Delete AirQualityBench.py with huggingface_hub

AirQualityBench.py

@@ -1,95 +0,0 @@
-"""AirQualityBench dataset loading script for Hugging Face datasets."""
-
-import os
-import h5py
-import numpy as np
-import datasets
-
-
-_CITATION = """@article{airqualitybench2025,
-  title={AirQualityBench: A Global-Scale Air Quality Forecasting Benchmark
-         for Spatio-Temporal Graph Neural Networks},
-  author={...},
-  journal={arXiv preprint arXiv:2605.05854},
-  year={2025}
-}"""
-
-_DESCRIPTION = """AirQualityBench is a global-scale air quality forecasting benchmark
-featuring 3,720 monitoring stations across the world with **authentic missing patterns**
-and physical-scale evaluation. The dataset spans 5 years (2021–2025) of hourly
-measurements across 6 primary pollutants: PM2.5, PM10, NO2, O3, SO2, CO.
-"""
-
-_HOMEPAGE = "https://github.com/Star-Learning/AirQualityBench"
-_LICENSE = "MIT"
-
-NUM_STATIONS = 3720
-NUM_POLLUTANTS = 6
-
-
-class AirQualityBench(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="train", description="Training set (2021–2023)"),
-        datasets.BuilderConfig(name="validation", description="Validation set (2024)"),
-        datasets.BuilderConfig(name="test", description="Test set (2025)"),
-        datasets.BuilderConfig(name="2021", description="Year 2021 hourly data"),
-        datasets.BuilderConfig(name="2022", description="Year 2022 hourly data"),
-        datasets.BuilderConfig(name="2023", description="Year 2023 hourly data"),
-        datasets.BuilderConfig(name="2024", description="Year 2024 hourly data"),
-        datasets.BuilderConfig(name="2025", description="Year 2025 hourly data"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "train"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features({
-                "timestamp_idx": datasets.Value("int32"),
-                "values": datasets.Array2D(
-                    shape=(NUM_STATIONS, NUM_POLLUTANTS), dtype="float32"
-                ),
-                "masks": datasets.Array2D(
-                    shape=(NUM_STATIONS, NUM_POLLUTANTS), dtype="int8"
-                ),
-            }),
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = os.path.dirname(__file__) or "."
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"data_dir": data_dir, "config": self.config.name},
-            )
-        ]
-
-    def _generate_examples(self, data_dir, config):
-        config_year_map = {
-            "2021": [2021], "2022": [2022], "2023": [2023],
-            "2024": [2024], "2025": [2025],
-            "train": [2021, 2022, 2023],
-            "validation": [2024],
-            "test": [2025],
-        }
-        years = config_year_map[config]
-
-        global_idx = 0
-        for year in years:
-            file_path = os.path.join(data_dir, f"aq_compact_{year}.h5")
-            with h5py.File(file_path, "r") as f:
-                values = f["values"][:]
-                masks = f["masks"][:]
-
-            for t in range(values.shape[0]):
-                yield global_idx, {
-                    "timestamp_idx": t,
-                    "values": values[t].astype(np.float32),
-                    "masks": masks[t].astype(np.int8),
-                }
-                global_idx += 1