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Support categorical data for hist. (#7695)

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* Extract partitioner from hist.
* Implement categorical data support by passing the gradient index directly into the partitioner.
* Organize/update document.
* Remove code for negative hessian.
This commit is contained in:
Jiaming Yuan
2022-02-25 03:47:14 +08:00
committed by GitHub
parent f60d95b0ba
commit 83a66b4994
15 changed files with 402 additions and 498 deletions
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7
python-package/xgboost/core.py
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@@ -582,10 +582,11 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
.. versionadded:: 1.3.0
.. note:: This parameter is experimental
Experimental support of specializing for categorical features. Do not set
to True unless you are interested in development. Currently it's only
available for `gpu_hist` and `approx` tree methods. Also, JSON/UBJSON
serialization format is required. (XGBoost 1.6 for approx)
to True unless you are interested in development. Also, JSON/UBJSON
serialization format is required.
"""
if group is not None and qid is not None:

17
python-package/xgboost/sklearn.py
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@@ -206,10 +206,11 @@ __model_doc = f'''
.. versionadded:: 1.5.0
Experimental support for categorical data. Do not set to true unless you are
interested in development. Only valid when `gpu_hist` or `approx` is used along
with dataframe as input. Also, JSON/UBJSON serialization format is
required. (XGBoost 1.6 for approx)
.. note:: This parameter is experimental
Experimental support for categorical data. When enabled, cudf/pandas.DataFrame
should be used to specify categorical data type. Also, JSON/UBJSON
serialization format is required.
max_cat_to_onehot : Optional[int]
@@ -220,9 +221,8 @@ __model_doc = f'''
A threshold for deciding whether XGBoost should use one-hot encoding based split
for categorical data. When number of categories is lesser than the threshold
then one-hot encoding is chosen, otherwise the categories will be partitioned
into children nodes. Only relevant for regression and binary
classification. Also, ``approx`` or ``gpu_hist`` tree method is required. See
:doc:`Categorical Data </tutorials/categorical>` for details.
into children nodes. Only relevant for regression and binary classification.
See :doc:`Categorical Data </tutorials/categorical>` for details.
eval_metric : Optional[Union[str, List[str], Callable]]
@@ -846,7 +846,8 @@ class XGBModel(XGBModelBase):
callbacks = self.callbacks if self.callbacks is not None else callbacks
tree_method = params.get("tree_method", None)
if self.enable_categorical and tree_method not in ("gpu_hist", "approx"):
cat_support = {"gpu_hist", "approx", "hist"}
if self.enable_categorical and tree_method not in cat_support:
raise ValueError(
"Experimental support for categorical data is not implemented for"
" current tree method yet."