def __init__(self, modelId=None, splitRatio=1.0, labelCol="label", weightCol=None, featuresCols=[], allStringColumnsToCategorical=True, columnsToCategorical=[],
nfolds=0, keepCrossValidationPredictions=False, keepCrossValidationFoldAssignment=False, parallelizeCrossValidation=True,
seed=-1, distribution="AUTO", ntrees=50, maxDepth=5, minRows=10.0, nbins=20, nbinsCats=1024, minSplitImprovement=1e-5,
histogramType="AUTO", r2Stopping=java_max_double_value,
nbinsTopLevel=1<<10, buildTreeOneNode=False, scoreTreeInterval=0,
sampleRate=1.0, sampleRatePerClass=None, colSampleRateChangePerLevel=1.0, colSampleRatePerTree=1.0,
learnRate=0.1, learnRateAnnealing=1.0, colSampleRate=1.0, maxAbsLeafnodePred=java_max_double_value,
predNoiseBandwidth=0.0, convertUnknownCategoricalLevelsToNa=False, foldCol=None, predictionCol="prediction",
detailedPredictionCol="detailed_prediction", withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False, **deprecatedArgs):
Initializer.load_sparkling_jar()
super(H2OGBM, self).__init__()
self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.algos.H2OGBM", self.uid)
self._setDefault(modelId=None, splitRatio=1.0, labelCol="label", weightCol=None, featuresCols=[], allStringColumnsToCategorical=True, columnsToCategorical=[],
nfolds=0, keepCrossValidationPredictions=False, keepCrossValidationFoldAssignment=False, parallelizeCrossValidation=True,
seed=-1, distribution="AUTO",
ntrees=50, maxDepth=5, minRows=10.0, nbins=20, nbinsCats=1024, minSplitImprovement=1e-5,
histogramType="AUTO",
r2Stopping=_jvm().Double.MAX_VALUE, nbinsTopLevel=1<<10, buildTreeOneNode=False, scoreTreeInterval=0,
sampleRate=1.0, sampleRatePerClass=None, colSampleRateChangePerLevel=1.0, colSampleRatePerTree=1.0,
learnRate=0.1, learnRateAnnealing=1.0, colSampleRate=1.0, maxAbsLeafnodePred=_jvm().Double.MAX_VALUE,
predNoiseBandwidth=0.0, convertUnknownCategoricalLevelsToNa=False, foldCol=None,
predictionCol="prediction", detailedPredictionCol="detailed_prediction", withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False)
kwargs = get_input_kwargs(self)
self.setParams(**kwargs)
def setParams(self, modelId=None, splitRatio=1.0, labelCol="label", weightCol=None, featuresCols=[], allStringColumnsToCategorical=True, columnsToCategorical=[],
nfolds=0, keepCrossValidationPredictions=False, keepCrossValidationFoldAssignment=False,parallelizeCrossValidation=True,
seed=-1, distribution="AUTO", ntrees=50, maxDepth=5, minRows=10.0, nbins=20, nbinsCats=1024, minSplitImprovement=1e-5,
histogramType="AUTO", r2Stopping=java_max_double_value,
nbinsTopLevel=1<<10, buildTreeOneNode=False, scoreTreeInterval=0,
sampleRate=1.0, sampleRatePerClass=None, colSampleRateChangePerLevel=1.0, colSampleRatePerTree=1.0,
learnRate=0.1, learnRateAnnealing=1.0, colSampleRate=1.0, maxAbsLeafnodePred=java_max_double_value,
predNoiseBandwidth=0.0, convertUnknownCategoricalLevelsToNa=False, foldCol=None, predictionCol="prediction",
detailedPredictionCol="detailed_prediction", withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False, **deprecatedArgs):
kwargs = get_input_kwargs(self)
validateEnumValue(self._H2OAlgoCommonParams__getDistributionEnum(), kwargs, "distribution")
validateEnumValue(self._H2OSharedTreeParams__getHistogramTypeEnum(), kwargs, "histogramType")
# we need to convert double arguments manually to floats as if we assign integer to double, py4j thinks that
# the whole type is actually int and we get class cast exception
double_types = ["minRows", "predNoiseBandwidth", "splitRatio", "learnRate", "colSampleRate", "learnRateAnnealing", "maxAbsLeafnodePred"
