misc/{,py-}xgboost: Update 1.5.2 -> 1.6.0

Reported by:	portscout

5 files changed, 22 insertions, 383 deletions

diff --git a/misc/py-xgboost/Makefile b/misc/py-xgboost/Makefile
index ecce53c63555..bbe49e906a87 100644
--- a/misc/py-xgboost/Makefile
+++ b/misc/py-xgboost/Makefile
@@ -1,6 +1,6 @@
 PORTNAME=	xgboost
 DISTVERSIONPREFIX=	v
-DISTVERSION=	1.5.2
+DISTVERSION=	1.6.0
 CATEGORIES=	misc # machine-learning
 PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
 
diff --git a/misc/py-xgboost/distinfo b/misc/py-xgboost/distinfo
index 754ae83217d4..21125d76240c 100644
--- a/misc/py-xgboost/distinfo
+++ b/misc/py-xgboost/distinfo
@@ -1,3 +1,3 @@
-TIMESTAMP = 1643047834
-SHA256 (dmlc-xgboost-v1.5.2_GH0.tar.gz) = d77c71aa125bd7292cd670e5a575850d48aba03d1b05d75d791f3580748b1b0e
-SIZE (dmlc-xgboost-v1.5.2_GH0.tar.gz) = 1645645
+TIMESTAMP = 1650230831
+SHA256 (dmlc-xgboost-v1.6.0_GH0.tar.gz) = 035706af59d842d03e23d6eb2c9a146ae7c2832f66aed2a4703b4c32209cce40
+SIZE (dmlc-xgboost-v1.6.0_GH0.tar.gz) = 1755620
diff --git a/misc/py-xgboost/files/patch-2to3 b/misc/py-xgboost/files/patch-2to3
deleted file mode 100644
index 54eac41ed210..000000000000
--- a/misc/py-xgboost/files/patch-2to3
+++ /dev/null
@@ -1,375 +0,0 @@
---- xgboost/callback.py.orig	2022-01-17 08:52:31 UTC
-+++ xgboost/callback.py
-@@ -319,7 +319,7 @@ def _aggcv(rlist):
-             cvmap[(metric_idx, k)].append(float(v))
-     msg = idx
-     results = []
--    for (metric_idx, k), v in sorted(cvmap.items(), key=lambda x: x[0][0]):
-+    for (metric_idx, k), v in sorted(list(cvmap.items()), key=lambda x: x[0][0]):
-         v = numpy.array(v)
-         if not isinstance(msg, STRING_TYPES):
-             msg = msg.decode()
-@@ -595,10 +595,10 @@ class EarlyStopping(TrainingCallback):
-                         evals_log: TrainingCallback.EvalsLog) -> bool:
-         epoch += self.starting_round  # training continuation
-         msg = 'Must have at least 1 validation dataset for early stopping.'
--        assert len(evals_log.keys()) >= 1, msg
-+        assert len(list(evals_log.keys())) >= 1, msg
-         data_name = ''
-         if self.data:
--            for d, _ in evals_log.items():
-+            for d, _ in list(evals_log.items()):
-                 if d == self.data:
-                     data_name = d
-             if not data_name:
-@@ -672,8 +672,8 @@ class EvaluationMonitor(TrainingCallback):
- 
-         msg: str = f'[{epoch}]'
-         if rabit.get_rank() == self.printer_rank:
--            for data, metric in evals_log.items():
--                for metric_name, log in metric.items():
-+            for data, metric in list(evals_log.items()):
-+                for metric_name, log in list(metric.items()):
-                     stdv: Optional[float] = None
-                     if isinstance(log[-1], tuple):
-                         score = log[-1][0]
---- xgboost/compat.py.orig	2022-01-17 08:52:31 UTC
-+++ xgboost/compat.py
-@@ -48,14 +48,14 @@ except ImportError:
- 
- # sklearn
- try:
--    from sklearn.base import BaseEstimator
--    from sklearn.base import RegressorMixin, ClassifierMixin
--    from sklearn.preprocessing import LabelEncoder
-+    from .sklearn.base import BaseEstimator
-+    from .sklearn.base import RegressorMixin, ClassifierMixin
-+    from .sklearn.preprocessing import LabelEncoder
- 
-     try:
--        from sklearn.model_selection import KFold, StratifiedKFold
-+        from .sklearn.model_selection import KFold, StratifiedKFold
-     except ImportError:
--        from sklearn.cross_validation import KFold, StratifiedKFold
-+        from .sklearn.cross_validation import KFold, StratifiedKFold
- 
-     SKLEARN_INSTALLED = True
- 
-@@ -71,7 +71,7 @@ try:
-         def to_json(self):
-             '''Returns a JSON compatible dictionary'''
-             meta = {}
--            for k, v in self.__dict__.items():
-+            for k, v in list(self.__dict__.items()):
-                 if isinstance(v, np.ndarray):
-                     meta[k] = v.tolist()
-                 else:
-@@ -82,7 +82,7 @@ try:
-             # pylint: disable=attribute-defined-outside-init
-             '''Load the encoder back from a JSON compatible dict.'''
