[CI] Fix PyLint errors.

python-package/xgboost/callback.py

@@ -23,7 +23,13 @@
 import numpy
 
 from . import collective
-from .core import Booster, DMatrix, XGBoostError, _parse_eval_str
+from .core import (
+ Booster,
+ DMatrix,
+ XGBoostError,
+ _deprecate_positional_args,
+ _parse_eval_str,
+)
 
 __all__ = [
  "TrainingCallback",
@@ -346,8 +352,10 @@ class EarlyStopping(TrainingCallback):
  """
 
  # pylint: disable=too-many-arguments
+ @_deprecate_positional_args
  def __init__(
  self,
+ *,
  rounds: int,
  metric_name: Optional[str] = None,
  data_name: Optional[str] = None,
@@ -375,7 +383,7 @@ def before_training(self, model: _Model) -> _Model:
  return model
 
  def _update_rounds(
- self, score: _Score, name: str, metric: str, model: _Model, epoch: int
+ self, *, score: _Score, name: str, metric: str, model: _Model, epoch: int
  ) -> bool:
  def get_s(value: _Score) -> float:
  """get score if it's cross validation history."""
@@ -471,7 +479,9 @@ def after_iteration(
 
  # The latest score
  score = data_log[metric_name][-1]
- return self._update_rounds(score, data_name, metric_name, model, epoch)
+ return self._update_rounds(
+ score=score, name=data_name, metric=metric_name, model=model, epoch=epoch
+ )
 
  def after_training(self, model: _Model) -> _Model:
  if not self.save_best:

python-package/xgboost/core.py

@@ -905,7 +905,7 @@ def __init__(
  return
 
  handle, feature_names, feature_types = dispatch_data_backend(
- data,
+ data=data,
  missing=self.missing,
  threads=self.nthread,
  feature_names=feature_names,
@@ -1692,6 +1692,7 @@ class ExtMemQuantileDMatrix(DMatrix):
  def __init__( # pylint: disable=super-init-not-called
  self,
  data: DataIter,
+ *,
  missing: Optional[float] = None,
  nthread: Optional[int] = None,
  max_bin: Optional[int] = None,
@@ -2343,9 +2344,11 @@ def eval(self, data: DMatrix, name: str = "eval", iteration: int = 0) -> str:
  return self.eval_set([(data, name)], iteration)
 
  # pylint: disable=too-many-function-args
+ @_deprecate_positional_args
  def predict(
  self,
  data: DMatrix,
+ *,
  output_margin: bool = False,
  pred_leaf: bool = False,
  pred_contribs: bool = False,
@@ -2478,9 +2481,11 @@ def assign_type(t: int) -> None:
  return _prediction_output(shape, dims, preds, False)
 
  # pylint: disable=too-many-statements
+ @_deprecate_positional_args
  def inplace_predict(
  self,
  data: DataType,
+ *,
  iteration_range: IterationRange = (0, 0),
  predict_type: str = "value",
  missing: float = np.nan,

python-package/xgboost/dask/__init__.py

@@ -339,8 +339,8 @@ def __init__(
 
  self._init = client.sync(
  self._map_local_data,
- client,
- data,
+ client=client,
+ data=data,
  label=label,
  weights=weight,
  base_margin=base_margin,
@@ -355,6 +355,7 @@ def __await__(self) -> Generator:
 
  async def _map_local_data(
  self,
+ *,
  client: "distributed.Client",
  data: _DataT,
  label: Optional[_DaskCollection] = None,
@@ -589,6 +590,7 @@ def __init__(
  self,
  data: List[Any],
  label: Optional[List[Any]] = None,
+ *,
  weight: Optional[List[Any]] = None,
  base_margin: Optional[List[Any]] = None,
  qid: Optional[List[Any]] = None,
@@ -712,6 +714,7 @@ def _create_fn_args(self, worker_addr: str) -> Dict[str, Any]:
 
 
 def _create_quantile_dmatrix(
+ *,
  feature_names: Optional[FeatureNames],
  feature_types: Optional[Union[Any, List[Any]]],
  feature_weights: Optional[Any],
@@ -757,6 +760,7 @@ def _create_quantile_dmatrix(
 
 
 def _create_dmatrix(
+ *,
  feature_names: Optional[FeatureNames],
  feature_types: Optional[Union[Any, List[Any]]],
  feature_weights: Optional[Any],
@@ -927,6 +931,7 @@ def _get_dmatrices(
 
 
 async def _train_async(
+ *,
  client: "distributed.Client",
  global_config: Dict[str, Any],
  dconfig: Optional[Dict[str, Any]],
@@ -947,7 +952,7 @@ async def _train_async(
  _rabit_args = await _get_rabit_args(len(workers), dconfig, client)
  _check_distributed_params(params)
 
- def dispatched_train(
+ def dispatched_train( # pylint: disable=too-many-positional-arguments
  parameters: Dict,
  rabit_args: Dict[str, Union[str, int]],
  train_id: int,
@@ -1115,6 +1120,7 @@ def _maybe_dataframe(
 
 
 async def _direct_predict_impl( # pylint: disable=too-many-branches
+ *,
  mapped_predict: Callable,
  booster: "distributed.Future",
  data: _DataT,
@@ -1249,6 +1255,7 @@ async def _predict_async(
  global_config: Dict[str, Any],
  model: Union[Booster, Dict, "distributed.Future"],
  data: _DataT,
+ *,
  output_margin: bool,
  missing: float,
  pred_leaf: bool,
@@ -1304,7 +1311,12 @@ def mapped_predict(
  )
  )
  return await _direct_predict_impl(
- mapped_predict, _booster, data, None, _output_shape, meta
+ mapped_predict=mapped_predict,
+ booster=_booster,
+ data=data,
+ base_margin=None,
+ output_shape=_output_shape,
+ meta=meta,
  )
 
  output_shape, _ = await client.compute(
@@ -1392,10 +1404,12 @@ def dispatched_predict(booster: Booster, part: Dict[str, Any]) -> numpy.ndarray:
  return predictions
 
 
+@_deprecate_positional_args
 def predict( # pylint: disable=unused-argument
  client: Optional["distributed.Client"],
  model: Union[TrainReturnT, Booster, "distributed.Future"],
  data: Union[DaskDMatrix, _DataT],
+ *,
  output_margin: bool = False,
  missing: float = numpy.nan,
  pred_leaf: bool = False,
@@ -1447,6 +1461,7 @@ def predict( # pylint: disable=unused-argument
 
 
 async def _inplace_predict_async( # pylint: disable=too-many-branches
+ *,
  client: "distributed.Client",
  global_config: Dict[str, Any],
  model: Union[Booster, Dict, "distributed.Future"],
@@ -1501,14 +1516,21 @@ def mapped_predict(
  )
  )
  return await _direct_predict_impl(
- mapped_predict, booster, data, base_margin, shape, meta
+ mapped_predict=mapped_predict,
+ booster=booster,
+ data=data,
+ base_margin=base_margin,
+ output_shape=shape,
+ meta=meta,
  )
 
 
+@_deprecate_positional_args
 def inplace_predict( # pylint: disable=unused-argument
  client: Optional["distributed.Client"],
  model: Union[TrainReturnT, Booster, "distributed.Future"],
  data: _DataT,
+ *,
  iteration_range: IterationRange = (0, 0),
  predict_type: str = "value",
  missing: float = numpy.nan,
@@ -1615,6 +1637,7 @@ class DaskScikitLearnBase(XGBModel):
  async def _predict_async(
  self,
  data: _DataT,
+ *,
  output_margin: bool,
  validate_features: bool,
  base_margin: Optional[_DaskCollection],
@@ -1652,9 +1675,11 @@ async def _predict_async(
  )
  return predts
 
+ @_deprecate_positional_args
  def predict(
  self,
  X: _DataT,
+ *,
  output_margin: bool = False,
  validate_features: bool = True,
  base_margin: Optional[_DaskCollection] = None,
@@ -1765,6 +1790,7 @@ async def _fit_async(
  self,
  X: _DataT,
  y: _DaskCollection,
+ *,
  sample_weight: Optional[_DaskCollection],
  base_margin: Optional[_DaskCollection],
  eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]],
@@ -1855,6 +1881,7 @@ async def _fit_async(
  self,
  X: _DataT,
  y: _DaskCollection,
+ *,
  sample_weight: Optional[_DaskCollection],
  base_margin: Optional[_DaskCollection],
  eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]],
@@ -1999,13 +2026,18 @@ def predict_proba(
  async def _predict_async(
  self,
  data: _DataT,
+ *,
  output_margin: bool,
  validate_features: bool,
  base_margin: Optional[_DaskCollection],
  iteration_range: Optional[IterationRange],
  ) -> _DaskCollection:
  pred_probs = await super()._predict_async(
- data, output_margin, validate_features, base_margin, iteration_range
+ data,
+ output_margin=output_margin,
+ validate_features=validate_features,
+ base_margin=base_margin,
+ iteration_range=iteration_range,
  )
  if output_margin:
  return pred_probs
@@ -2049,6 +2081,7 @@ async def _fit_async(
  self,
  X: _DataT,
  y: _DaskCollection,
+ *,
  group: Optional[_DaskCollection],
  qid: Optional[_DaskCollection],
  sample_weight: Optional[_DaskCollection],