"minSplitImprovement", "r2Stopping", "sampleRate", "colSampleRateChangePerLevel", "colSampleRatePerTree"]
set_double_values(kwargs, double_types)
# We need to also map all doubles in the arrays
arrayToDoubleArray("sampleRatePerClass", kwargs)
return self._set(**kwargs)
def setParams(self,
featuresCols=[],
labelCol="label",
allStringColumnsToCategorical=True,
columnsToCategorical=[],
splitRatio=1.0,
foldCol=None,
weightCol=None,
ignoredCols=[],
includeAlgos=None,
excludeAlgos=None,
projectName=None,
maxRuntimeSecs=3600.0,
stoppingRounds=3,
stoppingTolerance=0.001,
stoppingMetric="AUTO",
nfolds=5,
convertUnknownCategoricalLevelsToNa=True,
seed=-1,
sortMetric="AUTO",
balanceClasses=False,
classSamplingFactors=None,
maxAfterBalanceSize=5.0,
keepCrossValidationPredictions=True,
keepCrossValidationModels=True,
maxModels=0,
predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False,
**deprecatedArgs):
kwargs = get_input_kwargs(self)
validateEnumValues(self._H2OAutoMLParams__getAutomlAlgoEnum(),
kwargs,
"includeAlgos",
nullEnabled=True)
validateEnumValues(self._H2OAutoMLParams__getAutomlAlgoEnum(),
kwargs,
"excludeAlgos",
nullEnabled=True)
validateEnumValue(self._H2OAutoMLParams__getStoppingMetricEnum(),
kwargs, "stoppingMetric")
validateEnumValue(self._H2OAutoMLParams__getSortMetricEnum(), kwargs,
"sortMetric")
if "projectName" in kwargs and kwargs["projectName"] is None:
kwargs["projectName"] = ''.join(
random.choice(string.ascii_letters) for i in range(30))
# we need to convert double arguments manually to floats as if we assign integer to double, py4j thinks that
# the whole type is actually int and we get class cast exception
double_types = [
"maxRuntimeSecs", "stoppingTolerance", "splitRatio",
"maxAfterBalanceSize"
]
set_double_values(kwargs, double_types)
return self._set(**kwargs)
def __init__(self, keep=False, columns=[]):
Initializer.load_sparkling_jar()
super(ColumnPruner, self).__init__()
self._java_obj = self._new_java_obj(
"ai.h2o.sparkling.ml.features.ColumnPruner", self.uid)
self._setDefault(keep=False, columns=[])
kwargs = get_input_kwargs(self)
self.setParams(**kwargs)
def __init__(self,
predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
featuresCols=[],
foldCol=None,
weightCol=None,
splitRatio=1.0,
seed=-1,
nfolds=0,
allStringColumnsToCategorical=True,
columnsToCategorical=[],
convertUnknownCategoricalLevelsToNa=False,
convertInvalidNumbersToNa=False,
modelId=None,
keepCrossValidationPredictions=False,
keepCrossValidationFoldAssignment=False,
parallelizeCrossValidation=True,
distribution="AUTO",
maxIterations=10,
standardize=True,
init="Furthest",
userPoints=None,
estimateK=False,
k=2,
**deprecatedArgs):
Initializer.load_sparkling_jar()
super(H2OKMeans, self).__init__()
self._java_obj = self._new_java_obj(
"ai.h2o.sparkling.ml.algos.H2OKMeans", self.uid)
self._setDefault(predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
featuresCols=[],
foldCol=None,
weightCol=None,
splitRatio=1.0,
seed=-1,
nfolds=0,
allStringColumnsToCategorical=True,
columnsToCategorical=[],
convertUnknownCategoricalLevelsToNa=False,
convertInvalidNumbersToNa=False,
modelId=None,
keepCrossValidationPredictions=False,
keepCrossValidationFoldAssignment=False,
parallelizeCrossValidation=True,
distribution="AUTO",
maxIterations=10,
standardize=True,
init="Furthest",
userPoints=None,
estimateK=False,
k=2)
kwargs = get_input_kwargs(self)
self.setParams(**kwargs)
def __init__(self,
featuresCols=[],
algo=None,
splitRatio=1.0,
hyperParameters={},
labelCol="label",
weightCol=None,
allStringColumnsToCategorical=True,
columnsToCategorical=[],
strategy="Cartesian",
maxRuntimeSecs=0.0,
maxModels=0,
seed=-1,
stoppingRounds=0,
