---

name: dataset-loaders description: "Guides safe selection and use of PyTorch Geometric Temporal dataset loaders, including signatures, return signal types, cache and network side effects, and dataset-specific window parameters." disable-model-invocation: true metadata: disco-role: operating license: MIT

Dataset Loaders

Use this sub-skill when the task is to choose or call a built-in loader from torch_geometric_temporal.dataset, understand what get_dataset returns, plan traffic/PDE/JSON data access, or inspect loader signatures without triggering downloads.

Do not instantiate a loader until you have decided whether network access and cache writes are acceptable. Many loaders fetch data in __init__, not in get_dataset.

Route here for

• Selecting among public *DatasetLoader classes exported by torch_geometric_temporal.dataset.
• Checking constructor signatures, get_dataset arguments, return signal types, and loader-specific lags/window/feature parameters.
• Planning raw_data_dir for METR-LA, PeMS-Bay, PeMS, PeMS-All-LA, and WindmillLarge before construction.
• Avoiding unintended downloads, partial cache files, zip extraction surprises, or unavailable WindmillSmall/WindmillMedium loaders.
• Explaining JSON/web loaders, traffic loaders, synthetic PDE loaders, and index-only PeMS loaders at a planning level.

Route elsewhere

• Ordinary snapshot iteration, temporal_signal_split, slicing, and creating custom signals from arrays: use the temporal-signals sub-skill.
• get_index_dataset, dataloader return tuple unpacking, index=True, allGPU, world_size, ddp_rank, Dask-DDP, and sequence-to-sequence index batches: use the index-batching sub-skill.
• Model/training loops after a loader returns snapshots: use the recurrent-layers or attention-and-hetero-layers sub-skill.

Safe planning workflow

1. Inspect without constructing. Run or read scripts/list_dataset_loaders.py to list exported loader classes and method signatures. This imports classes but never instantiates them.
2. Choose by task and signal type. Use references/dataset-loader-reference.md to match the loader to StaticGraphTemporalSignal, DynamicGraphTemporalSignal, or index-only behavior.
3. Classify side effects. Read references/data-sources.md before constructing any loader. Treat constructor-time downloads as the default unless the reference says the loader is unavailable or can be fully pre-staged.
4. Plan parameters before the first call. Decide lags, frames, num_timesteps_in, num_timesteps_out, target_var, feature_vars, event_id, N, and feature_mode up front; changing them after construction is usually safe, but construction may already have downloaded data.
5. Use an isolated cache for traffic loaders. For loaders with raw_data_dir, pre-stage the expected files or point to an expendable cache directory. Do not use a shared working directory when evaluating unknown prompts.
6. Validate only after loading is allowed. Once data access is approved, instantiate the loader, call get_dataset, inspect one snapshot's edge_index, edge_attr, x, and y, then hand off iterator mechanics or model loops to the appropriate sub-skill.

Quick loader selection

• Small/static JSON benchmarks: ChickenpoxDatasetLoader, PedalMeDatasetLoader, WikiMathsDatasetLoader, MontevideoBusDatasetLoader, MTMDatasetLoader.
• Dynamic graph JSON benchmarks: EnglandCovidDatasetLoader, TwitterTennisDatasetLoader.
• Traffic window forecasting: METRLADatasetLoader or PemsBayDatasetLoader with get_dataset(num_timesteps_in=..., num_timesteps_out=...).
• Index-only large PeMS: PemsDatasetLoader and PemsAllLADatasetLoader do not provide ordinary get_dataset; plan index batching separately.
• Synthetic PDE data: AdvectionDiffusionDatasetLoader, SIDiffusionDatasetLoader, WaveEquationDatasetLoader download remote NumPy/PyTorch payloads at construction and return static graph temporal signals.
• Avoid unavailable exports: WindmillOutputSmallDatasetLoader and WindmillOutputMediumDatasetLoader are exported but raise a runtime error in the inspected source because their original host is unavailable.

References

• references/dataset-loader-reference.md - class-by-class arguments, output types, side effects, and index support notes.
• references/data-sources.md - remote source families, cache/pre-stage expectations, and no-download alternatives.
• references/troubleshooting.md - failed downloads, missing optional packages, cache extraction, shape assumptions, and index-only loader errors.