Merge branch 'kdey2' of gitea-s2i2s.isti.cnr.it:moreo/QuaPy into kdey2
This commit is contained in:
commit
3686e820fe
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@ -17,6 +17,8 @@ import quapy.functional as F
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epsilon = 1e-10
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BANDWIDTH_RANGE = (0.001, 0.2)
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class KDEyMLauto(KDEyML):
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def __init__(self, classifier: BaseEstimator = None, val_split=5, random_state=None, optim='two_steps'):
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self.classifier = qp._get_classifier(classifier)
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@ -218,7 +220,7 @@ class KDEyMLauto(KDEyML):
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def choose_bandwidth_maxlikelihood_search(self, tr_posteriors, tr_y, te_posteriors, classes):
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n_classes = len(classes)
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init_prev = np.full(fill_value=1 / n_classes, shape=(n_classes,))
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init_prev = F.uniform_prevalence(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[0])
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@ -258,7 +260,7 @@ def optim_minimize(loss: Callable, init_prev: np.ndarray, return_loss=False):
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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)
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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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# 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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@ -268,7 +270,7 @@ def optim_minimize(loss: Callable, init_prev: np.ndarray, return_loss=False):
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class KDEyMLauto2(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, target='likelihood'):
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def __init__(self, classifier: BaseEstimator=None, val_split=5, bandwidth=0.1, random_state=None, reduction=100, max_reduced=500, target='likelihood', search='grid'):
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"""
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reduction: number of examples per class for automatically setting the bandwidth
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"""
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@ -281,8 +283,10 @@ class KDEyMLauto2(KDEyML):
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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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assert target in ['likelihood', 'likelihood+'] or target in qp.error.QUANTIFICATION_ERROR_NAMES, 'unknown target for auto'
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assert target in ['likelihood'] or target in qp.error.QUANTIFICATION_ERROR_NAMES, 'unknown target for auto'
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assert search in ['grid', 'optim'], 'unknown value for search'
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self.target = target
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self.search = search
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def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
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if self.bandwidth == 'auto':
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@ -303,65 +307,42 @@ class KDEyMLauto2(KDEyML):
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if len(train) > tr_length:
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train = train.sampling(tr_length)
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init_prev = np.full(fill_value=1 / n_classes, shape=(n_classes,))
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init_prev = F.uniform_prevalence(n_classes=n_classes)
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repeats = 25
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prot = UPP(val, sample_size=self.reduction, repeats=repeats, random_state=self.random_state)
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if self.target == 'likelihood+':
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def eval_bandwidth(bandwidth):
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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_densities = [self.pdf(kde_i, sample) for kde_i in mix_densities]
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def neg_loglikelihood_bandwidth(bandwidth):
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mix_densities = self.get_mixture_components(*train.Xy, train.classes_, bandwidth)
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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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test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
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nll = -np.sum(test_loglikelihood)
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return nll
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loss_accum = 0
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for (sample, prevtrue) in prot():
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test_densities = [self.pdf(kde_i, sample) for kde_i in mix_densities]
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if self.target == 'likelihood':
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loss_fn = neg_loglikelihood_prev
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else:
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loss_fn = lambda prev_hat: qp.error.from_name(self.target)(prev, prev_hat)
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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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test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
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nll = -np.sum(test_loglikelihood)
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return nll
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pred_prev, neglikelihood = optim_minimize(loss_fn, init_prev, return_loss=True)
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loss_accum += neglikelihood
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return loss_accum
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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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return loss_accum
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r = optimize.minimize_scalar(neg_loglikelihood_bandwidth, bounds=(0.00001, 0.2))
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if self.search == 'optim':
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r = optimize.minimize_scalar(eval_bandwidth, bounds=(0.001, 0.2), options={'xatol': 0.005})
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best_band = r.x
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best_loss_value = 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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best_loss_value = None
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init_prev = np.full(fill_value=1 / n_classes, shape=(n_classes,))
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for bandwidth in np.logspace(-4, np.log10(0.2), 20):
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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, 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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test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
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return -np.sum(test_loglikelihood)
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if self.target == 'likelihood':
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loss_fn = neg_loglikelihood_prev_
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else:
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loss_fn = lambda prev_hat: qp.error.from_name(self.target)(prev, prev_hat)
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pred_prev, loss_val = optim_minimize(loss_fn, init_prev, return_loss=True)
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loss_accum += loss_val
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if best_loss_value is None or loss_accum < best_loss_value:
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best_loss_value = loss_accum
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best_band = bandwidth
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elif self.search=='grid':
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nit=20
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band_evals = [(band, eval_bandwidth(band)) for band in np.logspace(-4, np.log10(0.2), num=nit)]
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best_band, best_loss_value = sorted(band_evals, key=lambda x:x[1])[0]
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print(f'found bandwidth={best_band:.8f} after {nit=} iterations loss_val={best_loss_value:.5f})')
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self.bandwidth_ = best_band
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@ -22,7 +22,7 @@ def newLR():
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# typical hyperparameters explored for Logistic Regression
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logreg_grid = {
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'C': np.logspace(-3,3,7),
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'C': np.logspace(-4,4,9),
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'class_weight': [None, 'balanced']
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}
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@ -34,14 +34,16 @@ def wrap_hyper(classifier_hyper_grid: dict):
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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', 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-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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('KDEy-scott', KDEyML(newLR(), bandwidth='scott'), wrap_hyper(logreg_grid)),
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('KDEy-silver', KDEyML(newLR(), bandwidth='silverman'), wrap_hyper(logreg_grid)),
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('KDEy-NLL', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood', search='grid'), wrap_hyper(logreg_grid)),
