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cleaning dir KDEy

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Alejandro Moreo Fernandez 2024-09-17 10:39:39 +02:00
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commit b485205c7c
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KDEy/experiments.py
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import numpy as np
from sklearn.linear_model import LogisticRegression
from os.path import join
import quapy as qp
from quapy.protocol import UPP
from quapy.method.aggregative import KDEyML
DEBUG = True
qp.environ["SAMPLE_SIZE"] = 100 if DEBUG else 500
val_repeats = 100 if DEBUG else 500
test_repeats = 100 if DEBUG else 500
if DEBUG:
qp.environ["DEFAULT_CLS"] = LogisticRegression()
test_results = {}
val_choice = {}
bandwidth_range = np.linspace(0.01, 0.20, 20)
if DEBUG:
bandwidth_range = np.linspace(0.01, 0.20, 10)
def datasets():
for dataset_name in qp.datasets.UCI_MULTICLASS_DATASETS[:4]:
dataset = qp.datasets.fetch_UCIMulticlassDataset(dataset_name)
if DEBUG:
dataset = dataset.reduce(random_state=0)
yield dataset
def experiment_dataset(dataset):
train, test = dataset.train_test
test_gen = UPP(test, repeats=test_repeats)
# bandwidth chosen during model selection in validation
train_tr, train_va = train.split_stratified(random_state=0)
kdey = KDEyML(random_state=0)
modsel = qp.model_selection.GridSearchQ(
model=kdey,
param_grid={'bandwidth': bandwidth_range},
protocol=UPP(train_va, repeats=val_repeats),
refit=False,
n_jobs=-1
).fit(train_tr)
chosen_bandwidth = modsel.best_params_['bandwidth']
modsel_choice = float(chosen_bandwidth)
# results in test
print(f"testing KDEy in {dataset.name}")
dataset_results = []
for b in bandwidth_range:
kdey = KDEyML(bandwidth=b, random_state=0)
kdey.fit(train)
mae = qp.evaluation.evaluate(kdey, protocol=test_gen, error_metric='mae', verbose=True)
print(f'bandwidth={b}: {mae:.5f}')
dataset_results.append((float(b), float(mae)))
return modsel_choice, dataset_results
def plot_bandwidth(val_choice, test_results):
for dataset_name in val_choice.keys():
import matplotlib.pyplot as plt
bandwidths, results = zip(*test_results[dataset_name])
# Crear la gráfica
plt.figure(figsize=(8, 6))
# Graficar los puntos de datos
plt.plot(bandwidths, results, marker='o')
# Agregar la línea vertical en bandwidth_chosen
plt.axvline(x=val_choice[dataset_name], color='r', linestyle='--', label=f'Bandwidth elegido: {val_choice[dataset_name]}')
# Agregar etiquetas y título
plt.xlabel('Bandwidth')
plt.ylabel('Resultado')
plt.title('Gráfica de Bandwidth vs Resultado')
# Mostrar la leyenda
plt.legend()
# Mostrar la gráfica
plt.grid(True)
plt.show()
for dataset in datasets():
if DEBUG:
result_path = f'./results/debug/{dataset.name}.pkl'
else:
result_path = f'./results/{dataset.name}.pkl'
modsel_choice, dataset_results = qp.util.pickled_resource(result_path, experiment_dataset, dataset)
val_choice[dataset.name] = modsel_choice
test_results[dataset.name] = dataset_results
print(f'Dataset = {dataset.name}')
print(modsel_choice)
print(dataset_results)
plot_bandwidth(val_choice, test_results)