Indentation Issue

app.py

@@ -1436,227 +1436,227 @@ def run_model(model_name):
             )
             
     elif reduction_method == "t-nse":
-    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]
+        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]
-    )
+        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)}
 
-    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)
-        )
+        # -------------------------------------------------------------------------
+        # Crear plot interactivo con Bokeh
+        st.subheader("t-SNE - Real: Visualización Interactiva")
 
-    p.legend.title = "Label"
-    p.legend.location = "top_right"
-    p.xaxis.axis_label = "t-SNE 1"
-    p.yaxis.axis_label = "t-SNE 2"
+        source = ColumnDataSource(df_real_only)
 
-    st.bokeh_chart(p, use_container_width=True)
+        hover = HoverTool(tooltips=[
+            ("Index", "$index"),
+            ("Label", "@label"),
+            ("TSNE1", "@TSNE1"),
+            ("TSNE2", "@TSNE2")
+        ])
 
-    # -------------------------------------------------------------------------
-    # BLOQUE: Heatmap de Características (adaptado a TSNE1 y TSNE2)
-    st.markdown("## Heatmap de Características")
-    try:
-        df_heat = pd.read_csv(f"data/heatmaps_{model_name.lower()}.csv")
-    except Exception as e:
-        st.error(f"Error al cargar heatmaps.csv: {e}")
-        df_heat = None
-    
-    if df_heat is not None:
-        if 'img' not in df_all["real"].columns:
-            st.error("La columna 'img' no se encuentra en las muestras reales para hacer el merge con heatmaps.csv.")
-        else:
-            # Crear columna 'name' en las muestras reales (si aún no existe)
-            df_all["real"]["name"] = df_all["real"]["img"].apply(
-                lambda x: x.split("/")[-1].replace(".png", "") if isinstance(x, str) else x
-            )
-            # Merge con heatmaps.csv
-            df_heatmap_base = pd.merge(df_all["real"], df_heat, on="name", how="inner")
-            
-            # Opciones de features
-            feature_options = [col for col in df_heat.columns if col != "name"]
-            selected_feature = st.selectbox("Select heatmap feature:", 
-                                            options=feature_options, key=f"heatmap_{model_name}")
-            select_extra_dataset_hm = st.selectbox("Select a dataset:", 
-                                            options=model_options_with_default, key=f"heatmap_extra_dataset_{model_name}")
-            
-            # Definir un rango fijo y rejilla
-            x_min, x_max = -1, 1
-            y_min, y_max = -1, 1
-            grid_size = 50
-            x_bins = np.linspace(x_min, x_max, grid_size + 1)
-            y_bins = np.linspace(y_min, y_max, grid_size + 1)
-            
-            # Solo una combinación: TSNE1 vs TSNE2
-            x_comp, y_comp = "TSNE1", "TSNE2"
-            st.markdown(f"### Heatmap: {x_comp} vs {y_comp}")
-            
-            df_heatmap = df_heatmap_base.copy()
-            df_heatmap["x"] = df_heatmap[x_comp]
-            df_heatmap["y"] = df_heatmap[y_comp]
-            
-            # Si la feature seleccionada no es numérica, convertir
-            cat_mapping = None
-            if df_heatmap[selected_feature].dtype == bool or not pd.api.types.is_numeric_dtype(df_heatmap[selected_feature]):
-                cat = df_heatmap[selected_feature].astype('category')
-                cat_mapping = list(cat.cat.categories)
-                df_heatmap[selected_feature] = cat.cat.codes
-            
-            # Calcular estadística binned
-            heat_stat, x_edges, y_edges, binnumber = binned_statistic_2d(
-                df_heatmap['x'], df_heatmap['y'], df_heatmap[selected_feature],
-                statistic='mean', bins=[x_bins, y_bins]
+        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)
             )
-            heatmap_data = heat_stat.T
-            
-            # Color mapper
-            if selected_feature in model_options:
-                color_mapper = LinearColorMapper(
-                    palette=red_green_palette,
-                    low=0,
-                    high=1,
-                    nan_color='rgba(0, 0, 0, 0)'
-                )
+
+        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)
+
+        # -------------------------------------------------------------------------
+        # BLOQUE: Heatmap de Características (adaptado a TSNE1 y TSNE2)
+        st.markdown("## Heatmap de Características")
+        try:
+            df_heat = pd.read_csv(f"data/heatmaps_{model_name.lower()}.csv")
+        except Exception as e:
+            st.error(f"Error al cargar heatmaps.csv: {e}")
+            df_heat = None
+        
+        if df_heat is not None:
+            if 'img' not in df_all["real"].columns:
+                st.error("La columna 'img' no se encuentra en las muestras reales para hacer el merge con heatmaps.csv.")
             else:
-                color_mapper = LinearColorMapper(
-                    palette="Viridis256",
-                    low=np.nanmin(heatmap_data),
-                    high=np.nanmax(heatmap_data),
-                    nan_color='rgba(0, 0, 0, 0)'
+                # Crear columna 'name' en las muestras reales (si aún no existe)
