From b485205c7cf95efb26e42066ef1b1df611ddc3b2 Mon Sep 17 00:00:00 2001
From: Alejandro Moreo <alejandro.moreo@isti.cnr.it>
Date: Tue, 17 Sep 2024 10:39:39 +0200
Subject: [PATCH] cleaning dir KDEy

---
 KDEy/experiments.py | 108 --------------------------------------------
 1 file changed, 108 deletions(-)
 delete mode 100644 KDEy/experiments.py

diff --git a/KDEy/experiments.py b/KDEy/experiments.py
deleted file mode 100644
index fb279fc..0000000
--- a/KDEy/experiments.py
+++ /dev/null
@@ -1,108 +0,0 @@
-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)
-
-
-
-
-