Lighter App

app.py

@@ -718,36 +718,36 @@ def run_model(model_name):
         st.write(f"Trustworthiness: {result['trustworthiness']:.4f}")
         st.write(f"Continuity: {result['continuity']:.4f}")
 
-    # Mostrar los plots de loadings si se usó PCA (para el conjunto combinado)
-    if reduction_method == "PCA" and result.get("pca_model") is not None:
-        # pca_model = result["pca_model"]
-        pca_model = result["pca_model"].named_steps["pca"]
-        components = pca_model.components_  # Shape: (n_components, n_features)
+    # # Mostrar los plots de loadings si se usó PCA (para el conjunto combinado)
+    # if reduction_method == "PCA" and result.get("pca_model") is not None:
+    #     # pca_model = result["pca_model"]
+    #     pca_model = result["pca_model"].named_steps["pca"]
+    #     components = pca_model.components_  # Shape: (n_components, n_features)
         
-        st.subheader("Pesos de las Componentes Principales (Loadings) - Conjunto Combinado")
-        for i, comp in enumerate(components):
-            source = ColumnDataSource(data=dict(
-                dimensions=embedding_cols,
-                weight=comp
-            ))
-            p = figure(x_range=embedding_cols, title=f"Componente Principal {i+1}",
-                       plot_height=400, plot_width=600,
-                       toolbar_location="above",
-                       tools="pan,wheel_zoom,reset,save,hover",
-                       active_scroll="wheel_zoom")
+    #     st.subheader("Pesos de las Componentes Principales (Loadings) - Conjunto Combinado")
+    #     for i, comp in enumerate(components):
+    #         source = ColumnDataSource(data=dict(
+    #             dimensions=embedding_cols,
+    #             weight=comp
+    #         ))
+    #         p = figure(x_range=embedding_cols, title=f"Componente Principal {i+1}",
+    #                    plot_height=400, plot_width=600,
+    #                    toolbar_location="above",
+    #                    tools="pan,wheel_zoom,reset,save,hover",
+    #                    active_scroll="wheel_zoom")
 
-            # Establecer fondo blanco
-            p.background_fill_color = "white"
-            # Mostrar solo grilla horizontal
-            p.xgrid.grid_line_color = None
-            p.ygrid.grid_line_color = "gray"
-            p.vbar(x='dimensions', top='weight', width=0.8, source=source)
-            p.xaxis.major_label_text_font_size = '0pt'
-            hover = HoverTool(tooltips=[("Dimensión", "@dimensions"), ("Peso", "@weight")])
-            p.add_tools(hover)
-            p.xaxis.axis_label = "Dimensiones originales"
-            p.yaxis.axis_label = "Peso"
-            st.bokeh_chart(p)
+    #         # Establecer fondo blanco
+    #         p.background_fill_color = "white"
+    #         # Mostrar solo grilla horizontal
+    #         p.xgrid.grid_line_color = None
+    #         p.ygrid.grid_line_color = "gray"
+    #         p.vbar(x='dimensions', top='weight', width=0.8, source=source)
+    #         p.xaxis.major_label_text_font_size = '0pt'
+    #         hover = HoverTool(tooltips=[("Dimensión", "@dimensions"), ("Peso", "@weight")])
+    #         p.add_tools(hover)
+    #         p.xaxis.axis_label = "Dimensiones originales"
+    #         p.yaxis.axis_label = "Peso"
+    #         st.bokeh_chart(p)
     
     data_table, df_table, source_table = create_table(result["df_distances"])
     real_subset_names = list(df_table.columns[1:])
@@ -755,56 +755,56 @@ 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)
@@ -856,32 +856,32 @@ def run_model(model_name):
         st.table(variance_df_real)
         
         # Mostrar los plots de loadings para cada componente
-        st.subheader("PCA - Real: Component Loadings")
-        st.markdown("### Pesos de las Componentes Principales (Loadings) - Conjunto Combinado")
-        for i, comp in enumerate(reducer_real.named_steps["pca"].components_):
-            source = ColumnDataSource(data=dict(
-                dimensions=embedding_cols,
-                weight=comp
-            ))
-            p = figure(
-                x_range=embedding_cols,
-                title=f"Componente Principal {i+1}",
-                plot_height=400,
-                plot_width=600,
-                toolbar_location="above",
-                tools="pan,wheel_zoom,reset,save,hover",
-                active_scroll="wheel_zoom"
-            )
-            p.background_fill_color = "white"
-            p.xgrid.grid_line_color = None
-            p.ygrid.grid_line_color = "gray"
-            p.vbar(x='dimensions', top='weight', width=0.8, source=source,
-                fill_color="#2b83ba", line_color="#2b83ba")
-            p.xaxis.axis_label = "Dimensiones Originales"
-            p.xaxis.major_label_text_font_size = '0pt'
-            hover = p.select_one(HoverTool)
-            hover.tooltips = [("Dimensión", "@dimensions"), ("Peso", "@weight")]
-            st.bokeh_chart(p)
+        # st.subheader("PCA - Real: Component Loadings")
+        # st.markdown("### Pesos de las Componentes Principales (Loadings) - Conjunto Combinado")
+        # for i, comp in enumerate(reducer_real.named_steps["pca"].components_):
+        #     source = ColumnDataSource(data=dict(
+        #         dimensions=embedding_cols,
+        #         weight=comp
+        #     ))
+        #     p = figure(
+        #         x_range=embedding_cols,
+        #         title=f"Componente Principal {i+1}",
+        #         plot_height=400,
+        #         plot_width=600,
+        #         toolbar_location="above",
+        #         tools="pan,wheel_zoom,reset,save,hover",
+        #         active_scroll="wheel_zoom"
+        #     )
+        #     p.background_fill_color = "white"
+        #     p.xgrid.grid_line_color = None
+        #     p.ygrid.grid_line_color = "gray"
+        #     p.vbar(x='dimensions', top='weight', width=0.8, source=source,
+        #         fill_color="#2b83ba", line_color="#2b83ba")
+        #     p.xaxis.axis_label = "Dimensiones Originales"
+        #     p.xaxis.major_label_text_font_size = '0pt'
+        #     hover = p.select_one(HoverTool)
+        #     hover.tooltips = [("Dimensión", "@dimensions"), ("Peso", "@weight")]
+        #     st.bokeh_chart(p)
         
         # -------------------------------------------------------------------------
         # 2. Proyección de todos los subconjuntos usando los loadings de df_real (para PC completos)