fix eom recursion; add fit_predict

fast_hdbscan/cluster_trees.py

@@ -450,21 +450,16 @@ def extract_clusters_bcubed(condensed_tree, cluster_tree, data_labels, allow_vir
 
 @numba.njit()
 def score_condensed_tree_nodes(condensed_tree):
-    result = {0: np.float32(0.0) for i in range(0)}
+    root = condensed_tree.parent[0]
+    result = {root: np.float32(0.0)}
 
     for i in range(condensed_tree.parent.shape[0]):
-        parent = condensed_tree.parent[i]
-        if parent in result:
-            result[parent] += condensed_tree.lambda_val[i] * condensed_tree.child_size[i]
-        else:
-            result[parent] = condensed_tree.lambda_val[i] * condensed_tree.child_size[i]
-
         if condensed_tree.child_size[i] > 1:
             child = condensed_tree.child[i]
-            if child in result:
-                result[child] -= condensed_tree.lambda_val[i] * condensed_tree.child_size[i]
-            else:
-                result[child] = -condensed_tree.lambda_val[i] * condensed_tree.child_size[i]
+            result[child] = -condensed_tree.lambda_val[i] * condensed_tree.child_size[i]
+
+        parent = condensed_tree.parent[i]
+        result[parent] += condensed_tree.lambda_val[i] * condensed_tree.child_size[i]
 
     return result
 
@@ -493,7 +488,7 @@ def unselect_below_node(node, cluster_tree, selected_clusters):
 
 @numba.njit(fastmath=True)
 def eom_recursion(node, cluster_tree, node_scores, node_sizes, selected_clusters, max_cluster_size):
-    current_score = node_scores[node]
+    current_score = max(node_scores[node], 0.0) # floating point errors can make score negative!
     current_size = node_sizes[node]
 
     children = cluster_tree.child[cluster_tree.parent == node]

fast_hdbscan/hdbscan.py

@@ -392,6 +392,10 @@ def fit(self, X, y=None, sample_weight=None, **fit_params):
 
         return self
 
+    def fit_predict(self, X, y=None, sample_weight=None, **fit_params):
+        self.fit(X, y, sample_weight, **fit_params)
+        return self.labels_
+
     def dbscan_clustering(self, epsilon):
         check_is_fitted(
             self,