234 lines
13 KiB
Python
234 lines
13 KiB
Python
from pathlib import Path
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from sklearn.linear_model import LogisticRegression
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from copy import deepcopy as cp
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import quapy as qp
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from BayesianKDEy.commons import KDEyReduce
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from _bayeisan_kdey import BayesianKDEy
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from _bayesian_mapls import BayesianMAPLS
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from commons import experiment_path, KDEyCLR, RESULT_DIR, MockClassifierFromPosteriors, KDEyScaledB, KDEyFresh
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# import datasets
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from datasets import LeQuaHandler, UCIMulticlassHandler, DatasetHandler, VisualDataHandler, CIFAR100Handler
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from temperature_calibration import temp_calibration
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from build.lib.quapy.data import LabelledCollection
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from quapy.method.aggregative import DistributionMatchingY as DMy, AggregativeQuantifier, EMQ, CC
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from quapy.model_selection import GridSearchQ
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from quapy.data import Dataset
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from quapy.method.confidence import BayesianCC, AggregativeBootstrap
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from quapy.method.aggregative import KDEyML, ACC
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from quapy.protocol import UPP
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import numpy as np
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from tqdm import tqdm
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from collections import defaultdict
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from time import time
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def methods(data_handler: DatasetHandler):
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"""
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Returns a tuple (name, quantifier, hyperparams, bayesian/bootstrap_constructor), where:
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- name: is a str representing the name of the method (e.g., 'BayesianKDEy')
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- quantifier: is the base model (e.g., KDEyML())
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- hyperparams: is a dictionary for the quantifier (e.g., {'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]})
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- bayesian/bootstrap_constructor: is a function that instantiates the bayesian o bootstrap method with the
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quantifier with optimized hyperparameters
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"""
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if False: # isinstance(data_handler, VisualDataHandler):
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Cls = MockClassifierFromPosteriors
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cls_hyper = {}
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val_split = data_handler.get_validation().Xy # use this specific collection
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pass
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else:
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Cls = LogisticRegression
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cls_hyper = {'classifier__C': np.logspace(-4,4,9), 'classifier__class_weight': ['balanced', None]}
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val_split = 5 # k-fold cross-validation
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acc_hyper = cls_hyper
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# emq_hyper = {'calib': ['nbvs', 'bcts', 'ts', 'vs'], **cls_hyper}
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hdy_hyper = {'nbins': [3,4,5,8,16,32], **cls_hyper}
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kdey_hyper = {'bandwidth': np.logspace(-3, -1, 10), **cls_hyper}
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kdey_hyper_clr = {'bandwidth': np.logspace(-2, 2, 10), **cls_hyper}
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multiclass_method = 'multiclass'
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only_binary = 'only_binary'
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only_multiclass = 'only_multiclass'
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# surrogate quantifiers
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acc = ACC(Cls(), val_split=val_split)
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hdy = DMy(Cls(), val_split=val_split)
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kde_gau = KDEyML(Cls(), val_split=val_split)
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kde_gau_scale = KDEyScaledB(Cls(), val_split=val_split)
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kde_gau_pca = KDEyReduce(Cls(), val_split=val_split, n_components=5)
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kde_gau_pca10 = KDEyReduce(Cls(), val_split=val_split, n_components=10)
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kde_ait = KDEyCLR(Cls(), val_split=val_split)
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emq = EMQ(Cls(), exact_train_prev=False, val_split=val_split)
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# Bootstrap approaches:
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# --------------------------------------------------------------------------------------------------------
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#yield 'BootstrapACC', acc, acc_hyper, lambda hyper: _AggregativeBootstrap(ACC(Cls()), n_test_samples=1000, random_state=0), multiclass_method
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#yield 'BootstrapEMQ', emq, on_calib_error='backup', val_split=5), emq_hyper, lambda hyper: _AggregativeBootstrap(EMQ(Cls(), on_calib_error='backup', calib=hyper['calib'], val_split=5), n_test_samples=1000, random_state=0), multiclass_method
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#yield 'BootstrapHDy', hdy, hdy_hyper, lambda hyper: _AggregativeBootstrap(DMy(Cls(), **hyper), n_test_samples=1000, random_state=0), multiclass_method
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#yield 'BootstrapKDEy', kde_gau, kdey_hyper, lambda hyper: _AggregativeBootstrap(KDEyML(Cls(), **hyper), n_test_samples=1000, random_state=0, verbose=True), multiclass_method
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# Bayesian approaches:
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# --------------------------------------------------------------------------------------------------------
