class OrthoForestDMLModel extends Model[OrthoForestDMLModel] with OrthoForestDMLParams with ComplexParamsWritable with Wrappable with SynapseMLLogging
Model produced by OrthoForestDMLEstimator.
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- OrthoForestDMLModel
- SynapseMLLogging
- Wrappable
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- PythonWrappable
- BaseWrappable
- ComplexParamsWritable
- MLWritable
- OrthoForestDMLParams
- HasOutputCol
- HasMinSampleLeaf
- HasMaxDepth
- HasNumTrees
- DoubleMLParams
- HasParallelismInjected
- HasParallelism
- HasWeightCol
- HasMaxIter
- HasFeaturesCol
- HasOutcomeCol
- HasTreatmentCol
- Model
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def
$[T](param: Param[T]): T
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==(arg0: Any): Boolean
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def
asInstanceOf[T0]: T0
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def
awaitFutures[T](futures: Array[Future[T]]): Seq[T]
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lazy val
classNameHelper: String
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final
def
clear(param: Param[_]): OrthoForestDMLModel.this.type
- Definition Classes
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def
clone(): AnyRef
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- protected[lang]
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def
companionModelClassName: String
- Attributes
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- Definition Classes
- BaseWrappable
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val
confidenceLevel: DoubleParam
- Definition Classes
- DoubleMLParams
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val
confounderVecCol: Param[String]
- Definition Classes
- OrthoForestDMLParams
-
def
copy(extra: ParamMap): OrthoForestDMLModel
- Definition Classes
- OrthoForestDMLModel → Model → Transformer → PipelineStage → Params
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
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lazy val
copyrightLines: String
- Attributes
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- Definition Classes
- BaseWrappable
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final
def
defaultCopy[T <: Params](extra: ParamMap): T
- Attributes
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def
dotnetAdditionalMethods: String
- Definition Classes
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def
dotnetClass(): String
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lazy val
dotnetClassName: String
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lazy val
dotnetClassNameString: String
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lazy val
dotnetClassWrapperName: String
- Attributes
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- DotnetWrappable
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lazy val
dotnetCopyrightLines: String
- Attributes
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- DotnetWrappable
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def
dotnetExtraEstimatorImports: String
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def
dotnetExtraMethods: String
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lazy val
dotnetInternalWrapper: Boolean
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def
dotnetMLReadWriteMethods: String
- Attributes
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lazy val
dotnetNamespace: String
- Attributes
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- Definition Classes
- DotnetWrappable
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lazy val
dotnetObjectBaseClass: String
- Attributes
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- DotnetWrappable
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def
dotnetParamGetter(p: Param[_]): String
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- DotnetWrappable
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def
dotnetParamGetters: String
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def
dotnetParamSetter(p: Param[_]): String
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def
dotnetParamSetters: String
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def
dotnetWrapAsTypeMethod: String
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def
eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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def
explainParam(param: Param[_]): String
- Definition Classes
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def
explainParams(): String
- Definition Classes
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final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
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final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
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val
featuresCol: Param[String]
The name of the features column
The name of the features column
- Definition Classes
- HasFeaturesCol
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def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
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- @throws( classOf[java.lang.Throwable] )
- val forest: TransformerArrayParam
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final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
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final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
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def
getConfidenceLevel: Double
- Definition Classes
- DoubleMLParams
-
def
getConfounderVecCol: String
- Definition Classes
- OrthoForestDMLParams
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final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
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def
getExecutionContextProxy: ExecutionContext
- Definition Classes
- HasParallelismInjected
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def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
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def
getHeterogeneityVecCol: String
- Definition Classes
- OrthoForestDMLParams
