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change in minimize -> minimize_scalar in TKDE2

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Alejandro Moreo Fernandez 2024-09-25 17:00:22 +02:00
parent da006ee89a
commit 531badffc8
2 changed files with 17 additions and 13 deletions
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26
KDEy/kdey_devel.py
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@ -68,11 +68,11 @@ class KDEyMLauto(KDEyML):
current_prevalence, current_bandwidth = self.optim_minimize_both_fine(current_bandwidth, current_prevalence, tr_posteriors, tr_y, te_posteriors, classes)
# check converngece
prev_convergence = all(np.isclose(previous_prevalence, current_prevalence, atol=0.0001))
prev_convergence = all(np.isclose(previous_prevalence, current_prevalence, atol=0.01))
if isinstance(current_bandwidth, float):
band_convergence = np.isclose(previous_bandwidth, current_bandwidth, atol=0.0001)
band_convergence = np.isclose(previous_bandwidth, current_bandwidth, atol=0.001)
else:
band_convergence = all(np.isclose(previous_bandwidth, current_bandwidth, atol=0.0001))
band_convergence = all(np.isclose(previous_bandwidth, current_bandwidth, atol=0.001))
convergence = band_convergence and prev_convergence
@ -94,17 +94,21 @@ class KDEyMLauto(KDEyML):
def optim_minimize_bandwidth(self, current_bandwidth, current_prev, tr_posteriors, tr_y, te_posteriors, classes):
def neg_loglikelihood_bandwidth(bandwidth):
mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth[0])
# bandwidth = bandwidth[0]
mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth)
test_densities = [self.pdf(kde_i, te_posteriors) for kde_i in mix_densities]
test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(current_prev, test_densities))
test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
return -np.sum(test_loglikelihood)
nll = -np.sum(test_loglikelihood)
# print(f'\t{bandwidth=:.10f}\t{nll=:.10f}')
return nll
bounds = [(0.00001, 1)]
r = optimize.minimize(neg_loglikelihood_bandwidth, x0=[current_bandwidth], method='SLSQP', bounds=bounds)
print(f'iterations-bandwidth={r.nit}')
assert r.success, 'Process did not converge!'
return r.x[0]
# bounds = [(0.00001, 0.2)]
# r = optimize.minimize(neg_loglikelihood_bandwidth, x0=[current_bandwidth], method='SLSQP', bounds=bounds)
r = optimize.minimize_scalar(neg_loglikelihood_bandwidth, bounds=(0.00001, 0.2))
# print(f'iterations-bandwidth={r.nit}')
assert r.success, f'Process did not converge! {r.message}'
return r.x
def optim_minimize_both(self, current_bandwidth, current_prev, tr_posteriors, tr_y, te_posteriors, classes):
n_classes = len(current_prev)
@ -117,7 +121,7 @@ class KDEyMLauto(KDEyML):
test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
return -np.sum(test_loglikelihood)
bounds = [(0, 1) for _ in range(n_classes)] + [(0.00001, 1)]
bounds = [(0, 1) for _ in range(n_classes)] + [(0.00001, 0.2)]
constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x[:n_classes])})
prevalence_bandwidth = np.append(current_prev, current_bandwidth)
r = optimize.minimize(neg_loglikelihood_bandwidth, x0=prevalence_bandwidth, method='SLSQP', bounds=bounds, constraints=constraints)

4
KDEy/quantification_evaluation.py
View File

@ -35,7 +35,7 @@ METHODS = [
('PACC', PACC(newLR()), wrap_hyper(logreg_grid)),
('EMQ', EMQ(newLR()), wrap_hyper(logreg_grid)),
('KDEy-ML', KDEyML(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
('KDEy-MLred', KDEyMLred(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
# ('KDEy-MLred', KDEyMLred(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
('KDEy-ML-scott', KDEyML(newLR(), bandwidth='scott'), wrap_hyper(logreg_grid)),
('KDEy-ML-silver', KDEyML(newLR(), bandwidth='silverman'), wrap_hyper(logreg_grid)),
('KDEy-ML-autoLike', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood'), wrap_hyper(logreg_grid)),
@ -55,7 +55,7 @@ TRANSDUCTIVE_METHODS = [
#('TKDEy-ML', KDEyMLauto(newLR()), None),
# ('TKDEy-MLboth', KDEyMLauto(newLR(), optim='both'), None),
# ('TKDEy-MLbothfine', KDEyMLauto(newLR(), optim='both_fine'), None),
# ('TKDEy-ML2', KDEyMLauto(newLR()), None),
('TKDEy-ML2', KDEyMLauto(newLR(), optim='two_steps'), None),
# ('TKDEy-MLike', KDEyMLauto(newLR(), optim='max_likelihood'), None),
# ('TKDEy-MLike2', KDEyMLauto(newLR(), optim='max_likelihood2'), None),
#('TKDEy-ML3', KDEyMLauto(newLR()), None),