Rescale PCA output

app.py

@@ -8,6 +8,7 @@ from bokeh.palettes import Reds9, Blues9, Oranges9, Purples9, Greys9, BuGn9, Gre
 from sklearn.decomposition import PCA
 from sklearn.manifold import TSNE, trustworthiness
 from sklearn.metrics import pairwise_distances
+from sklearn.preprocessing import MinMaxScaler
 import io
 import ot
 from sklearn.linear_model import LinearRegression
@@ -456,6 +457,8 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
                          learning_rate=tsne_params["learning_rate"])
     
     reduced = reducer.fit_transform(df_combined[embedding_cols].values)
+    scaler = MinMaxScaler(feature_range=(-1, 1))
+    reduced = scaler.fit_transform(reduced)
     # Guardamos el embedding completo (por ejemplo, 4 dimensiones en PCA)
     df_combined['embedding'] = list(reduced)
     # Si el embedding es 2D, asignamos x e y para visualización
@@ -790,6 +793,9 @@ def run_model(model_name):
         df_real_only = embeddings["real"].copy()
         pca_real = PCA(n_components=N_COMPONENTS)
         reduced_real = pca_real.fit_transform(df_real_only[embedding_cols].values)
+
+        scaler_real = MinMaxScaler(feature_range=(-1, 1))
+        reduced_real = scaler_real.fit_transform(reduced_real)
         
         # Agregar columnas PC1, PC2, … a df_real_only
         for i in range(reduced_real.shape[1]):