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('KDEy-NLL+', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood', search='optim'), wrap_hyper(logreg_grid)),
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('KDEy-AE', KDEyMLauto2(newLR(), bandwidth='auto', target='mae', search='grid'), wrap_hyper(logreg_grid)),
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('KDEy-AE+', KDEyMLauto2(newLR(), bandwidth='auto', target='mae', search='optim'), wrap_hyper(logreg_grid)),
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('KDEy-RAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mrae', search='grid'), wrap_hyper(logreg_grid)),
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('KDEy-RAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mrae', search='optim'), wrap_hyper(logreg_grid)),
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]
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@ -80,12 +82,80 @@ def show_results(result_path):
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print(pv)
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def run_experiment(method_name, quantifier, param_grid):
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print('Init method', method_name)
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with open(global_result_path + '.csv', 'at') as csv:
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for dataset in qp.datasets.UCI_MULTICLASS_DATASETS:
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print('init', dataset)
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# run_experiment(global_result_path, method_name, quantifier, param_grid, dataset)
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local_result_path = os.path.join(Path(global_result_path).parent, method_name + '_' + dataset + '.dataframe')
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if os.path.exists(local_result_path):
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print(f'result file {local_result_path} already exist; skipping')
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report = qp.util.load_report(local_result_path)
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else:
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with qp.util.temp_seed(SEED):
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data = qp.datasets.fetch_UCIMulticlassDataset(dataset, verbose=True)
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train, test = data.train_test
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transductive_names = [name for (name, *_) in TRANSDUCTIVE_METHODS]
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if method_name not in transductive_names:
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if len(param_grid) == 0:
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t_init = time()
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quantifier.fit(train)
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train_time = time() - t_init
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else:
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# model selection (train)
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train, val = train.split_stratified(random_state=SEED)
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protocol = UPP(val, repeats=n_bags_val)
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modsel = GridSearchQ(
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quantifier, param_grid, protocol, refit=True, n_jobs=-1, verbose=1, error='mae'
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)
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t_init = time()
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try:
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modsel.fit(train)
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print(f'best params {modsel.best_params_}')
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print(f'best score {modsel.best_score_}')
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quantifier = modsel.best_model()
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except:
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print('something went wrong... trying to fit the default model')
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quantifier.fit(train)
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train_time = time() - t_init
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else:
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# transductive
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t_init = time()
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quantifier.fit(train) # <-- nothing actually (proyects the X into posteriors only)
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train_time = time() - t_init
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# test
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t_init = time()
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protocol = UPP(test, repeats=n_bags_test)
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report = qp.evaluation.evaluation_report(