+                df_all["real"]["name"] = df_all["real"]["img"].apply(
+                    lambda x: x.split("/")[-1].replace(".png", "") if isinstance(x, str) else x
                 )
-            
-            # Figura heatmap
-            heatmap_fig = figure(title=f"Heatmap de '{selected_feature}' ({x_comp} vs {y_comp})",
-                                x_range=(x_min, x_max), y_range=(y_min, y_max),
-                                width=600, height=600,
-                                tools="pan,wheel_zoom,reset,save", active_scroll="wheel_zoom", tooltips=TOOLTIPS,
-                                sizing_mode="fixed")
-            heatmap_fig.match_aspect = True
-            heatmap_fig.xaxis.axis_label = x_comp
-            heatmap_fig.yaxis.axis_label = y_comp
-
-            heatmap_fig.image(image=[heatmap_data], x=x_min, y=y_min,
-                            dw=x_max - x_min, dh=y_max - y_min,
-                            color_mapper=color_mapper)
-            
-            # Barra de color
-            color_bar = ColorBar(color_mapper=color_mapper, location=(0, 0))
-            if cat_mapping is not None:
-                ticks = list(range(len(cat_mapping)))
-                color_bar.ticker = FixedTicker(ticks=ticks)
-                categories_json = json.dumps(cat_mapping)
-                color_bar.formatter = FuncTickFormatter(code=f"""
-                    var categories = {categories_json};
-                    var index = Math.round(tick);
-                    if(index >= 0 && index < categories.length) {{
-                        return categories[index];
-                    }} else {{
-                        return "";
-                    }}
-                """)
-            heatmap_fig.add_layout(color_bar, 'right')
-            
-            # Tooltips
-            source_points = ColumnDataSource(data={
-                'x': df_heatmap['x'],
-                'y': df_heatmap['y'],
-                'img': df_heatmap['img'],
-                'label': df_heatmap['name']
-            })
-            invisible_renderer = heatmap_fig.circle('x', 'y', size=10, source=source_points, fill_alpha=0, line_alpha=0.5)
-            
-            if select_extra_dataset_hm != "-":
-                df_extra = df_all["synthetic"][df_all["synthetic"]["source"] == select_extra_dataset_hm].copy()
-                df_extra["x"] = df_extra[x_comp]
-                df_extra["y"] = df_extra[y_comp]
-                if 'name' not in df_extra.columns:
-                    df_extra["name"] = df_extra["img"].apply(lambda x: x.split("/")[-1].replace(".png", "") if isinstance(x, str) else x)
-                source_extra_points = ColumnDataSource(data={
-                    'x': df_extra['x'],
-                    'y': df_extra['y'],
-                    'img': df_extra['img'],
-                    'label': df_extra['name']
+                # Merge con heatmaps.csv
+                df_heatmap_base = pd.merge(df_all["real"], df_heat, on="name", how="inner")
+                
+                # Opciones de features
+                feature_options = [col for col in df_heat.columns if col != "name"]
+                selected_feature = st.selectbox("Select heatmap feature:", 
+                                                options=feature_options, key=f"heatmap_{model_name}")
+                select_extra_dataset_hm = st.selectbox("Select a dataset:", 
+                                                options=model_options_with_default, key=f"heatmap_extra_dataset_{model_name}")
+                
+                # Definir un rango fijo y rejilla
+                x_min, x_max = -1, 1
+                y_min, y_max = -1, 1
+                grid_size = 50
+                x_bins = np.linspace(x_min, x_max, grid_size + 1)
+                y_bins = np.linspace(y_min, y_max, grid_size + 1)
+                
+                # Solo una combinación: TSNE1 vs TSNE2
+                x_comp, y_comp = "TSNE1", "TSNE2"
+                st.markdown(f"### Heatmap: {x_comp} vs {y_comp}")
+                
+                df_heatmap = df_heatmap_base.copy()
+                df_heatmap["x"] = df_heatmap[x_comp]
+                df_heatmap["y"] = df_heatmap[y_comp]
+                
+                # Si la feature seleccionada no es numérica, convertir
+                cat_mapping = None
+                if df_heatmap[selected_feature].dtype == bool or not pd.api.types.is_numeric_dtype(df_heatmap[selected_feature]):
+                    cat = df_heatmap[selected_feature].astype('category')
+                    cat_mapping = list(cat.cat.categories)
+                    df_heatmap[selected_feature] = cat.cat.codes
+                
+                # Calcular estadística binned
+                heat_stat, x_edges, y_edges, binnumber = binned_statistic_2d(
+                    df_heatmap['x'], df_heatmap['y'], df_heatmap[selected_feature],
+                    statistic='mean', bins=[x_bins, y_bins]
+                )
+                heatmap_data = heat_stat.T
+                
+                # Color mapper
+                if selected_feature in model_options:
+                    color_mapper = LinearColorMapper(
+                        palette=red_green_palette,
+                        low=0,
+                        high=1,
+                        nan_color='rgba(0, 0, 0, 0)'
+                    )
