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- # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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- """Chaotic Dynamical Systems (Dysts) dataset."""
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- from dataclasses import dataclass
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-
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- import pandas as pd
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-
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- import datasets
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-
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-
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- _CITATION = """\
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- @article{gilpin2023model,
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- title={Model scale versus domain knowledge in statistical forecasting of chaotic systems},
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- author={Gilpin, William},
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- journal={Physical Review Research},
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- volume={5},
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- number={4},
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- pages={043252},
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- year={2023},
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- publisher={APS}
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- }
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- """
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-
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- _DESCRIPTION = """\
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- A collection of long multivariate time series, each of which comes from a chaotic
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- dynamical system. The subdirectories coarse, medium, and fine each contain 135 .csv
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- files, each of which contains a single multivariate time series of length 10,000. The
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- number of channels varies depending on the specific dynamical system.
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- """
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-
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- _HOMEPAGE = "https://github.com/williamgilpin/dysts"
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-
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- _LICENSE = "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/"
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-
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- # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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- # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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- # _URLS = {
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- # "h1": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/Dysts-small/Dystsh1.csv",
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- # "h2": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/Dysts-small/Dystsh2.csv",
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- # "m1": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/Dysts-small/Dystsm1.csv",
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- # "m2": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/Dysts-small/Dystsm2.csv",
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- # }
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-
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- @dataclass
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- class DystsBuilderConfig(datasets.BuilderConfig):
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- """Dysts builder config."""
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- prediction_length: int = 100
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- multivariate: bool = True
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-
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-
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- class Dysts(datasets.GeneratorBasedBuilder):
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- """Chaotic Dynamical Systems (Dysts) dataset"""
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-
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- VERSION = datasets.Version("1.0.0")
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-
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- # You will be able to load one or the other configurations in the following list with
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- # data = datasets.load_dataset('Dysts', 'h1')
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- # data = datasets.load_dataset('Dysts', 'm2')
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- BUILDER_CONFIGS = [
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- DystsBuilderConfig(
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- name="coarse",
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- version=VERSION,
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- description="Time series sampled at a coarse resolution of 10 points per period.",
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- ),
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- DystsBuilderConfig(
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- name="medium",
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- version=VERSION,
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- description="Time series sampled at a coarse resolution of 30 points per period.",
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- ),
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- DystsBuilderConfig(
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- name="m2",
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- version=VERSION,
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- description="Time series sampled at a coarse resolution of 100 points per period.",
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- ),
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- ]
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-
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- DEFAULT_CONFIG_NAME = "medium"
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-
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- def _info(self):
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- if self.config.multivariate:
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- features = datasets.Features(
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- {
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- "start": datasets.Value("Index"),
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- "target": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
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- "feat_static_cat": datasets.Sequence(datasets.Value("uint64")),
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- "item_id": datasets.Value("string"),
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- }
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- )
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- else:
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- features = datasets.Features(
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- {
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- "start": datasets.Value("Index"),
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- "target": datasets.Sequence(datasets.Value("float32")),
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- "feat_static_cat": datasets.Sequence(datasets.Value("uint64")),
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- "feat_dynamic_real": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
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- "item_id": datasets.Value("string"),
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- }
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- )
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-
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- return datasets.DatasetInfo(
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- # This is the description that will appear on the datasets page.
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- description=_DESCRIPTION,
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- # This defines the different columns of the dataset and their types
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- features=features, # Here we define them above because they are different between the two configurations
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- # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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- # specify them. They'll be used if as_supervised=True in builder.as_dataset.
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- # supervised_keys=("sentence", "label"),
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- # Homepage of the dataset for documentation
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- homepage=_HOMEPAGE,
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- # License for the dataset if available
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- license=_LICENSE,
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- # Citation for the dataset
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- # urls = _URLS[self.config.name]
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- # filepath = dl_manager.download_and_extract(urls)
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- filepath = ""
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-
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={
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- "filepath": filepath,
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- "split": "train",
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- },
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.TEST,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={
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- "filepath": filepath,
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- "split": "test",
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- },
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={
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- "filepath": filepath,
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- "split": "val",
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- },
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- ),
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- ]
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-
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- # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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- def _generate_examples(self, filepath, split):
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- data = pd.read_csv(filepath, parse_dates=True, index_col=0)
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- # data = np.loadtxt(f"./{granularity}/{equation_name}_{granularity}.csv", delimiter=",", skiprows=1)
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- start_date = data.index.min()
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-
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- # if self.config.name in ["m1", "m2"]:
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- # factor = 4 # 15-min frequency
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- # else:
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- # factor = 1 # hourly frequency
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- # train_end_index = 12 * 30 * 24 * factor # 1 year
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- train_end_index = 7000
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-
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- if split == "val":
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- end_index = train_end_index + 1500
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- else:
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- end_index = train_end_index + 3000
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-
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- if self.config.multivariate:
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- if split in ["test", "val"]:
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- # rolling windows of prediction_length for val and test
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- for i, index in enumerate(
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- range(
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- train_end_index,
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- end_index,
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- self.config.prediction_length,
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- )
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- ):
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- yield i, {
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- "start": start_date,
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- "target": data[: index + self.config.prediction_length].values.astype("float32").T,
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- "feat_static_cat": [0],
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- "item_id": "0",
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- }
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- else:
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- yield 0, {
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- "start": start_date,
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- "target": data[:train_end_index].values.astype("float32").T,
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- "feat_static_cat": [0],
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- "item_id": "0",
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- }
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- else:
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- if split in ["test", "val"]:
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- # rolling windows of prediction_length for val and test
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- for i, index in enumerate(
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- range(
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- train_end_index,
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- end_index,
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- self.config.prediction_length,
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- )
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- ):
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- target = data[: index + self.config.prediction_length].values.astype("float32")
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- feat_dynamic_real = data[: index + self.config.prediction_length].values.T.astype("float32")
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- yield i, {
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- "start": start_date,
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- "target": target,
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- "feat_dynamic_real": feat_dynamic_real,
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- "feat_static_cat": [0],
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- "item_id": "OT",
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- }
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- else:
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- target = data[:train_end_index].values.astype("float32")
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- feat_dynamic_real = data[:train_end_index].values.T.astype("float32")
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- yield 0, {
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- "start": start_date,
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- "target": target,
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- "feat_dynamic_real": feat_dynamic_real,
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- "feat_static_cat": [0],
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- "item_id": "OT",
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- }