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)
@@ -1435,6 +1435,66 @@ def run_model(model_name):
                 key=f"download_pca_coordinates_{model_name}"
             )
             
+    elif reduction_method == "t-SNE":
+        st.markdown("## t-SNE - Solo Muestras Reales")
+        # -------------------------------------------------------------------------
+        # 1. t-SNE sobre las muestras reales
+        df_real_only = embeddings["real"].copy()
+        
+        reducer_real = TSNE(n_components=2, perplexity=30, random_state=42)
+        reduced_real = reducer_real.fit_transform(df_real_only[embedding_cols].values)
+        
+        # Agregar columnas TSNE1, TSNE2
+        df_real_only['TSNE1'] = reduced_real[:, 0]
+        df_real_only['TSNE2'] = reduced_real[:, 1]
+
+        unique_labels_real = sorted(df_real_only['label'].unique().tolist())
+        
+        # Mapeo de colores para las muestras reales usando la paleta Reds9
+        num_labels = len(unique_labels_real)
+        if num_labels <= 9:
+            red_palette = Reds9[:num_labels]
+        else:
+            red_palette = (Reds9 * ((num_labels // 9) + 1))[:num_labels]
+        real_color_mapping = {label: red_palette[i] for i, label in enumerate(unique_labels_real)}
+
+        # -------------------------------------------------------------------------
+        # Crear plot interactivo con Bokeh
+        st.subheader("t-SNE - Real: Visualización Interactiva")
+
+        source = ColumnDataSource(df_real_only)
+
+        hover = HoverTool(tooltips=[
+            ("Index", "$index"),
+            ("Label", "@label"),
+            ("TSNE1", "@TSNE1"),
+            ("TSNE2", "@TSNE2")
+        ])
+
+        p = figure(
+            width=800,
+            height=600,
+            title="t-SNE sobre muestras reales",
+            tools=["pan", "wheel_zoom", "box_zoom", "reset", hover]
+        )
+
+        for label in unique_labels_real:
+            subset = df_real_only[df_real_only['label'] == label]
+            p.scatter(
+                x=subset["TSNE1"],
+                y=subset["TSNE2"],
+                size=8,
+                color=real_color_mapping[label],
+                alpha=0.7,
+                legend_label=str(label)
+            )
+
+        p.legend.title = "Label"
+        p.legend.location = "top_right"
+        p.xaxis.axis_label = "t-SNE 1"
+        p.yaxis.axis_label = "t-SNE 2"
+
+        st.bokeh_chart(p, use_container_width=True)
 
 def main():
     config_style()