class DoubleMLModel extends Model[DoubleMLModel] with DoubleMLParams with ComplexParamsWritable with Wrappable with SynapseMLLogging

Model produced by DoubleMLEstimator.

Linear Supertypes
SynapseMLLogging, Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, ComplexParamsWritable, MLWritable, DoubleMLParams, HasParallelismInjected, HasParallelism, HasWeightCol, HasMaxIter, HasFeaturesCol, HasOutcomeCol, HasTreatmentCol, Model[DoubleMLModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. DoubleMLModel
  2. SynapseMLLogging
  3. Wrappable
  4. DotnetWrappable
  5. RWrappable
  6. PythonWrappable
  7. BaseWrappable
  8. ComplexParamsWritable
  9. MLWritable
  10. DoubleMLParams
  11. HasParallelismInjected
  12. HasParallelism
  13. HasWeightCol
  14. HasMaxIter
  15. HasFeaturesCol
  16. HasOutcomeCol
  17. HasTreatmentCol
  18. Model
  19. Transformer
  20. PipelineStage
  21. Logging
  22. Params
  23. Serializable
  24. Serializable
  25. Identifiable
  26. AnyRef
  27. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

Value Members

  1. final def clear(param: Param[_]): DoubleMLModel.this.type
    Definition Classes
    Params
  2. val confidenceLevel: DoubleParam
    Definition Classes
    DoubleMLParams
  3. def copy(extra: ParamMap): DoubleMLModel
    Definition Classes
    DoubleMLModel → Model → Transformer → PipelineStage → Params
  4. def dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  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. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  11. def getAvgTreatmentEffect: Double
  12. def getConfidenceInterval: Array[Double]
  13. def getConfidenceLevel: Double
    Definition Classes
    DoubleMLParams
  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. final def getMaxIter: Int
    Definition Classes
    HasMaxIter
  18. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  19. def getOutcomeCol: String
    Definition Classes
    HasOutcomeCol
  20. def getOutcomeModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  21. def getPValue: Double
  22. def getParallelism: Int
    Definition Classes
    HasParallelism
  23. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  24. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  25. def getRawTreatmentEffects: Array[Double]
  26. def getSampleSplitRatio: Array[Double]
    Definition Classes
    DoubleMLParams
  27. def getTreatmentCol: String
    Definition Classes
    HasTreatmentCol
  28. def getTreatmentModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  29. def getWeightCol: String

    Definition Classes
    HasWeightCol
  30. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  31. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  32. def hasParent: Boolean
    Definition Classes
    Model
  33. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  34. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  35. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  36. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  37. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  38. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  39. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  40. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  41. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  42. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  43. val outcomeCol: Param[String]
    Definition Classes
    HasOutcomeCol
  44. val outcomeModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  45. val parallelism: IntParam
    Definition Classes
    HasParallelism
  46. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  47. var parent: Estimator[DoubleMLModel]
    Definition Classes
    Model
  48. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  49. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  50. val rawTreatmentEffects: DoubleArrayParam
  51. val sampleSplitRatio: DoubleArrayParam
    Definition Classes
    DoubleMLParams
  52. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  53. final def set[T](param: Param[T], value: T): DoubleMLModel.this.type
    Definition Classes
    Params
  54. def setConfidenceLevel(value: Double): DoubleMLModel.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
  55. def setFeaturesCol(value: String): DoubleMLModel.this.type

    Definition Classes
    HasFeaturesCol
  56. def setMaxIter(value: Int): DoubleMLModel.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
  57. def setOutcomeCol(value: String): DoubleMLModel.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
  58. def setOutcomeModel(value: Estimator[_ <: Model[_]]): DoubleMLModel.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
  59. def setParallelism(value: Int): DoubleMLModel.this.type
    Definition Classes
    DoubleMLParams
  60. def setParent(parent: Estimator[DoubleMLModel]): DoubleMLModel
    Definition Classes
    Model
  61. def setRawTreatmentEffects(v: Array[Double]): DoubleMLModel.this.type
  62. def setSampleSplitRatio(value: Array[Double]): DoubleMLModel.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
  63. def setTreatmentCol(value: String): DoubleMLModel.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
  64. def setTreatmentModel(value: Estimator[_ <: Model[_]]): DoubleMLModel.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
  65. def setWeightCol(value: String): DoubleMLModel.this.type

    Definition Classes
    HasWeightCol
  66. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  67. def transform(dataset: Dataset[_]): DataFrame

    :: Experimental :: DoubleMLEstimator transform function is still experimental, and its behavior could change in the future.

    :: Experimental :: DoubleMLEstimator transform function is still experimental, and its behavior could change in the future.

    Definition Classes
    DoubleMLModel → Transformer
    Annotations
    @Experimental()
  68. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  69. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  70. def transformSchema(schema: StructType): StructType
    Definition Classes
    DoubleMLModel → PipelineStage
    Annotations
    @DeveloperApi()
  71. val treatmentCol: Param[String]
    Definition Classes
    HasTreatmentCol
  72. val treatmentModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  73. val uid: String
    Definition Classes
    DoubleMLModel → SynapseMLLogging → Identifiable
  74. val weightCol: Param[String]

    The name of the weight column

    The name of the weight column

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