Deprecate positional arguments.

python-package/xgboost/core.py

@@ -12,6 +12,8 @@
 import sys
 import json
 import warnings
+from functools import wraps
+from inspect import signature, Parameter
 
 import numpy as np
 import scipy.sparse
@@ -369,6 +371,58 @@ def next(self, input_data):
         raise NotImplementedError()
 
 
+# Notice for `_deprecate_positional_args`
+# Authors: Olivier Grisel
+#          Gael Varoquaux
+#          Andreas Mueller
+#          Lars Buitinck
+#          Alexandre Gramfort
+#          Nicolas Tresegnie
+#          Sylvain Marie
+# License: BSD 3 clause
+def _deprecate_positional_args(f):
+    """Decorator for methods that issues warnings for positional arguments
+
+    Using the keyword-only argument syntax in pep 3102, arguments after the
+    * will issue a warning when passed as a positional argument.
+
+    Modifed from sklearn utils.validation.
+
+    Parameters
+    ----------
+    f : function
+        function to check arguments on
+    """
+    sig = signature(f)
+    kwonly_args = []
+    all_args = []
+
+    for name, param in sig.parameters.items():
+        if param.kind == Parameter.POSITIONAL_OR_KEYWORD:
+            all_args.append(name)
+        elif param.kind == Parameter.KEYWORD_ONLY:
+            kwonly_args.append(name)
+
+    @wraps(f)
+    def inner_f(*args, **kwargs):
+        extra_args = len(args) - len(all_args)
+        if extra_args > 0:
+            # ignore first 'self' argument for instance methods
+            args_msg = [
+                '{}={}'.format(name, arg) for name, arg in zip(
+                    kwonly_args[:extra_args], args[-extra_args:])
+            ]
+            warnings.warn(
+                "Pass {} as keyword args.  Passing these as positional "
+                "arguments will be considered as error in future releases.".
+                format(", ".join(args_msg)), FutureWarning)
+        for k, arg in zip(sig.parameters, args):
+            kwargs[k] = arg
+        return f(**kwargs)
+
+    return inner_f
+
+
 class DMatrix:                  # pylint: disable=too-many-instance-attributes
     """Data Matrix used in XGBoost.
 
@@ -461,7 +515,8 @@ def __del__(self):
             _check_call(_LIB.XGDMatrixFree(self.handle))
             self.handle = None
 
-    def set_info(self,
+    @_deprecate_positional_args
+    def set_info(self, *,
                  label=None, weight=None, base_margin=None,
                  group=None,
                  label_lower_bound=None,

python-package/xgboost/sklearn.py

@@ -5,7 +5,7 @@
 import warnings
 import json
 import numpy as np
-from .core import Booster, DMatrix, XGBoostError
+from .core import Booster, DMatrix, XGBoostError, _deprecate_positional_args
 from .training import train
 from .data import _is_cudf_df, _is_cudf_ser, _is_cupy_array
 
@@ -248,6 +248,40 @@ def __init__(self, max_depth=None, learning_rate=None, n_estimators=100,
         self.gpu_id = gpu_id
         self.validate_parameters = validate_parameters
 
+    def _wrap_evaluation_matrices(self, X, y, group,
+                                  sample_weight, train_dmatrix: DMatrix,
+                                  eval_set, sample_weight_eval_set, eval_group,
+                                  label_transform=lambda x: x):
+        '''Convert array_like evaluation matrices into DMatrix'''
+        if eval_group is None and eval_set is not None:
+            eval_group = [None] * len(eval_set)
+
+        if eval_set is not None:
+            if sample_weight_eval_set is None:
+                sample_weight_eval_set = [None] * len(eval_set)
+            else:
+                assert len(sample_weight_eval_set) == len(eval_set)
+
+            evals = []
+            for i, (valid_X, valid_y) in enumerate(eval_set):
+                if valid_X is X and valid_y is y and \
+                   sample_weight_eval_set[i] is sample_weight and eval_group[i] is group:
+                    evals.append(train_dmatrix)
+                else:
+                    m = DMatrix(valid_X,
+                                label=label_transform(valid_y),
+                                missing=self.missing, weight=sample_weight_eval_set[i],
+                                nthread=self.n_jobs)
+                    m.set_info(group=eval_group[i])
+                    evals.append(m)
+
+            nevals = len(evals)
+            eval_names = ["validation_{}".format(i) for i in range(nevals)]
+            evals = list(zip(evals, eval_names))
+        else:
+            evals = ()
+        return evals
+
     def _more_tags(self):
         '''Tags used for scikit-learn data validation.'''
         return {'allow_nan': True, 'no_validation': True}
@@ -445,6 +479,7 @@ def load_model(self, fname):
         # Delete the attribute after load
         self.get_booster().set_attr(scikit_learn=None)
 
+    @_deprecate_positional_args
     def fit(self, X, y, sample_weight=None, base_margin=None,
             eval_set=None, eval_metric=None, early_stopping_rounds=None,
             verbose=True, xgb_model=None, sample_weight_eval_set=None,
@@ -523,22 +558,10 @@ def fit(self, X, y, sample_weight=None, base_margin=None,
 
         evals_result = {}
 
-        if eval_set is not None:
-            if not isinstance(eval_set[0], (list, tuple)):
-                raise TypeError('Unexpected input type for `eval_set`')
-            if sample_weight_eval_set is None:
-                sample_weight_eval_set = [None] * len(eval_set)
-            else:
-                assert len(eval_set) == len(sample_weight_eval_set)
-            evals = list(
-                DMatrix(eval_set[i][0], label=eval_set[i][1], missing=self.missing,
-                        weight=sample_weight_eval_set[i], nthread=self.n_jobs)
-                for i in range(len(eval_set)))
-            evals = list(zip(evals, ["validation_{}".format(i) for i in
-                                     range(len(evals))]))
-        else:
-            evals = ()
-
+        evals = self._wrap_evaluation_matrices(
+            X, y, group=None, sample_weight=sample_weight, train_dmatrix=train_dmatrix,
+            eval_set=eval_set, sample_weight_eval_set=sample_weight_eval_set,
+            eval_group=None)
         params = self.get_xgb_params()
 
         if callable(self.objective):
@@ -774,10 +797,12 @@ def intercept_(self):
 ''')
 class XGBClassifier(XGBModel, XGBClassifierBase):
     # pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
-    def __init__(self, objective="binary:logistic", use_label_encoder=True, **kwargs):
+    @_deprecate_positional_args
+    def __init__(self, *, objective="binary:logistic", use_label_encoder=True, **kwargs):
         self.use_label_encoder = use_label_encoder
         super().__init__(objective=objective, **kwargs)
 
+    @_deprecate_positional_args
     def fit(self, X, y, sample_weight=None, base_margin=None,
             eval_set=None, eval_metric=None,
             early_stopping_rounds=None, verbose=True, xgb_model=None,
@@ -851,24 +876,6 @@ def fit(self, X, y, sample_weight=None, base_margin=None,
             label_transform = (lambda x: x)
         training_labels = label_transform(y)
 
-        if eval_set is not None:
-            if sample_weight_eval_set is None:
-                sample_weight_eval_set = [None] * len(eval_set)
-            else:
-                assert len(sample_weight_eval_set) == len(eval_set)
-            evals = list(
-                DMatrix(eval_set[i][0],
-                        label=label_transform(eval_set[i][1]),
-                        missing=self.missing, weight=sample_weight_eval_set[i],
-                        nthread=self.n_jobs)
-                for i in range(len(eval_set))
-            )
-            nevals = len(evals)
-            eval_names = ["validation_{}".format(i) for i in range(nevals)]
-            evals = list(zip(evals, eval_names))
-        else:
-            evals = ()
-
         if len(X.shape) != 2:
             # Simply raise an error here since there might be many
             # different ways of reshaping
@@ -882,6 +889,10 @@ def fit(self, X, y, sample_weight=None, base_margin=None,
                                 base_margin=base_margin,
                                 missing=self.missing, nthread=self.n_jobs)
         train_dmatrix.set_info(feature_weights=feature_weights)
+        evals = self._wrap_evaluation_matrices(
+            X, y, group=None, sample_weight=sample_weight, train_dmatrix=train_dmatrix,
+            eval_set=eval_set, sample_weight_eval_set=sample_weight_eval_set,
+            eval_group=None, label_transform=label_transform)
 
