use sparse weights instead

Author: Sinacam

libmultilabel/linear/tree.py

@@ -223,6 +223,7 @@ def _train_node(y: sparse.csr_matrix,
         node.model = linear.train_1vsrest(
             meta_y, x, options, False
         )
+        node.model.weights = sparse.csr_matrix(node.model.weights)
 
 
 def _flatten_model(root: Node) -> tuple[linear.FlatModel, np.ndarray]:
@@ -257,7 +258,7 @@ def visit(node):
 
     model = linear.FlatModel(
         name='flattened-tree',
-        weights=np.hstack(weights),
+        weights=sparse.hstack(weights, 'csr'),
         bias=bias,
         thresholds=0,
     )