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adding bootstrap-emq

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Alejandro Moreo Fernandez 2025-12-08 12:31:23 +01:00
parent 7b75954f9b
commit 7cb4bd550f
1 changed files with 12 additions and 12 deletions
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24
BayesianKDEy/full_experiments.py
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@ -63,21 +63,21 @@ def methods():
only_binary = 'only_binary' only_binary = 'only_binary'
only_multiclass = 'only_multiclass' only_multiclass = 'only_multiclass'
yield 'BootstrapACC', ACC(LR()), acc_hyper, lambda hyper: AggregativeBootstrap(ACC(LR()), n_test_samples=1000, random_state=0), multiclass_method # yield 'BootstrapACC', ACC(LR()), acc_hyper, lambda hyper: AggregativeBootstrap(ACC(LR()), n_test_samples=1000, random_state=0), multiclass_method
yield 'BayesianACC', ACC(LR()), acc_hyper, lambda hyper: BayesianCC(LR(), mcmc_seed=0), multiclass_method # yield 'BayesianACC', ACC(LR()), acc_hyper, lambda hyper: BayesianCC(LR(), mcmc_seed=0), multiclass_method
yield 'BootstrapEMQ', EMQ(LR(), on_calib_error='backup'), emq_hyper, lambda hyper: AggregativeBootstrap(EMQ(LR(), on_calib_error='backup'), n_test_samples=1000, random_state=0), multiclass_method yield 'BootstrapEMQ', EMQ(LR(), on_calib_error='backup'), emq_hyper, lambda hyper: AggregativeBootstrap(EMQ(LR(), on_calib_error='backup', calib=hyper['calib'], val_split=None if hyper['calib'] is None else 5z), n_test_samples=1000, random_state=0), multiclass_method
yield 'BootstrapHDy', DMy(LR()), hdy_hyper, lambda hyper: AggregativeBootstrap(DMy(LR(), **hyper), n_test_samples=1000, random_state=0), multiclass_method # yield 'BootstrapHDy', DMy(LR()), hdy_hyper, lambda hyper: AggregativeBootstrap(DMy(LR(), **hyper), n_test_samples=1000, random_state=0), multiclass_method
yield 'BayesianHDy', DMy(LR()), hdy_hyper, lambda hyper: PQ(LR(), stan_seed=0, **hyper), only_binary # yield 'BayesianHDy', DMy(LR()), hdy_hyper, lambda hyper: PQ(LR(), stan_seed=0, **hyper), only_binary
#
yield 'BootstrapKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: AggregativeBootstrap(KDEyML(LR(), **hyper), n_test_samples=1000, random_state=0, verbose=True), multiclass_method # yield 'BootstrapKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: AggregativeBootstrap(KDEyML(LR(), **hyper), n_test_samples=1000, random_state=0, verbose=True), multiclass_method
yield 'BayesianKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: BayesianKDEy(mcmc_seed=0, **hyper), multiclass_method # yield 'BayesianKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: BayesianKDEy(mcmc_seed=0, **hyper), multiclass_method
yield 'BayesianKDEy*', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, **hyper), multiclass_method # yield 'BayesianKDEy*', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, **hyper), multiclass_method
yield 'BayKDEy*CLR', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore='clr', step_size=.15, **hyper), multiclass_method # yield 'BayKDEy*CLR', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore='clr', step_size=.15, **hyper), multiclass_method
# yield 'BayKDEy*CLR2', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore='clr', step_size=.05, **hyper), multiclass_method # yield 'BayKDEy*CLR2', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore='clr', step_size=.05, **hyper), multiclass_method
yield 'BayKDEy*ILR', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore='ilr', step_size=.15, **hyper), only_multiclass # yield 'BayKDEy*ILR', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore='ilr', step_size=.15, **hyper), only_multiclass
yield 'BayKDEy*ILR2', KDEyILR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='ilr', mcmc_seed=0, explore='ilr', step_size=.1, **hyper), only_multiclass # yield 'BayKDEy*ILR2', KDEyILR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='ilr', mcmc_seed=0, explore='ilr', step_size=.1, **hyper), only_multiclass
def model_selection(train: LabelledCollection, point_quantifier: AggregativeQuantifier, grid: dict): def model_selection(train: LabelledCollection, point_quantifier: AggregativeQuantifier, grid: dict):