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# yield 'BayesianACC', acc, acc_hyper, lambda hyper: BayesianCC(Cls(), val_split=val_split, mcmc_seed=0), multiclass_method
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#yield 'BayesianHDy', hdy, hdy_hyper, lambda hyper: PQ(Cls(), val_split=val_split, stan_seed=0, **hyper), only_binary
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# yield f'BaKDE-Ait-numpyro', kde_ait, kdey_hyper_clr, lambda hyper: BayesianKDEy(Cls(), kernel='aitchison', mcmc_seed=0, engine='numpyro', val_split=val_split, **hyper), multiclass_method
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#yield f'BaKDE-Gau-numpyro', kde_gau, kdey_hyper, lambda hyper: BayesianKDEy(Cls(), kernel='gaussian', mcmc_seed=0, engine='numpyro', val_split=val_split, **hyper), multiclass_method
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#yield f'BaKDE-Gau-scale', kde_gau_scale, kdey_hyper, lambda hyper: BayesianKDEy(Cls(), kernel='gaussian', mcmc_seed=0, engine='numpyro', val_split=val_split, **hyper), multiclass_method
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yield f'BaKDE-Gau-pca5', kde_gau_pca, kdey_hyper, lambda hyper: BayesianKDEy(Cls(), reduce=5, kernel='gaussian', mcmc_seed=0, engine='numpyro', val_split=val_split, **hyper), multiclass_method
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yield f'BaKDE-Gau-pca5*', kde_gau_pca, kdey_hyper, lambda hyper: BayesianKDEy(Cls(), reduce=5, temperature=None, kernel='gaussian', mcmc_seed=0, engine='numpyro', val_split=val_split, **hyper), multiclass_method
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yield f'BaKDE-Gau-pca10', kde_gau_pca10, kdey_hyper, lambda hyper: BayesianKDEy(Cls(), reduce=10, kernel='gaussian', mcmc_seed=0, engine='numpyro', val_split=val_split, **hyper), multiclass_method
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yield f'BaKDE-Gau-pca10*', kde_gau_pca10, kdey_hyper, lambda hyper: BayesianKDEy(Cls(), reduce=10, temperature=None, kernel='gaussian', mcmc_seed=0, engine='numpyro', val_split=val_split, **hyper), multiclass_method
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# yield f'BaKDE-Gau-H0', KDEyFresh(Cls(), bandwidth=0.4), cls_hyper, lambda hyper: BayesianKDEy(Cls(), bandwidth=0.4, kernel='gaussian', mcmc_seed=0, engine='numpyro', **hyper), multiclass_method
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# yield f'BaKDE-Gau-H1', KDEyFresh(Cls(), bandwidth=1.), cls_hyper, lambda hyper: BayesianKDEy(Cls(), bandwidth=1., kernel='gaussian', mcmc_seed=0, engine='numpyro', **hyper), multiclass_method
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# yield f'BaKDE-Gau-H2', KDEyFresh(Cls(), bandwidth=1.5), cls_hyper, lambda hyper: BayesianKDEy(Cls(), bandwidth=1.5,
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# kernel='gaussian',
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# mcmc_seed=0,
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# engine='numpyro',
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# **hyper), multiclass_method
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# yield f'BaKDE-Ait-T*', kde_ait, kdey_hyper_clr, lambda hyper: BayesianKDEy(Cls(),kernel='aitchison', mcmc_seed=0, engine='numpyro', temperature=None, val_split=val_split, **hyper), multiclass_method
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#yield f'BaKDE-Gau-T*', kde_gau, kdey_hyper, lambda hyper: BayesianKDEy(Cls(), kernel='gaussian', mcmc_seed=0, engine='numpyro', temperature=None, val_split=val_split, **hyper), multiclass_method
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# yield 'BayEMQ', emq, acc_hyper, lambda hyper: BayesianMAPLS(Cls(), prior='uniform', temperature=1, exact_train_prev=False, val_split=val_split), multiclass_method
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# yield 'BayEMQ*', emq, acc_hyper, lambda hyper: BayesianMAPLS(Cls(), prior='uniform', temperature=None, exact_train_prev=False, val_split=val_split), multiclass_method
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def model_selection(dataset: DatasetHandler, point_quantifier: AggregativeQuantifier, grid: dict):
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with qp.util.temp_seed(0):
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if isinstance(point_quantifier, KDEyScaledB) and 'bandwidth' in grid:
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def scale_bandwidth(bandwidth, n_classes, beta=0.5):
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return bandwidth * np.power(n_classes, beta)
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n = dataset.get_training().n_classes
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grid['bandwidth'] = [scale_bandwidth(b, n) for b in grid['bandwidth']]
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print('bandwidth scaled')
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print(f'performing model selection for {point_quantifier.__class__.__name__} with grid {grid}')
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# model selection
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if len(grid)>0:
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train, val_prot = dataset.get_train_valprot_for_modsel()
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mod_sel = GridSearchQ(
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model=point_quantifier,
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param_grid=grid,
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protocol=val_prot,
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refit=False,
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n_jobs=-1,
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verbose=True
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).fit(*train.Xy)
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best_params = mod_sel.best_params_
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else:
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best_params = {}
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return best_params
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def temperature_calibration(dataset: DatasetHandler, uncertainty_quantifier):
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temperature = None
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if hasattr(uncertainty_quantifier, 'temperature'):
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if uncertainty_quantifier.temperature is None:
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print('calibrating temperature')
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train, val_prot = dataset.get_train_valprot_for_modsel()
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if dataset.name.startswith('LeQua'):
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temp_grid=[100., 500, 1000, 5_000, 10_000, 50_000]
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else:
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temp_grid=[.5, 1., 1.5, 2., 5., 10., 100., 1000.]