stoppingTolerance=0.001,
stoppingMetric="AUTO",
nfolds=0,
selectBestModelBy="AUTO",
selectBestModelDecreasing=True,
foldCol=None,
convertUnknownCategoricalLevelsToNa=True,
predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False,
**deprecatedArgs):
Initializer.load_sparkling_jar()
super(H2OGridSearch, self).__init__()
self._java_obj = self._new_java_obj(
"ai.h2o.sparkling.ml.algos.H2OGridSearch", self.uid)
self._setDefault(featuresCols=[],
algo=None,
splitRatio=1.0,
hyperParameters={},
labelCol="label",
weightCol=None,
allStringColumnsToCategorical=True,
columnsToCategorical=[],
strategy="Cartesian",
maxRuntimeSecs=0.0,
maxModels=0,
seed=-1,
stoppingRounds=0,
stoppingTolerance=0.001,
stoppingMetric="AUTO",
nfolds=0,
selectBestModelBy="AUTO",
selectBestModelDecreasing=True,
foldCol=None,
convertUnknownCategoricalLevelsToNa=True,
predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False)
kwargs = get_input_kwargs(self)
self.setParams(**kwargs)
def __init__(self, foldCol=None, labelCol="label", inputCols=[], holdoutStrategy = "None",
blendedAvgEnabled=False, blendedAvgInflectionPoint=10.0, blendedAvgSmoothing=20.0, noise=0.01, noiseSeed=-1):
Initializer.load_sparkling_jar()
super(H2OTargetEncoder, self).__init__()
self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.features.H2OTargetEncoder", self.uid)
self._setDefault(foldCol=None, labelCol="label", inputCols=[], holdoutStrategy="None",
blendedAvgEnabled=False, blendedAvgInflectionPoint=10.0, blendedAvgSmoothing=20.0, noise=0.01, noiseSeed=-1)
kwargs = get_input_kwargs(self)
self.setParams(**kwargs)
def setParams(self, featuresCols=[], labelCol="label", allStringColumnsToCategorical=True, columnsToCategorical=[], splitRatio=1.0, foldCol=None,
weightCol=None, ignoredCols=[], includeAlgos=None, excludeAlgos=None, projectName=None, maxRuntimeSecs=3600.0, stoppingRounds=3,
stoppingTolerance=0.001, stoppingMetric="AUTO", nfolds=5, convertUnknownCategoricalLevelsToNa=True, seed=-1,
sortMetric="AUTO", balanceClasses=False, classSamplingFactors=None, maxAfterBalanceSize=5.0, keepCrossValidationPredictions=True,
keepCrossValidationModels=True, maxModels=0, predictionCol="prediction", detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False, convertInvalidNumbersToNa=False, **deprecatedArgs):
kwargs = get_input_kwargs(self)
return self._set(**kwargs)
def setParams(self, foldCol=None, labelCol="label", inputCols=[], holdoutStrategy = "None",
blendedAvgEnabled=False, blendedAvgInflectionPoint=10.0, blendedAvgSmoothing=20.0, noise=0.01, noiseSeed=-1):
kwargs = get_input_kwargs(self)
validateEnumValue(self.__getHoldoutStrategyEnumName(), kwargs, "holdoutStrategy")
# we need to convert double arguments manually to floats as if we assign integer to double, py4j thinks that
# the whole type is actually int and we get class cast exception
double_types = ["blendedAvgInflectionPoint", "blendedAvgSmoothing", "noise"]
set_double_values(kwargs, double_types)
return self._set(**kwargs)
def setParams(self, modelId=None, splitRatio=1.0, labelCol="label", weightCol=None, featuresCols=[], allStringColumnsToCategorical=True, columnsToCategorical=[],
nfolds=0, keepCrossValidationPredictions=False, keepCrossValidationFoldAssignment=False, parallelizeCrossValidation=True,
seed=-1, distribution="AUTO", epochs=10.0, l1=0.0, l2=0.0, hidden=[200,200], reproducible=False, convertUnknownCategoricalLevelsToNa=False,
foldCol=None, predictionCol="prediction", detailedPredictionCol="detailed_prediction", withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False, **deprecatedArgs):
kwargs = get_input_kwargs(self)
