示例#1
0
 def __init__(self,
              maxIterations=10,
              standardize=True,
              init="Furthest",
              userPoints=None,
              estimateK=False,
              k=2,
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              foldCol=None,
              weightCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              allStringColumnsToCategorical=True,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True):
     Initializer.load_sparkling_jar()
     super(H2OKMeans, self).__init__()
     self._java_obj = self._new_java_obj(
         "ai.h2o.sparkling.ml.algos.H2OKMeans", self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
 def __init__(self,
              epochs=10.0,
              l1=0.0,
              l2=0.0,
              hidden=[200, 200],
              reproducible=False,
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              labelCol="label",
              foldCol=None,
              weightCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              allStringColumnsToCategorical=True,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True):
     Initializer.load_sparkling_jar()
     super(H2ODeepLearning, self).__init__()
     self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.algos.H2ODeepLearning", self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
示例#3
0
 def __init__(self,
              algo=None,
              hyperParameters={},
              strategy="Cartesian",
              maxRuntimeSecs=0.0,
              maxModels=0,
              stoppingRounds=0,
              stoppingTolerance=0.001,
              stoppingMetric="AUTO",
              selectBestModelBy="AUTO",
              parallelism=1,
              labelCol="label",
              foldCol=None,
              weightCol=None,
              offsetCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              allStringColumnsToCategorical=True,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True,
              **DeprecatedParams):
     Initializer.load_sparkling_jar()
     super(H2OGridSearch, self).__init__()
     self._java_obj = self._new_java_obj(
         "ai.h2o.sparkling.ml.algos.H2OGridSearch", self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
示例#4
0
    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 __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._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
示例#6
0
 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)
示例#7
0
    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)
示例#8
0
 def __init__(self,
              standardize=True,
              family="gaussian",
              link="family_default",
              solver="AUTO",
              tweedieVariancePower=0.0,
              tweedieLinkPower=0.0,
              alphaValue=None,
              lambdaValue=None,
              missingValuesHandling="MeanImputation",
              prior=-1.0,
              lambdaSearch=False,
              nlambdas=-1,
              nonNegative=False,
              exactLambdas=False,
              lambdaMinRatio=-1.0,
              maxIterations=-1,
              intercept=True,
              betaEpsilon=1e-4,
              objectiveEpsilon=-1.0,
              gradientEpsilon=-1.0,
              objReg=-1.0,
              computePValues=False,
              removeCollinearCols=False,
              interactions=None,
              interactionPairs=None,
              earlyStopping=True,
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              labelCol="label",
              foldCol=None,
              weightCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              allStringColumnsToCategorical=True,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True,
              **DeprecatedParams):
     Initializer.load_sparkling_jar()
     super(H2OGLM, self).__init__()
     self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.algos.H2OGLM", self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     Utils.propagateValueFromDeprecatedProperty(kwargs, "alpha", "alphaValue")
     Utils.propagateValueFromDeprecatedProperty(kwargs, "lambda_", "lambdaValue")
     self._set(**kwargs)
示例#9
0
    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)
示例#10
0
 def __init__(self,
              learnRate=0.1,
              learnRateAnnealing=1.0,
              colSampleRate=1.0,
              maxAbsLeafnodePred=Utils.javaDoubleMaxValue,
              predNoiseBandwidth=0.0,
              quantileAlpha=0.5,
              ntrees=50,
              maxDepth=5,
              minRows=10.0,
              nbins=20,
              nbinsCats=1024,
              minSplitImprovement=1e-5,
              histogramType="AUTO",
              nbinsTopLevel=1 << 10,
              buildTreeOneNode=False,
              scoreTreeInterval=0,
              sampleRate=1.0,
              sampleRatePerClass=None,
              colSampleRateChangePerLevel=1.0,
              colSampleRatePerTree=1.0,
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              labelCol="label",