+                else:
+                    color_mapper = LinearColorMapper(
+                        palette="Viridis256",
+                        low=np.nanmin(heatmap_data),
+                        high=np.nanmax(heatmap_data),
+                        nan_color='rgba(0, 0, 0, 0)'
+                    )
+                
+                # Figura heatmap
+                heatmap_fig = figure(title=f"Heatmap de '{selected_feature}' ({x_comp} vs {y_comp})",
+                                    x_range=(x_min, x_max), y_range=(y_min, y_max),
+                                    width=600, height=600,
+                                    tools="pan,wheel_zoom,reset,save", active_scroll="wheel_zoom", tooltips=TOOLTIPS,
+                                    sizing_mode="fixed")
+                heatmap_fig.match_aspect = True
+                heatmap_fig.xaxis.axis_label = x_comp
+                heatmap_fig.yaxis.axis_label = y_comp
+
+                heatmap_fig.image(image=[heatmap_data], x=x_min, y=y_min,
+                                dw=x_max - x_min, dh=y_max - y_min,
+                                color_mapper=color_mapper)
+                
+                # Barra de color
+                color_bar = ColorBar(color_mapper=color_mapper, location=(0, 0))
+                if cat_mapping is not None:
+                    ticks = list(range(len(cat_mapping)))
+                    color_bar.ticker = FixedTicker(ticks=ticks)
+                    categories_json = json.dumps(cat_mapping)
+                    color_bar.formatter = FuncTickFormatter(code=f"""
+                        var categories = {categories_json};
+                        var index = Math.round(tick);
+                        if(index >= 0 && index < categories.length) {{
+                            return categories[index];
+                        }} else {{
+                            return "";
+                        }}
+                    """)
+                heatmap_fig.add_layout(color_bar, 'right')
+                
+                # Tooltips
+                source_points = ColumnDataSource(data={
+                    'x': df_heatmap['x'],
+                    'y': df_heatmap['y'],
+                    'img': df_heatmap['img'],
+                    'label': df_heatmap['name']
                 })
-                heatmap_fig.circle('x', 'y', size=5, source=source_extra_points, fill_alpha=0, line_alpha=0.5, color="purple")
-            
-            hover_tool_points = HoverTool(renderers=[invisible_renderer], tooltips=TOOLTIPS)
-            heatmap_fig.add_tools(hover_tool_points)
+                invisible_renderer = heatmap_fig.circle('x', 'y', size=10, source=source_points, fill_alpha=0, line_alpha=0.5)
+                
+                if select_extra_dataset_hm != "-":
+                    df_extra = df_all["synthetic"][df_all["synthetic"]["source"] == select_extra_dataset_hm].copy()
+                    df_extra["x"] = df_extra[x_comp]
+                    df_extra["y"] = df_extra[y_comp]
+                    if 'name' not in df_extra.columns:
+                        df_extra["name"] = df_extra["img"].apply(lambda x: x.split("/")[-1].replace(".png", "") if isinstance(x, str) else x)
+                    source_extra_points = ColumnDataSource(data={
+                        'x': df_extra['x'],
+                        'y': df_extra['y'],
+                        'img': df_extra['img'],
+                        'label': df_extra['name']
+                    })
+                    heatmap_fig.circle('x', 'y', size=5, source=source_extra_points, fill_alpha=0, line_alpha=0.5, color="purple")
+                
+                hover_tool_points = HoverTool(renderers=[invisible_renderer], tooltips=TOOLTIPS)
+                heatmap_fig.add_tools(hover_tool_points)
+                
+                st.bokeh_chart(heatmap_fig)
+
+        # -------------------------------------------------------------------------
+        # Botón para descargar df_all (Embeddings en t-SNE Space)
+        if st.button("Download Embeddings in t-SNE Space", key=f"click_download_tsne_coordinates_{model_name}"):
+            df_all_tsne = {}
+            for key, df in df_all.items():
+                if key == "synthetic":
+                    for source in df["source"].unique():
+                        df_subset = df[df["source"] == source].copy()
+                        if "img" in df_subset.columns and "name" not in df_subset.columns:
+                            df_subset["name"] = df_subset["img"].apply(lambda x: x.split("/")[-1].replace(".png", "") if isinstance(x, str) else x)
+                        tsne_cols = [col for col in df_subset.columns if col.startswith("TSNE") or col == "name"]
+                        sheet_name = f"synthetic_{source}"
+                        df_all_tsne[sheet_name] = df_subset[tsne_cols].copy()
+                else:
+                    tsne_cols = [col for col in df.columns if col.startswith("TSNE") or col == "name"]
+                    df_all_tsne[key] = df[tsne_cols].copy()
+
+            excel_buffer = io.BytesIO()
+            with pd.ExcelWriter(excel_buffer, engine='openpyxl') as writer:
+                for key_name, df in df_all_tsne.items():
+                    df.to_excel(writer, sheet_name=key_name, index=False)
+            excel_buffer.seek(0)
             
