Duplicate from autogluon/chronos_datasets_extra

Co-authored-by: Oleksandr Shchur <shchuro@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+pretty_name: Chronos datasets (extra)
+annotations_creators:
+- no-annotation
+source_datasets:
+- original
+task_categories:
+- time-series-forecasting
+task_ids:
+- univariate-time-series-forecasting
+- multivariate-time-series-forecasting
+license: apache-2.0
+dataset_info:
+- config_name: ETTh
+  features:
+  - name: id
+    dtype: string
+  - name: timestamp
+    sequence: timestamp[ns]
+  - name: HUFL
+    sequence: float64
+  - name: HULL
+    sequence: float64
+  - name: MUFL
+    sequence: float64
+  - name: MULL
+    sequence: float64
+  - name: LUFL
+    sequence: float64
+  - name: LULL
+    sequence: float64
+  - name: OT
+    sequence: float64
+  splits:
+  - name: train
+    num_bytes: 2229840
+    num_examples: 2
+  download_size: 0
+  dataset_size: 2229840
+- config_name: ETTm
+  features:
+  - name: id
+    dtype: string
+  - name: timestamp
+    sequence: timestamp[ms]
+  - name: HUFL
+    sequence: float64
+  - name: HULL
+    sequence: float64
+  - name: MUFL
+    sequence: float64
+  - name: MULL
+    sequence: float64
+  - name: LUFL
+    sequence: float64
+  - name: LULL
+    sequence: float64
+  - name: OT
+    sequence: float64
+  splits:
+  - name: train
+    num_bytes: 8919120
+    num_examples: 2
+  download_size: 0
+  dataset_size: 8919120
+- config_name: brazilian_cities_temperature
+  features:
+  - name: id
+    dtype: string
+  - name: timestamp
+    sequence: timestamp[ms]
+  - name: temperature
+    sequence: float32
+  splits:
+  - name: train
+    num_bytes: 109234
+    num_examples: 12
+  download_size: 0
+  dataset_size: 109234
+- config_name: spanish_energy_and_weather
+  features:
+  - name: timestamp
+    sequence: timestamp[ms]
+  - name: generation_biomass
+    sequence: float64
+  - name: generation_fossil_brown_coal/lignite
+    sequence: float64
+  - name: generation_fossil_gas
+    sequence: float64
+  - name: generation_fossil_hard_coal
+    sequence: float64
+  - name: generation_fossil_oil
+    sequence: float64
+  - name: generation_hydro_pumped_storage_consumption
+    sequence: float64
+  - name: generation_hydro_run-of-river_and_poundage
+    sequence: float64
+  - name: generation_hydro_water_reservoir
+    sequence: float64
+  - name: generation_nuclear
+    sequence: float64
+  - name: generation_other
+    sequence: float64
+  - name: generation_other_renewable
+    sequence: float64
+  - name: generation_solar
+    sequence: float64
+  - name: generation_waste
+    sequence: float64
+  - name: generation_wind_onshore
+    sequence: float64
+  - name: total_load_actual
+    sequence: float64
+  - name: price_actual
+    sequence: float64
+  - name: Barcelona_temp
+    sequence: float64
+  - name: Bilbao_temp
+    sequence: float64
+  - name: Madrid_temp
+    sequence: float64
+  - name: Seville_temp
+    sequence: float64
+  - name: Valencia_temp
+    sequence: float64
+  - name: Barcelona_temp_min
+    sequence: float64
+  - name: Bilbao_temp_min
+    sequence: float64
+  - name: Madrid_temp_min
+    sequence: float64
+  - name: Seville_temp_min
+    sequence: float64
+  - name: Valencia_temp_min
+    sequence: float64
+  - name: Barcelona_temp_max
+    sequence: float64
+  - name: Bilbao_temp_max
+    sequence: float64
+  - name: Madrid_temp_max
+    sequence: float64
+  - name: Seville_temp_max
+    sequence: float64
+  - name: Valencia_temp_max
+    sequence: float64
+  - name: Barcelona_pressure
+    sequence: float64
+  - name: Bilbao_pressure
+    sequence: float64
+  - name: Madrid_pressure
+    sequence: float64
+  - name: Seville_pressure
+    sequence: float64
+  - name: Valencia_pressure
+    sequence: float64
+  - name: Barcelona_humidity
+    sequence: float64
+  - name: Bilbao_humidity
+    sequence: float64
+  - name: Madrid_humidity
+    sequence: float64
+  - name: Seville_humidity
+    sequence: float64
+  - name: Valencia_humidity
+    sequence: float64
+  - name: Barcelona_wind_speed
+    sequence: float64
+  - name: Bilbao_wind_speed
+    sequence: float64
+  - name: Madrid_wind_speed
+    sequence: float64
+  - name: Seville_wind_speed
+    sequence: float64
+  - name: Valencia_wind_speed
+    sequence: float64
+  - name: Barcelona_wind_deg
+    sequence: float64
+  - name: Bilbao_wind_deg
+    sequence: float64
+  - name: Madrid_wind_deg
+    sequence: float64
+  - name: Seville_wind_deg
+    sequence: float64
+  - name: Valencia_wind_deg
+    sequence: float64
+  - name: Barcelona_rain_1h
+    sequence: float64
+  - name: Bilbao_rain_1h
+    sequence: float64
+  - name: Madrid_rain_1h
+    sequence: float64
+  - name: Seville_rain_1h
+    sequence: float64
+  - name: Valencia_rain_1h
+    sequence: float64
+  - name: Barcelona_snow_3h
+    sequence: float64
+  - name: Bilbao_snow_3h
+    sequence: float64
+  - name: Madrid_snow_3h
+    sequence: float64
+  - name: Seville_snow_3h
+    sequence: float64
+  - name: Valencia_snow_3h
+    sequence: float64
+  - name: Barcelona_clouds_all
+    sequence: float64
+  - name: Bilbao_clouds_all
+    sequence: float64
+  - name: Madrid_clouds_all
+    sequence: float64
+  - name: Seville_clouds_all
+    sequence: float64
+  - name: Valencia_clouds_all
+    sequence: float64
+  splits:
+  - name: train
+    num_bytes: 18794572
+    num_examples: 1
+  download_size: 0
+  dataset_size: 18794572
+---
+
+# Chronos datasets
+
+Time series datasets used for training and evaluation of the [Chronos](https://github.com/amazon-science/chronos-forecasting) forecasting models.
+
+This repository contains scripts for constructing datasets that cannot be hosted in the [main Chronos datasets repository](https://huggingface.co/datasets/autogluon/chronos_datasets) due to license restrictions.
+
+
+## Usage
+
+Datasets can be loaded using the 🤗 [`datasets`](https://huggingface.co/docs/datasets/en/index) library
+```python
+import datasets
+
+ds = datasets.load_dataset("autogluon/chronos_datasets_extra", "ETTh", split="train", trust_remote_code=True)
+ds.set_format("numpy")  # sequences returned as numpy arrays
+```
+
+For more information about the data format and usage please refer to [`autogluon/chronos_datasets`](https://huggingface.co/datasets/autogluon/chronos_datasets).
+
+
+## License
+Different datasets available in this collection are distributed under different open source licenses. Please see `ds.info.license` and `ds.info.homepage` for each individual dataset.
+
+The dataset script provided in this repository (`chronos_datasets_extra.py`) is available under the Apache 2.0 License.

chronos_datasets_extra.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.
+import tempfile
+from pathlib import Path
+
+import datasets
+import pandas as pd
+
+_VERSION = "1.0.0"
+
+_DESCRIPTION = "Chronos datasets"
+
+_CITATION = """
+@article{ansari2024chronos,
+  author  = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Wang, Hao and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang},
+  title   = {Chronos: Learning the Language of Time Series},
+  journal = {arXiv preprint arXiv:2403.07815},
+  year    = {2024}
+}
+"""
+
+
+_ETTH = "ETTh"
+_ETTM = "ETTm"
+_SPANISH_ENERGY_AND_WEATHER = "spanish_energy_and_weather"
+_BRAZILIAN_TEMPERATURE = "brazilian_cities_temperature"
+
+
+class ChronosExtraConfig(datasets.BuilderConfig):
+    def __init__(
+        self,
+        name: str,
+        license: str = None,
+        homepage: str = None,
+        **kwargs,
+    ):
+        super().__init__(name=name, **kwargs)
+        self.license = license
+        self.homepage = homepage
+
+
+class ChronosExtraBuilder(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = ChronosExtraConfig
+    BUILDER_CONFIGS = [
+        ChronosExtraConfig(
+            name=_ETTH,
+            license="CC BY-ND 4.0",
+            homepage="https://github.com/zhouhaoyi/ETDataset",
+            version=_VERSION,
+        ),
+        ChronosExtraConfig(
+            name=_ETTM,
+            license="CC BY-ND 4.0",
+            homepage="https://github.com/zhouhaoyi/ETDataset",
+            version=_VERSION,
+        ),
+        ChronosExtraConfig(
+            name=_BRAZILIAN_TEMPERATURE,
+            license="Database Contents License (DbCL) v1.0",
+            homepage="https://www.kaggle.com/datasets/volpatto/temperature-timeseries-for-some-brazilian-cities",
+            version=_VERSION,
+        ),
+        ChronosExtraConfig(
+            name=_SPANISH_ENERGY_AND_WEATHER,
+            homepage="https://www.kaggle.com/datasets/nicholasjhana/energy-consumption-generation-prices-and-weather",
+            version=_VERSION,
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            version=self.config.version,
+            license=self.config.license,
