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/
estimator.py
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/
estimator.py
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# type: ignore
"""Xgboost pyspark integration submodule for estimator API."""
# pylint: disable=too-many-ancestors
from pyspark.ml.param.shared import HasProbabilityCol, HasRawPredictionCol
from xgboost import XGBClassifier, XGBRanker, XGBRegressor
from .core import (
SparkXGBClassifierModel,
SparkXGBRankerModel,
SparkXGBRegressorModel,
_set_pyspark_xgb_cls_param_attrs,
_SparkXGBEstimator,
)
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 :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
: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.
SparkXGBRegressor doesn't support setting `base_margin` explicitly as well, but support
another param called `base_margin_col`. see doc below for more details.
SparkXGBRegressor doesn't support `validate_features` and `output_margin` param.
SparkXGBRegressor doesn't support setting `nthread` xgboost param, instead, the `nthread`
param for each xgboost worker will be set equal to `spark.task.cpus` config value.
callbacks:
The export and import of the callback functions are at best effort.
For details, see :py:attr:`xgboost.spark.SparkXGBRegressor.callbacks` param doc.
validation_indicator_col
For params related to `xgboost.XGBRegressor` training
with evaluation dataset's supervision, set
:py:attr:`xgboost.spark.SparkXGBRegressor.validation_indicator_col`
parameter instead of setting the `eval_set` parameter in `xgboost.XGBRegressor`
fit method.
weight_col:
To specify the weight of the training and validation dataset, set
:py:attr:`xgboost.spark.SparkXGBRegressor.weight_col` parameter instead of setting
`sample_weight` and `sample_weight_eval_set` parameter in `xgboost.XGBRegressor`
fit method.
xgb_model:
Set the value to be the instance returned by
:func:`xgboost.spark.SparkXGBRegressorModel.get_booster`.
num_workers:
Integer that specifies the number of XGBoost workers to use.
Each XGBoost worker corresponds to one spark task.
use_gpu:
Boolean that specifies whether the executors are running on GPU
instances.
base_margin_col:
To specify the base margins of the training and validation
dataset, set :py:attr:`xgboost.spark.SparkXGBRegressor.base_margin_col` parameter
instead of setting `base_margin` and `base_margin_eval_set` in the
`xgboost.XGBRegressor` fit method. Note: this isn't available for distributed
training.
.. Note:: The Parameters chart above contains parameters that need special handling.
For a full list of parameters, see entries with `Param(parent=...` below.
.. Note:: This API is experimental.
Examples
--------
>>> from xgboost.spark import SparkXGBRegressor
>>> from pyspark.ml.linalg import Vectors
>>> df_train = spark.createDataFrame([
... (Vectors.dense(1.0, 2.0, 3.0), 0, False, 1.0),
... (Vectors.sparse(3, {1: 1.0, 2: 5.5}), 1, False, 2.0),
... (Vectors.dense(4.0, 5.0, 6.0), 2, True, 1.0),
... (Vectors.sparse(3, {1: 6.0, 2: 7.5}), 3, True, 2.0),
... ], ["features", "label", "isVal", "weight"])
>>> df_test = spark.createDataFrame([
... (Vectors.dense(1.0, 2.0, 3.0), ),
... (Vectors.sparse(3, {1: 1.0, 2: 5.5}), )
... ], ["features"])
>>> xgb_regressor = SparkXGBRegressor(max_depth=5, missing=0.0,
... validation_indicator_col='isVal', weight_col='weight',
... early_stopping_rounds=1, eval_metric='rmse')
>>> xgb_reg_model = xgb_regressor.fit(df_train)
>>> xgb_reg_model.transform(df_test)
"""
def __init__(self, **kwargs):
super().__init__()
self.setParams(**kwargs)
@classmethod
def _xgb_cls(cls):
return XGBRegressor
@classmethod
def _pyspark_model_cls(cls):
return SparkXGBRegressorModel
def _validate_params(self):
super()._validate_params()
if self.isDefined(self.qid_col):
raise ValueError(
"Spark Xgboost regressor estimator does not support `qid_col` param."
