134 lines
4.4 KiB
Python
134 lines
4.4 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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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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SAMPLE_SIZE=150
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qp.environ['SAMPLE_SIZE'] = SAMPLE_SIZE
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epsilon = 1e-10
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# n_bags_test = 2
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DATASETS = [qp.datasets.UCI_MULTICLASS_DATASETS[21]]
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# DATASETS = qp.datasets.UCI_MULTICLASS_DATASETS
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for i, dataset in enumerate(DATASETS):
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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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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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# protocol = UPP(test, repeats=n_bags_test)
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#
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# for sample, prev in protocol():
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# print(f'sample-prev = {F.strprev(prev)}')
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# prev = np.asarray([0.2, 0.3, 0.5])
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# prev = np.asarray([0.33, 0.33, 0.34])
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# prev = train_prev
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# sample = test.sampling(SAMPLE_SIZE, *prev, random_state=1)
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# print(f'sample-prev = {F.strprev(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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tr_posteriors, tr_y = kde.classif_predictions.Xy
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for it, (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_val = []
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# for bandwidth in np.linspace(0.01, 0.2, 50):
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for bandwidth in np.logspace(-3, 0.5, 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_val.append(likelihood)
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import matplotlib.pyplot as plt
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# Crear la figura
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fig, ax1 = plt.subplots(figsize=(8, 6))
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# Pintar las series ae_error, rae_error, y kld_error en el primer eje Y
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ax1.plot(xaxis, ae_error, label='AE Error', marker='o', color='b')
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ax1.plot(xaxis, rae_error, label='RAE Error', marker='s', color='g')
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ax1.plot(xaxis, kld_error, label='KLD Error', marker='^', color='r')
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ax1.plot(xaxis, mse_error, label='MSE Error', marker='^', color='c')
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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('Error 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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ax2.plot(xaxis, likelihood_val, label='(neg)Likelihood', marker='x', color='purple')
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# Configurar etiquetas para el segundo eje Y
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ax2.set_ylabel('Likelihood Value')
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ax2.legend(loc='upper right')
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# Mostrar el gráfico
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plt.title('Error Metrics vs Bandwidth')
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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/fig{it}.png')
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plt.close()
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