# 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", }