)
_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
: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
: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.
SparkXGBClassifier doesn't support setting `base_margin` explicitly as well, but support
another param called `base_margin_col`. see doc below for more details.
SparkXGBClassifier doesn't support setting `output_margin`, but we can get output margin
from the raw prediction column. See `raw_prediction_col` param doc below for more details.
SparkXGBClassifier doesn't support `validate_features` and `output_margin` param.
SparkXGBRegressor doesn't support setting `nthread` xgboost param, instead, the `nthread`
param for each xgboost worker will be set equal to `spark.task.cpus` config value.
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.
raw_prediction_col:
The `output_margin=True` is implicitly supported by the
`rawPredictionCol` output column, which is always returned with the predicted margin
values.
validation_indicator_col:
For params related to `xgboost.XGBClassifier` training with
evaluation dataset's supervision,
set :py:attr:`xgboost.spark.SparkXGBClassifier.validation_indicator_col`
parameter instead of setting the `eval_set` parameter in `xgboost.XGBClassifier`
fit method.
weight_col:
To specify the weight of the training and validation dataset, set
:py:attr:`xgboost.spark.SparkXGBClassifier.weight_col` parameter instead of setting
`sample_weight` and `sample_weight_eval_set` parameter in `xgboost.XGBClassifier`
fit method.
xgb_model:
Set the value to be the instance returned by
:func:`xgboost.spark.SparkXGBClassifierModel.get_booster`.
num_workers:
Integer that specifies the number of XGBoost workers to use.
Each XGBoost worker corresponds to one spark task.
use_gpu:
Boolean that specifies whether the executors are running on GPU
instances.
base_margin_col:
To specify the base margins of the training and validation
dataset, set :py:attr:`xgboost.spark.SparkXGBClassifier.base_margin_col` parameter
instead of setting `base_margin` and `base_margin_eval_set` in the
`xgboost.XGBClassifier` fit method. Note: this isn't available for distributed
training.
.. Note:: The Parameters chart above contains parameters that need special handling.
For a full list of parameters, see entries with `Param(parent=...` below.
.. Note:: This API is experimental.
Examples
--------
>>> from xgboost.spark import SparkXGBClassifier
>>> from pyspark.ml.linalg import Vectors
>>> df_train = spark.createDataFrame([
... (Vectors.dense(1.0, 2.0, 3.0), 0, False, 1.0),
... (Vectors.sparse(3, {1: 1.0, 2: 5.5}), 1, False, 2.0),
... (Vectors.dense(4.0, 5.0, 6.0), 0, True, 1.0),
... (Vectors.sparse(3, {1: 6.0, 2: 7.5}), 1, True, 2.0),
... ], ["features", "label", "isVal", "weight"])
>>> df_test = spark.createDataFrame([
... (Vectors.dense(1.0, 2.0, 3.0), ),
... ], ["features"])
>>> xgb_classifier = SparkXGBClassifier(max_depth=5, missing=0.0,
... validation_indicator_col='isVal', weight_col='weight',
... early_stopping_rounds=1, eval_metric='logloss')
>>> xgb_clf_model = xgb_classifier.fit(df_train)
>>> xgb_clf_model.transform(df_test).show()
"""
def __init__(self, **kwargs):
super().__init__()
self.setParams(**kwargs)
@classmethod
def _xgb_cls(cls):
return XGBClassifier
@classmethod
def _pyspark_model_cls(cls):
return SparkXGBClassifierModel
def _validate_params(self):
super()._validate_params()
if self.isDefined(self.qid_col):
raise ValueError(
"Spark Xgboost classifier estimator does not support `qid_col` param."
)
_set_pyspark_xgb_cls_param_attrs(SparkXGBClassifier, SparkXGBClassifierModel)
class SparkXGBRanker(_SparkXGBEstimator):
"""SparkXGBRanker 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`
SparkXGBRanker automatically supports most of the parameters in
`xgboost.XGBClassifier` constructor and most of the parameters used in
:py:class:`xgboost.XGBClassifier` fit and predict method.