-             meta = {}
--            for k, v in doc.items():
-+            for k, v in list(doc.items()):
-                 if k == 'classes_':
-                     self.classes_ = np.array(v)
-                     continue
---- xgboost/core.py.orig	2022-01-17 08:52:31 UTC
-+++ xgboost/core.py
-@@ -142,7 +142,7 @@ def _expect(expectations, got):
- 
- def _log_callback(msg: bytes) -> None:
-     """Redirect logs from native library into Python console"""
--    print(py_str(msg))
-+    print((py_str(msg)))
- 
- 
- def _get_log_callback_func():
-@@ -479,7 +479,7 @@ def _deprecate_positional_args(f):
-     kwonly_args = []
-     all_args = []
- 
--    for name, param in sig.parameters.items():
-+    for name, param in list(sig.parameters.items()):
-         if param.kind == Parameter.POSITIONAL_OR_KEYWORD:
-             all_args.append(name)
-         elif param.kind == Parameter.KEYWORD_ONLY:
-@@ -1346,7 +1346,7 @@ class Booster(object):
-     def _configure_metrics(self, params: Union[Dict, List]) -> Union[Dict, List]:
-         if isinstance(params, dict) and 'eval_metric' in params \
-            and isinstance(params['eval_metric'], list):
--            params = dict((k, v) for k, v in params.items())
-+            params = dict((k, v) for k, v in list(params.items()))
-             eval_metrics = params['eval_metric']
-             params.pop("eval_metric", None)
-             params = list(params.items())
-@@ -1577,7 +1577,7 @@ class Booster(object):
-         **kwargs
-             The attributes to set. Setting a value to None deletes an attribute.
-         """
--        for key, value in kwargs.items():
-+        for key, value in list(kwargs.items()):
-             if value is not None:
-                 if not isinstance(value, STRING_TYPES):
-                     raise ValueError("Set Attr only accepts string values")
-@@ -1650,7 +1650,7 @@ class Booster(object):
-            value of the specified parameter, when params is str key
-         """
-         if isinstance(params, Mapping):
--            params = params.items()
-+            params = list(params.items())
-         elif isinstance(params, STRING_TYPES) and value is not None:
-             params = [(params, value)]
-         for key, val in params:
---- xgboost/dask.py.orig	2022-01-17 08:52:31 UTC
-+++ xgboost/dask.py
-@@ -49,9 +49,9 @@ from .sklearn import _cls_predict_proba
- from .sklearn import XGBRanker
- 
- if TYPE_CHECKING:
--    from dask import dataframe as dd
--    from dask import array as da
--    import dask
-+    from .dask import dataframe as dd
-+    from .dask import array as da
-+    from . import dask
-     import distributed
- else:
-     dd = LazyLoader('dd', globals(), 'dask.dataframe')
-@@ -152,7 +152,7 @@ def _start_tracker(n_workers: int) -> Dict[str, Any]:
- 
- def _assert_dask_support() -> None:
-     try:
--        import dask  # pylint: disable=W0621,W0611
-+        from . import dask  # pylint: disable=W0621,W0611
-     except ImportError as e:
-         raise ImportError(
-             "Dask needs to be installed in order to use this module"
-@@ -394,7 +394,7 @@ class DaskDMatrix:
-         # [(x0, x1, ..), (y0, y1, ..), ..] in delayed form
- 
