Skip to content

Reproducibility

Every number, figure, and table in this project is regenerated from a fixed seed by one command.

Environment

  • Python 3.10 to 3.12. On Windows, use the launcher: py -3.11.
  • Verified stack: Qiskit 1.4.5, qiskit-machine-learning 0.8.4, scikit-learn 1.8, pandas 3.0, numpy 2.4.

Install the package with its development tools:

py -3.11 -m pip install -e ".[dev]"

Reproduce everything

py -3.11 scripts/reproduce_all.py

This runs all five pipeline stages (data, classical baseline, QSVM, VQC and QNN, reports) and then refreshes the README results table. Override the defaults when you want a quicker or larger run:

py -3.11 scripts/reproduce_all.py --n 200 --k 6 --reps 2 --maxiter 60
Flag Default Meaning
--n 200 quantum training subsample (the kernel is O(N squared))
--k 6 features selected for the quantum encoding (qubits)
--reps 2 feature-map and ansatz repetitions
--maxiter 60 COBYLA iterations for the VQC and QNN

Individual stages are also available as scripts: scripts/download_data.py, scripts/train_baseline.py, scripts/train_qsvm.py, and scripts/train_vqc_qnn.py.

Determinism and the data caveat

All randomness is seeded with RANDOM_SEED = 42: the splits, the model fits, and the bootstrap resampling. Re-running the pipeline reproduces the same metrics.

Without Kaggle credentials the pipeline uses the synthetic fallback, so the numbers shown on this site come from synthetic data. They are internally consistent and reproducible, but they are not the real WiDS results. To run on the real data, place a Kaggle API token at ~/.kaggle/kaggle.json and re-run.

Rebuild this site

py -3.11 -m pip install -e ".[docs]"
py -3.11 scripts/sync_docs_assets.py    # tables + chart data + demo model + figures
py -3.11 -m mkdocs serve                # preview at http://127.0.0.1:8000/
py -3.11 -m mkdocs build --strict       # production build into site/

scripts/sync_docs_assets.py regenerates the results table, the chart data, and the demo model, then copies the figures into the site. The deploy workflow runs the same steps in CI.

Tests

py -3.11 -m pytest

The suite is deterministic and offline (it uses the synthetic generator), and it includes a check that the exported demo model's JavaScript math matches scikit-learn, and that the docs tables carry no fabricated numbers.