Show Continuity and Trustworthiness for PCA

app.py

@@ -771,6 +771,7 @@ def run_model(model_name):
             tsne_params = {"perplexity": perplexity_val, "learning_rate": learning_rate_val}
     
     result = compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, reduction_method=reduction_method, distance_metric=distance_metric.lower())
+    print(result)
     
     reg_metrics = pd.DataFrame({
         "Slope": [result["slope"]],
@@ -787,10 +788,10 @@ def run_model(model_name):
             "Explained Variance": result["explained_variance"]
         })
         st.table(variance_df)
-    elif reduction_method == "t-SNE":
-        st.subheader("t-SNE Quality Metrics")
-        st.write(f"Trustworthiness: {result['trustworthiness']:.4f}")
-        st.write(f"Continuity: {result['continuity']:.4f}")
+    # 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}")
 
     # # 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: