class OrthoForestDMLModel extends Model[OrthoForestDMLModel] with OrthoForestDMLParams with ComplexParamsWritable with Wrappable with SynapseMLLogging

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Inherited
  1. OrthoForestDMLModel
  2. SynapseMLLogging
  3. Wrappable
  4. RWrappable
  5. PythonWrappable
  6. BaseWrappable
  7. ComplexParamsWritable
  8. MLWritable
  9. OrthoForestDMLParams
  10. HasOutputCol
  11. HasMinSampleLeaf
  12. HasMaxDepth
  13. HasNumTrees
  14. DoubleMLParams
  15. HasParallelismInjected
  16. HasParallelism
  17. HasWeightCol
  18. HasMaxIter
  19. HasFeaturesCol
  20. HasOutcomeCol
  21. HasTreatmentCol
  22. Model
  23. Transformer
  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 OrthoForestDMLModel()
  2. new OrthoForestDMLModel(uid: String)

Value Members

  1. final def clear(param: Param[_]): OrthoForestDMLModel.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): OrthoForestDMLModel
    Definition Classes
    OrthoForestDMLModel → Model → Transformer → 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. val forest: TransformerArrayParam
  11. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  12. def getConfidenceLevel: Double
    Definition Classes
    DoubleMLParams
  13. def getConfounderVecCol: String
    Definition Classes
    OrthoForestDMLParams
  14. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  15. def getExecutionContextProxy: ExecutionContext
    Definition Classes
    HasParallelismInjected
  16. def getFeaturesCol: String

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

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

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

    The name of the output column

    The name of the output column

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

    Set confounder vector column

    Set confounder vector column

    Definition Classes
    OrthoForestDMLParams
  69. def setFeaturesCol(value: String): OrthoForestDMLModel.this.type

    Definition Classes
    HasFeaturesCol
  70. def setForest(v: Array[DecisionTreeRegressionModel]): OrthoForestDMLModel.this.type
  71. def setHeterogeneityVecCol(value: String): OrthoForestDMLModel.this.type

    Set heterogeneity vector column

    Set heterogeneity vector column

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

    Set outcome residual column

    Set outcome residual column

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

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

    Set output column for effect upper bound

    Set output column for effect upper bound

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

    Set output column for effect lower bound

    Set output column for effect lower bound

    Definition Classes
    OrthoForestDMLParams
  82. def setParallelism(value: Int): OrthoForestDMLModel.this.type
    Definition Classes
    DoubleMLParams
  83. def setParent(parent: Estimator[OrthoForestDMLModel]): OrthoForestDMLModel
    Definition Classes
    Model
  84. 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
  85. 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
  86. 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
  87. def setTreatmentResidualCol(value: String): OrthoForestDMLModel.this.type

    Set treatment residual column

    Set treatment residual column

    Definition Classes
    OrthoForestDMLParams
  88. def setWeightCol(value: String): OrthoForestDMLModel.this.type

    Definition Classes
    HasWeightCol
  89. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  90. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    OrthoForestDMLModel → Transformer
  91. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  92. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  93. def transformSchema(schema: StructType): StructType
    Definition Classes
    OrthoForestDMLModel → PipelineStage
  94. val treatmentCol: Param[String]
    Definition Classes
    HasTreatmentCol
  95. val treatmentModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  96. val treatmentResidualCol: Param[String]
    Definition Classes
    OrthoForestDMLParams
  97. val uid: String
    Definition Classes
    OrthoForestDMLModel → SynapseMLLogging → Identifiable
  98. val weightCol: Param[String]

    The name of the weight column

    The name of the weight column

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