         self._Booster = train(xgb_options, train_dmatrix,
                               self.get_num_boosting_rounds(),
@@ -1063,7 +1074,8 @@ def evals_result(self):
 ''')
 class XGBRFClassifier(XGBClassifier):
     # pylint: disable=missing-docstring
-    def __init__(self,
+    @_deprecate_positional_args
+    def __init__(self, *,
                  learning_rate=1,
                  subsample=0.8,
                  colsample_bynode=0.8,
@@ -1091,7 +1103,8 @@ def get_num_boosting_rounds(self):
     ['estimators', 'model', 'objective'])
 class XGBRegressor(XGBModel, XGBRegressorBase):
     # pylint: disable=missing-docstring
-    def __init__(self, objective="reg:squarederror", **kwargs):
+    @_deprecate_positional_args
+    def __init__(self, *, objective="reg:squarederror", **kwargs):
         super().__init__(objective=objective, **kwargs)
 
 
@@ -1103,7 +1116,8 @@ def __init__(self, objective="reg:squarederror", **kwargs):
 ''')
 class XGBRFRegressor(XGBRegressor):
     # pylint: disable=missing-docstring
-    def __init__(self, learning_rate=1, subsample=0.8, colsample_bynode=0.8,
+    @_deprecate_positional_args
+    def __init__(self, *, learning_rate=1, subsample=0.8, colsample_bynode=0.8,
                  reg_lambda=1e-5, **kwargs):
         super().__init__(learning_rate=learning_rate, subsample=subsample,
                          colsample_bynode=colsample_bynode,
@@ -1159,14 +1173,16 @@ def get_num_boosting_rounds(self):
 ''')
 class XGBRanker(XGBModel):
     # pylint: disable=missing-docstring,too-many-arguments,invalid-name
-    def __init__(self, objective='rank:pairwise', **kwargs):
+    @_deprecate_positional_args
+    def __init__(self, *, objective='rank:pairwise', **kwargs):
         super().__init__(objective=objective, **kwargs)
         if callable(self.objective):
             raise ValueError(
                 "custom objective function not supported by XGBRanker")
         if "rank:" not in self.objective:
             raise ValueError("please use XGBRanker for ranking task")
 
+    @_deprecate_positional_args
     def fit(self, X, y, group, sample_weight=None, base_margin=None,
             eval_set=None, sample_weight_eval_set=None,
             eval_group=None, eval_metric=None,
@@ -1267,11 +1283,6 @@ def fit(self, X, y, group, sample_weight=None, base_margin=None,
                 raise ValueError(
                     "group is required for all eval datasets for ranking task")
 
-        def _dmat_init(group, **params):
-            ret = DMatrix(**params)
-            ret.set_group(group)
-            return ret
-
         self.n_features_in_ = X.shape[1]
 
         train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,
@@ -1280,24 +1291,13 @@ def _dmat_init(group, **params):
         train_dmatrix.set_info(feature_weights=feature_weights)
         train_dmatrix.set_group(group)
 
-        evals_result = {}
-
-        if eval_set is not None:
-            if sample_weight_eval_set is None:
-                sample_weight_eval_set = [None] * len(eval_set)
-            evals = [_dmat_init(eval_group[i],
-                                data=eval_set[i][0],
-                                label=eval_set[i][1],
-                                missing=self.missing,
-                                weight=sample_weight_eval_set[i],
-                                nthread=self.n_jobs)
-                     for i in range(len(eval_set))]
-            nevals = len(evals)
-            eval_names = ["eval_{}".format(i) for i in range(nevals)]
-            evals = list(zip(evals, eval_names))
-        else:
-            evals = ()
+        evals = self._wrap_evaluation_matrices(
+            X, y, group=group, sample_weight=sample_weight, train_dmatrix=train_dmatrix,
+            eval_set=eval_set,
+            sample_weight_eval_set=sample_weight_eval_set,
+            eval_group=eval_group)
 
+        evals_result = {}
         params = self.get_xgb_params()
 
         feval = eval_metric if callable(eval_metric) else None

tests/python/test_with_sklearn.py

@@ -32,7 +32,7 @@ def test_binary_classification():
     from sklearn.datasets import load_digits
     from sklearn.model_selection import KFold
 
-    digits = load_digits(2)
+    digits = load_digits(n_class=2)
     y = digits['target']
     X = digits['data']
     kf = KFold(n_splits=2, shuffle=True, random_state=rng)
@@ -166,7 +166,7 @@ def test_stacking_classification():
 def test_feature_importances_weight():
     from sklearn.datasets import load_digits
 
-    digits = load_digits(2)
+    digits = load_digits(n_class=2)
     y = digits['target']
     X = digits['data']
     xgb_model = xgb.XGBClassifier(random_state=0,
@@ -204,7 +204,7 @@ def test_feature_importances_weight():
 def test_feature_importances_gain():
     from sklearn.datasets import load_digits
 
-    digits = load_digits(2)
+    digits = load_digits(n_class=2)
     y = digits['target']
     X = digits['data']
     xgb_model = xgb.XGBClassifier(
@@ -243,7 +243,7 @@ def test_feature_importances_gain():
 def test_select_feature():
     from sklearn.datasets import load_digits
     from sklearn.feature_selection import SelectFromModel
-    digits = load_digits(2)
+    digits = load_digits(n_class=2)
     y = digits['target']
     X = digits['data']
     cls = xgb.XGBClassifier()
@@ -376,7 +376,7 @@ def logregobj(y_true, y_pred):
         hess = y_pred * (1.0 - y_pred)
         return grad, hess
 
-    digits = load_digits(2)
+    digits = load_digits(n_class=2)
     y = digits['target']
     X = digits['data']
     kf = KFold(n_splits=2, shuffle=True, random_state=rng)
@@ -473,7 +473,7 @@ def test_sklearn_nfolds_cv():
     from sklearn.datasets import load_digits
     from sklearn.model_selection import StratifiedKFold
 
-    digits = load_digits(3)
+    digits = load_digits(n_class=3)
     X = digits['data']
     y = digits['target']
     dm = xgb.DMatrix(X, label=y)
@@ -505,7 +505,7 @@ def test_sklearn_nfolds_cv():
 def test_split_value_histograms():
     from sklearn.datasets import load_digits
 
-    digits_2class = load_digits(2)
+    digits_2class = load_digits(n_class=2)
 
     X = digits_2class['data']
     y = digits_2class['target']
@@ -591,7 +591,7 @@ def test_sklearn_clone():
 
 def test_sklearn_get_default_params():
     from sklearn.datasets import load_digits
-    digits_2class = load_digits(2)
+    digits_2class = load_digits(n_class=2)
     X = digits_2class['data']
     y = digits_2class['target']
     cls = xgb.XGBClassifier()
@@ -889,6 +889,11 @@ def test_parameter_validation():
     assert len(output) == 0
 
 
+def test_deprecate_position_arg():
+    with pytest.warns(FutureWarning):
+        xgb.XGBRegressor(3, learning_rate=0.1)
+
+
 @pytest.mark.skipif(**tm.no_pandas())
 def test_pandas_input():
     import pandas as pd