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quantifier, protocol, error_metrics=['mae', 'mrae', 'kld'], verbose=True
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)
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test_time = time() - t_init
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report['tr_time'] = train_time
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report['te_time'] = test_time
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report.to_csv(local_result_path)
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means = report.mean(numeric_only=True)
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csv.write(f'{method_name}\t{dataset}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\t{means["kld"]:.5f}\t{means["tr_time"]:.3f}\t{means["te_time"]:.3f}\n')
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csv.flush()
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if __name__ == '__main__':
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qp.environ['SAMPLE_SIZE'] = 500
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qp.environ['N_JOBS'] = -1
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n_bags_val = 25
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n_bags_test = 100
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n_bags_val = 100
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n_bags_test = 500
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result_dir = f'results_quantification/ucimulti'
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os.makedirs(result_dir, exist_ok=True)
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@ -95,69 +165,6 @@ if __name__ == '__main__':
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csv.write(f'Method\tDataset\tMAE\tMRAE\tKLD\tTR-TIME\tTE-TIME\n')
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for method_name, quantifier, param_grid in METHODS + TRANSDUCTIVE_METHODS:
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print('Init method', method_name)
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with open(global_result_path + '.csv', 'at') as csv:
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for dataset in qp.datasets.UCI_MULTICLASS_DATASETS:
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print('init', dataset)
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# run_experiment(global_result_path, method_name, quantifier, param_grid, dataset)
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local_result_path = os.path.join(Path(global_result_path).parent, method_name + '_' + dataset + '.dataframe')
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if os.path.exists(local_result_path):
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print(f'result file {local_result_path} already exist; skipping')
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report = qp.util.load_report(local_result_path)
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else:
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with qp.util.temp_seed(SEED):
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data = qp.datasets.fetch_UCIMulticlassDataset(dataset, verbose=True)
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train, test = data.train_test
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transductive_names = [name for (name, *_) in TRANSDUCTIVE_METHODS]
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if method_name not in transductive_names:
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if len(param_grid) == 0:
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t_init = time()
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quantifier.fit(train)
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train_time = time() - t_init
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else:
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# model selection (train)
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train, val = train.split_stratified(random_state=SEED)
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protocol = UPP(val, repeats=n_bags_val)
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modsel = GridSearchQ(
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quantifier, param_grid, protocol, refit=True, n_jobs=-1, verbose=1, error='mae'
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)
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t_init = time()
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try:
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modsel.fit(train)
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print(f'best params {modsel.best_params_}')
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print(f'best score {modsel.best_score_}')
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quantifier = modsel.best_model()
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except:
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print('something went wrong... trying to fit the default model')
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quantifier.fit(train)
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train_time = time() - t_init
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else:
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# transductive
