Enable generalization of min_samples_split

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

Author: adam2392

sklearn/tree/_classes.py

@@ -110,6 +110,7 @@ class BaseDecisionTree(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta):
         "min_samples_split": [
             Interval(Integral, 2, None, closed="left"),
             Interval(RealNotInt, 0.0, 1.0, closed="right"),
+            StrOptions({"sqrt", "log2"}),
         ],
         "min_samples_leaf": [
             Interval(Integral, 1, None, closed="left"),
@@ -364,13 +365,18 @@ def _fit(
         else:  # float
             min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples))
 
-        if isinstance(self.min_samples_split, numbers.Integral):
+        if isinstance(self.min_samples_split, str):
+            if self.min_samples_split == "sqrt":
+                min_samples_split = max(1, int(np.sqrt(self.n_features_in_)))
+            elif self.min_samples_split == "log2":
+                min_samples_split = max(1, int(np.log2(self.n_features_in_)))
+        elif isinstance(self.min_samples_split, numbers.Integral):
             min_samples_split = self.min_samples_split
         else:  # float
             min_samples_split = int(ceil(self.min_samples_split * n_samples))
             min_samples_split = max(2, min_samples_split)
-
         min_samples_split = max(min_samples_split, 2 * min_samples_leaf)
+        self.min_samples_split_ = min_samples_split
 
         if isinstance(self.max_features, str):
             if self.max_features == "auto":

sklearn/tree/tests/test_tree.py

@@ -2413,8 +2413,8 @@ def test_min_sample_split_1_error(Tree):
     # min_samples_split=1 is invalid
     tree = Tree(min_samples_split=1)
     msg = (
-        r"'min_samples_split' .* must be an int in the range \[2, inf\) "
-        r"or a float in the range \(0.0, 1.0\]"
+        r"'min_samples_split' .* must be an int in the range \[2, inf\)"
+        r".* a float in the range \(0.0, 1.0\]"
     )
     with pytest.raises(ValueError, match=msg):
         tree.fit(X, y)