269 lines
9.3 KiB
Python
269 lines
9.3 KiB
Python
import pickle
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import os
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from time import time
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from collections import defaultdict
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from tqdm import tqdm
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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, optim_minimize
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from method._kdey import KDEBase
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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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from pathlib import Path
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from quapy import functional as F
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import matplotlib.pyplot as plt
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SEED = 1
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def newLR():
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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, markersize=3)
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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')
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# Mostrar el gráfico
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plt.title(dataset)
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# plt.show()
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os.makedirs('./plots/likelihood/', exist_ok=True)
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plt.savefig(f'./plots/likelihood/{dataset}-fig{suffix}.png')
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plt.close()
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def plot_stack(xaxis, metrics_measurements, metrics_names, figname):
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n_measures = len(metrics_measurements)//2
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fig, axs = plt.subplots(n_measures, 1, figsize=(8, 3*n_measures))
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indexes = np.arange(len(metrics_measurements))
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axs_idx = 0
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# colors = ['b', 'g', 'r', 'c', 'purple']
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for m_te, m_tr in zip(indexes[:-1:2], indexes[1::2]):
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metric_te, metric_tr = metrics_measurements[m_te], metrics_measurements[m_tr]
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metric_te_name, metric_tr_name = metrics_names[m_te], metrics_names[m_tr]
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metric_mean_tr = np.mean(metric_tr, axis=0)
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metric_std_tr = np.std(metric_tr, axis=0)
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metric_mean_te = np.mean(metric_te, axis=0)
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metric_std_te = np.std(metric_te, axis=0)
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axs[axs_idx].plot(xaxis, metric_mean_tr, label=metric_tr_name, marker='o', color='r', markersize=3)
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axs[axs_idx].fill_between(xaxis, metric_mean_tr - metric_std_tr, metric_mean_tr + metric_std_tr, color='r', alpha=0.2)
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minx = np.argmin(metric_mean_tr)
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axs[axs_idx].axvline(xaxis[minx], color='r', linestyle='--', linewidth=1)
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axs[axs_idx].plot(xaxis, metric_mean_te, label=metric_te_name, marker='o', color='b', markersize=3)
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axs[axs_idx].fill_between(xaxis, metric_mean_te - metric_std_te, metric_mean_te + metric_std_te, color='b', alpha=0.2)
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minx = np.argmin(metric_mean_te)
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axs[axs_idx].axvline(xaxis[minx], color='b', linestyle='--', linewidth=1)
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# axs[axs_idx].set_title(f'{metric_te_name} and {metric_tr_name}')
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axs[axs_idx].legend(loc='lower right')
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axs[axs_idx].set_xscale('log')
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if axs_idx==0:
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axs[axs_idx].set_title(dataset)
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if axs_idx < len(indexes)//2 -1:
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axs[axs_idx].set_xticks([])
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axs_idx += 1
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# Ajustar el espaciado entre los subplots
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plt.tight_layout()
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# Mostrar el gráfico
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# Mostrar el gráfico
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# plt.title(dataset)
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# plt.show()
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os.makedirs('./plots/likelihood/', exist_ok=True)
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plt.savefig(f'./plots/likelihood/{figname}.png')
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plt.close()
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def generate_data(from_train=False):
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data = qp.datasets.fetch_UCIMulticlassDataset(dataset)
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n_classes = data.n_classes
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print(f'{i=}')
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print(f'{dataset=}')
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print(f'{n_classes=}')
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print(len(data.training))
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print(len(data.test))
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train, test = data.train_test
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if from_train:
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train, test = train.split_stratified(0.5)
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train_prev = train.prevalence()
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test_prev = test.prevalence()
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print(f'train-prev = {F.strprev(train_prev)}')
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print(f'test-prev = {F.strprev(test_prev)}')
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repeats = 10
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prot = UPP(test, sample_size=SAMPLE_SIZE, repeats=repeats)
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kde = KDEyMLauto(newLR())
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kde.fit(train)
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AE_error, RAE_error, MSE_error, KLD_error, LIKE_value = [], [], [], [], []
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tr_posteriors, tr_y = kde.classif_predictions.Xy
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for sample_no, (sample, prev) in tqdm(enumerate(prot()), total=repeats):
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te_posteriors = kde.classifier.predict_proba(sample)
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classes = train.classes_
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xaxis = []
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ae_error = []
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rae_error = []
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mse_error = []
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kld_error = []
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likelihood_value = []
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# for bandwidth in np.linspace(0.01, 0.2, 50):
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for bandwidth in np.logspace(-4, np.log10(0.2), 50):
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mix_densities = kde.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth)
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test_densities = [kde.pdf(kde_i, te_posteriors) 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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init_prev = np.full(fill_value=1 / n_classes, shape=(n_classes,))
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pred_prev, likelihood = optim_minimize(neg_loglikelihood_prev, init_prev, return_loss=True)
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xaxis.append(bandwidth)
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ae_error.append(qp.error.ae(prev, pred_prev))
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rae_error.append(qp.error.rae(prev, pred_prev))
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mse_error.append(qp.error.mse(prev, pred_prev))
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kld_error.append(qp.error.kld(prev, pred_prev))
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likelihood_value.append(likelihood)
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AE_error.append(ae_error)
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RAE_error.append(rae_error)
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MSE_error.append(mse_error)
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KLD_error.append(kld_error)
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LIKE_value.append(likelihood_value)
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return xaxis, AE_error, RAE_error, MSE_error, KLD_error, LIKE_value
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def normalize_metric(Error_matrix):
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max_val, min_val = np.max(Error_matrix), np.min(Error_matrix)
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return (np.asarray(Error_matrix) - min_val) / (max_val - min_val)
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SAMPLE_SIZE=150
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qp.environ['SAMPLE_SIZE'] = SAMPLE_SIZE
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show_ae = True
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show_rae = True
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show_mse = False
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show_kld = False
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normalize = True
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epsilon = 1e-10
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DATASETS = qp.datasets.UCI_MULTICLASS_DATASETS
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for i, dataset in enumerate(tqdm(DATASETS, desc='processing datasets', total=len(DATASETS))):
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xaxis, AE_error_te, RAE_error_te, MSE_error_te, KLD_error_te, LIKE_value_te = qp.util.pickled_resource(
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f'./plots/likelihood/pickles/{dataset}.pkl', generate_data, False
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)
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xaxis, AE_error_tr, RAE_error_tr, MSE_error_tr, KLD_error_tr, LIKE_value_tr = qp.util.pickled_resource(
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f'./plots/likelihood/pickles/{dataset}_tr.pkl', generate_data, True
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)
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# Test measurements
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# ----------------------------------------------------------------------------------------------------
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measurements = []
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measurement_names = []
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if show_ae:
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measurements.append(AE_error_te)
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measurement_names.append('AE(te)')
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measurements.append(AE_error_tr)
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measurement_names.append('AE(tr)')
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if show_rae:
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measurements.append(RAE_error_te)
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measurement_names.append('RAE(te)')
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measurements.append(RAE_error_tr)
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measurement_names.append('RAE(tr)')
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if show_kld:
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measurements.append(KLD_error_te)
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measurement_names.append('KLD(te)')
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measurements.append(KLD_error_tr)
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measurement_names.append('KLD(tr)')
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if show_mse:
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measurements.append(MSE_error_te)
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measurement_names.append('MSE(te)')
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measurements.append(MSE_error_tr)
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measurement_names.append('MSE(tr)')
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measurements.append(normalize_metric(LIKE_value_te))
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measurements.append(normalize_metric(LIKE_value_tr))
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measurement_names.append('NLL(te)')
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measurement_names.append('NLL(tr)')
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if normalize:
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measurements = [normalize_metric(m) for m in measurements]
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# plot(xaxis, measurements, measurement_names, suffix='AVE')
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# Train-Test measurements
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# ----------------------------------------------------------------------------------------------------
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# measurements = []
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# measurement_names = []
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# measurements.append(normalize_metric(LIKE_value_te))
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# measurements.append(normalize_metric(LIKE_value_tr))
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# measurement_names.append('NLL(te)')
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# measurement_names.append('NLL(tr)')
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# plot(xaxis, measurements, measurement_names, suffix='AVEtr')
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plot_stack(xaxis, measurements, measurement_names, figname=f'{i}.png')
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