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t_init = time()
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quantifier.fit(train) # <-- nothing actually (proyects the X into posteriors only)
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train_time = time() - t_init
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# test
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t_init = time()
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protocol = UPP(test, repeats=n_bags_test)
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report = qp.evaluation.evaluation_report(
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quantifier, protocol, error_metrics=['mae', 'mrae', 'kld'], verbose=True
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)
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test_time = time() - t_init
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report['tr_time'] = train_time
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report['te_time'] = test_time
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report.to_csv(local_result_path)
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means = report.mean(numeric_only=True)
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csv.write(f'{method_name}\t{dataset}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\t{means["kld"]:.5f}\t{means["tr_time"]:.3f}\t{means["te_time"]:.3f}\n')
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csv.flush()
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run_experiment(method_name, quantifier, param_grid)
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show_results(global_result_path)
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@ -21,7 +21,127 @@ SEED = 1
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def newLR():
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return LogisticRegression(max_iter=1000)#, C=1, class_weight='balanced')
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return LogisticRegression(max_iter=1000)
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def plot(xaxis, metrics_measurements, metrics_names, suffix):
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fig, ax1 = plt.subplots(figsize=(8, 6))
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def add_plot(ax, mean_error, std_error, name, color, marker):
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ax.plot(xaxis, mean_error, label=name, marker=marker, color=color)
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if std_error is not None:
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ax.fill_between(xaxis, mean_error - std_error, mean_error + std_error, color=color, alpha=0.2)
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colors = ['b', 'g', 'r', 'c', 'purple']
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def get_mean_std(measurement):
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measurement = np.asarray(measurement)
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measurement_mean = np.mean(measurement, axis=0)
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if measurement.ndim == 2:
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measurement_std = np.std(measurement, axis=0)
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else:
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measurement_std = None
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return measurement_mean, measurement_std
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for i, (measurement, name) in enumerate(zip(metrics_measurements, metrics_names)):
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color = colors[i%len(colors)]
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add_plot(ax1, *get_mean_std(measurement), name, color=color, marker='o')
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ax1.set_xscale('log')
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# Configurar etiquetas para el primer eje Y
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ax1.set_xlabel('Bandwidth')
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ax1.set_ylabel('Normalized value')
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ax1.grid(True)
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ax1.legend(loc='upper left')
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# Crear un segundo eje Y que comparte el eje X
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# ax2 = ax1.twinx()
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# Pintar likelihood_val en el segundo eje Y
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# add_plot(ax2, *get_mean_std(likelihood_measurements), name='NLL', color='purple', marker='x')
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# Configurar etiquetas para el segundo eje Y
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# ax1.set_ylabel('neg log likelihood')
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# ax1.legend(loc='upper right')
|
||||
|
||||
# Mostrar el gráfico
|
||||
plt.title(dataset)
|
||||
# plt.show()
|
||||
os.makedirs('./plots/likelihood/', exist_ok=True)
|
||||
|
||||
plt.savefig(f'./plots/likelihood/{dataset}-fig{suffix}.png')
|
||||
plt.close()
|
||||
|
||||
|
||||
def generate_data(from_train=False):
