Working..

Signed-off-by: Adam Li <adam2392@gmail.com>

Author: adam2392

sklearn/tree/_criterion.pxd

@@ -11,7 +11,7 @@
 
 # See _criterion.pyx for implementation details.
 
-# from libcpp.vector cimport vector
+from libcpp.vector cimport vector
 
 from ._tree cimport DTYPE_t          # Type of X
 from ._tree cimport DOUBLE_t         # Type of y, sample_weight

sklearn/tree/_criterion.pyx

@@ -34,7 +34,9 @@ from ._utils cimport WeightedMedianCalculator
 cdef double EPSILON = 10 * np.finfo('double').eps
 
 cdef class BaseCriterion:
-    """This is an abstract interface for criterion. For example, a tree model could
+    """This is an abstract interface for criterion.
+
+    For example, a tree model could
     be either supervisedly, or unsupervisedly computing impurity on samples of
     covariates, or labels, or both. Although scikit-learn currently only contains
     supervised tree methods, this class enables 3rd party packages to leverage

sklearn/tree/_splitter.pxd

@@ -97,7 +97,7 @@ cdef class BaseSplitter:
 cdef class Splitter(BaseSplitter):
     cdef public Criterion criterion      # Impurity criterion
     cdef const DOUBLE_t[:, ::1] y
-    
+
     cdef int init(
         self,
         object X,