Elbow as Max Silohuette

app.py

@@ -617,24 +617,32 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
 
         silhouette = np.max(silhouette_vals)
     
-    inertias = []
-    silohuettes_test = []
-    K = range(1, 20)
+    # inertias = []
+    # K = range(1, 20)
 
     # for k in K:
     #     kmeans = KMeans(n_clusters=k, random_state=42)
     #     kmeans.fit(reduced_real)
     #     inertias.append(kmeans.inertia_)
+
+
     # kl = KneeLocator(K, inertias, curve="convex", direction="decreasing")
+    # elbow_k = kl.elbow
+
+
+    silhouettes_test = []
+    K = range(2, 20)
 
     for k in K:
-        kmeans = KMeans(n_clusters=k, random_state=42)
-        kmeans.fit(reduced_real)
-        labels_opt = kmeans.fit_predict(X)
-        silohuettes_test.append(silhouette_score(X, labels_opt))
+        kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
+        labels = kmeans.fit_predict(X)
+        sil = silhouette_score(X, labels)
+        silhouettes_test.append(sil)
+
+    inertias = silhouettes_test
+    best_k = K[np.argmax(silhouettes_test)]
+    elbow_k = best_k
 
-    kl = KneeLocator(K, silohuettes_test, curve="convex", direction="decreasing")
-    elbow_k = kl.elbow
     kmeans_opt = KMeans(n_clusters=elbow_k, random_state=42, n_init=10)
     labels_opt = kmeans_opt.fit_predict(X)
     silhouette_opt = silhouette_score(X, labels_opt)