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[doc] Integrate pyspark module into sphinx doc [skip ci] (#8066)

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This commit is contained in:
Jiaming Yuan
2022-07-17 10:46:09 +08:00
committed by GitHub
parent 579ab23b10
commit e28f6f6657
4 changed files with 47 additions and 13 deletions
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27
python-package/xgboost/spark/estimator.py
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@@ -15,12 +15,13 @@ class SparkXGBRegressor(_SparkXGBEstimator):
"""
SparkXGBRegressor is a PySpark ML estimator. It implements the XGBoost regression
algorithm based on XGBoost python library, and it can be used in PySpark Pipeline
and PySpark ML meta algorithms like CrossValidator/TrainValidationSplit/OneVsRest.
and PySpark ML meta algorithms like :py:class:`~pyspark.ml.tuning.CrossValidator`/
:py:class:`~pyspark.ml.tuning.TrainValidationSplit`/
:py:class:`~pyspark.ml.classification.OneVsRest`
SparkXGBRegressor automatically supports most of the parameters in
`xgboost.XGBRegressor` constructor and most of the parameters used in
`xgboost.XGBRegressor` fit and predict method (see `API docs <https://xgboost.readthedocs\
.io/en/latest/python/python_api.html#xgboost.XGBRegressor>`_ for details).
:py:class:`xgboost.XGBRegressor` fit and predict method.
SparkXGBRegressor doesn't support setting `gpu_id` but support another param `use_gpu`,
see doc below for more details.
@@ -65,7 +66,8 @@ class SparkXGBRegressor(_SparkXGBEstimator):
.. Note:: This API is experimental.
**Examples**
Examples
--------
>>> from xgboost.spark import SparkXGBRegressor
>>> from pyspark.ml.linalg import Vectors
@@ -104,15 +106,16 @@ _set_pyspark_xgb_cls_param_attrs(SparkXGBRegressor, SparkXGBRegressorModel)
class SparkXGBClassifier(_SparkXGBEstimator, HasProbabilityCol, HasRawPredictionCol):
"""
SparkXGBClassifier is a PySpark ML estimator. It implements the XGBoost classification
algorithm based on XGBoost python library, and it can be used in PySpark Pipeline
and PySpark ML meta algorithms like CrossValidator/TrainValidationSplit/OneVsRest.
"""SparkXGBClassifier is a PySpark ML estimator. It implements the XGBoost
classification algorithm based on XGBoost python library, and it can be used in
PySpark Pipeline and PySpark ML meta algorithms like
:py:class:`~pyspark.ml.tuning.CrossValidator`/
:py:class:`~pyspark.ml.tuning.TrainValidationSplit`/
:py:class:`~pyspark.ml.classification.OneVsRest`
SparkXGBClassifier automatically supports most of the parameters in
`xgboost.XGBClassifier` constructor and most of the parameters used in
`xgboost.XGBClassifier` fit and predict method (see `API docs <https://xgboost.readthedocs\
.io/en/latest/python/python_api.html#xgboost.XGBClassifier>`_ for details).
:py:class:`xgboost.XGBClassifier` fit and predict method.
SparkXGBClassifier doesn't support setting `gpu_id` but support another param `use_gpu`,
see doc below for more details.
@@ -127,6 +130,7 @@ class SparkXGBClassifier(_SparkXGBEstimator, HasProbabilityCol, HasRawPrediction
Parameters
----------
callbacks:
The export and import of the callback functions are at best effort. For
details, see :py:attr:`xgboost.spark.SparkXGBClassifier.callbacks` param doc.
@@ -166,7 +170,8 @@ class SparkXGBClassifier(_SparkXGBEstimator, HasProbabilityCol, HasRawPrediction
.. Note:: This API is experimental.
**Examples**
Examples
--------
>>> from xgboost.spark import SparkXGBClassifier
>>> from pyspark.ml.linalg import Vectors