              foldCol=None,
              weightCol=None,
              offsetCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              allStringColumnsToCategorical=True,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True,
              monotoneConstraints={},
              stoppingRounds=0,
              stoppingMetric="AUTO",
              stoppingTolerance=0.001):
     Initializer.load_sparkling_jar()
     super(H2OGBM, self).__init__()
     self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.algos.H2OGBM",
                                         self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
示例#11
0
 def __init__(self,
              maxIterations=10,
              standardize=True,
              init="Furthest",
              userPoints=None,
              estimateK=False,
              k=2,
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              keepCrossValidationModels=True,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              foldCol=None,
              weightCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True,
              quantileAlpha=0.5,
              tweediePower=1.5,
              maxCategoricalLevels=10,
              ignoredCols=None,
              ignoreConstCols=True,
              scoreEachIteration=False,
              customDistributionFunc=None,
              customMetricFunc=None,
              exportCheckpointsDir=None,
              stoppingRounds=0,
              stoppingMetric="AUTO",
              maxRuntimeSecs=0.0,
              clusterSizeConstraints=None,
              stoppingTolerance=0.001,
              foldAssignment="AUTO",
              categoricalEncoding="AUTO",
              huberAlpha=0.9):
     Initializer.load_sparkling_jar()
     super(H2OKMeans, self).__init__()
     self._java_obj = self._new_java_obj(
         "ai.h2o.sparkling.ml.algos.H2OKMeans", self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
示例#12
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 def __init__(self,
              binomialDoubleTrees=False,
              mtries=-1,
              ntrees=50,
              maxDepth=20,
              minRows=1,
              nbins=20,
              nbinsCats=1024,
              minSplitImprovement=1e-5,
              histogramType="AUTO",
              r2Stopping=Utils.javaDoubleMaxValue,
              nbinsTopLevel=1 << 10,
              buildTreeOneNode=False,
              scoreTreeInterval=0,
              sampleRate=1.0,
              sampleRatePerClass=None,
              colSampleRateChangePerLevel=1.0,
              colSampleRatePerTree=1.0,
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              labelCol="label",
              foldCol=None,
              weightCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              allStringColumnsToCategorical=True,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True):
     Initializer.load_sparkling_jar()
     super(H2ODRF, self).__init__()
     self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.algos.H2ODRF",
                                         self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
示例#13
0
 def __init__(self,
              ignoredCols=[],
              includeAlgos=[
                  "GLM", "DRF", "GBM", "DeepLearning", "StackedEnsemble",
                  "XGBoost"
              ],
              excludeAlgos=[],
              projectName=None,
              maxRuntimeSecs=0.0,
              stoppingRounds=3,
              stoppingTolerance=0.001,
              stoppingMetric="AUTO",
              sortMetric="AUTO",
              balanceClasses=False,
              classSamplingFactors=None,
              maxAfterBalanceSize=5.0,
              keepCrossValidationPredictions=False,
              keepCrossValidationModels=False,
              maxModels=0,
              labelCol="label",
              foldCol=None,
              weightCol=None,
              offsetCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=5,
              allStringColumnsToCategorical=True,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True,
              monotoneConstraints={}):
     Initializer.load_sparkling_jar()
     super(H2OAutoML, self).__init__()
     self._java_obj = self._new_java_obj(
         "ai.h2o.sparkling.ml.algos.H2OAutoML", self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
 def __init__(self,
              foldCol=None,
              labelCol="label",
              inputCols=[],
              outputCols=[],
              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._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     if 'inputCols' in kwargs:
         kwargs['inputCols'] = self._convertInputCols(kwargs['inputCols'])
     self._set(**kwargs)
     self.setInputCols(self.getInputCols())
示例#15
0
    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)
示例#16
0
    def __init__(self,
                 algo=None,
                 hyperParameters={},
                 strategy="Cartesian",
                 maxRuntimeSecs=0.0,
                 maxModels=0,