-         # delay the zipped result
--        parts = list(map(dask.delayed, zip(*parts)))  # pylint: disable=no-member
-+        parts = list(map(dask.delayed, list(zip(*parts))))  # pylint: disable=no-member
-         # At this point, the mental model should look like:
-         # [(x0, y0, ..), (x1, y1, ..), ..] in delayed form
- 
-@@ -414,7 +414,7 @@ class DaskDMatrix:
- 
-         worker_map: Dict[str, "distributed.Future"] = defaultdict(list)
- 
--        for key, workers in who_has.items():
-+        for key, workers in list(who_has.items()):
-             worker_map[next(iter(workers))].append(key_to_partition[key])
- 
-         self.worker_map = worker_map
-@@ -803,7 +803,7 @@ def _dmatrix_from_list_of_parts(
- async def _get_rabit_args(n_workers: int, client: "distributed.Client") -> List[bytes]:
-     '''Get rabit context arguments from data distribution in DaskDMatrix.'''
-     env = await client.run_on_scheduler(_start_tracker, n_workers)
--    rabit_args = [f"{k}={v}".encode() for k, v in env.items()]
-+    rabit_args = [f"{k}={v}".encode() for k, v in list(env.items())]
-     return rabit_args
- 
- # train and predict methods are supposed to be "functional", which meets the
-@@ -930,7 +930,7 @@ async def _train_async(
- 
-         results = await client.gather(futures, asynchronous=True)
- 
--        return list(filter(lambda ret: ret is not None, results))[0]
-+        return list([ret for ret in results if ret is not None])[0]
- 
- 
- def train(                      # pylint: disable=unused-argument
-@@ -1579,7 +1579,7 @@ class DaskScikitLearnBase(XGBModel):
- 
-     def __getstate__(self) -> Dict:
-         this = self.__dict__.copy()
--        if "_client" in this.keys():
-+        if "_client" in list(this.keys()):
-             del this["_client"]
-         return this
- 
-@@ -1711,7 +1711,7 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegress
-         callbacks: Optional[List[TrainingCallback]] = None,
-     ) -> "DaskXGBRegressor":
-         _assert_dask_support()
--        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
-+        args = {k: v for k, v in list(locals().items()) if k not in ("self", "__class__")}
-         return self._client_sync(self._fit_async, **args)
- 
- 
-@@ -1814,7 +1814,7 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassi
-         callbacks: Optional[List[TrainingCallback]] = None
-     ) -> "DaskXGBClassifier":
-         _assert_dask_support()
--        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
-+        args = {k: v for k, v in list(locals().items()) if k not in ("self", "__class__")}
-         return self._client_sync(self._fit_async, **args)
- 
-     async def _predict_proba_async(
-@@ -2002,7 +2002,7 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixI
-         callbacks: Optional[List[TrainingCallback]] = None
-     ) -> "DaskXGBRanker":
-         _assert_dask_support()
--        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
-+        args = {k: v for k, v in list(locals().items()) if k not in ("self", "__class__")}
-         return self._client_sync(self._fit_async, **args)
- 
-     # FIXME(trivialfis): arguments differ due to additional parameters like group and qid.
