Test

app.py

@@ -587,9 +587,10 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
     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)
+        # X = df_combined[embedding_cols].values
+        X = df_combined[real_mask][embedding_cols].values
+        trust = trustworthiness(X, reduced_real, n_neighbors=TSNE_NEIGHBOURS)
+        cont = compute_continuity(X, reduced_real, n_neighbors=TSNE_NEIGHBOURS)
         
     #     silhouette_clustering = DBSCAN(eps=0.1, min_samples=15).fit(reduced)
     #     silhouette_labels = silhouette_clustering.labels_
@@ -606,7 +607,7 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
         silhouette_vals = []
         for feature in feature_options:
             labels = df_heat[feature].values
-            silhouette = silhouette_score(reduced, labels)
+            silhouette = silhouette_score(reduced_real, labels)
             silhouette_vals.append(silhouette)
 
         silhouette = np.mean(silhouette_vals)