class OrthoForestDMLEstimator extends Estimator[OrthoForestDMLModel] with ComplexParamsWritable with OrthoForestDMLParams with Wrappable with SynapseMLLogging with HasOutcomeCol

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Inherited
  1. OrthoForestDMLEstimator
  2. SynapseMLLogging
  3. Wrappable
  4. DotnetWrappable
  5. RWrappable
  6. PythonWrappable
  7. BaseWrappable
  8. OrthoForestDMLParams
  9. HasOutputCol
  10. HasMinSampleLeaf
  11. HasMaxDepth
  12. HasNumTrees
  13. DoubleMLParams
  14. HasParallelismInjected
  15. HasParallelism
  16. HasWeightCol
  17. HasMaxIter
  18. HasFeaturesCol
  19. HasOutcomeCol
  20. HasTreatmentCol
  21. ComplexParamsWritable
  22. MLWritable
  23. Estimator
  24. PipelineStage
  25. Logging
  26. Params
  27. Serializable
  28. Serializable
  29. Identifiable
  30. AnyRef
  31. Any
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Visibility
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Instance Constructors

  1. new OrthoForestDMLEstimator()
  2. new OrthoForestDMLEstimator(uid: String)

Type Members

  1. type EstimatorWithPC = Estimator[_ <: Model[_] with HasPredictionCol] with HasPredictionCol

Value Members

  1. final def clear(param: Param[_]): OrthoForestDMLEstimator.this.type
    Definition Classes
    Params
  2. val confidenceLevel: DoubleParam
    Definition Classes
    DoubleMLParams
  3. val confounderVecCol: Param[String]
    Definition Classes
    OrthoForestDMLParams
  4. def copy(extra: ParamMap): Estimator[OrthoForestDMLModel]
    Definition Classes
    OrthoForestDMLEstimator → Estimator → PipelineStage → Params
  5. def dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  6. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  7. def explainParams(): String
    Definition Classes
    Params
  8. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  9. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  10. val featuresCol: Param[String]

    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol
  11. def fit(dataset: Dataset[_]): OrthoForestDMLModel

    Fits the OrthoForestDML model.

    Fits the OrthoForestDML model.

    dataset

    The input dataset to train.

    returns

    The trained DoubleML model, from which you can get Ate and Ci values

    Definition Classes
    OrthoForestDMLEstimator → Estimator
  12. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[OrthoForestDMLModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  13. def fit(dataset: Dataset[_], paramMap: ParamMap): OrthoForestDMLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  14. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): OrthoForestDMLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  15. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  16. def getConfidenceLevel: Double
    Definition Classes
    DoubleMLParams
  17. def getConfounderVecCol: String
    Definition Classes
    OrthoForestDMLParams
  18. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  19. def getExecutionContextProxy: ExecutionContext
    Definition Classes
    HasParallelismInjected
  20. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  21. def getHeterogeneityVecCol: String
    Definition Classes
    OrthoForestDMLParams
  22. def getMaxDepth: Int
    Definition Classes
    HasMaxDepth
  23. final def getMaxIter: Int
    Definition Classes
    HasMaxIter
  24. def getMinSamplesLeaf: Int
    Definition Classes
    HasMinSampleLeaf
  25. def getNumTrees: Int
    Definition Classes
    HasNumTrees
  26. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  27. def getOutcomeCol: String
    Definition Classes
    HasOutcomeCol
  28. def getOutcomeModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  29. def getOutcomeResidualCol: String
    Definition Classes
    OrthoForestDMLParams
  30. def getOutputCol: String

    Definition Classes
    HasOutputCol
  31. def getOutputHighCol: String
    Definition Classes
    OrthoForestDMLParams
  32. def getOutputLowCol: String
    Definition Classes
    OrthoForestDMLParams
  33. def getParallelism: Int
    Definition Classes
    HasParallelism
  34. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  35. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  36. def getSampleSplitRatio: Array[Double]
    Definition Classes
    DoubleMLParams
  37. def getTreatmentCol: String
    Definition Classes
    HasTreatmentCol
  38. def getTreatmentModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  39. def getTreatmentResidualCol: String
    Definition Classes
    OrthoForestDMLParams
  40. def getWeightCol: String

