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Experiments

This section documents how to reproduce the experiments run with the HGP library on the datasets used in our evaluation.

Each page covers, where applicable:

  • how to obtain the raw data
  • how to preprocess it into the HDF format consumed by the library
  • how to run a benchmark with scripts/run_benchmark.py

Datasets

  • PMLB: a suite of binary classification benchmarks, used to evaluate HGP across many small to medium datasets.
  • PaySim: synthetic mobile-money transactions for fraud detection, large and strongly imbalanced.
  • AEAC: Amazon employee access requests, where readable rules double as access-control policies.

Conventions

All scripts use a consistent --data_path argument to point at the dataset. Preprocessing scripts accept --data_path as the folder that holds the raw input and receives the generated .hdf files. scripts/run_benchmark.py and scripts/optuna_hypertuning.py take --data_path as the path to a single preprocessed .hdf file.