'],
	"split": "valid",
	},
	),
	]

	def _generate_examples(
	self, filepath,split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	""" Yields examples as (key, example) tuples. """
	count=-1
	for i,filepath in enumerate(filepath):
	loaded_f=pickle.load(open(filepath,'rb'))
	for j, func in enumerate(loaded_f):
	count+=1
	yield count,{
	"repository_name": str(func['nwo']),
	"function_path":str(func['path']),
	"function_identifier": str(func['identifier']),
	"language": str(func['language']),
	"function": str(func['function']),
	"docstring": str(func['docstring']),
	"function_url": str(func['url']),
	"context":str(func['context']),
	"license":str(func['license']),
	}