validateEnumValue(self._H2OAlgoCommonParams__getDistributionEnum(), kwargs, "distribution")
# we need to convert double arguments manually to floats as if we assign integer to double, py4j thinks that
# the whole type is actually int and we get class cast exception
double_types = ["splitRatio", "epochs", "l1", "l2"]
set_double_values(kwargs, double_types)
return self._set(**kwargs)
def setParams(self,
featuresCols=[],
algo=None,
splitRatio=1.0,
hyperParameters={},
labelCol="label",
weightCol=None,
allStringColumnsToCategorical=True,
columnsToCategorical=[],
strategy="Cartesian",
maxRuntimeSecs=0.0,
maxModels=0,
seed=-1,
stoppingRounds=0,
stoppingTolerance=0.001,
stoppingMetric="AUTO",
nfolds=0,
selectBestModelBy="AUTO",
selectBestModelDecreasing=True,
foldCol=None,
convertUnknownCategoricalLevelsToNa=True,
predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False,
**deprecatedArgs):
kwargs = get_input_kwargs(self)
validateEnumValue(self._H2OGridSearchParams__getStrategyEnum(), kwargs,
"strategy")
validateEnumValue(self._H2OGridSearchParams__getStoppingMetricEnum(),
kwargs, "stoppingMetric")
validateEnumValue(
self._H2OGridSearchParams__getSelectBestModelByEnum(), kwargs,
"selectBestModelBy")
# we need to convert double arguments manually to floats as if we assign integer to double, py4j thinks that
# the whole type is actually int and we get class cast exception
double_types = ["splitRatio", "stoppingTolerance", "maxRuntimeSecs"]
set_double_values(kwargs, double_types)
if "algo" in kwargs and kwargs["algo"] is not None:
tmp = kwargs["algo"]
del kwargs['algo']
self._java_obj.setAlgo(tmp._java_obj)
return self._set(**kwargs)
def __init__(self, modelId=None, splitRatio=1.0, labelCol="label", weightCol=None, featuresCols=[], allStringColumnsToCategorical=True, columnsToCategorical=[],
nfolds=0, keepCrossValidationPredictions=False, keepCrossValidationFoldAssignment=False, parallelizeCrossValidation=True,
seed=-1, distribution="AUTO", epochs=10.0, l1=0.0, l2=0.0, hidden=[200,200], reproducible=False,
convertUnknownCategoricalLevelsToNa=False, foldCol=None, predictionCol="prediction", detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False, convertInvalidNumbersToNa=False, **deprecatedArgs):
Initializer.load_sparkling_jar()
super(H2ODeepLearning, self).__init__()
self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.algos.H2ODeepLearning", self.uid)
self._setDefault(modelId=None, splitRatio=1.0, labelCol="label", weightCol=None, featuresCols=[], allStringColumnsToCategorical=True, columnsToCategorical=[],
nfolds=0, keepCrossValidationPredictions=False, keepCrossValidationFoldAssignment=False, parallelizeCrossValidation=True,
seed=-1, distribution="AUTO",
epochs=10.0, l1=0.0, l2=0.0, hidden=[200,200], reproducible=False, convertUnknownCategoricalLevelsToNa=False,
foldCol=None, predictionCol="prediction", detailedPredictionCol="detailed_prediction", withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False)
kwargs = get_input_kwargs(self)
self.setParams(**kwargs)
def setParams(self,
predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
featuresCols=[],
foldCol=None,
weightCol=None,
splitRatio=1.0,
seed=-1,
nfolds=0,
allStringColumnsToCategorical=True,
columnsToCategorical=[],
convertUnknownCategoricalLevelsToNa=False,
convertInvalidNumbersToNa=False,
modelId=None,
keepCrossValidationPredictions=False,
keepCrossValidationFoldAssignment=False,
parallelizeCrossValidation=True,
distribution="AUTO",
maxIterations=10,
standardize=True,
init="Furthest",
userPoints=None,
estimateK=False,
k=2,
**deprecatedArgs):
kwargs = get_input_kwargs(self)
validateEnumValue(self._H2OAlgoCommonParams__getDistributionEnum(),
