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app.py

@@ -12,8 +12,7 @@ import io
 import ot
 from sklearn.linear_model import LinearRegression
 
-# Usaremos 4 componentes para el embedding
-N_COMPONENTS = 100
+N_COMPONENTS = 2
 TSNE_NEIGHBOURS = 150
 
 TOOLTIPS = """
@@ -358,7 +357,7 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
     if reduction_method == "PCA":
         reducer = PCA(n_components=N_COMPONENTS)
     else:
-        reducer = TSNE(n_components=3, random_state=42, 
+        reducer = TSNE(n_components=2, random_state=42, 
                          perplexity=tsne_params["perplexity"], 
                          learning_rate=tsne_params["learning_rate"])