Cleanup configuration for constraints. (#7758)

doc/treemethod.rst

@@ -134,7 +134,7 @@ Following table summarizes some differences in supported features between 4 tree
 +------------------+-----------+---------------------+---------------------+------------------------+
 | categorical data | F         | T                   | T                   | T                      |
 +------------------+-----------+---------------------+---------------------+------------------------+
-| External memory  | F         | T                   | P                   | P                      |
+| External memory  | F         | T                   | T                   | P                      |
 +------------------+-----------+---------------------+---------------------+------------------------+
 | Distributed      | F         | T                   | T                   | T                      |
 +------------------+-----------+---------------------+---------------------+------------------------+

doc/tutorials/feature_interaction_constraint.rst

@@ -174,6 +174,14 @@ parameter:
                                      num_boost_round = 1000, evals = evallist,
                                      early_stopping_rounds = 10)
 
+**************************
+Using feature name instead
+**************************
+
+XGBoost's Python package supports using feature names instead of feature index for
+specifying the constraints. Given a data frame with columns ``["f0", "f1", "f2"]``, the
+feature interaction constraint can be specified as ``[["f0", "f2"]]``.
+
 **************
 Advanced topic
 **************

doc/tutorials/monotonic.rst

@@ -69,7 +69,7 @@ Then fitting with monotonicity constraints only requires adding a single paramet
 .. code-block:: python
 
   params_constrained = params.copy()
-  params_constrained['monotone_constraints'] = "(1,-1)"
+  params_constrained['monotone_constraints'] = (1,-1)
 
   model_with_constraints = xgb.train(params_constrained, dtrain,
                                      num_boost_round = 1000, evals = evallist,
@@ -90,3 +90,13 @@ monotonic constraints may produce unnecessarily shallow trees. This is because t
 split. Monotonic constraints may wipe out all available split candidates, in which case no
 split is made. To reduce the effect, you may want to increase the ``max_bin`` parameter to
 consider more split candidates.
+
+
+*******************
+Using feature names
+*******************
+
+XGBoost's Python package supports using feature names instead of feature index for
+specifying the constraints. Given a data frame with columns ``["f0", "f1", "f2"]``, the
+monotonic constraint can be specified as ``{"f0": 1, "f2": -1}``, and ``"f1"`` will
+default to ``0`` (no constraint).