summary plot updated

Experiments/evaluate_metrics.py

@@ -267,7 +267,7 @@ def collect_metrics(x_y_pairs, seed=0,
         lin_pred, lin_unc = linear_pred_unc(x_train, y_train, sigma_y, design_matrix, x, device=device)
         assert true_y is not None
         lin_coverage = linear_coverage(lin_pred, lin_unc, true_y)
-        metrics['lin'] = {'coverage': lin_coverage}
+        metrics['lin'] = {'true_coverage_numerical': lin_coverage}
         
     return metrics
 

Experiments/plot_summary.py

@@ -82,14 +82,14 @@ for i, ([(eiv_metric_mean, eiv_metric_std),
                     width = 1.0,
                     bottom = eiv_metric_mean - eiv_bar_size,
                     color=colors[0],
-                    alpha=1.0)
+                    alpha=0.5)
             plt.plot(i+1, noneiv_metric_mean, '^', color=colors[1], markersize=16, zorder=0)
             plt.bar(i+1,
                     height = 2 * k *noneiv_bar_size,
                     width = 1.0,
                     bottom = noneiv_metric_mean - k*  noneiv_bar_size,
                     color=colors[1],
-                    alpha=1.0)
+                    alpha=0.5)
 plt.ylim(bottom=0, top=ymax)
 ax = plt.gca()
 ax.set_xticks(np.arange(1,len(data_list)+1))
@@ -102,13 +102,14 @@ plt.savefig('results/figures/RMSE_bar_plot.pdf')
 
 metric = 'true_coverage_numerical'
 data_list = ['linear','quadratic','cubic','sine']
-colors = ['red', 'blue']
+colors = ['red', 'blue','black']
 ymax = 1.0
 minimal_bar_size = ymax * 1.5e-3
 # read out EiV and non-EiV results for all datasets
 metric_results = [
         (save_readout(results[data]['eiv'], metric),
-        save_readout(results[data]['noneiv'], metric))
+        save_readout(results[data]['noneiv'], metric),
+        save_readout(results[data]['lin'], metric))
             for data in data_list]
 
 # create figure
@@ -118,7 +119,8 @@ plt.gcf().canvas.manager.set_window_title('coverage (ground truth)')
 
 # plot bars
 for i, ([(eiv_metric_mean, eiv_metric_std),
-        (noneiv_metric_mean, noneiv_metric_std)],\
+        (noneiv_metric_mean, noneiv_metric_std),
+            (lin_metric_mean, lin_metric_std)],\
                 data) in\
                 enumerate(zip(metric_results, data_list)):
     if eiv_metric_mean is not None:
@@ -127,6 +129,7 @@ for i, ([(eiv_metric_mean, eiv_metric_std),
             assert noneiv_metric_std is not None
             eiv_bar_size = max(eiv_metric_std, minimal_bar_size)
             noneiv_bar_size = max(noneiv_metric_std, minimal_bar_size)
+            lin_bar_size = max(lin_metric_std, minimal_bar_size)
             plt.plot(i+1, eiv_metric_mean, '^', color=colors[0], markersize=16)
             plt.bar(i+1,
                     height = 2*eiv_bar_size,
@@ -141,6 +144,13 @@ for i, ([(eiv_metric_mean, eiv_metric_std),
                     bottom = noneiv_metric_mean - k*  noneiv_bar_size,
                     color=colors[1],
                     alpha=0.5)
+            plt.plot(i+1, lin_metric_mean, '^', color=colors[2], markersize=16)
+            plt.bar(i+1,
+                    height = 2 * k *lin_bar_size,
+                    width = 0.3,
+                    bottom = lin_metric_mean - k*  lin_bar_size,
+                    color=colors[2],
+                    alpha=0.5)
 plt.axhline(0.95,0.0,1.0,color='k', linestyle='dashed')
 plt.ylim(bottom=0, top=ymax)
 ax = plt.gca()