Fix Plot With Other Approach

app.py

@@ -515,7 +515,7 @@ def add_synthetic_dataset_to_fig(fig, df, labels, marker, color_mapping, group_l
         renderers[label + f" ({group_label})"] = r
     return renderers
 
-def get_color_maps(unique_subsets):
+def get_color_maps(unique_subsets, result):
     color_map = {}
     num_real = len(unique_subsets["real"])
     red_palette = Reds9[:num_real] if num_real <= 9 else (Reds9 * ((num_real // 9) + 1))[:num_real]
@@ -543,6 +543,7 @@ def get_color_maps(unique_subsets):
     num_pretrained = len(unique_subsets["pretrained"])
     purple_palette = Purples9[:num_pretrained] if num_pretrained <= 9 else (Purples9 * ((num_pretrained // 9) + 1))[:num_pretrained]
     color_map["pretrained"] = {label: purple_palette[i] for i, label in enumerate(sorted(unique_subsets["pretrained"]))}
+    color_map["kmeans"] = {k: Category10[10][i % 10] for i, k in enumerate(np.unique(result["classes_k"]))}
     
     return color_map
 
@@ -1086,7 +1087,7 @@ def run_model(model_name):
             unique_subsets["pretrained"] = []
         
         # Obtener mapeo de colores para cada subconjunto (función definida externamente)
-        color_maps = get_color_maps(unique_subsets)
+        color_maps = get_color_maps(unique_subsets, result)
         
         # Mapeo de marcadores para synthetic (por source)
         marker_mapping = {
@@ -1114,8 +1115,20 @@ def run_model(model_name):
         
         # Plotear las muestras reales, agrupadas por label
         # for label in unique_subsets["real"]:
-        for label_k, label in zip(result["classes_k"], unique_subsets["real"]):
-            subset = df_all["real"][df_all["real"]['label'] == label_k]
+        #     subset = df_all["real"][df_all["real"]['label'] == label]
+        #     source = ColumnDataSource(data={
+        #         'x': subset['x'],
+        #         'y': subset['y'],
+        #         'label': subset['label'],
+        #         'img': subset['img']
+        #     })
+        #     fig_all.circle('x', 'y', size=10,
+        #                 fill_color=color_maps["real"][label],
+        #                 line_color=color_maps["real"][label],
+        #                 legend_label=f"Real: {label}",
+        #                 source=source)
+        for label_k in np.unique(result["classes_k"]):
+            subset = df_all["real"][result["classes_k"] == label_k]
             source = ColumnDataSource(data={
                 'x': subset['x'],
                 'y': subset['y'],
@@ -1123,9 +1136,9 @@ def run_model(model_name):
                 'img': subset['img']
             })
             fig_all.circle('x', 'y', size=10,
-                        fill_color=color_maps["real"][label],
-                        line_color=color_maps["real"][label],
-                        legend_label=f"Real: {label_k}",
+                        fill_color=color_maps["kmeans"][label_k],
+                        line_color=color_maps["kmeans"][label_k],
+                        legend_label=f"KMeans: {label_k}",
                         source=source)
         
         show_real_only = st.checkbox("Show only real samples", value=True, key=f"show_real_only_{model_name}")