Datasets:

tulipa762
/

electricity_load_diagrams

	# coding=utf-8
	# Copyright 2022 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 Load Diagrams 2011-2014 time series dataset."""
	from pathlib import Path

	import pandas as pd

	import datasets

	from .utils import to_dict


	_CITATION = """\
	@inproceedings{10.1145/3209978.3210006,
	author = {Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao},
	title = {Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks},
	year = {2018},
	isbn = {9781450356572},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3209978.3210006},
	doi = {10.1145/3209978.3210006},
	booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
	pages = {95--104},
	numpages = {10},
	location = {Ann Arbor, MI, USA},
	series = {SIGIR '18}
	}
	"""

	_DESCRIPTION = """\
	This new dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014.
	"""

	_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014"

	_LICENSE = ""

	_URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/00321/LD2011_2014.txt.zip"


	class ElectricityLoadDiagramsConfig(datasets.BuilderConfig):
	"""A builder config with some added meta data."""

	freq: str = "1H"
	prediction_length: int = 24
	rolling_evaluations: int = 7


	class ElectricityLoadDiagrams(datasets.GeneratorBasedBuilder):
	"""Hourly electricity consumption of 370 points/clients."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	ElectricityLoadDiagramsConfig(
	name="uci",
	version=VERSION,
	description="Original UCI time series.",
	),
	ElectricityLoadDiagramsConfig(
	name="lstnet",
	version=VERSION,
	description="Electricity time series preprocessed as in LSTNet paper.",
	),
	]

	DEFAULT_CONFIG_NAME = "lstnet"

	def _info(self):
	features = datasets.Features(
	{
	"start": datasets.Value("timestamp[s]"),
	"target": datasets.Sequence(datasets.Value("float32")),
	"feat_static_cat": datasets.Sequence(datasets.Value("uint64")),
	# "feat_static_real": datasets.Sequence(datasets.Value("float32")),
	# "feat_dynamic_real": datasets.Sequence(datasets.Sequence(datasets.Value("uint64"))),
	# "feat_dynamic_cat": datasets.Sequence(datasets.Sequence(datasets.Value("uint64"))),
	"item_id": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(_URL)

	train_ts = []
	val_ts = []
	test_ts = []

	df = pd.read_csv(
	Path(data_dir) / "LD2011_2014.txt",
	sep=";",
	index_col=0,
	parse_dates=True,
	decimal=",",
	)
	df.sort_index(inplace=True)
	df = df.resample(self.config.freq).sum()
	unit = pd.tseries.frequencies.to_offset(self.config.freq).name

	if self.config.name == "uci":
	val_end_date = df.index.max() - pd.Timedelta(
	self.config.prediction_length * self.config.rolling_evaluations, unit
	)
	train_end_date = val_end_date - pd.Timedelta(self.config.prediction_length, unit)
	else:
	# concate the time series to be from 2012 till 2014
	df = df[(df.index.year >= 2012) & (df.index.year <= 2014)]

	# drop time series which are zero at the start
	df = df.T[df.iloc[0] > 0].T

	# tran/val/test split from LSTNet paper
	# validation ends at 8/10-th of the time series
	val_end_date = df.index[int(len(df) * (8 / 10)) - 1]
	# training ends at 6/10-th of the time series
	train_end_date = df.index[int(len(df) * (6 / 10)) - 1]

	for cat, (ts_id, ts) in enumerate(df.iteritems()):
	start_date = ts.ne(0).idxmax()

	sliced_ts = ts[start_date:train_end_date]
	train_ts.append(
	to_dict(
	target_values=sliced_ts.values,
	start=start_date,
	cat=[cat],
	item_id=ts_id,
	)
	)

	sliced_ts = ts[start_date:val_end_date]
	val_ts.append(
	to_dict(
	target_values=sliced_ts.values,
	start=start_date,
	cat=[cat],
	item_id=ts_id,
	)
	)

	for i in range(self.config.rolling_evaluations):
	for cat, (ts_id, ts) in enumerate(df.iteritems()):
	start_date = ts.ne(0).idxmax()

	test_end_date = val_end_date + pd.Timedelta(self.config.prediction_length * (i + 1), unit)
	sliced_ts = ts[start_date:test_end_date]
	test_ts.append(
	to_dict(
	target_values=sliced_ts.values,
	start=start_date,
	cat=[cat],
	item_id=ts_id,
	)
	)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"split": train_ts,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"split": test_ts,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"split": val_ts,
	},
	),
	]

	def _generate_examples(self, split):
	for key, row in enumerate(split):
	yield key, row