Visualizations of clustering models.

Signed-off-by: Oliver Ladores <oliver.ladores@wvsu.edu.ph>

app.py

@@ -116,8 +116,6 @@ def _(dataset_encoded, mo):
             {classification_report(y_test, y_pred_dectree)}
     ```
 
-    ## Conclusion
-
     {mo.callout("Classifiers don't work well with this dataset, let's try something else.", kind='info')}
     """)
     return (
@@ -172,12 +170,13 @@ def _(OrdinalEncoder, dataset, mo, pl):
 
         # df_kmeans_parts = pl.DataFrame(labels_kmeans, schema=pl.String)
         df_kmeans = X.with_columns(pl.lit(labels_kmeans, dtype=pl.String).alias('kmeans_cluster'))
+        df_spec = X.with_columns(pl.lit(labels_spec, dtype=pl.String).alias('spectral_cluster'))
 
         return mo.vstack([
             mo.md(f"""
             ## K-Means Clustering
 
-            ### External Metrics
+            ### External Metrics (Based on Cancer Stage Labels)
 
             Adjusted Rand Index (ARI): {adjusted_rand_score(y, labels_kmeans)}
 
@@ -200,7 +199,7 @@ def _(OrdinalEncoder, dataset, mo, pl):
 
             ## Spectral Clustering
 
-            ### External Metrics
+            ### External Metrics (Based on Cancer Stage Labels)
 
             Adjusted Rand Index (ARI): {adjusted_rand_score(y, labels_spec)}
 
@@ -222,12 +221,24 @@ def _(OrdinalEncoder, dataset, mo, pl):
 
             {mo.callout("Unsupervised clustering techniques do perform reasonably well, but does not correlate to other labels.", 'info')}
         """),
-        
-            alt.Chart(df_kmeans, autosize='pad').mark_circle().encode(
-                x='Genetic_Mutation',
-                y='Tumor_Size_mm',
-                color='kmeans_cluster'
-            )
+
+            mo.hstack([
+                alt.Chart(df_kmeans, autosize='pad').mark_rect().encode(
+                    alt.X('Genetic_Mutation:N'),
+                    y='Tumor_Size_mm',
+                    color='kmeans_cluster'
+                ).properties(
+                    width=325
+                ).interactive(),
+
+                alt.Chart(df_spec, autosize='pad').mark_rect().encode(
+                    alt.X('Genetic_Mutation:N'),
+                    y='Tumor_Size_mm',
+                    color='spectral_cluster'
+                ).properties(
+                    width=325
+                ).interactive(),
+            ])
         ])