Cleanup some pylint errors.

python-package/xgboost/core.py

@@ -1,11 +1,9 @@
-# coding: utf-8
 # pylint: disable=too-many-arguments, too-many-branches, invalid-name
-# pylint: disable=too-many-lines, too-many-locals, no-self-use
+# pylint: disable=too-many-lines, too-many-locals
 """Core XGBoost Library."""
-# pylint: disable=no-name-in-module,import-error
+from abc import ABC, abstractmethod
 from collections.abc import Mapping
 from typing import List, Optional, Any, Union, Dict, TypeVar
-# pylint: enable=no-name-in-module,import-error
 from typing import Callable, Tuple, cast, Sequence
 import ctypes
 import os
@@ -123,9 +121,8 @@ def _log_callback(msg: bytes) -> None:
 
 def _get_log_callback_func() -> Callable:
     """Wrap log_callback() method in ctypes callback type"""
-    # pylint: disable=invalid-name
-    CALLBACK = ctypes.CFUNCTYPE(None, ctypes.c_char_p)
-    return CALLBACK(_log_callback)
+    c_callback = ctypes.CFUNCTYPE(None, ctypes.c_char_p)
+    return c_callback(_log_callback)
 
 
 def _load_lib() -> ctypes.CDLL:
@@ -311,7 +308,7 @@ def _prediction_output(shape, dims, predts, is_cuda):
     return arr_predict
 
 
-class DataIter:  # pylint: disable=too-many-instance-attributes
+class DataIter(ABC):  # pylint: disable=too-many-instance-attributes
     """The interface for user defined data iterator.
 
     Parameters
@@ -333,9 +330,10 @@ def __init__(self, cache_prefix: Optional[str] = None) -> None:
         # Stage data in Python until reset or next is called to avoid data being free.
         self._temporary_data: Optional[Tuple[Any, Any]] = None
 
-    def _get_callbacks(
+    def get_callbacks(
         self, allow_host: bool, enable_categorical: bool
     ) -> Tuple[Callable, Callable]:
+        """Get callback functions for iterating in C."""
         assert hasattr(self, "cache_prefix"), "__init__ is not called."
         self._reset_callback = ctypes.CFUNCTYPE(None, ctypes.c_void_p)(
             self._reset_wrapper
@@ -369,7 +367,8 @@ def _handle_exception(self, fn: Callable, dft_ret: _T) -> _T:
             self._exception = e.with_traceback(tb)
         return dft_ret
 
-    def _reraise(self) -> None:
+    def reraise(self) -> None:
+        """Reraise the exception thrown during iteration."""
         self._temporary_data = None
         if self._exception is not None:
             #  pylint 2.7.0 believes `self._exception` can be None even with `assert
@@ -424,10 +423,12 @@ def data_handle(
         # pylint: disable=not-callable
         return self._handle_exception(lambda: self.next(data_handle), 0)
 
+    @abstractmethod
     def reset(self) -> None:
         """Reset the data iterator.  Prototype for user defined function."""
         raise NotImplementedError()
 
+    @abstractmethod
     def next(self, input_data: Callable) -> int:
         """Set the next batch of data.
 
@@ -642,8 +643,7 @@ def _init_from_iter(self, iterator: DataIter, enable_categorical: bool) -> None:
         }
         args = from_pystr_to_cstr(json.dumps(args))
         handle = ctypes.c_void_p()
-        # pylint: disable=protected-access
-        reset_callback, next_callback = it._get_callbacks(
+        reset_callback, next_callback = it.get_callbacks(
             True, enable_categorical
         )
         ret = _LIB.XGDMatrixCreateFromCallback(
@@ -654,8 +654,7 @@ def _init_from_iter(self, iterator: DataIter, enable_categorical: bool) -> None:
             args,
             ctypes.byref(handle),
         )
-        # pylint: disable=protected-access
-        it._reraise()
+        it.reraise()
         # delay check_call to throw intermediate exception first
         _check_call(ret)
         self.handle = handle
@@ -1225,8 +1224,7 @@ def _init(self, data, enable_categorical: bool, **meta) -> None:
             it = SingleBatchInternalIter(data=data, **meta)
 
         handle = ctypes.c_void_p()
-        # pylint: disable=protected-access
-        reset_callback, next_callback = it._get_callbacks(False, enable_categorical)
+        reset_callback, next_callback = it.get_callbacks(False, enable_categorical)
         if it.cache_prefix is not None:
             raise ValueError(
                 "DeviceQuantileDMatrix doesn't cache data, remove the cache_prefix "
@@ -1242,8 +1240,7 @@ def _init(self, data, enable_categorical: bool, **meta) -> None:
             ctypes.c_int(self.max_bin),
             ctypes.byref(handle),
         )
-        # pylint: disable=protected-access
-        it._reraise()
+        it.reraise()
         # delay check_call to throw intermediate exception first
         _check_call(ret)
         self.handle = handle
@@ -1281,6 +1278,21 @@ def _get_booster_layer_trees(model: "Booster") -> Tuple[int, int]:
     return num_parallel_tree, num_groups
 
 
+def _configure_metrics(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())
+        eval_metrics = params["eval_metric"]
+        params.pop("eval_metric", None)
+        params = list(params.items())
+        for eval_metric in eval_metrics:
+            params += [("eval_metric", eval_metric)]
+    return params
+
+
 class Booster:
     # pylint: disable=too-many-public-methods
     """A Booster of XGBoost.
@@ -1339,7 +1351,7 @@ def __init__(
             raise TypeError('Unknown type:', model_file)
 
         params = params or {}
-        params = self._configure_metrics(params.copy())
+        params = _configure_metrics(params.copy())
         params = self._configure_constraints(params)
         if isinstance(params, list):
             params.append(('validate_parameters', True))
@@ -1352,17 +1364,6 @@ def __init__(
         else:
             self.booster = 'gbtree'
 
-    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())
-            eval_metrics = params['eval_metric']
-            params.pop("eval_metric", None)
-            params = list(params.items())
-            for eval_metric in eval_metrics:
-                params += [('eval_metric', eval_metric)]
-        return params
-
     def _transform_monotone_constrains(self, value: Union[Dict[str, int], str]) -> str:
         if isinstance(value, str):
             return value
@@ -1395,7 +1396,6 @@ def _transform_interaction_constraints(
                 )
             return s + "]"
         except KeyError as e:
-            # pylint: disable=raise-missing-from
             raise ValueError(
                 "Constrained features are not a subset of training data feature names"
             ) from e

python-package/xgboost/dask.py

@@ -171,15 +171,15 @@ def _try_start_tracker(
             host_ip = addrs[0][0]
             port = addrs[0][1]
             rabit_context = RabitTracker(
-                hostIP=get_host_ip(host_ip),
+                host_ip=get_host_ip(host_ip),
                 n_workers=n_workers,
                 port=port,
                 use_logger=False,
             )
         else:
             assert isinstance(addrs[0], str) or addrs[0] is None
             rabit_context = RabitTracker(
-                hostIP=get_host_ip(addrs[0]), n_workers=n_workers, use_logger=False
+                host_ip=get_host_ip(addrs[0]), n_workers=n_workers, use_logger=False
             )
         env.update(rabit_context.worker_envs())
         rabit_context.start(n_workers)

python-package/xgboost/rabit.py

@@ -1,7 +1,6 @@
-# coding: utf-8
-# pylint: disable= invalid-name
 """Distributed XGBoost Rabit related API."""
 import ctypes
+from enum import IntEnum, unique
 import pickle
 from typing import Any, TypeVar, Callable, Optional, cast, List, Union
 
@@ -98,7 +97,7 @@ def get_processor_name() -> bytes:
     return buf.value
 
 
-T = TypeVar("T")
+T = TypeVar("T")                # pylint:disable=invalid-name
 
 
 def broadcast(data: T, root: int) -> T:
@@ -152,34 +151,35 @@ def broadcast(data: T, root: int) -> T:
 }
 
 
-class Op:                     # pylint: disable=too-few-public-methods
+@unique
+class Op(IntEnum):
     '''Supported operations for rabit.'''
     MAX = 0
     MIN = 1
     SUM = 2
     OR = 3
 
 
-def allreduce(
-    data: np.ndarray, op: int, prepare_fun: Optional[Callable[[np.ndarray], None]] = None
+def allreduce(                  # pylint:disable=invalid-name
+    data: np.ndarray, op: Op, prepare_fun: Optional[Callable[[np.ndarray], None]] = None
 ) -> np.ndarray:
     """Perform allreduce, return the result.
 
     Parameters
     ----------
-    data: numpy array
+    data :
         Input data.
-    op: int
+    op :
         Reduction operators, can be MIN, MAX, SUM, BITOR
-    prepare_fun: function
+    prepare_fun :
         Lazy preprocessing function, if it is not None, prepare_fun(data)
         will be called by the function before performing allreduce, to initialize the data
         If the result of Allreduce can be recovered directly,
         then prepare_fun will NOT be called
 
     Returns
     -------
-    result : array_like
+    result :
         The result of allreduce, have same shape as data
 
     Notes
@@ -196,7 +196,7 @@ def allreduce(
     if prepare_fun is None:
         _check_call(_LIB.RabitAllreduce(buf.ctypes.data_as(ctypes.c_void_p),
                                         buf.size, DTYPE_ENUM__[buf.dtype],
-                                        op, None, None))
+                                        int(op), None, None))
     else:
         func_ptr = ctypes.CFUNCTYPE(None, ctypes.c_void_p)