Set fixed colors for each plot component

gp_visualizer.py

@@ -95,6 +95,8 @@ class GPVisualizer:
             "show_predictions": True,
         }
 
+        self.plot_cmap = plt.get_cmap("tab20")
+
         self.css = """
 .hidden-button {
     display: none;
@@ -136,19 +138,25 @@ class GPVisualizer:
 
         if self.plot_options["show_training_data"]:
             if len(X_tr) > 1:
-                plt.scatter(X_tr.flatten(), y_tr, label='training data (R2=%.2f)' % (R2))
+                plt.scatter(X_tr.flatten(), y_tr, label='training data (R2=%.2f)' % (R2), color=self.plot_cmap(0))
             else:
-                plt.scatter(X_tr.flatten(), y_tr, label='training data')
+                plt.scatter(X_tr.flatten(), y_tr, label='training data', color=self.plot_cmap(0))
 
         if self.plot_options["show_true_function"]:
-            plt.plot(X_ts.flatten(), np.sin(2*np.pi*X_ts.flatten()), color='red', label='true function')
+            plt.plot(X_ts.flatten(), np.sin(2*np.pi*X_ts.flatten()), label='true function', color=self.plot_cmap(1))
 
         if self.plot_options["show_predictions"]:
-            plt.scatter(X_ts.flatten(), y_pred, marker='+', label='predictions')
+            plt.scatter(X_ts.flatten(), y_pred, marker='+', label='predictions', color=self.plot_cmap(2))
 
         if self.plot_options["show_confidence_interval"]:
-            plt.fill_between(X_ts.flatten(), y_pred - 1.96*y_std, y_pred + 1.96*y_std, alpha=0.5,
-                             label='95% confidence interval')
+            plt.fill_between(
+                X_ts.flatten(), 
+                y_pred - 1.96*y_std, 
+                y_pred + 1.96*y_std, 
+                alpha=0.2,
+                label='95% confidence interval', 
+                color=self.plot_cmap(3)
+            )
 
         plt.legend()