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def
getMaxDepth: Int
- Definition Classes
- HasMaxDepth
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final
def
getMaxIter: Int
- Definition Classes
- HasMaxIter
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def
getMinSamplesLeaf: Int
- Definition Classes
- HasMinSampleLeaf
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def
getNumTrees: Int
- Definition Classes
- HasNumTrees
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final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
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def
getOutcomeCol: String
- Definition Classes
- HasOutcomeCol
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def
getOutcomeModel: Estimator[_ <: Model[_]]
- Definition Classes
- DoubleMLParams
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def
getOutcomeResidualCol: String
- Definition Classes
- OrthoForestDMLParams
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def
getOutputCol: String
- Definition Classes
- HasOutputCol
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def
getOutputHighCol: String
- Definition Classes
- OrthoForestDMLParams
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def
getOutputLowCol: String
- Definition Classes
- OrthoForestDMLParams
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def
getParallelism: Int
- Definition Classes
- HasParallelism
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def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
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def
getParamInfo(p: Param[_]): ParamInfo[_]
- Definition Classes
- BaseWrappable
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def
getPayload(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], exception: Option[Exception]): Map[String, String]
- Attributes
- protected
- Definition Classes
- SynapseMLLogging
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def
getSampleSplitRatio: Array[Double]
- Definition Classes
- DoubleMLParams
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def
getTreatmentCol: String
- Definition Classes
- HasTreatmentCol
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def
getTreatmentModel: Estimator[_ <: Model[_]]
- Definition Classes
- DoubleMLParams
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def
getTreatmentResidualCol: String
- Definition Classes
- OrthoForestDMLParams
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def
getWeightCol: String
- Definition Classes
- HasWeightCol
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final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
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def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
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def
hasParent: Boolean
- Definition Classes
- Model
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def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
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val
heterogeneityVecCol: Param[String]
- Definition Classes
- OrthoForestDMLParams
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
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- Definition Classes
- Logging
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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final
def
isDefined(param: Param[_]): Boolean
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final
def
isInstanceOf[T0]: Boolean
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final
def
isSet(param: Param[_]): Boolean
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def
isTraceEnabled(): Boolean
- Attributes
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- Definition Classes
- Logging
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def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
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def
logBase(info: Map[String, String], featureName: Option[String]): Unit
- Attributes
- protected
- Definition Classes
- SynapseMLLogging
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def
logBase(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], featureName: Option[String]): Unit
- Attributes
- protected
- Definition Classes
- SynapseMLLogging
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def
logClass(featureName: String): Unit
- Definition Classes
- SynapseMLLogging
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logDebug(msg: ⇒ String): Unit
- Attributes
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- Definition Classes
- Logging
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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- Definition Classes
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def
logError(msg: ⇒ String): Unit
- Attributes
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- Definition Classes
- Logging
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def
logErrorBase(methodName: String, e: Exception): Unit
- Attributes
- protected
- Definition Classes
- SynapseMLLogging
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def
logFit[T](f: ⇒ T, columns: Int): T
- Definition Classes
- SynapseMLLogging
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
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def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logTransform[T](f: ⇒ T, columns: Int): T
- Definition Classes
- SynapseMLLogging
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def
logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
- Definition Classes
- SynapseMLLogging
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
makeDotnetFile(conf: CodegenConfig): Unit
- Definition Classes
- DotnetWrappable
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def
makePyFile(conf: CodegenConfig): Unit
- Definition Classes
- PythonWrappable
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def
makeRFile(conf: CodegenConfig): Unit
- Definition Classes
- RWrappable
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val
maxDepth: IntParam
- Definition Classes
- HasMaxDepth
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final
