new losses comparisons

src/models/eval_baseline_models.py

@@ -378,6 +378,21 @@ def check_classification(
     mae = mae * scale_paper
     rmse = rmse * scale_paper
     print(f"for GP paper scaled MAE: {mae}, paper scaled RMSE: {rmse}")
+
+        # now we do the same MAE and RMSE but only for the last point
+    mae = mean_absolute_error(true_label_gp[:, -1], pred_label_gp[:, -1])
+    rmse = mean_squared_error(true_label_gp[:, -1], pred_label_gp[:, -1], squared=False)
+    print(f"last point MAE: {mae}, last point RMSE: {rmse}")
+
+    # get back to original scale
+    mae = mae * 57.941
+    rmse = rmse * 57.941
+    print(f"last point scaled MAE: {mae}, last point scaled RMSE: {rmse}")
+
+    # get values for comparison with other paper
+    mae = mae * scale_paper
+    rmse = rmse * scale_paper
+    print(f"paper scaled last point MAE: {mae}, paper scaled last point RMSE: {rmse}")
 
     plot_beautiful_fig_gp(
         test_x[:3],
@@ -442,6 +457,21 @@ def check_classification(
     rmse = rmse * scale_paper
     print(f"for SVM paper scaled MAE: {mae}, paper scaled RMSE: {rmse}")
 
+    # now we do the same MAE and RMSE but only for the last point
+    mae = mean_absolute_error(true_label_svm[:, -1], pred_label_svm[:, -1])
+    rmse = mean_squared_error(true_label_svm[:, -1], pred_label_svm[:, -1], squared=False)
+    print(f"last point MAE: {mae}, last point RMSE: {rmse}")
+
+    # get back to original scale
+    mae = mae * 57.941
+    rmse = rmse * 57.941
+    print(f"last point scaled MAE: {mae}, last point scaled RMSE: {rmse}")
+
+    # get values for comparison with other paper
+    mae = mae * scale_paper
+    rmse = rmse * scale_paper
+    print(f"paper scaled last point MAE: {mae}, paper scaled last point RMSE: {rmse}")
+
     # Use your existing function to plot results
     plot_beautiful_fig(
         test_x[:3],

src/models/eval_saved_model.py

@@ -187,6 +187,21 @@ def eval():
     rmse = rmse * scale_paper
     print(f"for {log_name} paper scaled MAE: {mae}, paper scaled RMSE: {rmse}")
 
+    # now we do the same MAE and RMSE but only for the last point
+    mae = mean_absolute_error(new_test_y[:, -1], pred_y[:, -1])
+    rmse = mean_squared_error(new_test_y[:, -1], pred_y[:, -1], squared=False)
+    print(f"for {log_name} last point MAE: {mae}, last point RMSE: {rmse}")
+
+    # get back to original scale
+    mae = mae * 57.941
+    rmse = rmse * 57.941
+    print(f"for {log_name} last point scaled MAE: {mae}, last point scaled RMSE: {rmse}")
+
+    # get values for comparison with other paper
+    mae = mae * scale_paper
+    rmse = rmse * scale_paper
+    print(f"for {log_name} paper scaled last point MAE: {mae}, paper scaled last point RMSE: {rmse}")
+
     new_test_x = new_test_x.reshape(-1, 7)
 
     plot_beautiful_fig(new_test_x[:3], new_test_y[:3], pred_y[:3], "new_test", log_dir, 144.98, 57.94)