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glass_alloy_composition / glass_alloy_composition.py

bipin

updated dataloader

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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


	_DESCRIPTION = """\
	This is an alloy composition dataset
	"""

	_LICENSE = "MIT"

	# link to the dataset
	_URL = "https://drive.google.com/uc?export=download&id="
	_URLs = {
	'train': _URL+'1wAERHsEBvWvCgfiWjtodM_5lV2OPlapC',
	'test': _URL+'1TvC3R0gIjFNj2HWyvMZuuubv78vuZpSF',
	}

	class GlassAlloyComposition(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="train", version=VERSION, description="Training split of the complete dataset"),
	datasets.BuilderConfig(name="test", version=VERSION, description="Testing split of the complete dataset"),
	]

	DEFAULT_CONFIG_NAME = "train"

	def _info(self):
	"""Basic information about the dataset is specified here"""

	features = datasets.Features(
	{
	"alloy_composition": datasets.Value("string")
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	license=_LICENSE
	)

	def _split_generators(self, dl_manager):
	"""Generates the training and testing split of the dataset"""

	urls_to_download = _URLs
	downloaded_data = dl_manager.download_and_extract(urls_to_download)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_data['train']
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_data['test']
	},
	),
	]

	def _generate_examples(
	self, filepath
	):
	# Specify the format in which the data is to be returned
	with open(filepath, encoding="utf-8") as f:
	for i, line in enumerate(f.readlines()):
	_id = i
	row = ' '.join(w for w in line.strip().split(","))
	yield _id, {"alloy_composition": row}