add PCA for dimensions reduction on clustering

src/back/clustering_csv.py

@@ -3,6 +3,7 @@ import matplotlib.pyplot as plt
 from sklearn.cluster import KMeans, DBSCAN
 from sklearn.datasets import make_blobs, make_moons
 from mpl_toolkits.mplot3d import Axes3D
+from sklearn.decomposition import PCA
 
 def visualize_clusters_2d(X, labels, centers=None, title="Clusters"):
     plt.figure(figsize=(10, 7))
@@ -56,8 +57,11 @@ def calculate_cluster_statistics_dbscan(X, labels):
         })
     return stats
 
-def launch_cluster_knn(df, array_columns, n=3):
+def launch_cluster_knn(df, array_columns, n=3, dimensions=2):
     X = df[array_columns].values
+    if len(array_columns) > 3:
+        pca = PCA(dimensions)
+        X = pca.fit_transform(df)
     
     kmeans = KMeans(n_clusters=n, random_state=42)
     labels_kmeans = kmeans.fit_predict(X)
@@ -66,19 +70,23 @@ def launch_cluster_knn(df, array_columns, n=3):
     #     print(f"Cluster {stat['cluster']}: {stat['num_points']} points, Center: {stat['center']}")
 
     stats_kmeans = calculate_cluster_statistics_kmeans(X, labels_kmeans, centers_kmeans)
-    if len(array_columns) == 3:
+    if dimensions == 3:
         return visualize_clusters_3d(X, labels_kmeans, centers_kmeans, title="K-Means Clustering 3D")
     else:
         return visualize_clusters_2d(X, labels_kmeans, centers_kmeans, title="K-Means Clustering")
 
-def launch_cluster_dbscan(df, array_columns):
+def launch_cluster_dbscan(df, array_columns, dimensions=2):
     X = df[array_columns].values
+    if len(array_columns) > 3:
+        pca = PCA(dimensions)
+        X = pca.fit_transform(df)
+    
     dbscan = DBSCAN(eps=0.2, min_samples=5)
     labels_dbscan = dbscan.fit_predict(X)
     stats_dbscan = calculate_cluster_statistics_dbscan(X, labels_dbscan)
     # for stat in stats_dbscan:
     #     print(f"Cluster {stat['cluster']}: {stat['num_points']} points, Density: {stat['density']}")
-    if len(array_columns) == 3:
+    if dimensions == 3:
         return visualize_clusters_3d(X, labels_dbscan, title="DBSCAN Clustering 3D")
     else:
         return visualize_clusters_2d(X, labels_dbscan, title="DBSCAN Clustering")

src/pages/prediction.py

@@ -30,18 +30,22 @@ if 'df' in st.session_state:
     with tab1:
         st.header("Clustering")
         selected_columns = handle_column_multiselect(df, "clustering")
-            
+
+        if len(selected_columns) >= 3:
+            dimensions = st.radio("Reduce to dimensions X with PCA:",[2,3],index=0)
+        else:
+            dimensions = 2
         
         tab_names = ["K-means", "DBSCAN"] 
         tab11, tab12 = st.tabs(tab_names)
 
         with tab11:
             if st.button(f"Start {tab_names[0]}"):
-                st.pyplot(cc.launch_cluster_knn(df, selected_columns))
+                st.pyplot(cc.launch_cluster_knn(df, selected_columns, dimensions=dimensions))
 
         with tab12:
             if st.button(f"Start {tab_names[1]}"):
-                st.pyplot(cc.launch_cluster_dbscan(df, selected_columns))
+                st.pyplot(cc.launch_cluster_dbscan(df, selected_columns, dimensions))
 
     with tab2:
         st.header("Predictions")