Test Suite

QUiNN includes a comprehensive test suite in the tests/ directory with 14 test files covering all library modules (148 tests total). Run the full suite with:

pytest tests/ -q

Test Modules

File	What is tested	Tests
test_funcs.py	Analytical test functions (blundell, Sine, Sine10, Summation, Ackley, x5): output shapes, formulas, noise.	18
test_maps.py	Data mapping utilities (scale01ToDom, scaleDomTo01, Normalizer, Standardizer, Expon, Logar, ComposeMap, Domainizer): roundtrips, boundary values.	14
test_stats.py	Statistics helpers (get_stats, get_domain, intersect_domain, diam): mean/quantile computation, domain intersection.	10
test_rvar.py	Random variable classes (Gaussian_1d, GMM2_1d, MVN): sample shapes, log-probability formulas, sample statistics.	12
test_mlp.py	MLP and RNet construction: forward-pass shapes, parameter counts, activations (relu, tanh, sin), dropout, batch-norm, final transforms, residual connections.	16
test_nnwrap.py	NNWrap numpy/torch interop: flatten/unflatten roundtrip, predict with explicit weights, loss, gradient, and Hessian computation.	9
test_nnfit.py	nnfit training loop: loss decrease, validation split, mini-batch training, multi-output, weight decay.	7
test_losses.py	Bayesian losses (NegLogPost, NegLogPrior): anchor behaviour, symmetry, prior weighting, zero-residual baseline.	5
test_mcmc.py	MCMC samplers (AMCMC, HMC, MALA): chain shape, acceptance rate, log-posterior storage, custom proposal covariance.	8
test_ensemble.py	NN_Ens deep ensemble: creation, fit/predict, ensemble predictions, moment computation, data fraction, multi-output.	7
test_vi.py	BNet and NN_VI: Bayesian network creation, stochastic vs. deterministic forward, ELBO, uncertainty quantification.	8
test_solvers.py	NN_MCMC, NN_Laplace, NN_SWAG, NN_RMS: creation, fit, predict, ensemble prediction, MAP estimation.	14
test_tchutils.py	Torch utilities (tch, npy, flatten_params, recover_flattened): type conversions, gradient tracking, flatten/recover roundtrip.	9
test_xutils.py	General utilities (idt, savepk/loadpk, cartes_list, sample_sphere, get_opt_bw, get_pdf, safe_cholesky): identity function, serialisation, Cartesian products, KDE, Cholesky.	11

Coverage Summary

The tests exercise all major subsystems of QUiNN:

• Neural network architectures: MLP, RNet, BNet, NNWrap.

• Training: nnfit with various loss functions, optimizers, and schedules.

• UQ solvers: NN_MCMC (AMCMC, HMC, MALA), NN_Ens, NN_RMS, NN_VI, NN_Laplace, NN_SWAG.

• MCMC samplers: AMCMC, HMC, MALA independently.

• Bayesian losses: NegLogPost, NegLogPrior.

• Random variables: Gaussian_1d, GMM2_1d, MVN.

• Utilities: data maps, statistics, torch helpers, pickle, KDE, Cholesky.

• Test functions: blundell, Sine, Sine10, Ackley, etc.