Skip to content

[SPARK-30146][ML][PySpark] Add setWeightCol to GBTs in PySpark #26774

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
huaxingao wants to merge 2 commits into from
Closed

[SPARK-30146][ML][PySpark] Add setWeightCol to GBTs in PySpark #26774

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
12dd439
[SPARK-30146][ML][PySpark] Add setWeightCol to GBTs in PySpark
huaxingao Dec 6, 2019
9a83f92
fix python style test failure
huaxingao Dec 6, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
27 changes: 23 additions & 4 deletions python/pyspark/ml/classification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1617,6 +1617,8 @@ class GBTClassifier(JavaProbabilisticClassifier, _GBTClassifierParams,
... leafCol="leafId")
>>> gbt.setMaxIter(5)
GBTClassifier...
>>> gbt.setMinWeightFractionPerNode(0.049)
GBTClassifier...
>>> gbt.getMaxIter()
5
>>> gbt.getFeatureSubsetStrategy()
Expand Down Expand Up @@ -1684,14 +1686,15 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="logistic",
maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, impurity="variance",
featureSubsetStrategy="all", validationTol=0.01, validationIndicatorCol=None,
leafCol="", minWeightFractionPerNode=0.0):
leafCol="", minWeightFractionPerNode=0.0, weightCol=None):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
lossType="logistic", maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, \
impurity="variance", featureSubsetStrategy="all", validationTol=0.01, \
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0)
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0, \
weightCol=None)
"""
super(GBTClassifier, self).__init__()
self._java_obj = self._new_java_obj(
Expand All @@ -1711,14 +1714,16 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
lossType="logistic", maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0,
impurity="variance", featureSubsetStrategy="all", validationTol=0.01,
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0):
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
weightCol=None):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
lossType="logistic", maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, \
impurity="variance", featureSubsetStrategy="all", validationTol=0.01, \
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0)
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0, \
weightCol=None)
Sets params for Gradient Boosted Tree Classification.
"""
kwargs = self._input_kwargs
Expand Down Expand Up @@ -1826,6 +1831,20 @@ def setStepSize(self, value):
"""
return self._set(stepSize=value)

@since("3.0.0")
def setWeightCol(self, value):
"""
Sets the value of :py:attr:`weightCol`.
"""
return self._set(weightCol=value)

@since("3.0.0")
def setMinWeightFractionPerNode(self, value):
"""
Sets the value of :py:attr:`minWeightFractionPerNode`.
"""
return self._set(minWeightFractionPerNode=value)


class GBTClassificationModel(_TreeEnsembleModel, JavaProbabilisticClassificationModel,
_GBTClassifierParams, JavaMLWritable, JavaMLReadable):
Expand Down
28 changes: 24 additions & 4 deletions python/pyspark/ml/regression.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1226,6 +1226,8 @@ class GBTRegressor(JavaPredictor, _GBTRegressorParams, JavaMLWritable, JavaMLRea
>>> gbt = GBTRegressor(maxDepth=2, seed=42, leafCol="leafId")
>>> gbt.setMaxIter(5)
GBTRegressor...
>>> gbt.setMinWeightFractionPerNode(0.049)
GBTRegressor...
>>> gbt.getMaxIter()
5
>>> print(gbt.getImpurity())
Expand Down Expand Up @@ -1285,14 +1287,16 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None,
impurity="variance", featureSubsetStrategy="all", validationTol=0.01,
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0):
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
weightCol=None):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, \
impurity="variance", featureSubsetStrategy="all", validationTol=0.01, \
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0)
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
weightCol=None)
"""
super(GBTRegressor, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.regression.GBTRegressor", self.uid)
Expand All @@ -1311,14 +1315,16 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None,
impuriy="variance", featureSubsetStrategy="all", validationTol=0.01,
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0):
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
weightCol=None):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, \
impurity="variance", featureSubsetStrategy="all", validationTol=0.01, \
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0)
validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0, \
weightCol=None)
Sets params for Gradient Boosted Tree Regression.
"""
kwargs = self._input_kwargs
Expand Down Expand Up @@ -1432,6 +1438,20 @@ def setStepSize(self, value):
"""
return self._set(stepSize=value)

@since("3.0.0")
def setWeightCol(self, value):
"""
Sets the value of :py:attr:`weightCol`.
"""
return self._set(weightCol=value)

@since("3.0.0")
def setMinWeightFractionPerNode(self, value):
"""
Sets the value of :py:attr:`minWeightFractionPerNode`.
"""
return self._set(minWeightFractionPerNode=value)


class GBTRegressionModel(_TreeEnsembleModel, _GBTRegressorParams, JavaMLWritable, JavaMLReadable):
"""
Expand Down