                 stoppingRounds=0,
                 stoppingTolerance=0.001,
                 stoppingMetric="AUTO",
                 selectBestModelBy="AUTO",
                 selectBestModelDecreasing=True,
                 labelCol="label",
                 foldCol=None,
                 weightCol=None,
                 splitRatio=1.0,
                 seed=-1,
                 nfolds=0,
                 allStringColumnsToCategorical=True,
                 columnsToCategorical=[],
                 predictionCol="prediction",
                 detailedPredictionCol="detailed_prediction",
                 withDetailedPredictionCol=False,
                 featuresCols=[],
                 convertUnknownCategoricalLevelsToNa=False,
                 convertInvalidNumbersToNa=False):
        Initializer.load_sparkling_jar()
        super(H2OGridSearch, self).__init__()
        self._java_obj = self._new_java_obj(
            "ai.h2o.sparkling.ml.algos.H2OGridSearch", self.uid)
        self._setDefaultValuesFromJava(["algoParams"])
        kwargs = Utils.getInputKwargs(self)

        if "algo" in kwargs and kwargs["algo"] is not None:
            tmp = kwargs["algo"]
            del kwargs['algo']
            self._java_obj.setAlgo(tmp._java_obj)

        self._set(**kwargs)
示例#17
0
 def __init__(self,
              quietMode=True,
              ntrees=50,
              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",
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              labelCol="label",
              foldCol=None,
              weightCol=None,
              offsetCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              allStringColumnsToCategorical=True,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True,
              monotoneConstraints={},
              stoppingRounds=0,
              stoppingMetric="AUTO",
              stoppingTolerance=0.001,
              **DeprecatedParams):
     Initializer.load_sparkling_jar()
     super(H2OXGBoost, self).__init__()
     self._java_obj = self._new_java_obj(
         "ai.h2o.sparkling.ml.algos.H2OXGBoost", self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
示例#18
0
 def __init__(self,
              binomialDoubleTrees=False,
              mtries=-1,
              ntrees=50,
              maxDepth=20,
              minRows=1,
              nbins=20,
              nbinsCats=1024,
              minSplitImprovement=1e-5,
              histogramType="AUTO",
              nbinsTopLevel=1 << 10,
              buildTreeOneNode=False,
              scoreTreeInterval=0,
              sampleRate=1.0,
              sampleRatePerClass=None,
              colSampleRateChangePerLevel=1.0,
              colSampleRatePerTree=1.0,
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              labelCol="label",
              foldCol=None,
              weightCol=None,
              offsetCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True,
              stoppingRounds=0,
              stoppingMetric="AUTO",
              stoppingTolerance=0.001,
              customDistributionFunc=None,
              customMetricFunc=None,
              maxRuntimeSecs=0.0,
              exportCheckpointsDir=None,
              classSamplingFactors=None,
              huberAlpha=0.9,
              tweediePower=1.5,
              quantileAlpha=0.5,
              ignoredCols=None,
              ignoreConstCols=True,
              scoreEachIteration=False,
              maxCategoricalLevels=10,
              maxAfterBalanceSize=5.0,
              balanceClasses=False,
              calibrateModel=False,
              checkConstantResponse=True,
              foldAssignment="AUTO",
              categoricalEncoding="AUTO",
              keepCrossValidationModels=True):
     Initializer.load_sparkling_jar()
     super(H2ODRF, self).__init__()
     self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.algos.H2ODRF",
                                         self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
示例#19
0
 def __init__(self,
              standardize=True,
              family="gaussian",
              link="family_default",
              solver="AUTO",
              tweedieVariancePower=0.0,
              tweedieLinkPower=0.0,
              alphaValue=None,
              lambdaValue=None,
              missingValuesHandling="MeanImputation",
              prior=-1.0,
              lambdaSearch=False,
              nlambdas=-1,
              nonNegative=False,
              lambdaMinRatio=-1.0,
              maxIterations=-1,
              intercept=True,
              betaEpsilon=1e-4,
              objectiveEpsilon=-1.0,
              gradientEpsilon=-1.0,
              objReg=-1.0,
              computePValues=False,
              removeCollinearCols=False,
              interactions=None,
              interactionPairs=None,
              earlyStopping=True,
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              keepCrossValidationModels=True,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              labelCol="label",