val
maxIter: IntParam
- Definition Classes
- HasMaxIter
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val
minSamplesLeaf: IntParam
- Definition Classes
- HasMinSampleLeaf
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
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final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
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val
numTrees: IntParam
- Definition Classes
- HasNumTrees
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val
outcomeCol: Param[String]
- Definition Classes
- HasOutcomeCol
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val
outcomeModel: EstimatorParam
- Definition Classes
- DoubleMLParams
-
val
outcomeResidualCol: Param[String]
- Definition Classes
- OrthoForestDMLParams
-
val
outputCol: Param[String]
The name of the output column
The name of the output column
- Definition Classes
- HasOutputCol
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val
outputHighCol: Param[String]
- Definition Classes
- OrthoForestDMLParams
-
val
outputLowCol: Param[String]
- Definition Classes
- OrthoForestDMLParams
-
val
parallelism: IntParam
- Definition Classes
- HasParallelism
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[OrthoForestDMLModel]
- Definition Classes
- Model
-
def
pyAdditionalMethods: String
- Definition Classes
- PythonWrappable
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lazy val
pyClassDoc: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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lazy val
pyClassName: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyExtraEstimatorImports: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyExtraEstimatorMethods: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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lazy val
pyInheritedClasses: Seq[String]
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyInitFunc(): String
- Definition Classes
- PythonWrappable
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lazy val
pyInternalWrapper: Boolean
- Attributes
- protected
- Definition Classes
- OrthoForestDMLModel → PythonWrappable
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lazy val
pyObjectBaseClass: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamArg[T](p: Param[T]): String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamDefault[T](p: Param[T]): Option[String]
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamGetter(p: Param[_]): String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamSetter(p: Param[_]): String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamsArgs: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamsDefaults: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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lazy val
pyParamsDefinitions: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamsGetters: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
-
def
pyParamsSetters: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pythonClass(): String
- Attributes
- protected
- Definition Classes
- PythonWrappable
-
def
rClass(): String
- Attributes
- protected
- Definition Classes
- RWrappable
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def
rDocString: String
- Attributes
- protected
- Definition Classes
- RWrappable
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def
rExtraBodyLines: String
- Attributes
- protected
- Definition Classes
- RWrappable
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def
rExtraInitLines: String
- Attributes
- protected
- Definition Classes
- RWrappable
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lazy val
rFuncName: String
- Attributes
- protected
- Definition Classes
- RWrappable
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lazy val
rInternalWrapper: Boolean
- Attributes
- protected
- Definition Classes
- RWrappable
-
def
rParamArg[T](p: Param[T]): String
- Attributes
- protected
- Definition Classes
- RWrappable
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def
rParamsArgs: String
- Attributes
- protected
- Definition Classes
- RWrappable
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def
rSetterLines: String
- Attributes
- protected
- Definition Classes
- RWrappable
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val
sampleSplitRatio: DoubleArrayParam
- Definition Classes
- DoubleMLParams
-
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
def
set(paramPair: ParamPair[_]): OrthoForestDMLModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): OrthoForestDMLModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): OrthoForestDMLModel.this.type
- Definition Classes
- Params
-
def
setConfidenceLevel(value: Double): OrthoForestDMLModel.this.type
Set the higher bound percentile of ATE distribution.
Set the higher bound percentile of ATE distribution. Default is 0.975. lower bound value will be automatically calculated as 100*(1-confidenceLevel) That means by default we compute 95% confidence interval, it is [2.5%, 97.5%] percentile of ATE distribution
- Definition Classes
- DoubleMLParams
-
def
setConfounderVecCol(value: String): OrthoForestDMLModel.this.type
Set confounder vector column
Set confounder vector column
- Definition Classes
- OrthoForestDMLParams
-
final
def
setDefault(paramPairs: ParamPair[_]*): OrthoForestDMLModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): OrthoForestDMLModel.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
-
def
setFeaturesCol(value: String): OrthoForestDMLModel.this.type
- Definition Classes
- HasFeaturesCol
- def setForest(v: Array[DecisionTreeRegressionModel]): OrthoForestDMLModel.this.type
-
def
setHeterogeneityVecCol(value: String): OrthoForestDMLModel.this.type
Set heterogeneity vector column
Set heterogeneity vector column
- Definition Classes
- OrthoForestDMLParams
-
def
setMaxDepth(value: Int): OrthoForestDMLModel.this.type
Set max depth of the trees to be used in the forest
Set max depth of the trees to be used in the forest
- Definition Classes
- HasMaxDepth
-
def
setMaxIter(value: Int): OrthoForestDMLModel.this.type
Set the maximum number of confidence interval bootstrapping iterations.