-            st.bokeh_chart(heatmap_fig)
-
-    # -------------------------------------------------------------------------
-    # Botón para descargar df_all (Embeddings en t-SNE Space)
-    if st.button("Download Embeddings in t-SNE Space", key=f"click_download_tsne_coordinates_{model_name}"):
-        df_all_tsne = {}
-        for key, df in df_all.items():
-            if key == "synthetic":
-                for source in df["source"].unique():
-                    df_subset = df[df["source"] == source].copy()
-                    if "img" in df_subset.columns and "name" not in df_subset.columns:
-                        df_subset["name"] = df_subset["img"].apply(lambda x: x.split("/")[-1].replace(".png", "") if isinstance(x, str) else x)
-                    tsne_cols = [col for col in df_subset.columns if col.startswith("TSNE") or col == "name"]
-                    sheet_name = f"synthetic_{source}"
-                    df_all_tsne[sheet_name] = df_subset[tsne_cols].copy()
-            else:
-                tsne_cols = [col for col in df.columns if col.startswith("TSNE") or col == "name"]
-                df_all_tsne[key] = df[tsne_cols].copy()
-
-        excel_buffer = io.BytesIO()
-        with pd.ExcelWriter(excel_buffer, engine='openpyxl') as writer:
-            for key_name, df in df_all_tsne.items():
-                df.to_excel(writer, sheet_name=key_name, index=False)
-        excel_buffer.seek(0)
-        
-        st.download_button(
-            label="Download Embeddings in t-SNE Space",
-            data=excel_buffer,
-            file_name=f"df_all_tsne_{model_name.lower()}.xlsx",
-            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-            key=f"download_tsne_coordinates_{model_name}"
-        )
+            st.download_button(
+                label="Download Embeddings in t-SNE Space",
+                data=excel_buffer,
+                file_name=f"df_all_tsne_{model_name.lower()}.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+                key=f"download_tsne_coordinates_{model_name}"
+            )
 
 
 def main():