kwargs, "distribution")
validateEnumValue(self._H2OKMeansParams__getInitEnum(), kwargs, "init")
# We need to convert double arguments manually to floats as if we assign integer to double, py4j thinks that
# the whole type is actually int and we get class cast exception
double_types = ["splitRatio"]
set_double_values(kwargs, double_types)
if "userPoints" in kwargs:
kwargs["userPoints"] = getDoubleArrayArrayFromIntArrayArray(
kwargs["userPoints"])
return self._set(**kwargs)
def __init__(self, featuresCols=[], labelCol="label", allStringColumnsToCategorical=True, columnsToCategorical=[], splitRatio=1.0, foldCol=None,
weightCol=None, ignoredCols=[], includeAlgos=None, excludeAlgos=None, projectName=None, maxRuntimeSecs=3600.0, stoppingRounds=3,
stoppingTolerance=0.001, stoppingMetric="AUTO", nfolds=5, convertUnknownCategoricalLevelsToNa=True, seed=-1,
sortMetric="AUTO", balanceClasses=False, classSamplingFactors=None, maxAfterBalanceSize=5.0,
keepCrossValidationPredictions=True, keepCrossValidationModels=True, maxModels=0,
predictionCol="prediction", detailedPredictionCol="detailed_prediction", withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False, **deprecatedArgs):
Initializer.load_sparkling_jar()
super(H2OAutoML, self).__init__()
self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.algos.H2OAutoML", self.uid)
self._setDefault(featuresCols=[], labelCol="label", allStringColumnsToCategorical=True, columnsToCategorical=[], splitRatio=1.0, foldCol=None,
weightCol=None, ignoredCols=[], includeAlgos=None, excludeAlgos=None, projectName=None, maxRuntimeSecs=3600.0, stoppingRounds=3,
stoppingTolerance=0.001, stoppingMetric="AUTO", nfolds=5,
convertUnknownCategoricalLevelsToNa=True, seed=-1, sortMetric="AUTO", balanceClasses=False,
classSamplingFactors=None, maxAfterBalanceSize=5.0, keepCrossValidationPredictions=True,
keepCrossValidationModels=True, maxModels=0, predictionCol="prediction", detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False, convertInvalidNumbersToNa=False)
kwargs = get_input_kwargs(self)
self.setParams(**kwargs)
def setParams(self,
modelId=None,
splitRatio=1.0,
labelCol="label",
weightCol=None,
featuresCols=[],
allStringColumnsToCategorical=True,
columnsToCategorical=[],
nfolds=0,
keepCrossValidationPredictions=False,
keepCrossValidationFoldAssignment=False,
parallelizeCrossValidation=True,
seed=-1,
distribution="AUTO",
convertUnknownCategoricalLevelsToNa=False,
quietMode=True,
ntrees=50,
nEstimators=0,
maxDepth=6,
minRows=1.0,
minChildWeight=1.0,
learnRate=0.3,
eta=0.3,
learnRateAnnealing=1.0,
sampleRate=1.0,
subsample=1.0,
colSampleRate=1.0,
colSampleByLevel=1.0,
colSampleRatePerTree=1.0,
colsampleBytree=1.0,
maxAbsLeafnodePred=0.0,
maxDeltaStep=0.0,
scoreTreeInterval=0,
initialScoreInterval=4000,
scoreInterval=4000,
minSplitImprovement=0.0,
gamma=0.0,
nthread=-1,
maxBins=256,
maxLeaves=0,
minSumHessianInLeaf=100.0,
minDataInLeaf=0.0,
treeMethod="auto",
growPolicy="depthwise",
booster="gbtree",
dmatrixType="auto",
regLambda=0.0,
regAlpha=0.0,
sampleType="uniform",
normalizeType="tree",
rateDrop=0.0,
oneDrop=False,
skipDrop=0.0,
gpuId=0,
backend="auto",
foldCol=None,
predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False,
**deprecatedArgs):
kwargs = get_input_kwargs(self)
validateEnumValue(self._H2OAlgoCommonParams__getDistributionEnum(),
kwargs, "distribution")
validateEnumValue(self._H2OXGBoostParams__getTreeMethodEnum(), kwargs,
"treeMethod")
validateEnumValue(self._H2OXGBoostParams__getGrowPolicyEnum(), kwargs,
"growPolicy")
validateEnumValue(self._H2OXGBoostParams__getBoosterEnum(), kwargs,
"booster")