|
||||
data = qp.datasets.fetch_UCIMulticlassDataset(dataset)
|
||||
n_classes = data.n_classes
|
||||
print(f'{i=}')
|
||||
print(f'{dataset=}')
|
||||
print(f'{n_classes=}')
|
||||
print(len(data.training))
|
||||
print(len(data.test))
|
||||
|
||||
train, test = data.train_test
|
||||
if from_train:
|
||||
train, test = train.split_stratified(0.5)
|
||||
train_prev = train.prevalence()
|
||||
test_prev = test.prevalence()
|
||||
|
||||
print(f'train-prev = {F.strprev(train_prev)}')
|
||||
print(f'test-prev = {F.strprev(test_prev)}')
|
||||
|
||||
repeats = 10
|
||||
prot = UPP(test, sample_size=SAMPLE_SIZE, repeats=repeats)
|
||||
kde = KDEyMLauto(newLR())
|
||||
kde.fit(train)
|
||||
AE_error, RAE_error, MSE_error, KLD_error, LIKE_value = [], [], [], [], []
|
||||
tr_posteriors, tr_y = kde.classif_predictions.Xy
|
||||
for sample_no, (sample, prev) in tqdm(enumerate(prot()), total=repeats):
|
||||
te_posteriors = kde.classifier.predict_proba(sample)
|
||||
classes = train.classes_
|
||||
|
||||
xaxis = []
|
||||
ae_error = []
|
||||
rae_error = []
|
||||
mse_error = []
|
||||
kld_error = []
|
||||
likelihood_value = []
|
||||
|
||||
# for bandwidth in np.linspace(0.01, 0.2, 50):
|
||||
for bandwidth in np.logspace(-5, np.log10(0.2), 50):
|
||||
mix_densities = kde.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth)
|
||||
test_densities = [kde.pdf(kde_i, te_posteriors) for kde_i in mix_densities]
|
||||
|
||||
def neg_loglikelihood_prev(prev):
|
||||
test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(prev, test_densities))
|
||||
test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
|
||||
return -np.sum(test_loglikelihood)
|
||||
|
||||
init_prev = np.full(fill_value=1 / n_classes, shape=(n_classes,))
|
||||
pred_prev, likelihood = optim_minimize(neg_loglikelihood_prev, init_prev, return_loss=True)
|
||||
|
||||
xaxis.append(bandwidth)
|
||||
ae_error.append(qp.error.ae(prev, pred_prev))
|
||||
rae_error.append(qp.error.rae(prev, pred_prev))
|
||||
mse_error.append(qp.error.mse(prev, pred_prev))
|
||||
kld_error.append(qp.error.kld(prev, pred_prev))
|
||||
likelihood_value.append(likelihood)
|
||||
|
||||
AE_error.append(ae_error)
|
||||
RAE_error.append(rae_error)
|
||||
MSE_error.append(mse_error)
|
||||
KLD_error.append(kld_error)
|
||||
LIKE_value.append(likelihood_value)
|
||||
|
||||
return xaxis, AE_error, RAE_error, MSE_error, KLD_error, LIKE_value
|
||||
|
||||
|
||||
def normalize_metric(Error_matrix):
|
||||
max_val, min_val = np.max(Error_matrix), np.min(Error_matrix)
|
||||
return (np.asarray(Error_matrix) - min_val) / (max_val - min_val)
|
||||
|
||||
|
||||
SAMPLE_SIZE=150
|
||||
qp.environ['SAMPLE_SIZE'] = SAMPLE_SIZE
|
||||
|
|
@ -30,158 +150,57 @@ show_ae = True
|
|||
show_rae = True
|
||||
show_mse = False
|
||||
show_kld = True
|
||||
normalize = True
|
||||
|
||||
epsilon = 1e-10
|
||||
# n_bags_test = 2
|
||||
# DATASETS = [qp.datasets.UCI_MULTICLASS_DATASETS[21]]
|
||||
DATASETS = qp.datasets.UCI_MULTICLASS_DATASETS
|
||||
for i, dataset in enumerate(DATASETS):
|
||||
for i, dataset in enumerate(tqdm(DATASETS, desc='processing datasets', total=len(DATASETS))):
|
||||
|
||||
def generate_data():
|
||||
data = qp.datasets.fetch_UCIMulticlassDataset(dataset)
|
||||
n_classes = data.n_classes
|
||||
print(f'{i=}')
|
||||
print(f'{dataset=}')
|
||||
print(f'{n_classes=}')
|
||||
print(len(data.training))
|
||||
print(len(data.test))
|
||||
|
||||
train, test = data.train_test
|
||||
train_prev = train.prevalence()
|
||||
test_prev = test.prevalence()
|
||||
xaxis, AE_error_te, RAE_error_te, MSE_error_te, KLD_error_te, LIKE_value_te = qp.util.pickled_resource(
|
||||
f'./plots/likelihood/pickles/{dataset}.pkl', generate_data, False
|
||||
)
|
||||
|
||||
print(f'train-prev = {F.strprev(train_prev)}')
|
||||
print(f'test-prev = {F.strprev(test_prev)}')
|
||||
xaxis, AE_error_tr, RAE_error_tr, MSE_error_tr, KLD_error_tr, LIKE_value_tr = qp.util.pickled_resource(
|
||||
f'./plots/likelihood/pickles/{dataset}_tr.pkl', generate_data, True
|
||||
)
|
||||
|
||||
repeats = 10
|
||||
prot = UPP(test, sample_size=SAMPLE_SIZE, repeats=repeats)
|
||||
kde = KDEyMLauto(newLR())
|
||||
kde.fit(train)
|
||||
AE_error, RAE_error, MSE_error, KLD_error, LIKE_value = [], [], [], [], []
|
||||
tr_posteriors, tr_y = kde.classif_predictions.Xy
|
||||
for it, (sample, prev) in tqdm(enumerate(prot()), total=repeats):
|
||||
te_posteriors = kde.classifier.predict_proba(sample)
|
||||
classes = train.classes_
|
||||
|
||||
xaxis = []
|
||||
ae_error = []
|
||||
rae_error = []
|
||||
mse_error = []
|
||||
kld_error = []
|
||||
likelihood_value = []
|
||||
|
||||
# for bandwidth in np.linspace(0.01, 0.2, 50):
|
||||
for bandwidth in np.logspace(-5, 0.5, 50):
|
||||
mix_densities = kde.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth)
|
||||
test_densities = [kde.pdf(kde_i, te_posteriors) for kde_i in mix_densities]
|
||||
|
||||
def neg_loglikelihood_prev(prev):
|
||||
test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(prev, test_densities))
|
||||
test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
|
||||
return -np.sum(test_loglikelihood)
|
||||
|
||||
init_prev = np.full(fill_value=1 / n_classes, shape=(n_classes,))
|
||||
