Analysis of timings added.

section_4.2_visualize.py

@@ -30,10 +30,12 @@ path_for_results = os.path.abspath(os.path.join(os.path.dirname(__file__), 'resu
 dic_singular_vals = {}
 dic_accuracy_deep = {}
 dic_accuracy_beststd = {}
+dic_timings = {}
 for name_dataset in list_datasets:
     dic_singular_vals[name_dataset] = {}
     dic_accuracy_deep[name_dataset] = {}
     dic_accuracy_beststd[name_dataset] = {}
+    dic_timings[name_dataset] = {'list_timings_1L': [], 'list_timings_2L': []}
 
 
 ## Read all file, perform computations, save results
@@ -57,10 +59,13 @@ for idx_file, file in enumerate(os.listdir(path_for_results)):
 
     dic_singular_vals[name_dataset][idx_rerun] = ratio
 
-    # Compute and store accuracies
+    # Compute and store accuracies as well as timings
     dic_accuracy_deep[name_dataset][idx_rerun] = results['array_test_rmse_deep']
     dic_accuracy_beststd[name_dataset][idx_rerun] = np.min(results['array_test_rmse_cv'], axis=0)
 
+    dic_timings[name_dataset]['list_timings_1L'].append(np.array(results['list_timings_1L']))
+    dic_timings[name_dataset]['list_timings_2L'].append(np.array(results['list_timings_2L']))
+
 
 ## Calculate ranking where 2L performs best to worst: Use the average mean of improvement for this
 array_ratio = np.zeros(len(list_datasets))
@@ -78,10 +83,28 @@ indices_sorted = np.argsort(array_ratio)
 
 
 ## Print the calculated ratio: This shows, when 2L is superior
+print('List indicating when two-layered kernel optimization is beneficial:')
 for idx_sorted in indices_sorted:
     print('{:20}'.format(list_datasets[idx_sorted]), np.round(array_ratio[idx_sorted], 5))
 
 
+## Print timing numbers
+for idx_dataset, name_dataset in enumerate(list_datasets):
+    array_timings_1L = np.stack(dic_timings[name_dataset]['list_timings_1L'])
+    array_timings_2L = np.stack(dic_timings[name_dataset]['list_timings_2L'])
+
+    t_mean_1L = np.mean(array_timings_1L)
+    t_std_1L = np.std(array_timings_1L)
+
+    t_mean_2L = np.mean(array_timings_2L, axis=0)
+    t_std_2L = np.std(array_timings_2L, axis=0)
+
+    print('1L runtime: {:6.2f} +- {:5.2f}s. '.format(t_mean_1L, t_std_1L)
+          + '2L optim: {:6.2f} +- {:5.2f}. '.format(t_mean_2L[0, 0], t_std_2L[0, 0])
+          + '2L greedy: {:6.2f} +- {:5.2f}. '.format(t_mean_2L[0, 1], t_std_2L[0, 1])
+          + '(' + name_dataset + ')')
+
+
 ## Visualization of the ratio of singular values compared to sum of all singular values
 matplotlib.use('TKAgg')