From bfb64824105026329d418e8ecb9b58240ae9d864 Mon Sep 17 00:00:00 2001 From: Alejandro Moreo Date: Thu, 4 Dec 2025 10:34:38 +0100 Subject: [PATCH] launching kde** --- BayesianKDEy/full_experiments.py | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/BayesianKDEy/full_experiments.py b/BayesianKDEy/full_experiments.py index 2e0c493..ad8bbf4 100644 --- a/BayesianKDEy/full_experiments.py +++ b/BayesianKDEy/full_experiments.py @@ -35,18 +35,6 @@ class KDEyCLR(KDEyML): random_state=random_state, kernel='aitchison' ) -def methods__(): - acc_hyper = {} - hdy_hyper = {'nbins': [3,4,5,8,16,32]} - kdey_hyper = {'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2], 'classifier__C':[1]} - wrap_hyper = lambda dic: {f'quantifier__{k}':v for k,v in dic.items()} - # yield 'BootstrapACC', AggregativeBootstrap(ACC(LR()), n_test_samples=1000, random_state=0), wrap_hyper(acc_hyper) - # yield 'BootstrapHDy', AggregativeBootstrap(DMy(LR(), divergence='HD'), n_test_samples=1000, random_state=0), wrap_hyper(hdy_hyper) - yield 'BootstrapKDEy', AggregativeBootstrap(KDEyML(LR()), n_test_samples=1000, random_state=0), wrap_hyper(kdey_hyper) - # yield 'BayesianACC', BayesianCC(LR(), mcmc_seed=0), acc_hyper - # yield 'BayesianHDy', PQ(LR(), stan_seed=0), hdy_hyper - # yield 'BayesianKDEy', BayesianKDEy(LR(), mcmc_seed=0), kdey_hyper - def methods(): """ @@ -66,10 +54,12 @@ def methods(): yield 'BayesianACC', ACC(LR()), acc_hyper, lambda hyper: BayesianCC(LR(), mcmc_seed=0) yield 'BootstrapHDy', DMy(LR()), hdy_hyper, lambda hyper: AggregativeBootstrap(DMy(LR(), **hyper), n_test_samples=1000, random_state=0), + # yield 'BayesianHDy', PQ(LR(), stan_seed=0), hdy_hyper yield 'BootstrapKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: AggregativeBootstrap(KDEyML(LR(), **hyper), n_test_samples=1000, random_state=0, verbose=True), yield 'BayesianKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: BayesianKDEy(mcmc_seed=0, **hyper), yield 'BayesianKDEy*', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, **hyper), + yield 'BayKDEy*CLR', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore_CLR=True, step_size=.15, **hyper), def model_selection(train: LabelledCollection, point_quantifier: AggregativeQuantifier, grid: dict):