+            homepage=self.config.homepage,
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN),
+        ]
+
+    def _generate_examples(self):
+        if self.config.name in [_ETTH, _ETTM]:
+            yield from _ett_generator(self.config.name)
+        elif self.config.name == _SPANISH_ENERGY_AND_WEATHER:
+            yield from _spanish_energy_generator()
+        elif self.config.name == _BRAZILIAN_TEMPERATURE:
+            yield from _brazilian_temperature_generator()
+
+
+def _ett_generator(name: str):
+    for region in [1, 2]:
+        url = f"https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/{name}{region}.csv?download=1"
+        df = pd.read_csv(url, parse_dates=["date"])
+        df = df.rename(columns={"date": "timestamp"})
+        entry = {"id": f"{name}{region}"}
+        for col in df.columns:
+            entry[col] = df[col].to_numpy()
+        yield region, entry
+
+
+def _download_from_kaggle(dataset_name, download_path) -> None:
+    from kaggle.api.kaggle_api_extended import KaggleApi
+
+    api = KaggleApi()
+    api.authenticate()
+    api.dataset_download_files(dataset_name, path=download_path, unzip=True)
+
+
+def _spanish_energy_generator():
+    with tempfile.TemporaryDirectory() as download_path:
+        _download_from_kaggle(
+            "nicholasjhana/energy-consumption-generation-prices-and-weather",
+            download_path,
+        )
+        download_path = Path(download_path)
+        df_energy = pd.read_csv(download_path / "energy_dataset.csv")
+        df_energy["time"] = pd.to_datetime(df_energy["time"], utc=True)
+        df_energy.set_index("time", inplace=True)
+
+        # Drop non-informative columns / columns containing forecasts
+        constant_columns = df_energy.columns[df_energy.nunique() <= 1].to_list()
+        forecast_columns = [
+            col for col in df_energy.columns if "forecast" in col or "day ahead" in col
+        ]
+        columns_to_drop = constant_columns + forecast_columns
+        df_energy = df_energy.drop(columns_to_drop, axis=1)
+
+        entry = {"id": "0", "timestamp": df_energy.index.to_numpy(dtype="datetime64[ms]")}
+        for col in df_energy.columns:
+            saved_name = col.replace(" ", "_")
+            entry[saved_name] = df_energy[col].to_numpy(dtype="float64")
+
+        # Weather data
+        df_weather = pd.read_csv(download_path / "weather_features.csv")
+        df_weather["dt_iso"] = pd.to_datetime(df_weather["dt_iso"], utc=True)
+        df_weather = (
+            df_weather.rename(columns={"dt_iso": "time"})
+            .drop_duplicates(subset=["time", "city_name"], keep="first")
+            .set_index("time")
+        )
+        weather_features = [
+            "temp",
+            "temp_min",
+            "temp_max",
+            "pressure",
+            "humidity",
+            "wind_speed",
+            "wind_deg",
+            "rain_1h",
+            "snow_3h",
+            "clouds_all",
+        ]
+        for feature in weather_features:
+            for city, df_for_city in df_weather.groupby("city_name"):
+                saved_name = f"{city.lstrip()}_{feature}"
+                entry[saved_name] = df_for_city[feature].to_numpy(dtype="float64")
+                assert df_for_city.index.equals(df_energy.index)
+    yield 0, entry
+
+
+def _brazilian_temperature_generator():
+    months = [
+        "JAN",
+        "FEB",
+        "MAR",
+        "APR",
+        "MAY",
+        "JUN",
+        "JUL",
+        "AUG",
+        "SEP",
+        "OCT",
+        "NOV",
+        "DEC",
+    ]
+    with tempfile.TemporaryDirectory() as download_path:
+        _download_from_kaggle(
+            "volpatto/temperature-timeseries-for-some-brazilian-cities", download_path
+        )
+        for filename in sorted(Path(download_path).iterdir()):
+            city = filename.name.split("_", maxsplit=1)[1].split(".")[0]
+            df = pd.read_csv(filename)
+            df = df.set_index("YEAR")[months]
+            first_timestamp = f"{df.index[0]}-01-01"
+            df = df.stack()
+            df[df == 999.9] = float("nan")
+            entry = {
+                "id": city,
+                "timestamp": pd.date_range(
+                    first_timestamp, freq="MS", periods=len(df), unit="ms"
+                ).to_numpy(),
+                "temperature": df.to_numpy("float32"),
+            }
+            yield city, entry