validateEnumValue(self._H2OXGBoostParams__getDmatrixTypeEnum(), kwargs,
"dmatrixType")
validateEnumValue(self._H2OXGBoostParams__getSampleTypeEnum(), kwargs,
"sampleType")
validateEnumValue(self._H2OXGBoostParams__getNormalizeTypeEnum(),
kwargs, "normalizeType")
validateEnumValue(self._H2OXGBoostParams__getBackendEnum(), kwargs,
"backend")
# we need to convert double arguments manually to floats as if we assign integer to double, py4j thinks that
# the whole type is actually int and we get class cast exception
double_types = [
"splitRatio", "minRows", "minChildWeight", "learnRate", "eta",
"learnRateAnnealing"
"sampleRate", "subsample", "colSampleRate", "colSampleByLevel",
"colSampleRatePerTree", "colsampleBytree", "maxAbsLeafnodePred",
"maxDeltaStep", "minSplitImprovement", "gamma",
"minSumHessianInLeaf", "minDataInLeaf", "regLambda", "regAlpha",
"rateDrop", "skipDrop"
]
set_double_values(kwargs, double_types)
return self._set(**kwargs)
def __init__(self,
modelId=None,
splitRatio=1.0,
labelCol="label",
weightCol=None,
featuresCols=[],
allStringColumnsToCategorical=True,
columnsToCategorical=[],
nfolds=0,
keepCrossValidationPredictions=False,
keepCrossValidationFoldAssignment=False,
parallelizeCrossValidation=True,
seed=-1,
distribution="AUTO",
convertUnknownCategoricalLevelsToNa=False,
quietMode=True,
ntrees=50,
nEstimators=0,
maxDepth=6,
minRows=1.0,
minChildWeight=1.0,
learnRate=0.3,
eta=0.3,
learnRateAnnealing=1.0,
sampleRate=1.0,
subsample=1.0,
colSampleRate=1.0,
colSampleByLevel=1.0,
colSampleRatePerTree=1.0,
colsampleBytree=1.0,
maxAbsLeafnodePred=0.0,
maxDeltaStep=0.0,
scoreTreeInterval=0,
initialScoreInterval=4000,
scoreInterval=4000,
minSplitImprovement=0.0,
gamma=0.0,
nthread=-1,
maxBins=256,
maxLeaves=0,
minSumHessianInLeaf=100.0,
minDataInLeaf=0.0,
treeMethod="auto",
growPolicy="depthwise",
booster="gbtree",
dmatrixType="auto",
regLambda=0.0,
regAlpha=0.0,
sampleType="uniform",
normalizeType="tree",
rateDrop=0.0,
oneDrop=False,
skipDrop=0.0,
gpuId=0,
backend="auto",
foldCol=None,
predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False,
**deprecatedArgs):
Initializer.load_sparkling_jar()
super(H2OXGBoost, self).__init__()
self._java_obj = self._new_java_obj(
"ai.h2o.sparkling.ml.algos.H2OXGBoost", self.uid)
self._setDefault(modelId=None,
splitRatio=1.0,
labelCol="label",
weightCol=None,
featuresCols=[],
allStringColumnsToCategorical=True,
columnsToCategorical=[],
nfolds=0,
keepCrossValidationPredictions=False,
keepCrossValidationFoldAssignment=False,
parallelizeCrossValidation=True,
seed=-1,
distribution="AUTO",
convertUnknownCategoricalLevelsToNa=False,
quietMode=True,
ntrees=50,
nEstimators=0,
maxDepth=6,
minRows=1.0,
minChildWeight=1.0,
learnRate=0.3,
eta=0.3,
learnRateAnnealing=1.0,
sampleRate=1.0,
subsample=1.0,
colSampleRate=1.0,
colSampleByLevel=1.0,
colSampleRatePerTree=1.0,
colsampleBytree=1.0,
maxAbsLeafnodePred=0.0,
maxDeltaStep=0.0,
scoreTreeInterval=0,
initialScoreInterval=4000,
scoreInterval=4000,
minSplitImprovement=0.0,
gamma=0.0,
nthread=-1,
maxBins=256,
maxLeaves=0,
minSumHessianInLeaf=100.0,
minDataInLeaf=0.0,
treeMethod="auto",
growPolicy="depthwise",
booster="gbtree",
dmatrixType="auto",
regLambda=0.0,
regAlpha=0.0,
sampleType="uniform",
normalizeType="tree",
rateDrop=0.0,
oneDrop=False,
skipDrop=0.0,
gpuId=0,
backend="auto",
foldCol=None,
predictionCol="prediction",
detailedPredictionCol="detailed_prediction",
withDetailedPredictionCol=False,
convertInvalidNumbersToNa=False)
kwargs = get_input_kwargs(self)
self.setParams(**kwargs)
def setParams(self, keep=False, columns=[]):
kwargs = get_input_kwargs(self)
return self._set(**kwargs)