Update predict plot with CIPHER ASAP demo changes

pages/predict.py

@@ -186,33 +186,53 @@ with st.spinner('Computing predictions...'):
 
         if show_val:
             # selected_display_data = val_display_data[val_display_data.Name.isin(selected_nodes)]
-            selected_display_data = val_display_data[val_display_data.Name.isin(selected_nodes)].style.map(style_val, subset=val_relations)
+            selected_display_data = val_display_data[val_display_data.Name.isin(selected_nodes)].copy()
+            selected_display_data = selected_display_data.reset_index(drop=True).style.map(style_val, subset=val_relations)
         else:
-            selected_display_data = display_data[display_data.Name.isin(selected_nodes)]
+            selected_display_data = display_data[display_data.Name.isin(selected_nodes)].copy()
+            selected_display_data = selected_display_data.reset_index(drop=True)
+
+        st.markdown(f"Out of {target_nodes.shape[0]} {target_node_type} nodes, the selected nodes rank as follows:")
+        selected_display_data_with_rank = selected_display_data.copy()
+        selected_display_data_with_rank['Rank'] = selected_display_data_with_rank['Rank'].apply(lambda x: f"{x} (top {(100*x/target_nodes.shape[0]):.2f}% of predictions)")
 
         # Show filtered nodes
         if target_node_type not in ['disease', 'anatomy']:
-            st.dataframe(selected_display_data, use_container_width = True, hide_index = True,
+            st.dataframe(selected_display_data_with_rank, use_container_width = True, hide_index = True,
                         column_config={"Database": st.column_config.LinkColumn(width = "small",
                                                                                help = "Click to visit external database.",
                                                                                display_text = display_database)})
         else:
-            st.dataframe(selected_display_data, use_container_width = True)
+            st.dataframe(selected_display_data_with_rank, use_container_width = True)
+
+        # Show plot
+        st.markdown(f"In the plot below, the dashed lines represent the rank of the selected {target_node_type} nodes across all predictions for {source_node}.")
+
+        # Checkbox to show text labels
+        show_labels = st.checkbox("Show Text Labels?", value = False)
 
         # Plot rank vs. score using matplotlib
-        st.markdown("**Rank vs. Score**")
         fig, ax = plt.subplots(figsize = (10, 6))
-        ax.plot(display_data['Rank'], display_data['Score'])
+        ax.plot(display_data['Rank'], display_data['Score'], color = 'black', linewidth = 1.5, zorder = 2)
         ax.set_xlabel('Rank', fontsize = 12)
         ax.set_ylabel('Score', fontsize = 12)
         ax.set_xlim(1, display_data['Rank'].max())
 
+        # Get color palette
+        # palette = plt.cm.get_cmap('tab10', len(selected_display_data))
+        palette = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf"]
+
         # Add vertical line for selected nodes
         for i, node in selected_display_data.iterrows():
-            ax.axvline(node['Rank'], color = 'red', linestyle = '--', label = node['Name'])
-            ax.text(node['Rank'] + 100, node['Score'], node['Name'], fontsize = 10, color = 'red')
+            ax.scatter(node['Rank'], node['Score'], color = palette[i], zorder=3)
+            ax.axvline(node['Rank'], color = palette[i], linestyle = '--', linewidth = 1.5, label = node['Name'], zorder=3)
+            if show_labels:
+                ax.text(node['Rank'] + 100, node['Score'], node['Name'], fontsize = 10, color = palette[i], zorder=3)
+
+        # Add legend
+        ax.legend(loc = 'upper right', fontsize = 10)
+        ax.grid(alpha = 0.2, zorder=0)
 
-        # Show plot
         st.pyplot(fig)