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adding bootstrap variants

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Alejandro Moreo Fernandez 2025-11-17 12:37:12 +01:00
parent 12b431ef4b
commit fd62e73d2d
1 changed files with 10 additions and 4 deletions
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14
BayesianKDEy/full_experiments.py
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@ -6,13 +6,14 @@ from pathlib import Path
from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LogisticRegression
import quapy as qp import quapy as qp
from BayesianKDEy._bayeisan_kdey import BayesianKDEy from BayesianKDEy._bayeisan_kdey import BayesianKDEy
from method.aggregative import DistributionMatchingY as DMy
from quapy.method.base import BinaryQuantifier from quapy.method.base import BinaryQuantifier
from quapy.model_selection import GridSearchQ from quapy.model_selection import GridSearchQ
from quapy.data import Dataset from quapy.data import Dataset
# from BayesianKDEy.plot_simplex import plot_prev_points, plot_prev_points_matplot # from BayesianKDEy.plot_simplex import plot_prev_points, plot_prev_points_matplot
from quapy.method.confidence import ConfidenceIntervals, BayesianCC, PQ, WithConfidenceABC from quapy.method.confidence import ConfidenceIntervals, BayesianCC, PQ, WithConfidenceABC, AggregativeBootstrap
from quapy.functional import strprev from quapy.functional import strprev
from quapy.method.aggregative import KDEyML from quapy.method.aggregative import KDEyML, ACC
from quapy.protocol import UPP from quapy.protocol import UPP
import quapy.functional as F import quapy.functional as F
import numpy as np import numpy as np
@ -34,9 +35,14 @@ def new_classifier():
def methods(): def methods():
cls, cls_hyper = new_classifier() cls, cls_hyper = new_classifier()
hdy_hyper = {**cls_hyper, 'n_bins': [3,4,5,8,16,32]}
kdey_hyper = {**cls_hyper, 'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}
yield 'BootstrapACC', AggregativeBootstrap(ACC(clone(cls)), n_test_samples=1000), cls_hyper
yield 'BootstrapHDy', AggregativeBootstrap(DMy(clone(cls), divergence='HD'), n_test_samples=1000), hdy_hyper
yield 'BootstrapKDEy', AggregativeBootstrap(KDEyML(clone(cls)), n_test_samples=1000), kdey_hyper
# yield 'BayesianACC', BayesianCC(clone(cls), mcmc_seed=0), cls_hyper # yield 'BayesianACC', BayesianCC(clone(cls), mcmc_seed=0), cls_hyper
# yield 'BayesianHDy', PQ(clone(cls), stan_seed=0), {**cls_hyper, 'n_bins': [3,4,5,8,16,32]} # yield 'BayesianHDy', PQ(clone(cls), stan_seed=0), hdy_hyper
yield 'BayesianKDEy', BayesianKDEy(clone(cls), mcmc_seed=0), {**cls_hyper, 'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]} # yield 'BayesianKDEy', BayesianKDEy(clone(cls), mcmc_seed=0), kdey_hyper
def experiment(dataset: Dataset, method: WithConfidenceABC, grid: dict): def experiment(dataset: Dataset, method: WithConfidenceABC, grid: dict):