reduction kdey
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@ -13,6 +13,7 @@ import quapy.functional as F
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from sklearn.metrics.pairwise import rbf_kernel
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from scipy import optimize
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from tqdm import tqdm
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import quapy.functional as F
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epsilon = 1e-10
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@ -102,6 +103,7 @@ class KDEyMLauto(KDEyML):
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bounds = [(0.00001, 1)]
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r = optimize.minimize(neg_loglikelihood_bandwidth, x0=[current_bandwidth], method='SLSQP', bounds=bounds)
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print(f'iterations-bandwidth={r.nit}')
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assert r.success, 'Process did not converge!'
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return r.x[0]
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def optim_minimize_both(self, current_bandwidth, current_prev, tr_posteriors, tr_y, te_posteriors, classes):
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@ -120,6 +122,7 @@ class KDEyMLauto(KDEyML):
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prevalence_bandwidth = np.append(current_prev, current_bandwidth)
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r = optimize.minimize(neg_loglikelihood_bandwidth, x0=prevalence_bandwidth, method='SLSQP', bounds=bounds, constraints=constraints)
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print(f'iterations-both={r.nit}')
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assert r.success, 'Process did not converge!'
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prev_band = r.x
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current_prevalence = prev_band[:-1]
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current_bandwidth = prev_band[-1]
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@ -141,6 +144,7 @@ class KDEyMLauto(KDEyML):
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prevalence_bandwidth = np.concatenate((current_prev, current_bandwidth))
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r = optimize.minimize(neg_loglikelihood_bandwidth, x0=prevalence_bandwidth, method='SLSQP', bounds=bounds, constraints=constraints)
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print(f'iterations-both-fine={r.nit}')
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assert r.success, 'Process did not converge!'
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prev_band = r.x
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current_prevalence = prev_band[:n_classes]
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current_bandwidth = prev_band[n_classes:]
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@ -213,7 +217,7 @@ class KDEyMLauto(KDEyML):
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init_prev = np.full(fill_value=1 / n_classes, shape=(n_classes,))
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def neglikelihood_band(bandwidth):
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mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth)
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mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth[0])
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test_densities = [self.pdf(kde_i, te_posteriors) for kde_i in mix_densities]
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def neg_loglikelihood_prev(prev):
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@ -225,10 +229,11 @@ class KDEyMLauto(KDEyML):
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return neglikelihood
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bounds = [(0.0001, 1)]
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bounds = [(0.0001, 0.2)]
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r = optimize.minimize(neglikelihood_band, x0=[0.001], method='SLSQP', bounds=bounds)
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best_band = r.x[0]
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assert r.success, 'Process did not converge!'
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print(f'solved in nit={r.nit}')
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return best_band
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@ -247,8 +252,9 @@ def optim_minimize(loss: Callable, init_prev: np.ndarray, return_loss=False):
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# solutions are bounded to those contained in the unit-simplex
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bounds = tuple((0, 1) for _ in range(n_classes)) # values in [0,1]
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constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)}) # values summing up to 1
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r = optimize.minimize(loss, x0=init_prev, method='SLSQP', bounds=bounds, constraints=constraints, tol=1e-10)
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r = optimize.minimize(loss, x0=init_prev, method='SLSQP', bounds=bounds, constraints=constraints)
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# print(f'iterations-prevalence={r.nit}')
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assert r.success, 'Process did not converge!'
