Update plot in compute results to have two axis instead of two plots.

code/bolsonaro/visualization/plotter.py

@@ -57,10 +57,10 @@ class Plotter(object):
         ax.plot(x_value, mean, c=color_mean, label=label)
 
     @staticmethod
-    def plot_losses(file_path, all_experiment_scores, x_value, xlabel, ylabel, all_labels, title):
-        fig, ax = plt.subplots()
+    def plot_losses(file_path, all_experiment_scores_1, all_experiment_scores_2, x_value, xlabel, ylabel, all_labels, title):
+        fig, axes = plt.subplots(nrows=1, ncols=2)
 
-        n = len(all_experiment_scores)
+        n = len(len(all_experiment_scores_1))
 
         """
         Get as many different colors from the specified cmap (here nipy_spectral)
@@ -68,24 +68,25 @@ class Plotter(object):
         """
         colors = Plotter.get_colors_from_cmap(n)
 
-        # For each curve to plot
-        for i in range(n):
-            # Retreive the scores in a list for each seed
-            experiment_scores = list(all_experiment_scores[i].values())
-            # Compute the mean and the std for the CI
-            mean_experiment_scores = np.average(experiment_scores, axis=0)
-            std_experiment_scores = np.std(experiment_scores, axis=0)
-            # Plot the score curve with the CI
-            Plotter.plot_mean_and_CI(
-                ax=ax,
-                mean=mean_experiment_scores,
-                lb=mean_experiment_scores + std_experiment_scores,
-                ub=mean_experiment_scores - std_experiment_scores,
-                x_value=x_value,
-                color_mean=colors[i],
-                facecolor=colors[i],
-                label=all_labels[i]
-            )
+        for j, all_experiment_scores in enumerate([all_experiment_scores_1, all_experiment_scores_2]):
+            # For each curve to plot
+            for i in range(n):
+                # Retreive the scores in a list for each seed
+                experiment_scores = list(all_experiment_scores[i].values())
+                # Compute the mean and the std for the CI
+                mean_experiment_scores = np.average(experiment_scores, axis=0)
+                std_experiment_scores = np.std(experiment_scores, axis=0)
+                # Plot the score curve with the CI
+                Plotter.plot_mean_and_CI(
+                    ax=axes[j],
+                    mean=mean_experiment_scores,
+                    lb=mean_experiment_scores + std_experiment_scores,
+                    ub=mean_experiment_scores - std_experiment_scores,
+                    x_value=x_value,
+                    color_mean=colors[i],
+                    facecolor=colors[i],
+                    label=all_labels[i]
+                )
 
         plt.xlabel(xlabel)
         plt.ylabel(ylabel)

code/compute_results.py

@@ -175,10 +175,13 @@ if __name__ == "__main__":
         pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)
 
         Plotter.plot_losses(
-            file_path=output_path + os.sep + 'losses_with_params.png',
-            all_experiment_scores=[base_with_params_train_scores, base_with_params_dev_scores, base_with_params_test_scores,
+            file_path=output_path + os.sep + 'losses.png',
+            all_experiment_scores_1=[base_with_params_train_scores, base_with_params_dev_scores, base_with_params_test_scores,
                 random_with_params_train_scores, random_with_params_dev_scores, random_with_params_test_scores,
                 omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores],
+            all_experiment_scores_2=[base_wo_params_train_scores, base_wo_params_dev_scores, base_wo_params_test_scores,
+                random_wo_params_train_scores, random_wo_params_dev_scores, random_wo_params_test_scores,
+                omp_wo_params_train_scores, omp_wo_params_dev_scores, omp_wo_params_test_scores],
             x_value=with_params_extracted_forest_sizes,
             xlabel='Number of trees extracted',
             ylabel='MSE', # TODO: hardcoded
@@ -187,19 +190,6 @@ if __name__ == "__main__":
                 'omp_with_params_train', 'omp_with_params_dev', 'omp_with_params_test'],
             title='Loss values of {} using the best hyperparams'.format(args.dataset_name)
         )
-        Plotter.plot_losses(
-            file_path=output_path + os.sep + 'losses_wo_params.png',
-            all_experiment_scores=[base_wo_params_train_scores, base_wo_params_dev_scores, base_wo_params_test_scores,
-                random_wo_params_train_scores, random_wo_params_dev_scores, random_wo_params_test_scores,
-                omp_wo_params_train_scores, omp_wo_params_dev_scores, omp_wo_params_test_scores],
-            x_value=wo_params_extracted_forest_sizes,
-            xlabel='Number of trees extracted',
-            ylabel='MSE', # TODO: hardcoded
-            all_labels=['base_wo_params_train', 'base_wo_params_dev', 'base_wo_params_test',
-                'random_wo_params_train', 'random_wo_params_dev', 'random_wo_params_test',
-                'omp_wo_params_train', 'omp_wo_params_dev', 'omp_wo_params_test'],
-            title='Loss values of {} without using the best hyperparams'.format(args.dataset_name)
-        )
     else:
         raise ValueError('This stage number is not supported yet, but it will be!')