Rewrite approx (#7214)

This PR rewrites the approx tree method to use codebase from hist for better performance and code sharing.

The rewrite has many benefits:
- Support for both `max_leaves` and `max_depth`.
- Support for `grow_policy`.
- Support for mono constraint.
- Support for feature weights.
- Support for easier bin configuration (`max_bin`).
- Support for categorical data.
- Faster performance for most of the datasets. (many times faster)
- Support for prediction cache.
- Significantly better performance for external memory.
- Unites the code base between approx and hist.

tests/python/test_monotone_constraints.py

@@ -63,7 +63,6 @@ training_dset = xgb.DMatrix(x, label=y)
 
 
 class TestMonotoneConstraints:
-
     def test_monotone_constraints_for_exact_tree_method(self):
 
         # first check monotonicity for the 'exact' tree method
@@ -76,32 +75,23 @@ class TestMonotoneConstraints:
         )
         assert is_correctly_constrained(constrained_exact_method)
 
-    def test_monotone_constraints_for_depthwise_hist_tree_method(self):
-
-        # next check monotonicity for the 'hist' tree method
-        params_for_constrained_hist_method = {
-            'tree_method': 'hist', 'verbosity': 1,
-            'monotone_constraints': '(1, -1)'
+    @pytest.mark.parametrize(
+        "tree_method,policy",
+        [
+            ("hist", "depthwise"),
+            ("approx", "depthwise"),
+            ("hist", "lossguide"),
+            ("approx", "lossguide"),
+        ],
+    )
+    def test_monotone_constraints(self, tree_method: str, policy: str) -> None:
+        params_for_constrained = {
+            "tree_method": tree_method,
+            "grow_policy": policy,
+            "monotone_constraints": "(1, -1)",
         }
-        constrained_hist_method = xgb.train(
-            params_for_constrained_hist_method, training_dset
-        )
-
-        assert is_correctly_constrained(constrained_hist_method)
-
-    def test_monotone_constraints_for_lossguide_hist_tree_method(self):
-
-        # next check monotonicity for the 'hist' tree method
-        params_for_constrained_hist_method = {
-            'tree_method': 'hist', 'verbosity': 1,
-            'grow_policy': 'lossguide',
-            'monotone_constraints': '(1, -1)'
-        }
-        constrained_hist_method = xgb.train(
-            params_for_constrained_hist_method, training_dset
-        )
-
-        assert is_correctly_constrained(constrained_hist_method)
+        constrained = xgb.train(params_for_constrained, training_dset)
+        assert is_correctly_constrained(constrained)
 
     @pytest.mark.parametrize('format', [dict, list])
     def test_monotone_constraints_feature_names(self, format):