    Definition Classes
    HasWeightCol
  41. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  42. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  43. val heterogeneityVecCol: Param[String]
    Definition Classes
    OrthoForestDMLParams
  44. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  45. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  46. def logClass(): Unit
    Definition Classes
    SynapseMLLogging
  47. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  48. def logTrain[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  49. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  50. def logVerb[T](verb: String, f: ⇒ T, columns: Int = -1): T
    Definition Classes
    SynapseMLLogging
  51. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  52. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  53. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  54. val maxDepth: IntParam
    Definition Classes
    HasMaxDepth
  55. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  56. val minSamplesLeaf: IntParam
    Definition Classes
    HasMinSampleLeaf
  57. val numTrees: IntParam
    Definition Classes
    HasNumTrees
  58. val outcomeCol: Param[String]
    Definition Classes
    HasOutcomeCol
  59. val outcomeModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  60. val outcomeResidualCol: Param[String]
    Definition Classes
    OrthoForestDMLParams
  61. val outputCol: Param[String]

    The name of the output column

    The name of the output column

    Definition Classes
    HasOutputCol
  62. val outputHighCol: Param[String]
    Definition Classes
    OrthoForestDMLParams
  63. val outputLowCol: Param[String]
    Definition Classes
    OrthoForestDMLParams
  64. val parallelism: IntParam
    Definition Classes
    HasParallelism
  65. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  66. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  67. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  68. val sampleSplitRatio: DoubleArrayParam
    Definition Classes
    DoubleMLParams
  69. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  70. final def set[T](param: Param[T], value: T): OrthoForestDMLEstimator.this.type
    Definition Classes
    Params
  71. def setConfidenceLevel(value: Double): OrthoForestDMLEstimator.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
  72. def setConfounderVecCol(value: String): OrthoForestDMLEstimator.this.type

    Set confounder vector column

    Set confounder vector column

    Definition Classes
    OrthoForestDMLParams
  73. def setFeaturesCol(value: String): OrthoForestDMLEstimator.this.type

    Definition Classes
    HasFeaturesCol
  74. def setHeterogeneityVecCol(value: String): OrthoForestDMLEstimator.this.type

    Set heterogeneity vector column

    Set heterogeneity vector column

    Definition Classes
    OrthoForestDMLParams
  75. def setMaxDepth(value: Int): OrthoForestDMLEstimator.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
  76. def setMaxIter(value: Int): OrthoForestDMLEstimator.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
  77. def setMinSamplesLeaf(value: Int): OrthoForestDMLEstimator.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
  78. def setNumTrees(value: Int): OrthoForestDMLEstimator.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
  79. def setOutcomeCol(value: String): OrthoForestDMLEstimator.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
  80. def setOutcomeModel(value: Estimator[_ <: Model[_]]): OrthoForestDMLEstimator.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
  81. def setOutcomeResidualCol(value: String): OrthoForestDMLEstimator.this.type

    Set outcome residual column

    Set outcome residual column

    Definition Classes
    OrthoForestDMLParams
  82. def setOutputCol(value: String): OrthoForestDMLEstimator.this.type

    Definition Classes
    HasOutputCol
  83. def setOutputHighCol(value: String): OrthoForestDMLEstimator.this.type

    Set output column for effect upper bound

    Set output column for effect upper bound

    Definition Classes
    OrthoForestDMLParams
  84. def setOutputLowCol(value: String): OrthoForestDMLEstimator.this.type

    Set output column for effect lower bound

    Set output column for effect lower bound

    Definition Classes
    OrthoForestDMLParams
  85. def setParallelism(value: Int): OrthoForestDMLEstimator.this.type
    Definition Classes
    DoubleMLParams
  86. def setSampleSplitRatio(value: Array[Double]): OrthoForestDMLEstimator.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
  87. def setTreatmentCol(value: String): OrthoForestDMLEstimator.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
  88. def setTreatmentModel(value: Estimator[_ <: Model[_]]): OrthoForestDMLEstimator.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
  89. def setTreatmentResidualCol(value: String): OrthoForestDMLEstimator.this.type

    Set treatment residual column

    Set treatment residual column

    Definition Classes
    OrthoForestDMLParams
  90. def setWeightCol(value: String): OrthoForestDMLEstimator.this.type

    Definition Classes
    HasWeightCol
  91. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  92. def transformSchema(schema: StructType): StructType
    Definition Classes
    OrthoForestDMLEstimator → PipelineStage
  93. val treatmentCol: Param[String]
    Definition Classes
    HasTreatmentCol
  94. val treatmentModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  95. val treatmentResidualCol: Param[String]
    Definition Classes
    OrthoForestDMLParams
  96. val uid: String
    Definition Classes
    OrthoForestDMLEstimator → SynapseMLLogging → Identifiable
  97. val weightCol: Param[String]

    The name of the weight column

    The name of the weight column

    Definition Classes
    HasWeightCol
  98. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable