HIF integration¶

HyperBench uses HIF (Hypergraph Interchange Format) to represent hypergraphs.

Supported inputs: - .json (plain HIF). - .json.zst (Zstandard-compressed HIF).

Load built-in datasets¶

Many datasets are available as built-ins (downloaded and cached automatically):

from hyperbench.data import AlgebraDataset, SamplingStrategy

dataset = AlgebraDataset(sampling_strategy=SamplingStrategy.HYPEREDGE)
print(dataset.stats())

Built-in dataset classes include AlgebraDataset, AmazonDataset, CoraDataset, CourseraDataset, IMDBDataset, and more. See the Data API reference for the complete list.

Load a dataset from a local file¶

from hyperbench.data import Dataset

dataset = Dataset.from_path("path/to/hypergraph.json.zst")
print(dataset.stats())

Load a dataset from a URL¶

from hyperbench.data import Dataset

dataset = Dataset.from_url("https://example.com/hypergraph.json.zst")
print(dataset.stats())

Validate HIF files¶

If you have a plain .json file and want to validate it against the HIF schema:

from hyperbench.utils import validate_hif_json

is_valid = validate_hif_json("path/to/hypergraph.json")
print(is_valid)

How HIF maps into HyperBench¶

When loaded, HIF data is processed into an HData object (see HData API reference for details).

Next steps¶

• Model selection/customization: Models.
• Training loop (callbacks, devices, etc.): Training.
• Comparing multiple models consistently: Benchmarking.
• Outputs and logging: Loggers.
• Visualizing runs: TensorBoard.