Silohuette Score

app.py

@@ -7,7 +7,7 @@ from bokeh.layouts import column
 from bokeh.palettes import Reds9, Blues9, Oranges9, Purples9, Greys9, BuGn9, Greens9, RdYlGn11, linear_palette
 from sklearn.decomposition import PCA
 from sklearn.manifold import TSNE, trustworthiness
-from sklearn.metrics import pairwise_distances
+from sklearn.metrics import pairwise_distances, silhouette_score
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.pipeline import Pipeline
 from sklearn.base import BaseEstimator, TransformerMixin
@@ -577,10 +577,12 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
     
     trust = None
     cont = None
+    silhouette = None
     if reduction_method in ("t-SNE","PCA"):
         X = df_combined[embedding_cols].values
         trust = trustworthiness(X, reduced, n_neighbors=TSNE_NEIGHBOURS)
         cont = compute_continuity(X, reduced, n_neighbors=TSNE_NEIGHBOURS)
+        silhouette = silhouette_score(X, df_combined['label'])
     
     dfs_reduced, unique_subsets = split_versions(df_combined, reduced)
     
@@ -655,7 +657,8 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
         "df_distances": df_distances,
         "explained_variance": explained_variance,
         "trustworthiness": trust,
-        "continuity": cont
+        "continuity": cont,
+        "silhouette": silhouette
     }
     
     if reduction_method == "PCA":
@@ -790,8 +793,9 @@ def run_model(model_name):
         st.table(variance_df)
     # elif reduction_method == "t-SNE":
     st.subheader(f"{reduction_method} Quality Metrics")
-    st.write(f"Trustworthiness: {result['trustworthiness']:.4f}")
-    st.write(f"Continuity: {result['continuity']:.4f}")
+    st.write(f"Trustworthiness: {result['trustworthiness']:.2f}")
+    st.write(f"Continuity: {result['continuity']:.2f}")
+    st.write(f"Silhouette Score: {result['silhouette']:.2f}")
 
     # # 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: