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dysts.py

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+# 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.
+"""Chaotic Dynamical Systems (Dysts) dataset."""
+from dataclasses import dataclass
+
+import pandas as pd
+
+import datasets
+
+
+_CITATION = """\
+@article{gilpin2023model,
+  title={Model scale versus domain knowledge in statistical forecasting of chaotic systems},
+  author={Gilpin, William},
+  journal={Physical Review Research},
+  volume={5},
+  number={4},
+  pages={043252},
+  year={2023},
+  publisher={APS}
+}
+"""
+
+_DESCRIPTION = """\
+A collection of long multivariate time series, each of which comes from a chaotic 
+dynamical system. The subdirectories coarse, medium, and fine each contain 135 .csv 
+files, each of which contains a single multivariate time series of length 10,000. The 
+number of channels varies depending on the specific dynamical system.
+"""
+
+_HOMEPAGE = "https://github.com/williamgilpin/dysts"
+
+_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/Dysts-small/Dystsh1.csv",
+#     "h2": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/Dysts-small/Dystsh2.csv",
+#     "m1": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/Dysts-small/Dystsm1.csv",
+#     "m2": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/Dysts-small/Dystsm2.csv",
+# }
+
+@dataclass
+class DystsBuilderConfig(datasets.BuilderConfig):
+    """Dysts builder config."""
+    prediction_length: int = 100
+    multivariate: bool = True
+
+
+class Dysts(datasets.GeneratorBasedBuilder):
+    """Chaotic Dynamical Systems (Dysts) 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('Dysts', 'h1')
+    # data = datasets.load_dataset('Dysts', 'm2')
+    BUILDER_CONFIGS = [
+        DystsBuilderConfig(
+            name="coarse",
+            version=VERSION,
+            description="Time series sampled at a coarse resolution of 10 points per period.",
+        ),
+        DystsBuilderConfig(
+            name="medium",
+            version=VERSION,
+            description="Time series sampled at a coarse resolution of 30 points per period.",
+        ),
+        DystsBuilderConfig(
+            name="m2",
+            version=VERSION,
+            description="Time series sampled at a coarse resolution of 100 points per period.",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "medium"
+
+    def _info(self):
+        if self.config.multivariate:
+            features = datasets.Features(
+                {
+                    "start": datasets.Value("Index"),
+                    "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("Index"),
+                    "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)
+        filepath = ""
+
+        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": "val",
+                },
+            ),
+        ]
+
+    # 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)
+        # data = np.loadtxt(f"./{granularity}/{equation_name}_{granularity}.csv", delimiter=",", skiprows=1)
+        start_date = data.index.min()
+
+        # if self.config.name in ["m1", "m2"]:
+        #     factor = 4  # 15-min frequency
+        # else:
+        #     factor = 1  # hourly frequency
+        # train_end_index = 12 * 30 * 24 * factor  # 1 year
+        train_end_index = 7000
+
+        if split == "val":
+            end_index = train_end_index + 1500
+        else:
+            end_index = train_end_index + 3000
+
+        if self.config.multivariate:
+            if split in ["test", "val"]:
+                # rolling windows of prediction_length for val and test
+                for i, index in enumerate(
+                    range(
+                        train_end_index,
+                        end_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_index].values.astype("float32").T,
+                    "feat_static_cat": [0],
+                    "item_id": "0",
+                }
+        else:
+            if split in ["test", "val"]:
+                # rolling windows of prediction_length for val and test
+                for i, index in enumerate(
+                    range(
+                        train_end_index,
+                        end_index,
+                        self.config.prediction_length,
+                    )
+                ):
+                    target = data[: index + self.config.prediction_length].values.astype("float32")
+                    feat_dynamic_real = data[: 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[:train_end_index].values.astype("float32")
+                feat_dynamic_real = data[:train_end_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",
+                }