Update clustering.py

clustering.py

@@ -614,6 +614,7 @@ def perform_clustering(data, min_eps, max_eps=0.95, n=5, threshold_values=None,
     most_similar_comments = {}
 
     for distance_threshold in threshold_values:
+        distance_threshold = round(distance_threshold, 6)
         clustering = AgglomerativeClustering(
             n_clusters=None,
             distance_threshold=distance_threshold,
@@ -636,6 +637,7 @@ def perform_clustering(data, min_eps, max_eps=0.95, n=5, threshold_values=None,
                 embeddings_matrix, metric="euclidean"
             )
             ch_score = calinski_harabasz_score(euclidean_distances, labels)
+            ch_score = round(ch_score, 2)
         else:
             ch_score = -1  # Valor predeterminado si solo hay un clúster
         calinski_harabasz_scores[distance_threshold] = ch_score
@@ -643,6 +645,7 @@ def perform_clustering(data, min_eps, max_eps=0.95, n=5, threshold_values=None,
         # Calcular Silhouette Score
         if len(np.unique(labels)) > 1:
             sil_score = silhouette_score(embeddings_matrix, labels, metric="cosine")
+            sil_score = round(sil_score, 2)
         else:
             sil_score = -1  # Valor predeterminado si solo hay un clúster
         silhouette_scores[distance_threshold] = sil_score