T-nse Test

app.py

@@ -753,56 +753,56 @@ def run_model(model_name):
     reset_button = Button(label="Reset Colors", button_type="primary")
     line_source = ColumnDataSource(data={'x': [], 'y': []})
     
-    # if (reduction_method == "t-SNE" and N_COMPONENTS == 2) or (reduction_method == "PCA" and N_COMPONENTS == 2):
-    #     fig, real_renderers, synthetic_renderers, pretrained_renderers = create_figure(
-    #         result["dfs_reduced"],
-    #         result["unique_subsets"],
-    #         get_color_maps(result["unique_subsets"]),
-    #         model_name
-    #     )
-    #     fig.line('x', 'y', source=line_source, line_width=2, line_color='black')
-    #     centers_real = calculate_cluster_centers(result["dfs_reduced"]["real"], result["unique_subsets"]["real"])
-    #     real_centers_js = {k: [v[0], v[1]] for k, v in centers_real.items()}
-    #     synthetic_centers = {}
-    #     synth_labels = sorted(result["dfs_reduced"]["synthetic"]['label'].unique().tolist())
-    #     for label in synth_labels:
-    #         subset = result["dfs_reduced"]["synthetic"][result["dfs_reduced"]["synthetic"]['label'] == label]
-    #         if 'x' in subset.columns and 'y' in subset.columns:
-    #             synthetic_centers[label] = [subset['x'].mean(), subset['y'].mean()]
-    #     callback = CustomJS(args=dict(source=source_table, line_source=line_source,
-    #                               synthetic_centers=synthetic_centers,
-    #                               real_centers=real_centers_js,
-    #                               real_select=real_select),
-    #     code="""
-    #         var selected = source.selected.indices;
-    #         if (selected.length > 0) {
-    #             var idx = selected[0];
-    #             var data = source.data;
-    #             var synth_label = data['Synthetic'][idx];
-    #             var real_label = real_select.value;
-    #             var syn_coords = synthetic_centers[synth_label];
-    #             var real_coords = real_centers[real_label];
-    #             line_source.data = {'x': [syn_coords[0], real_coords[0]], 'y': [syn_coords[1], real_coords[1]]};
-    #             line_source.change.emit();
-    #         } else {
-    #             line_source.data = {'x': [], 'y': []};
-    #             line_source.change.emit();
-    #         }
-    #     """)
-    #     source_table.selected.js_on_change('indices', callback)
-    #     real_select.js_on_change('value', callback)
+    if (reduction_method == "t-SNE" and N_COMPONENTS == 2) or (reduction_method == "PCA" and N_COMPONENTS == 2):
+        fig, real_renderers, synthetic_renderers, pretrained_renderers = create_figure(
+            result["dfs_reduced"],
+            result["unique_subsets"],
+            get_color_maps(result["unique_subsets"]),
+            model_name
+        )
+        fig.line('x', 'y', source=line_source, line_width=2, line_color='black')
+        centers_real = calculate_cluster_centers(result["dfs_reduced"]["real"], result["unique_subsets"]["real"])
+        real_centers_js = {k: [v[0], v[1]] for k, v in centers_real.items()}
+        synthetic_centers = {}
+        synth_labels = sorted(result["dfs_reduced"]["synthetic"]['label'].unique().tolist())
+        for label in synth_labels:
+            subset = result["dfs_reduced"]["synthetic"][result["dfs_reduced"]["synthetic"]['label'] == label]
+            if 'x' in subset.columns and 'y' in subset.columns:
+                synthetic_centers[label] = [subset['x'].mean(), subset['y'].mean()]
+        callback = CustomJS(args=dict(source=source_table, line_source=line_source,
+                                  synthetic_centers=synthetic_centers,
+                                  real_centers=real_centers_js,
+                                  real_select=real_select),
+        code="""
+            var selected = source.selected.indices;
+            if (selected.length > 0) {
+                var idx = selected[0];
+                var data = source.data;
+                var synth_label = data['Synthetic'][idx];
+                var real_label = real_select.value;
+                var syn_coords = synthetic_centers[synth_label];
+                var real_coords = real_centers[real_label];
+                line_source.data = {'x': [syn_coords[0], real_coords[0]], 'y': [syn_coords[1], real_coords[1]]};
+                line_source.change.emit();
+            } else {
+                line_source.data = {'x': [], 'y': []};
+                line_source.change.emit();
+            }
+        """)
+        source_table.selected.js_on_change('indices', callback)
+        real_select.js_on_change('value', callback)
     
-    #     reset_callback = CustomJS(args=dict(line_source=line_source),
-    #     code="""
-    #         line_source.data = {'x': [], 'y': []};
-    #         line_source.change.emit();
-    #     """)
-    #     reset_button.js_on_event("button_click", reset_callback)
-    #     layout = column(fig, result["scatter_fig"], column(real_select, reset_button, data_table))
-    # else:
-    #     layout = column(result["scatter_fig"], column(real_select, reset_button, data_table))
+        reset_callback = CustomJS(args=dict(line_source=line_source),
+        code="""
+            line_source.data = {'x': [], 'y': []};
+            line_source.change.emit();
+        """)
+        reset_button.js_on_event("button_click", reset_callback)
+        layout = column(fig, result["scatter_fig"], column(real_select, reset_button, data_table))
+    else:
+        layout = column(result["scatter_fig"], column(real_select, reset_button, data_table))
     
-    # st.bokeh_chart(layout, use_container_width=True)
+    st.bokeh_chart(layout, use_container_width=True)
 
     buffer = io.BytesIO()
     df_table.to_excel(buffer, index=False)
@@ -816,7 +816,7 @@ def run_model(model_name):
         key=f"download_button_excel_{model_name}"
     )
 
-    if reduction_method == "PCA" or reduction_method == "t-SNE":
+    if reduction_method == "PCA":
         st.markdown("## PCA - Solo Muestras Reales")
         # -------------------------------------------------------------------------
         # 1. PCA sobre las muestras reales