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temperature = temp_calibration(uncertainty_quantifier, train, val_prot, temp_grid=temp_grid, n_jobs=-1, amplitude_threshold=.999)
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uncertainty_quantifier.temperature = temperature
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else:
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temperature = uncertainty_quantifier.temperature
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return temperature
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def experiment(dataset: DatasetHandler, point_quantifier: AggregativeQuantifier, method_name:str, grid: dict, uncertainty_quant_constructor, hyper_choice_path: Path):
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with qp.util.temp_seed(0):
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# model selection
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best_hyperparams = qp.util.pickled_resource(
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hyper_choice_path, model_selection, dataset, cp(point_quantifier), grid
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)
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print(f'{best_hyperparams=}')
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t_init = time()
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uncertainty_quantifier = uncertainty_quant_constructor(best_hyperparams)
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temperature = temperature_calibration(dataset, uncertainty_quantifier)
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training, test_generator = dataset.get_train_testprot_for_eval()
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uncertainty_quantifier.fit(*training.Xy)
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tr_time = time() - t_init
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# test
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train_prevalence = training.prevalence()
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results = defaultdict(list)
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pbar = tqdm(enumerate(test_generator()), total=test_generator.total())
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for i, (sample_X, true_prevalence) in pbar:
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t_init = time()
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point_estimate, region = uncertainty_quantifier.predict_conf(sample_X)
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ttime = time()-t_init
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results['true-prevs'].append(true_prevalence)
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results['point-estim'].append(point_estimate)
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results['shift'].append(qp.error.ae(true_prevalence, train_prevalence))
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results['ae'].append(qp.error.ae(prevs_true=true_prevalence, prevs_hat=point_estimate))
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results['rae'].append(qp.error.rae(prevs_true=true_prevalence, prevs_hat=point_estimate))
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results['sre'].append(qp.error.sre(prevs_true=true_prevalence, prevs_hat=point_estimate, prevs_train=train_prevalence))
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results['coverage'].append(region.coverage(true_prevalence))
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results['amplitude'].append(region.montecarlo_proportion(n_trials=50_000))
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results['test-time'].append(ttime)
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results['samples'].append(region.samples)
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pbar.set_description(f'{method_name} MAE={np.mean(results["ae"]):.5f} W={np.mean(results["sre"]):.5f} Cov={np.mean(results["coverage"]):.5f} AMP={np.mean(results["amplitude"]):.5f}')
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report = {
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'optim_hyper': best_hyperparams,
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'train_time': tr_time,
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'train-prev': train_prevalence,
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'results': {k:np.asarray(v) for k,v in results.items()},
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'temperature': temperature
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}
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return report
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def check_skip_experiment(method_scope, dataset: DatasetHandler):
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if method_scope == 'only_binary' and not dataset.is_binary():
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return True
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if method_scope == 'only_multiclass' and dataset.is_binary():
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return True
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return False
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if __name__ == '__main__':
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result_dir = RESULT_DIR
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for data_handler in [CIFAR100Handler]:#, UCIMulticlassHandler,LeQuaHandler, VisualDataHandler, CIFAR100Handler]:
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for dataset in data_handler.iter():
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qp.environ['SAMPLE_SIZE'] = dataset.sample_size
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print(f'dataset={dataset.name}')
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#if dataset.name != 'abalone':
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# continue
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problem_type = 'binary' if dataset.is_binary() else 'multiclass'
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for method_name, surrogate_quant, hyper_params, withconf_constructor, method_scope in methods(dataset):
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if check_skip_experiment(method_scope, dataset):
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continue
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result_path = experiment_path(result_dir / problem_type, dataset.name, method_name)
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hyper_path = experiment_path(result_dir / 'hyperparams' / problem_type, dataset.name, surrogate_quant.__class__.__name__)
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report = qp.util.pickled_resource(
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result_path, experiment, dataset, surrogate_quant, method_name, hyper_params, withconf_constructor, hyper_path
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)
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print(f'dataset={dataset.name}, '
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f'method={method_name}: '
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f'mae={report["results"]["ae"].mean():.5f}, '
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f'W={report["results"]["sre"].mean():.5f}, '
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f'coverage={report["results"]["coverage"].mean():.5f}, '
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f'amplitude={report["results"]["amplitude"].mean():.5f}, ')
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