FIX fix performance regression in trees with low-cardinality features (…

…scikit-learn#23410)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

sklearn/tree/_splitter.pyx

@@ -26,7 +26,6 @@ from ._utils cimport log
 from ._utils cimport rand_int
 from ._utils cimport rand_uniform
 from ._utils cimport RAND_R_MAX
-from ..utils._sorting cimport simultaneous_sort
 
 cdef double INFINITY = np.inf
 
@@ -342,7 +341,7 @@ cdef class BestSplitter(BaseDenseSplitter):
             for i in range(start, end):
                 Xf[i] = self.X[samples[i], current.feature]
 
-            simultaneous_sort(&Xf[start], &samples[start], end - start)
+            sort(&Xf[start], &samples[start], end - start)
 
             if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
                 features[f_j], features[n_total_constants] = features[n_total_constants], features[f_j]
@@ -1161,11 +1160,11 @@ cdef class BestSparseSplitter(BaseSparseSplitter):
             current.feature = features[f_j]
             self.extract_nnz(current.feature, &end_negative, &start_positive,
                              &is_samples_sorted)
-
             # Sort the positive and negative parts of `Xf`
-            simultaneous_sort(&Xf[start], &samples[start], end_negative - start)
+            sort(&Xf[start], &samples[start], end_negative - start)
             if start_positive < end:
-                simultaneous_sort(&Xf[start_positive], &samples[start_positive], end - start_positive)
+                sort(&Xf[start_positive], &samples[start_positive],
+                     end - start_positive)
 
             # Update index_to_samples to take into account the sort
             for p in range(start, end_negative):