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

Model produced by DoubleMLEstimator.

Linear Supertypes
SynapseMLLogging, Wrappable, 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. RWrappable
  5. PythonWrappable
  6. BaseWrappable
  7. ComplexParamsWritable
  8. MLWritable
  9. DoubleMLParams
  10. HasParallelismInjected
  11. HasParallelism
  12. HasWeightCol
  13. HasMaxIter
  14. HasFeaturesCol
  15. HasOutcomeCol
  16. HasTreatmentCol
  17. Model
  18. Transformer
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. 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 explainParam(param: Param[_]): String
    Definition Classes
    Params
  5. def explainParams(): String
    Definition Classes
    Params
  6. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  7. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  8. val featuresCol: Param[String]

    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol
  9. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  10. def getAvgTreatmentEffect: Double
  11. def getConfidenceInterval: Array[Double]
  12. def getConfidenceLevel: Double
    Definition Classes
    DoubleMLParams
  13. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  14. def getExecutionContextProxy: ExecutionContext
    Definition Classes
    HasParallelismInjected
  15. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  16. final def getMaxIter: Int
    Definition Classes
    HasMaxIter
  17. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  18. def getOutcomeCol: String
    Definition Classes
    HasOutcomeCol
  19. def getOutcomeModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  20. def getPValue: Double
  21. def getParallelism: Int
    Definition Classes
    HasParallelism
  22. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  23. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  24. def getRawTreatmentEffects: Array[Double]
  25. def getSampleSplitRatio: Array[Double]
    Definition Classes
    DoubleMLParams
  26. def getTreatmentCol: String
    Definition Classes
    HasTreatmentCol
  27. def getTreatmentModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  28. def getWeightCol: String

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

    Definition Classes
    HasFeaturesCol
  54. 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
  55. 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
  56. 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
  57. def setParallelism(value: Int): DoubleMLModel.this.type
    Definition Classes
    DoubleMLParams
  58. def setParent(parent: Estimator[DoubleMLModel]): DoubleMLModel
    Definition Classes
    Model
  59. def setRawTreatmentEffects(v: Array[Double]): DoubleMLModel.this.type
  60. 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
  61. 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
  62. 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
  63. def setWeightCol(value: String): DoubleMLModel.this.type

    Definition Classes
    HasWeightCol
  64. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  65. 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()
  66. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  67. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  68. def transformSchema(schema: StructType): StructType
    Definition Classes
    DoubleMLModel → PipelineStage
    Annotations
    @DeveloperApi()
  69. val treatmentCol: Param[String]
    Definition Classes
    HasTreatmentCol
  70. val treatmentModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  71. val uid: String
    Definition Classes
    DoubleMLModel → SynapseMLLogging → Identifiable
  72. val weightCol: Param[String]

    The name of the weight column

    The name of the weight column

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