SparkXGBRanker doesn't support setting `gpu_id` but support another param `use_gpu`,
see doc below for more details.
SparkXGBRanker doesn't support setting `base_margin` explicitly as well, but support
another param called `base_margin_col`. see doc below for more details.
SparkXGBRanker doesn't support setting `output_margin`, but we can get output margin
from the raw prediction column. See `raw_prediction_col` param doc below for more details.
SparkXGBRanker doesn't support `validate_features` and `output_margin` param.
SparkXGBRanker doesn't support setting `nthread` xgboost param, instead, the `nthread`
param for each xgboost worker will be set equal to `spark.task.cpus` config value.
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.
validation_indicator_col:
For params related to `xgboost.XGBClassifier` training with
evaluation dataset's supervision,
set :py:attr:`xgboost.spark.SparkXGBClassifier.validation_indicator_col`
parameter instead of setting the `eval_set` parameter in `xgboost.XGBClassifier`
fit method.
weight_col:
To specify the weight of the training and validation dataset, set
:py:attr:`xgboost.spark.SparkXGBClassifier.weight_col` parameter instead of setting
`sample_weight` and `sample_weight_eval_set` parameter in `xgboost.XGBClassifier`
fit method.
xgb_model:
Set the value to be the instance returned by
:func:`xgboost.spark.SparkXGBClassifierModel.get_booster`.
num_workers:
Integer that specifies the number of XGBoost workers to use.
Each XGBoost worker corresponds to one spark task.
use_gpu:
Boolean that specifies whether the executors are running on GPU
instances.
base_margin_col:
To specify the base margins of the training and validation
dataset, set :py:attr:`xgboost.spark.SparkXGBRanker.base_margin_col` parameter
instead of setting `base_margin` and `base_margin_eval_set` in the
`xgboost.XGBRanker` fit method.
qid_col:
To specify the qid of the training and validation
dataset, set :py:attr:`xgboost.spark.SparkXGBRanker.qid_col` parameter
instead of setting `qid` / `group`, `eval_qid` / `eval_group` in the
`xgboost.XGBRanker` fit method.
.. Note:: The Parameters chart above contains parameters that need special handling.
For a full list of parameters, see entries with `Param(parent=...` below.
.. Note:: This API is experimental.
Examples
--------
>>> from xgboost.spark import SparkXGBClassifier
>>> from pyspark.ml.linalg import Vectors
>>> df_train = spark.createDataFrame([
... (Vectors.dense(1.0, 2.0, 3.0), 0, False, 1.0),
... (Vectors.sparse(3, {1: 1.0, 2: 5.5}), 1, False, 2.0),
... (Vectors.dense(4.0, 5.0, 6.0), 0, True, 1.0),
... (Vectors.sparse(3, {1: 6.0, 2: 7.5}), 1, True, 2.0),
... ], ["features", "label", "isVal", "weight"])
>>> df_test = spark.createDataFrame([
... (Vectors.dense(1.0, 2.0, 3.0), ),
... ], ["features"])
>>> xgb_classifier = SparkXGBClassifier(max_depth=5, missing=0.0,
... validation_indicator_col='isVal', weight_col='weight',
... early_stopping_rounds=1, eval_metric='logloss')
>>> xgb_clf_model = xgb_classifier.fit(df_train)
>>> xgb_clf_model.transform(df_test).show()
"""
def __init__(self, **kwargs):
super().__init__()
self.setParams(**kwargs)
@classmethod
def _xgb_cls(cls):
return XGBRanker
@classmethod
def _pyspark_model_cls(cls):
return SparkXGBRankerModel
def _validate_params(self):
super()._validate_params()
if not self.isDefined(self.qid_col):
raise ValueError(
"Spark Xgboost ranker estimator requires setting `qid_col` param."
)
_set_pyspark_xgb_cls_param_attrs(SparkXGBRanker, SparkXGBRankerModel)