Include Scaling for UMAP

app.py

@@ -579,12 +579,16 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
         ])
 
     elif reduction_method == "UMAP":
-        reducer = umap.UMAP(n_components=N_COMPONENTS, 
-            random_state=42, 
-            n_neighbors=15, 
-            min_dist=0.1,
-            metric='cosine'
-        )
+        reducer = Pipeline([
+            ("umap", umap.UMAP(
+                n_components=N_COMPONENTS,
+                random_state=42,
+                n_neighbors=15,
+                min_dist=0.1,
+                metric='cosine'
+            )),
+            ("rel_scaler", RelativeScaler())
+        ])
 
     else:
         reducer = TSNE(n_components=2, random_state=42, 
@@ -1062,14 +1066,18 @@ def run_model(model_name):
                 ("pca", PCA(n_components=N_COMPONENTS)),
                 ("rel_scaler", RelativeScaler())
             ])
-        
+
         elif reduction_method == "UMAP":
-            reducer_real = umap.UMAP(n_components=N_COMPONENTS, 
-                random_state=42, 
-                n_neighbors=15, 
-                min_dist=0.1,
-                metric='cosine'
-            )
+            reducer_real = Pipeline([
+                ("umap", umap.UMAP(
+                    n_components=N_COMPONENTS,
+                    random_state=42,
+                    n_neighbors=15,
+                    min_dist=0.1,
+                    metric='cosine'
+                )),
+                ("rel_scaler", RelativeScaler())
+            ])
 
         reduced_real = reducer_real.fit_transform(df_real_only[embedding_cols].values)