Minor changes to plotting - highlight the best or reference point

obsidian/plotting/plotly.py

@@ -51,18 +51,34 @@ def visualize_inputs(campaign: Campaign) -> Figure:
         + ['Correlation Matrix']
         + [X.columns[i] for i in range(cols, n_dim)]
     )
+    # if campaign.optimizer is fitted, then X_best_f_idx is identified
+    if 'X_best_f_idx' in dir(campaign.optimizer):
+        marker_shapes = ['diamond' if rowInd in [campaign.optimizer.X_best_f_idx] else 'circle' for rowInd in range(campaign.X.shape[0])]
+    else:
+        marker_shapes = ['circle']*campaign.X.shape[0]
 
     for i, param in enumerate(X.columns):
         row_i = i // cols + 1
         col_i = i % cols + 1
         fig.add_trace(go.Scatter(x=X.index, y=X[param],
                                  mode='markers', name=param,
-                                 marker=dict(color=color_list[i]),
+                                 marker=dict(color=color_list[i], symbol=marker_shapes),
                                  showlegend=False),
                       row=row_i, col=col_i)
         fig.update_xaxes(tickvals=np.around(np.linspace(0, campaign.m_exp, 5)),
                          row=row_i, col=col_i)
 
+    # Add note to explain the shape of markers
+    if hasattr(campaign.optimizer, 'X_best_f_idx'):
+        fig.add_annotation(
+            text="Note: The diamond markers denote samples that achieve the best sum of targets.",
+            showarrow=False,
+            xref="paper", yref="paper",
+            x=0,
+            y=-0.2,
+            font=dict(style="italic")
+        )
+
     # Calculate the correlation matrix
     X_u = campaign.X_space.unit_map(X)
     corr_matrix = X_u.corr()
@@ -325,8 +341,9 @@ def factor_plot(optimizer: Optimizer,
         Y_mu_ref = Y_pred_ref[y_name+('_t (pred)' if f_transform else ' (pred)')].values
         fig.add_trace(go.Scatter(x=X_ref.iloc[:, feature_id].values, y=Y_mu_ref,
                                  mode='markers',
+                                 marker=dict(symbol='diamond'),
                                  line={'color': obsidian_colors.teal},
-                                 name='Ref'),
+                                 name='Reference'),
                       )
     fig.update_xaxes(title_text=X_name)
     fig.update_yaxes(title_text=y_name)
@@ -544,7 +561,19 @@ def optim_progress(campaign: Campaign,
         marker=marker_dict,
         customdata=campaign.data[X_names],
         name='Data'))
-
+
+    # Highlight the best samples
+    if hasattr(campaign.optimizer, 'X_best_f_idx'):
+        fig.add_trace(go.Scatter(x=pd.Series(out_exp.iloc[campaign.optimizer.X_best_f_idx, 0]),
+                                 y=pd.Series(out_exp.iloc[campaign.optimizer.X_best_f_idx, 1]),
+                                 mode='markers',
+                                 marker=dict(symbol='diamond-open', size=14),
+                                 line={'color': 'black'},
+                                 legendgroup='marker_shape', showlegend=True,
+                                 name='Best')
+                      )
+        fig.update_layout(showlegend=True)
+
     template = ["<b>"+str(param.name)+"</b>: "+" %{customdata["+str(i)+"]"
                 + (":.3G}"if isinstance(param, Param_Continuous) else "}") + "<br>"
                 for i, param in enumerate(campaign.X_space)]