              foldCol=None,
              weightCol=None,
              offsetCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True,
              balanceClasses=False,
              quantileAlpha=0.5,
              stoppingMetric="AUTO",
              stoppingTolerance=0.0001,
              stoppingRounds=3,
              categoricalEncoding="AUTO",
              exportCheckpointsDir=None,
              ignoredCols=None,
              ignoreConstCols=True,
              classSamplingFactors=None,
              maxAfterBalanceSize=5.0,
              maxCategoricalLevels=10,
              HGLM=False,
              customDistributionFunc=None,
              customMetricFunc=None,
              startval=None,
              theta=0.0000000001,
              tweediePower=1.5,
              scoreEachIteration=False,
              huberAlpha=0.9,
              maxActivePredictors=-1,
              foldAssignment="AUTO",
              calcLike=False,
              maxRuntimeSecs=0.0,
              **DeprecatedParams):
     Initializer.load_sparkling_jar()
     super(H2OGLM, self).__init__()
     self._java_obj = self._new_java_obj("ai.h2o.sparkling.ml.algos.H2OGLM",
                                         self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)
示例#20
0
 def __init__(self,
              epochs=10.0,
              l1=0.0,
              l2=0.0,
              hidden=[200, 200],
              reproducible=False,
              activation="Rectifier",
              quantileAlpha=0.5,
              modelId=None,
              keepCrossValidationPredictions=False,
              keepCrossValidationFoldAssignment=False,
              keepCrossValidationModels=True,
              parallelizeCrossValidation=True,
              distribution="AUTO",
              labelCol="label",
              foldCol=None,
              weightCol=None,
              offsetCol=None,
              splitRatio=1.0,
              seed=-1,
              nfolds=0,
              columnsToCategorical=[],
              predictionCol="prediction",
              detailedPredictionCol="detailed_prediction",
              withDetailedPredictionCol=False,
              featuresCols=[],
              convertUnknownCategoricalLevelsToNa=False,
              convertInvalidNumbersToNa=False,
              namedMojoOutputColumns=True,
              stoppingRounds=5,
              stoppingMetric="AUTO",
              stoppingTolerance=0.001,
              inputDropoutRatio=0.0,
              shuffleTrainingData=False,
              rateDecay=1.0,
              singleNodeMode=False,
              ignoredCols=None,
              ignoreConstCols=True,
              hiddenDropoutRatios=None,
              useAllFactorLevels=True,
              missingValuesHandling="MeanImputation",
              maxCategoricalFeatures=2147483647,
              fastMode=True,
              sparse=False,
              scoreTrainingSamples=10000,
              adaptiveRate=True,
              initialWeightScale=1.0,
              customMetricFunc=None,
              customDistributionFunc=None,
              autoencoder=False,
              classificationStop=0.0,
              standardize=True,
              targetRatioCommToComp=0.05,
              classSamplingFactors=None,
              elasticAveragingMovingRate=0.9,
              quietMode=False,
              scoreValidationSampling="Uniform",
              epsilon=.00000001,
              trainSamplesPerIteration=-2,
              diagnostics=True,
              momentumStable=0.0,
              rate=0.005,
              regressionStop=0.000001,
              initialWeightDistribution="UniformAdaptive",
              sparsityBeta=0.0,
              variableImportances=True,
              loss="Automatic",
              rateAnnealing=0.000001,
              scoreDutyCycle=0.1,
              maxRuntimeSecs=0.0,
              exportCheckpointsDir=None,
              nesterovAcceleratedGradient=True,
              momentumRamp=1000000.0,
              rho=0.99,
              scoreInterval=5.0,
              balanceClasses=False,
              elasticAveraging=False,
              averageActivation=0.0,
              forceLoadBalance=True,
              categoricalEncoding="AUTO",
              momentumStart=0.0,
              maxAfterBalanceSize=5.0,
              tweediePower=1.5,
              huberAlpha=0.9,
              overwriteWithBestModel=True,
              scoreEachIteration=False,
              exportWeightsAndBiases=False,
              foldAssignment="AUTO",
              maxW2=sys.float_info.max,
              elasticAveragingRegularization=0.001,
              replicateTrainingData=True,
              miniBatchSize=1,
              scoreValidationSamples=0,
              maxCategoricalLevels=10):
     Initializer.load_sparkling_jar()
     super(H2ODeepLearning, self).__init__()
     self._java_obj = self._new_java_obj(
         "ai.h2o.sparkling.ml.algos.H2ODeepLearning", self.uid)
     self._setDefaultValuesFromJava()
     kwargs = Utils.getInputKwargs(self)
     self._set(**kwargs)