moreo
/
QuaPy
1
0
Fork 1
Code Issues Pull Requests Releases Wiki Activity

adding bootstrap variants

This commit is contained in:
Alejandro Moreo Fernandez 2025-11-17 12:37:12 +01:00
parent 12b431ef4b
commit fd62e73d2d
1 changed files with 10 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
BayesianKDEy/full_experiments.py
View File

@ -6,13 +6,14 @@ from pathlib import Path
from sklearn.linear_model import LogisticRegression
import quapy as qp
from BayesianKDEy._bayeisan_kdey import BayesianKDEy
from method.aggregative import DistributionMatchingY as DMy
from quapy.method.base import BinaryQuantifier
from quapy.model_selection import GridSearchQ
from quapy.data import Dataset
# 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.method.aggregative import KDEyML
from quapy.method.aggregative import KDEyML, ACC
from quapy.protocol import UPP
import quapy.functional as F
import numpy as np
@ -34,9 +35,14 @@ def new_classifier():
def methods():
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 'BayesianHDy', PQ(clone(cls), stan_seed=0), {**cls_hyper, 'n_bins': [3,4,5,8,16,32]}
yield 'BayesianKDEy', BayesianKDEy(clone(cls), mcmc_seed=0), {**cls_hyper, 'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}
# yield 'BayesianHDy', PQ(clone(cls), stan_seed=0), hdy_hyper
# yield 'BayesianKDEy', BayesianKDEy(clone(cls), mcmc_seed=0), kdey_hyper
def experiment(dataset: Dataset, method: WithConfidenceABC, grid: dict):