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[SPARK-14107][PYSPARK][ML] Add seed as named argument to GBTs in pyspark #11944

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[SPARK-14107][PYSPARK][ML] Add seed as named argument to GBTs in pyspark #11944

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12 changes: 6 additions & 6 deletions python/pyspark/ml/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -520,7 +520,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
>>> si_model = stringIndexer.fit(df)
>>> td = si_model.transform(df)
>>> gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed")
>>> gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed", seed=42)
>>> model = gbt.fit(td)
>>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
True
Expand All @@ -543,19 +543,19 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
def __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):
maxIter=20, stepSize=0.1, seed=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)
lossType="logistic", maxIter=20, stepSize=0.1, seed=None)
"""
super(GBTClassifier, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.classification.GBTClassifier", self.uid)
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
lossType="logistic", maxIter=20, stepSize=0.1)
lossType="logistic", maxIter=20, stepSize=0.1, seed=None)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)

Expand All @@ -564,12 +564,12 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
def 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):
lossType="logistic", maxIter=20, stepSize=0.1, seed=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)
lossType="logistic", maxIter=20, stepSize=0.1, seed=None)
Sets params for Gradient Boosted Tree Classification.
"""
kwargs = self.setParams._input_kwargs
Expand Down
13 changes: 7 additions & 6 deletions python/pyspark/ml/regression.py
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Expand Up @@ -641,7 +641,7 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
>>> df = sqlContext.createDataFrame([
... (1.0, Vectors.dense(1.0)),
... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> gbt = GBTRegressor(maxIter=5, maxDepth=2)
>>> gbt = GBTRegressor(maxIter=5, maxDepth=2, seed=42)
>>> model = gbt.fit(df)
>>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
True
Expand All @@ -664,18 +664,19 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
def __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):
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=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)
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None)
"""
super(GBTRegressor, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.regression.GBTRegressor", self.uid)
self._setDefault(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)
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1,
seed=None)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)

Expand All @@ -684,12 +685,12 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
def 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):
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=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)
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None)
Sets params for Gradient Boosted Tree Regression.
"""
kwargs = self.setParams._input_kwargs
Expand Down