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if return_loss:
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return r.x, r.fun
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else:
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@ -299,27 +305,36 @@ class KDEyMLauto2(KDEyML):
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if self.target == 'likelihood+':
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def neg_loglikelihood_band_(bandwidth):
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bandwidth=bandwidth[0]
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mix_densities = self.get_mixture_components(*train.Xy, train.classes_, bandwidth)
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loss_accum = 0
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for (sample, prevtrue) in prot():
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test_densities2 = [self.pdf(kde_i, sample) for kde_i in mix_densities]
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for (sample, prev) in tqdm(prot(), total=repeats):
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test_densities = [self.pdf(kde_i, sample) for kde_i in mix_densities]
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def neg_loglikelihood_prev_(prev):
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test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(prev, test_densities))
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def neg_loglikelihood_prev(prev):
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test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(prev, test_densities2))
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test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
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return -np.sum(test_loglikelihood)
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nll = -np.sum(test_loglikelihood)
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# print(f'\t\tprev={F.strprev(prev)} got {nll=}')
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return nll
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pred_prev, loss_val = optim_minimize(neg_loglikelihood_prev_, init_prev, return_loss=True)
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loss_accum += loss_val
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init_prev = np.full(fill_value=1 / n_classes, shape=(n_classes,))
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pred_prev, neglikelihood = optim_minimize(neg_loglikelihood_prev, init_prev, return_loss=True)
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# print(f'\t\tprev={F.strprev(pred_prev)} (true={F.strprev(prev)}) got {neglikelihood=}')
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loss_accum += neglikelihood
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print(f'\t{bandwidth=:.8f} got {loss_accum=:.8f}')
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return loss_accum
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bounds = [tuple((0.0001, 0.2))]
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init_bandwidth = 0.05
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r = optimize.minimize(neg_loglikelihood_band_, x0=[init_bandwidth], method='SLSQP', bounds=bounds)
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init_bandwidth = 0.1
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r = optimize.minimize(neg_loglikelihood_band_, x0=[init_bandwidth], method='Nelder-Mead', bounds=bounds, tol=1)
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best_band = r.x[0]
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best_loss_val = r.fun
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nit = r.nit
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assert r.success, 'Process did not converge!'
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#found bandwidth=0.00994664 after nit=3 iterations loss_val=-212247.24305)
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else:
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best_band = None
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@ -350,5 +365,24 @@ class KDEyMLauto2(KDEyML):
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best_band = bandwidth
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nit=20
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print(f'found bandwidth={best_band:.4f} after {nit=} iterations') # (loss_val={best_loss_val:.5f})')
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print(f'found bandwidth={best_band:.8f} after {nit=} iterations loss_val={best_loss_val:.5f})')
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self.bandwidth_ = best_band
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class KDEyMLred(KDEyML):
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def __init__(self, classifier: BaseEstimator=None, val_split=5, bandwidth=0.1, random_state=None, reduction=100, max_reduced=500):
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self.classifier = qp._get_classifier(classifier)
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self.val_split = val_split
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self.bandwidth = KDEBase._check_bandwidth(bandwidth)
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self.reduction = reduction
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self.max_reduced = max_reduced
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self.random_state = random_state
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def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
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n_classes = classif_predictions.n_classes
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tr_length = min(self.reduction * n_classes, self.max_reduced)
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if len(classif_predictions) > tr_length:
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classif_predictions = classif_predictions.sampling(tr_length)
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self.mix_densities = self.get_mixture_components(*classif_predictions.Xy, data.classes_, self.bandwidth)
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return self
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@ -7,7 +7,7 @@ import numpy as np
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from sklearn.linear_model import LogisticRegression
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import quapy as qp
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from KDEy.kdey_devel import KDEyMLauto, KDEyMLauto2
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from KDEy.kdey_devel import KDEyMLauto, KDEyMLauto2, KDEyMLred
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from quapy.method.aggregative import PACC, EMQ, KDEyML
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from quapy.model_selection import GridSearchQ
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from quapy.protocol import UPP
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@ -35,10 +35,11 @@ METHODS = [
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('PACC', PACC(newLR()), wrap_hyper(logreg_grid)),
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('EMQ', EMQ(newLR()), wrap_hyper(logreg_grid)),
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('KDEy-ML', KDEyML(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
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('KDEy-MLred', KDEyMLred(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
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('KDEy-ML-scott', KDEyML(newLR(), bandwidth='scott'), wrap_hyper(logreg_grid)),
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('KDEy-ML-silver', KDEyML(newLR(), bandwidth='silverman'), wrap_hyper(logreg_grid)),
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('KDEy-ML-autoLike', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood'), wrap_hyper(logreg_grid)),
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('KDEy-ML-autoLike+', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood+'), wrap_hyper(logreg_grid)),
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# ('KDEy-ML-autoLike+', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood+'), wrap_hyper(logreg_grid)), <-- no funciona
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('KDEy-ML-autoAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mae'), wrap_hyper(logreg_grid)),
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('KDEy-ML-autoRAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mrae'), wrap_hyper(logreg_grid)),
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]
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