Set the maximum number of confidence interval bootstrapping iterations. Default is 1, which means it does not calculate confidence interval. To get Ci values please set a meaningful value
- Definition Classes
- DoubleMLParams
-
def
setMinSamplesLeaf(value: Int): OrthoForestDMLModel.this.type
Set number of samples in the leaf node of trees to be used in the forest
Set number of samples in the leaf node of trees to be used in the forest
- Definition Classes
- HasMinSampleLeaf
-
def
setNumTrees(value: Int): OrthoForestDMLModel.this.type
Set number of trees to be used in the forest
Set number of trees to be used in the forest
- Definition Classes
- HasNumTrees
-
def
setOutcomeCol(value: String): OrthoForestDMLModel.this.type
Set name of the column which will be used as outcome
Set name of the column which will be used as outcome
- Definition Classes
- HasOutcomeCol
-
def
setOutcomeModel(value: Estimator[_ <: Model[_]]): OrthoForestDMLModel.this.type
Set outcome model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'
Set outcome model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'
- Definition Classes
- DoubleMLParams
-
def
setOutcomeResidualCol(value: String): OrthoForestDMLModel.this.type
Set outcome residual column
Set outcome residual column
- Definition Classes
- OrthoForestDMLParams
-
def
setOutputCol(value: String): OrthoForestDMLModel.this.type
- Definition Classes
- HasOutputCol
-
def
setOutputHighCol(value: String): OrthoForestDMLModel.this.type
Set output column for effect upper bound
Set output column for effect upper bound
- Definition Classes
- OrthoForestDMLParams
-
def
setOutputLowCol(value: String): OrthoForestDMLModel.this.type
Set output column for effect lower bound
Set output column for effect lower bound
- Definition Classes
- OrthoForestDMLParams
-
def
setParallelism(value: Int): OrthoForestDMLModel.this.type
- Definition Classes
- DoubleMLParams
-
def
setParent(parent: Estimator[OrthoForestDMLModel]): OrthoForestDMLModel
- Definition Classes
- Model
-
def
setSampleSplitRatio(value: Array[Double]): OrthoForestDMLModel.this.type
Set the sample split ratio, default is Array(0.5, 0.5)
Set the sample split ratio, default is Array(0.5, 0.5)
- Definition Classes
- DoubleMLParams
-
def
setTreatmentCol(value: String): OrthoForestDMLModel.this.type
Set name of the column which will be used as treatment
Set name of the column which will be used as treatment
- Definition Classes
- HasTreatmentCol
-
def
setTreatmentModel(value: Estimator[_ <: Model[_]]): OrthoForestDMLModel.this.type
Set treatment model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'
Set treatment model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'
- Definition Classes
- DoubleMLParams
-
def
setTreatmentResidualCol(value: String): OrthoForestDMLModel.this.type
Set treatment residual column
Set treatment residual column
- Definition Classes
- OrthoForestDMLParams
-
def
setWeightCol(value: String): OrthoForestDMLModel.this.type
- Definition Classes
- HasWeightCol
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
val
thisStage: Params
- Attributes
- protected
- Definition Classes
- BaseWrappable
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
def
transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- OrthoForestDMLModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
def
transformSchema(schema: StructType): StructType
- Definition Classes
- OrthoForestDMLModel → PipelineStage
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
-
val
treatmentCol: Param[String]
- Definition Classes
- HasTreatmentCol
-
val
treatmentModel: EstimatorParam
- Definition Classes
- DoubleMLParams
-
val
treatmentResidualCol: Param[String]
- Definition Classes
- OrthoForestDMLParams
-
val
uid: String
- Definition Classes
- OrthoForestDMLModel → SynapseMLLogging → Identifiable
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
val
weightCol: Param[String]
The name of the weight column
The name of the weight column
- Definition Classes
- HasWeightCol
-
def
write: MLWriter
- Definition Classes
- ComplexParamsWritable → MLWritable