pred_prev, likelihood = optim_minimize(neg_loglikelihood_prev, init_prev, return_loss=True)
|
||||
|
||||
xaxis.append(bandwidth)
|
||||
ae_error.append(qp.error.ae(prev, pred_prev))
|
||||
rae_error.append(qp.error.rae(prev, pred_prev))
|
||||
mse_error.append(qp.error.mse(prev, pred_prev))
|
||||
kld_error.append(qp.error.kld(prev, pred_prev))
|
||||
likelihood_value.append(likelihood)
|
||||
|
||||
AE_error.append(ae_error)
|
||||
RAE_error.append(rae_error)
|
||||
MSE_error.append(mse_error)
|
||||
KLD_error.append(kld_error)
|
||||
LIKE_value.append(likelihood_value)
|
||||
|
||||
return xaxis, AE_error, RAE_error, MSE_error, KLD_error, LIKE_value
|
||||
|
||||
xaxis, AE_error, RAE_error, MSE_error, KLD_error, LIKE_value = qp.util.pickled_resource(
|
||||
f'./plots/likelihood/pickles/{dataset}.pkl', generate_data)
|
||||
|
||||
for row in range(len(AE_error)):
|
||||
|
||||
# Crear la figura
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
fig, ax1 = plt.subplots(figsize=(8, 6))
|
||||
|
||||
# Pintar las series ae_error, rae_error, y kld_error en el primer eje Y
|
||||
if show_ae:
|
||||
ax1.plot(xaxis, AE_error[row], label='AE', marker='o', color='b')
|
||||
if show_rae:
|
||||
ax1.plot(xaxis, RAE_error[row], label='RAE', marker='s', color='g')
|
||||
if show_kld:
|
||||
ax1.plot(xaxis, KLD_error[row], label='KLD', marker='^', color='r')
|
||||
if show_mse:
|
||||
ax1.plot(xaxis, MSE_error[row], label='MSE', marker='^', color='c')
|
||||
ax1.set_xscale('log')
|
||||
|
||||
# Configurar etiquetas para el primer eje Y
|
||||
ax1.set_xlabel('Bandwidth')
|
||||
ax1.set_ylabel('Error Value')
|
||||
ax1.grid(True)
|
||||
ax1.legend(loc='upper left')
|
||||
|
||||
# Crear un segundo eje Y que comparte el eje X
|
||||
ax2 = ax1.twinx()
|
||||
|
||||
# Pintar likelihood_val en el segundo eje Y
|
||||
ax2.plot(xaxis, LIKE_value[row], label='(neg)Likelihood', marker='x', color='purple')
|
||||
|
||||
# Configurar etiquetas para el segundo eje Y
|
||||
ax2.set_ylabel('Likelihood Value')
|
||||
ax2.legend(loc='upper right')
|
||||
|
||||
# Mostrar el gráfico
|
||||
plt.title('Error Metrics vs Bandwidth')
|
||||
# plt.show()
|
||||
os.makedirs('./plots/likelihood/', exist_ok=True)
|
||||
plt.savefig(f'./plots/likelihood/{dataset}-fig{row}.png')
|
||||
plt.close()
|
||||
|
||||
# Crear la figura con las medias
|
||||
# Test measurements
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
fig, ax1 = plt.subplots(figsize=(8, 6))
|
||||
measurements = []
|
||||
measurement_names = []
|
||||
if show_ae:
|
||||
measurements.append(AE_error_te)
|
||||
measurement_names.append('AE')
|
||||
if show_rae:
|
||||
measurements.append(RAE_error_te)
|
||||
measurement_names.append('RAE')
|
||||
if show_kld:
|
||||
measurements.append(KLD_error_te)
|
||||
measurement_names.append('KLD')
|
||||
if show_mse:
|
||||
measurements.append(MSE_error_te)
|
||||
measurement_names.append('MSE')
|
||||
measurements.append(normalize_metric(LIKE_value_te))
|
||||
measurements.append(normalize_metric(LIKE_value_tr))
|
||||
measurement_names.append('NLL(te)')
|
||||
measurement_names.append('NLL(tr)')
|
||||
|
||||
def add_plot(ax, vals_error, name, color, marker, show):
|
||||
if not show:
|
||||
return
|
||||
vals_error = np.asarray(vals_error)
|
||||
vals_ave = np.mean(vals_error, axis=0)
|
||||
vals_std = np.std(vals_error, axis=0)
|
||||
ax.plot(xaxis, vals_ave, label=name, marker=marker, color=color)
|
||||
ax.fill_between(xaxis, vals_ave - vals_std, vals_ave + vals_std, color=color, alpha=0.2)
|
||||
if normalize:
|
||||
measurements = [normalize_metric(m) for m in measurements]
|
||||
|
||||
add_plot(ax1, AE_error, 'AE', color='b', marker='o', show=show_ae)
|
||||
add_plot(ax1, RAE_error, 'RAE', color='g', marker='s', show=show_rae)
|
||||
add_plot(ax1, KLD_error, 'KLD', color='r', marker='^', show=show_kld)
|
||||
add_plot(ax1, MSE_error, 'MSE', color='c', marker='^', show=show_mse)
|
||||
ax1.set_xscale('log')
|
||||
# plot(xaxis, measurements, measurement_names, suffix='AVE')
|
||||
|
||||
# Configurar etiquetas para el primer eje Y
|
||||
ax1.set_xlabel('Bandwidth')
|
||||
ax1.set_ylabel('Error Value')
|
||||
ax1.grid(True)
|
||||
ax1.legend(loc='upper left')
|
||||
|
||||
# Crear un segundo eje Y que comparte el eje X
|
||||
ax2 = ax1.twinx()
|
||||
|
||||
# Pintar likelihood_val en el segundo eje Y
|
||||
add_plot(ax2, LIKE_value, '(neg)Likelihood', color='purple', marker='x', show=True)
|
||||
|
||||
# Configurar etiquetas para el segundo eje Y
|
||||
ax2.set_ylabel('Likelihood Value')
|
||||
ax2.legend(loc='upper right')
|
||||
|
||||
# Mostrar el gráfico
|
||||
plt.title('Error Metrics vs Bandwidth')
|
||||
# plt.show()
|
||||
os.makedirs('./plots/likelihood/', exist_ok=True)
|
||||
plt.savefig(f'./plots/likelihood/{dataset}-figAve.png')
|
||||
plt.close()
|
||||
# Train-Test measurements
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
# measurements = []
|
||||
# measurement_names = []
|
||||
# measurements.append(normalize_metric(LIKE_value_te))
|
||||
# measurements.append(normalize_metric(LIKE_value_tr))
|
||||
# measurement_names.append('NLL(te)')
|
||||
# measurement_names.append('NLL(tr)')
|
||||
plot(xaxis, measurements, measurement_names, suffix='AVEtr')
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue