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. RWrappable
  5. PythonWrappable
  6. BaseWrappable
  7. OrthoForestDMLParams
  8. HasOutputCol
  9. HasMinSampleLeaf
  10. HasMaxDepth
  11. HasNumTrees
  12. DoubleMLParams
  13. HasParallelismInjected
  14. HasParallelism
  15. HasWeightCol
  16. HasMaxIter
  17. HasFeaturesCol
  18. HasOutcomeCol
  19. HasTreatmentCol
  20. ComplexParamsWritable
  21. MLWritable
  22. Estimator
  23. PipelineStage
  24. Logging
  25. Params
  26. Serializable
  27. Serializable
  28. Identifiable
  29. AnyRef
  30. 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 explainParam(param: Param[_]): String
    Definition Classes
    Params
  6. def explainParams(): String
    Definition Classes
    Params
  7. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  8. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  9. val featuresCol: Param[String]

    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol
  10. 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
  11. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[OrthoForestDMLModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  12. def fit(dataset: Dataset[_], paramMap: ParamMap): OrthoForestDMLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  13. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): OrthoForestDMLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  14. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  15. def getConfidenceLevel: Double
    Definition Classes
    DoubleMLParams
  16. def getConfounderVecCol: String
    Definition Classes
    OrthoForestDMLParams
  17. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  18. def getExecutionContextProxy: ExecutionContext
    Definition Classes
    HasParallelismInjected
  19. def getFeaturesCol: String

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

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

    Definition Classes
    HasWeightCol
  40. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  41. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  42. val heterogeneityVecCol: Param[String]
    Definition Classes
    OrthoForestDMLParams
  43. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  44. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  45. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  46. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  47. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  48. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  49. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  50. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  51. val maxDepth: IntParam
    Definition Classes
    HasMaxDepth
  52. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  53. val minSamplesLeaf: IntParam
    Definition Classes
    HasMinSampleLeaf
  54. val numTrees: IntParam
    Definition Classes
    HasNumTrees
  55. val outcomeCol: Param[String]
    Definition Classes
    HasOutcomeCol
  56. val outcomeModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  57. val outcomeResidualCol: Param[String]
    Definition Classes
    OrthoForestDMLParams
  58. val outputCol: Param[String]

    The name of the output column

    The name of the output column

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

    Set confounder vector column

    Set confounder vector column

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

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

    Set heterogeneity vector column

    Set heterogeneity vector column

    Definition Classes
    OrthoForestDMLParams
  72. 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
  73. 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
  74. 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
  75. 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
  76. 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
  77. 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
  78. def setOutcomeResidualCol(value: String): OrthoForestDMLEstimator.this.type

    Set outcome residual column

    Set outcome residual column

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

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

    Set output column for effect upper bound

    Set output column for effect upper bound

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

    Set output column for effect lower bound

    Set output column for effect lower bound

    Definition Classes
    OrthoForestDMLParams
  82. def setParallelism(value: Int): OrthoForestDMLEstimator.this.type
    Definition Classes
    DoubleMLParams
  83. 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
  84. 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
  85. 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
  86. def setTreatmentResidualCol(value: String): OrthoForestDMLEstimator.this.type

    Set treatment residual column

    Set treatment residual column

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

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

    The name of the weight column

    The name of the weight column

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