Datasets:
ett

	# 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.
	"""Electricity Transformer Temperature (ETT) dataset."""
	from dataclasses import dataclass

	import pandas as pd

	import datasets


	_CITATION = """\
	@inproceedings{haoyietal-informer-2021,
	author = {Haoyi Zhou and
	Shanghang Zhang and
	Jieqi Peng and
	Shuai Zhang and
	Jianxin Li and
	Hui Xiong and
	Wancai Zhang},
	title = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},
	booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},
	volume = {35},
	number = {12},
	pages = {11106--11115},
	publisher = {{AAAI} Press},
	year = {2021},
	}
	"""

	_DESCRIPTION = """\
	The data of Electricity Transformers from two separated counties
	in China collected for two years at hourly and 15-min frequencies.
	Each data point consists of the target value "oil temperature" and
	6 power load features. The train/val/test is 12/4/4 months.
	"""

	_HOMEPAGE = "https://github.com/zhouhaoyi/ETDataset"

	_LICENSE = "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/"

	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLS = {
	"h1": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv",
	"h2": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh2.csv",
	"m1": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTm1.csv",
	"m2": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTm2.csv",
	}


	@dataclass
	class ETTBuilderConfig(datasets.BuilderConfig):
	"""ETT builder config."""

	prediction_length: int = 24
	multivariate: bool = False


	class ETT(datasets.GeneratorBasedBuilder):
	"""Electricity Transformer Temperature (ETT) dataset"""

	VERSION = datasets.Version("1.0.0")

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('ett', 'h1')
	# data = datasets.load_dataset('ett', 'm2')
	BUILDER_CONFIGS = [
	ETTBuilderConfig(
	name="h1",
	version=VERSION,
	description="Time series from first county at hourly frequency.",
	),
	ETTBuilderConfig(
	name="h2",
	version=VERSION,
	description="Time series from second county at hourly frequency.",
	),
	ETTBuilderConfig(
	name="m1",
	version=VERSION,
	description="Time series from first county at 15-min frequency.",
	),
	ETTBuilderConfig(
	name="m2",
	version=VERSION,
	description="Time series from second county at 15-min frequency.",
	),
	]

	DEFAULT_CONFIG_NAME = "h1" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	if self.config.multivariate:
	features = datasets.Features(
	{
	"start": datasets.Value("timestamp[s]"),
	"target": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
	"feat_static_cat": datasets.Sequence(datasets.Value("uint64")),
	"item_id": datasets.Value("string"),
	}
	)
	else:
	features = datasets.Features(
	{
	"start": datasets.Value("timestamp[s]"),
	"target": datasets.Sequence(datasets.Value("float32")),
	"feat_static_cat": datasets.Sequence(datasets.Value("uint64")),
	"feat_dynamic_real": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
	"item_id": datasets.Value("string"),
	}
	)

	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 _split_generators(self, dl_manager):
	urls = _URLS[self.config.name]
	filepath = dl_manager.download_and_extract(urls)

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

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	data = pd.read_csv(filepath, parse_dates=True, index_col=0)
	start_date = data.index.min()

	if self.config.name in ["m1", "m2"]:
	factor = 4 # 15-min frequency
	else:
	factor = 1 # hourly frequency
	train_end_date_index = 12 * 30 * 24 * factor # 1 year

	if split == "dev":
	end_date_index = train_end_date_index + 4 * 30 * 24 * factor # 1 year + 4 months
	else:
	end_date_index = train_end_date_index + 8 * 30 * 24 * factor # 1 year + 8 months

	if self.config.multivariate:
	if split in ["test", "dev"]:
	# rolling windows of prediction_length for dev and test
	for i, index in enumerate(
	range(
	train_end_date_index,
	end_date_index,
	self.config.prediction_length,
	)
	):
	yield i, {
	"start": start_date,
	"target": data[: index + self.config.prediction_length].values.astype("float32").T,
	"feat_static_cat": [0],
	"item_id": "0",
	}
	else:
	yield 0, {
	"start": start_date,
	"target": data[:train_end_date_index].values.astype("float32").T,
	"feat_static_cat": [0],
	"item_id": "0",
	}
	else:
	if split in ["test", "dev"]:
	# rolling windows of prediction_length for dev and test
	for i, index in enumerate(
	range(
	train_end_date_index,
	end_date_index,
	self.config.prediction_length,
	)
	):
	target = data["OT"][: index + self.config.prediction_length].values.astype("float32")
	feat_dynamic_real = data[["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL"]][
	: index + self.config.prediction_length
	].values.T.astype("float32")
	yield i, {
	"start": start_date,
	"target": target,
	"feat_dynamic_real": feat_dynamic_real,
	"feat_static_cat": [0],
	"item_id": "OT",
	}
	else:
	target = data["OT"][:train_end_date_index].values.astype("float32")
	feat_dynamic_real = data[["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL"]][
	:train_end_date_index
	].values.T.astype("float32")
	yield 0, {
	"start": start_date,
	"target": target,
	"feat_dynamic_real": feat_dynamic_real,
	"feat_static_cat": [0],
	"item_id": "OT",
	}