-@@ -2067,7 +2067,7 @@ class DaskXGBRFRegressor(DaskXGBRegressor):
-         callbacks: Optional[List[TrainingCallback]] = None
-     ) -> "DaskXGBRFRegressor":
-         _assert_dask_support()
--        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
-+        args = {k: v for k, v in list(locals().items()) if k not in ("self", "__class__")}
-         _check_rf_callback(early_stopping_rounds, callbacks)
-         super().fit(**args)
-         return self
-@@ -2131,7 +2131,7 @@ class DaskXGBRFClassifier(DaskXGBClassifier):
-         callbacks: Optional[List[TrainingCallback]] = None
-     ) -> "DaskXGBRFClassifier":
-         _assert_dask_support()
--        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
-+        args = {k: v for k, v in list(locals().items()) if k not in ("self", "__class__")}
-         _check_rf_callback(early_stopping_rounds, callbacks)
-         super().fit(**args)
-         return self
---- xgboost/plotting.py.orig	2022-01-17 08:52:31 UTC
-+++ xgboost/plotting.py
-@@ -81,7 +81,7 @@ def plot_importance(booster, ax=None, height=0.2,
-         tuples = sorted(tuples, key=lambda x: x[1])[-max_num_features:]
-     else:
-         tuples = sorted(tuples, key=lambda x: x[1])
--    labels, values = zip(*tuples)
-+    labels, values = list(zip(*tuples))
- 
-     if ax is None:
-         _, ax = plt.subplots(1, 1)
-@@ -177,13 +177,13 @@ def to_graphviz(booster, fmap='', num_trees=0, rankdir
-     # squash everything back into kwargs again for compatibility
-     parameters = 'dot'
-     extra = {}
--    for key, value in kwargs.items():
-+    for key, value in list(kwargs.items()):
-         extra[key] = value
- 
-     if rankdir is not None:
-         kwargs['graph_attrs'] = {}
-         kwargs['graph_attrs']['rankdir'] = rankdir
--    for key, value in extra.items():
-+    for key, value in list(extra.items()):
-         if kwargs.get("graph_attrs", None) is not None:
-             kwargs['graph_attrs'][key] = value
-         else:
---- xgboost/sklearn.py.orig	2022-01-17 08:52:31 UTC
-+++ xgboost/sklearn.py
-@@ -455,7 +455,7 @@ class XGBModel(XGBModelBase):
-         booster : a xgboost booster of underlying model
-         """
-         if not self.__sklearn_is_fitted__():
--            from sklearn.exceptions import NotFittedError
-+            from .sklearn.exceptions import NotFittedError
-             raise NotFittedError('need to call fit or load_model beforehand')
-         return self._Booster
- 
-@@ -476,7 +476,7 @@ class XGBModel(XGBModelBase):
- 
-         # this concatenates kwargs into parameters, enabling `get_params` for
-         # obtaining parameters from keyword parameters.
--        for key, value in params.items():
-+        for key, value in list(params.items()):
-             if hasattr(self, key):
-                 setattr(self, key, value)
-             else:
-@@ -526,14 +526,14 @@ class XGBModel(XGBModelBase):
-             internal = {}
-             while stack:
-                 obj = stack.pop()
--                for k, v in obj.items():
-+                for k, v in list(obj.items()):
-                     if k.endswith('_param'):
--                        for p_k, p_v in v.items():
-+                        for p_k, p_v in list(v.items()):
-                             internal[p_k] = p_v
-                     elif isinstance(v, dict):
-                         stack.append(v)
- 
--            for k, v in internal.items():
-+            for k, v in list(internal.items()):
-                 if k in params and params[k] is None:
-                     params[k] = parse_parameter(v)
-         except ValueError:
-@@ -549,7 +549,7 @@ class XGBModel(XGBModelBase):
-             "enable_categorical"
-         }
-         filtered = {}
--        for k, v in params.items():
-+        for k, v in list(params.items()):
-             if k not in wrapper_specific and not callable(v):
-                 filtered[k] = v
-         return filtered
-@@ -568,7 +568,7 @@ class XGBModel(XGBModelBase):
- 
-     def save_model(self, fname: Union[str, os.PathLike]) -> None:
-         meta = {}
--        for k, v in self.__dict__.items():
-+        for k, v in list(self.__dict__.items()):
-             if k == '_le':
-                 meta['_le'] = self._le.to_json()
-                 continue
-@@ -607,7 +607,7 @@ class XGBModel(XGBModelBase):
-             return
-         meta = json.loads(meta_str)
-         states = {}
--        for k, v in meta.items():
-+        for k, v in list(meta.items()):
-             if k == '_le':
-                 self._le = XGBoostLabelEncoder()
-                 self._le.from_json(v)
-@@ -660,7 +660,7 @@ class XGBModel(XGBModelBase):
- 
-     def _set_evaluation_result(self, evals_result: TrainingCallback.EvalsLog) -> None:
-         if evals_result:
--            for val in evals_result.items():
-+            for val in list(evals_result.items()):
-                 evals_result_key = list(val[1].keys())[0]
-                 evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
-             self.evals_result_ = evals_result
-@@ -1455,7 +1455,7 @@ class XGBRFClassifier(XGBClassifier):
-         feature_weights: Optional[array_like] = None,
-         callbacks: Optional[List[TrainingCallback]] = None
-     ) -> "XGBRFClassifier":
--        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
-+        args = {k: v for k, v in list(locals().items()) if k not in ("self", "__class__")}
-         _check_rf_callback(early_stopping_rounds, callbacks)
-         super().fit(**args)
-         return self
-@@ -1526,7 +1526,7 @@ class XGBRFRegressor(XGBRegressor):
-         feature_weights: Optional[array_like] = None,
-         callbacks: Optional[List[TrainingCallback]] = None
-     ) -> "XGBRFRegressor":
--        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
-+        args = {k: v for k, v in list(locals().items()) if k not in ("self", "__class__")}
-         _check_rf_callback(early_stopping_rounds, callbacks)
-         super().fit(**args)
-         return self
---- xgboost/training.py.orig	2022-01-17 08:52:31 UTC
-+++ xgboost/training.py
-@@ -452,7 +452,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, st
-         if 'eval_metric' in params:
-             params['eval_metric'] = _metrics
-     else:
--        params = dict((k, v) for k, v in params.items())
-+        params = dict((k, v) for k, v in list(params.items()))
- 
-     if (not metrics) and 'eval_metric' in params:
-         if isinstance(params['eval_metric'], list):
-@@ -506,7 +506,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, st
-             results[key + '-std'].append(std)
- 
-         if should_break:
--            for k in results.keys():  # pylint: disable=consider-iterating-dictionary
-+            for k in list(results.keys()):  # pylint: disable=consider-iterating-dictionary
-                 results[k] = results[k][:(booster.best_iteration + 1)]
-             break
-     if as_pandas:
diff --git a/misc/py-xgboost/files/patch-CMakeLists.txt b/misc/py-xgboost/files/patch-CMakeLists.txt
index 4b498a226047..593bcfbf2b9d 100644
--- a/misc/py-xgboost/files/patch-CMakeLists.txt
+++ b/misc/py-xgboost/files/patch-CMakeLists.txt
@@ -17,10 +17,10 @@
  if (BUILD_STATIC_LIB AND (R_LIB OR JVM_BINDINGS))
    message(SEND_ERROR "Cannot build a static library libxgboost.a when R or JVM packages are enabled.")
  endif (BUILD_STATIC_LIB AND (R_LIB OR JVM_BINDINGS))
-@@ -160,7 +160,7 @@ endif (USE_NCCL)
- 
- # dmlc-core
- msvc_use_static_runtime()
+@@ -168,7 +168,7 @@ msvc_use_static_runtime()
+ if (FORCE_SHARED_CRT)
+   set(DMLC_FORCE_SHARED_CRT ON)
+ endif ()
 -add_subdirectory(${xgboost_SOURCE_DIR}/dmlc-core)
 +#add_subdirectory(${xgboost_SOURCE_DIR}/dmlc-core)
  
diff --git a/misc/py-xgboost/pkg-plist b/misc/py-xgboost/pkg-plist
new file mode 100644
index 000000000000..1ddb5b1777da
--- /dev/null
+++ b/misc/py-xgboost/pkg-plist
@@ -0,0 +1,14 @@
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/__init__%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/_typing%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/callback%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/compat%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/config%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/core%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/dask%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/data%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/libpath%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/plotting%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/rabit%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/sklearn%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/tracker%%PYTHON_EXT_SUFFIX%%.pyc
+%%PYTHON_SITELIBDIR%%/xgboost/__pycache__/training%%PYTHON_EXT_SUFFIX%%.pyc