improving plots debug

KDEy/quantification_evaluation_debug.py

@@ -28,7 +28,7 @@ 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)
+        ax.plot(xaxis, mean_error, label=name, marker=marker, color=color, markersize=3)
         if std_error is not None:
             ax.fill_between(xaxis, mean_error - std_error, mean_error + std_error, color=color, alpha=0.2)
 
@@ -74,6 +74,56 @@ def plot(xaxis, metrics_measurements, metrics_names, suffix):
     plt.close()
 
 
+def plot_stack(xaxis, metrics_measurements, metrics_names, suffix):
+
+    # Crear la figura y los ejes (4 bloques verticales)
+    fig, axs = plt.subplots(4, 1, figsize=(8, 12))
+
+    x = xaxis
+
+    indexes = np.arange(len(metrics_measurements))
+    axs_idx = 0
+    # colors = ['b', 'g', 'r', 'c', 'purple']
+    for m_te, m_tr in zip(indexes[:-1:2], indexes[1::2]):
+        metric_te, metric_tr = metrics_measurements[m_te], metrics_measurements[m_tr]
+        metric_te_name, metric_tr_name = metrics_names[m_te], metrics_names[m_tr]
+
+        metric_mean_tr = np.mean(metric_tr, axis=0)
+        metric_std_tr = np.std(metric_tr, axis=0)
+        metric_mean_te = np.mean(metric_te, axis=0)
+        metric_std_te = np.std(metric_te, axis=0)
+
+        axs[axs_idx].plot(xaxis, metric_mean_tr, label=metric_tr_name, marker='o', color='r', markersize=3)
+        axs[axs_idx].fill_between(xaxis, metric_mean_tr - metric_std_tr, metric_mean_tr + metric_std_tr, color='r', alpha=0.2)
+        minx = np.argmin(metric_mean_tr)
+        axs[axs_idx].axvline(xaxis[minx], color='r', linestyle='--', linewidth=1)
+
+        axs[axs_idx].plot(xaxis, metric_mean_te, label=metric_te_name, marker='o', color='b', markersize=3)
+        axs[axs_idx].fill_between(xaxis, metric_mean_te - metric_std_te, metric_mean_te + metric_std_te, color='b', alpha=0.2)
+        minx = np.argmin(metric_mean_te)
+        axs[axs_idx].axvline(xaxis[minx], color='b', linestyle='--', linewidth=1)
+
+        # axs[axs_idx].set_title(f'{metric_te_name} and {metric_tr_name}')
+        axs[axs_idx].legend(loc='lower right')
+        if axs_idx < len(indexes)//2 -1:
+            axs[axs_idx].set_xticks([])
+
+        axs_idx += 1
+
+    # Ajustar el espaciado entre los subplots
+    plt.tight_layout()
+
+    # Mostrar el gráfico
+
+    # 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
@@ -110,7 +160,7 @@ def generate_data(from_train=False):
         likelihood_value = []
 
         # for bandwidth in np.linspace(0.01, 0.2, 50):
-        for bandwidth in np.logspace(-5, np.log10(0.2), 50):
+        for bandwidth in np.logspace(-4, 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]
 
@@ -172,16 +222,24 @@ for i, dataset in enumerate(tqdm(DATASETS, desc='processing datasets', total=len
     measurement_names = []
     if show_ae:
         measurements.append(AE_error_te)
-        measurement_names.append('AE')
+        measurement_names.append('AE(te)')
+        measurements.append(AE_error_tr)
+        measurement_names.append('AE(tr)')
     if show_rae:
         measurements.append(RAE_error_te)
-        measurement_names.append('RAE')
+        measurement_names.append('RAE(te)')
+        measurements.append(RAE_error_tr)
+        measurement_names.append('RAE(tr)')
     if show_kld:
         measurements.append(KLD_error_te)
-        measurement_names.append('KLD')
+        measurement_names.append('KLD(te)')
+        measurements.append(KLD_error_tr)
+        measurement_names.append('KLD(tr)')
     if show_mse:
         measurements.append(MSE_error_te)
-        measurement_names.append('MSE')
+        measurement_names.append('MSE(te)')
+        measurements.append(MSE_error_tr)
+        measurement_names.append('MSE(tr)')
     measurements.append(normalize_metric(LIKE_value_te))
     measurements.append(normalize_metric(LIKE_value_tr))
     measurement_names.append('NLL(te)')
@@ -200,7 +258,8 @@ for i, dataset in enumerate(tqdm(DATASETS, desc='processing datasets', total=len
     # measurements.append(normalize_metric(LIKE_value_tr))
     # measurement_names.append('NLL(te)')
     # measurement_names.append('NLL(tr)')
-    plot(xaxis, measurements, measurement_names, suffix='AVEtr')
+    # plot(xaxis, measurements, measurement_names, suffix='AVEtr')
+    plot_stack(xaxis, measurements, measurement_names, suffix='AVEtr')