Add DenseClus Implementation notebook for jumpstart

denseclus/DenseClus.py

@@ -612,8 +612,8 @@ def evaluate(self) -> np.array:
         clustered = labels >= 0
 
         if isinstance(self.hdbscan_, dict) or self.umap_combine_method == "ensemble":
-            print(f"DBCV score {self.hdbscan_['hdb_numerical'].relative_validity_}")
-            print(f"DBCV score {self.hdbscan_['hdb_categorical'].relative_validity_}")
+            print(f"DBCV numerical score {self.hdbscan_['hdb_numerical'].relative_validity_}")
+            print(f"DBCV categorical score {self.hdbscan_['hdb_categorical'].relative_validity_}")
             embedding_len = self.numerical_umap_.embedding_.shape[0]
             coverage = np.sum(clustered) / embedding_len
             print(f"Coverage {coverage}")

notebooks/02_TuningWithHDBSCAN.ipynb

@@ -365,6 +365,8 @@
    ],
    "source": [
     "# we will make our own scorer for DBCV\n",
+    "\n",
+    "\n",
     "def dbcv_score(X, labels):\n",
     "    return validity_index(X, labels)\n",
     "\n",