diff --git a/KDEy/kdey_devel.py b/KDEy/kdey_devel.py index 958c870..1e6d08b 100644 --- a/KDEy/kdey_devel.py +++ b/KDEy/kdey_devel.py @@ -258,7 +258,7 @@ def optim_minimize(loss: Callable, init_prev: np.ndarray, return_loss=False): constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)}) # values summing up to 1 r = optimize.minimize(loss, x0=init_prev, method='SLSQP', bounds=bounds, constraints=constraints) # print(f'iterations-prevalence={r.nit}') - assert r.success, 'Process did not converge!' + # assert r.success, 'Process did not converge!' if return_loss: return r.x, r.fun else: diff --git a/KDEy/quantification_evaluation.py b/KDEy/quantification_evaluation.py index 06d8b7f..ea0f43c 100644 --- a/KDEy/quantification_evaluation.py +++ b/KDEy/quantification_evaluation.py @@ -39,7 +39,7 @@ METHODS = [ ('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)), - ('KDEy-ML-autoLike+', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood+'), wrap_hyper(logreg_grid)), + ('KDEy-ML-autoLike+', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood+'), wrap_hyper(logreg_grid)), ('KDEy-ML-autoAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mae'), wrap_hyper(logreg_grid)), ('KDEy-ML-autoRAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mrae'), wrap_hyper(logreg_grid)), ] diff --git a/KDEy/quantification_evaluation_debug.py b/KDEy/quantification_evaluation_debug.py index a2f5e69..61d4517 100644 --- a/KDEy/quantification_evaluation_debug.py +++ b/KDEy/quantification_evaluation_debug.py @@ -21,7 +21,127 @@ SEED = 1 def newLR(): - return LogisticRegression(max_iter=1000)#, C=1, class_weight='balanced') + return LogisticRegression(max_iter=1000) + + +def plot(xaxis, metrics_measurements, metrics_names, suffix): + fig, ax1 = plt.subplots(figsize=(8, 6)) + + def add_plot(ax, mean_error, std_error, name, color, marker): + ax.plot(xaxis, mean_error, label=name, marker=marker, color=color) + if std_error is not None: + ax.fill_between(xaxis, mean_error - std_error, mean_error + std_error, color=color, alpha=0.2) + + colors = ['b', 'g', 'r', 'c', 'purple'] + + def get_mean_std(measurement): + measurement = np.asarray(measurement) + measurement_mean = np.mean(measurement, axis=0) + if measurement.ndim == 2: + measurement_std = np.std(measurement, axis=0) + else: + measurement_std = None + return measurement_mean, measurement_std + + for i, (measurement, name) in enumerate(zip(metrics_measurements, metrics_names)): + color = colors[i%len(colors)] + add_plot(ax1, *get_mean_std(measurement), name, color=color, marker='o') + + ax1.set_xscale('log') + + # Configurar etiquetas para el primer eje Y + ax1.set_xlabel('Bandwidth') + ax1.set_ylabel('Normalized 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, *get_mean_std(likelihood_measurements), name='NLL', color='purple', marker='x') + + # Configurar etiquetas para el segundo eje Y + # ax1.set_ylabel('neg log likelihood') + # 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 +150,55 @@ 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(LIKE_value_te) + measurement_names.append('NLL') - 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')