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[breaking] Remove label encoder deprecated in 1.3. (#7357)

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This commit is contained in:
Jiaming Yuan
2021-10-28 13:24:29 +08:00
committed by GitHub
parent d05754f558
commit 3c4aa9b2ea
7 changed files with 74 additions and 83 deletions
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73
python-package/xgboost/sklearn.py
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@@ -1,5 +1,4 @@
# coding: utf-8
# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, R0912, C0302
# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, too-many-lines
"""Scikit-Learn Wrapper interface for XGBoost."""
import copy
import warnings
@@ -278,14 +277,13 @@ def _wrap_evaluation_matrices(
eval_qid: Optional[List[Any]],
create_dmatrix: Callable,
enable_categorical: bool,
label_transform: Callable = lambda x: x,
) -> Tuple[Any, Optional[List[Tuple[Any, str]]]]:
"""Convert array_like evaluation matrices into DMatrix. Perform validation on the way.
"""
train_dmatrix = create_dmatrix(
data=X,
label=label_transform(y),
label=y,
group=group,
qid=qid,
weight=sample_weight,
@@ -333,7 +331,7 @@ def _wrap_evaluation_matrices(
else:
m = create_dmatrix(
data=valid_X,
label=label_transform(valid_y),
label=valid_y,
weight=sample_weight_eval_set[i],
group=eval_group[i],
qid=eval_qid[i],
@@ -1112,9 +1110,6 @@ def _cls_predict_proba(n_classes: int, prediction: PredtT, vstack: Callable) ->
['model', 'objective'], extra_parameters='''
n_estimators : int
Number of boosting rounds.
use_label_encoder : bool
(Deprecated) Use the label encoder from scikit-learn to encode the labels. For new
code, we recommend that you set this parameter to False.
''')
class XGBClassifier(XGBModel, XGBClassifierBase):
# pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
@@ -1123,10 +1118,13 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
self,
*,
objective: _SklObjective = "binary:logistic",
use_label_encoder: bool = True,
use_label_encoder: bool = False,
**kwargs: Any
) -> None:
# must match the parameters for `get_params`
self.use_label_encoder = use_label_encoder
if use_label_encoder is True:
raise ValueError("Label encoder was removed in 1.6.")
super().__init__(objective=objective, **kwargs)
@_deprecate_positional_args
@@ -1148,51 +1146,32 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
callbacks: Optional[List[TrainingCallback]] = None
) -> "XGBClassifier":
# pylint: disable = attribute-defined-outside-init,too-many-statements
can_use_label_encoder = True
label_encoding_check_error = (
"The label must consist of integer "
"labels of form 0, 1, 2, ..., [num_class - 1]."
)
label_encoder_deprecation_msg = (
"The use of label encoder in XGBClassifier is deprecated and will be "
"removed in a future release. To remove this warning, do the "
"following: 1) Pass option use_label_encoder=False when constructing "
"XGBClassifier object; and 2) Encode your labels (y) as integers "
"starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]."
)
evals_result: TrainingCallback.EvalsLog = {}
if _is_cudf_df(y) or _is_cudf_ser(y):
import cupy as cp # pylint: disable=E0401
self.classes_ = cp.unique(y.values)
self.n_classes_ = len(self.classes_)
can_use_label_encoder = False
expected_classes = cp.arange(self.n_classes_)
if (
self.classes_.shape != expected_classes.shape
or not (self.classes_ == expected_classes).all()
):
raise ValueError(label_encoding_check_error)
elif _is_cupy_array(y):
import cupy as cp # pylint: disable=E0401
self.classes_ = cp.unique(y)
self.n_classes_ = len(self.classes_)
can_use_label_encoder = False
expected_classes = cp.arange(self.n_classes_)
if (
self.classes_.shape != expected_classes.shape
or not (self.classes_ == expected_classes).all()
):
raise ValueError(label_encoding_check_error)
else:
self.classes_ = np.unique(np.asarray(y))
self.n_classes_ = len(self.classes_)
if not self.use_label_encoder and (
not np.array_equal(self.classes_, np.arange(self.n_classes_))
):
raise ValueError(label_encoding_check_error)
expected_classes = np.arange(self.n_classes_)
if (
self.classes_.shape != expected_classes.shape
or not (self.classes_ == expected_classes).all()
):
raise ValueError(
f"Invalid classes inferred from unique values of `y`. "
f"Expected: {expected_classes}, got {self.classes_}"
)
params = self.get_xgb_params()
@@ -1211,18 +1190,6 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
params["objective"] = "multi:softprob"
params["num_class"] = self.n_classes_
if self.use_label_encoder:
if not can_use_label_encoder:
raise ValueError('The option use_label_encoder=True is incompatible with inputs ' +
'of type cuDF or cuPy. Please set use_label_encoder=False when ' +
'constructing XGBClassifier object. NOTE: ' +
label_encoder_deprecation_msg)
warnings.warn(label_encoder_deprecation_msg, UserWarning)
self._le = XGBoostLabelEncoder().fit(y)
label_transform = self._le.transform
else:
label_transform = lambda x: x
model, feval, params = self._configure_fit(xgb_model, eval_metric, params)
train_dmatrix, evals = _wrap_evaluation_matrices(
missing=self.missing,
@@ -1240,7 +1207,6 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
eval_qid=None,
create_dmatrix=lambda **kwargs: DMatrix(nthread=self.n_jobs, **kwargs),
enable_categorical=self.enable_categorical,
label_transform=label_transform,
)
self._Booster = train(
@@ -1403,9 +1369,6 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
extra_parameters='''
n_estimators : int
Number of trees in random forest to fit.
use_label_encoder : bool
(Deprecated) Use the label encoder from scikit-learn to encode the labels. For new
code, we recommend that you set this parameter to False.
''')
class XGBRFClassifier(XGBClassifier):
# pylint: disable=missing-docstring
@@ -1416,14 +1379,12 @@ class XGBRFClassifier(XGBClassifier):
subsample: float = 0.8,
colsample_bynode: float = 0.8,
reg_lambda: float = 1e-5,
use_label_encoder: bool = True,
**kwargs: Any
):
super().__init__(learning_rate=learning_rate,
subsample=subsample,
colsample_bynode=colsample_bynode,
reg_lambda=reg_lambda,
use_label_encoder=use_label_encoder,
**kwargs)
def get_xgb_params(self) -> Dict[str, Any]: