Packages

class AFTSurvivalRegression extends Estimator[AFTSurvivalRegressionModel] with AFTSurvivalRegressionParams with DefaultParamsWritable with Logging

:: Experimental :: Fit a parametric survival regression model named accelerated failure time (AFT) model (see Accelerated failure time model (Wikipedia)) based on the Weibull distribution of the survival time.

Annotations
@Experimental() @Since( "1.6.0" )
Linear Supertypes
DefaultParamsWritable, MLWritable, AFTSurvivalRegressionParams, HasAggregationDepth, HasFitIntercept, HasTol, HasMaxIter, HasPredictionCol, HasLabelCol, HasFeaturesCol, Estimator[AFTSurvivalRegressionModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. AFTSurvivalRegression
  2. DefaultParamsWritable
  3. MLWritable
  4. AFTSurvivalRegressionParams
  5. HasAggregationDepth
  6. HasFitIntercept
  7. HasTol
  8. HasMaxIter
  9. HasPredictionCol
  10. HasLabelCol
  11. HasFeaturesCol
  12. Estimator
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new AFTSurvivalRegression()
    Annotations
    @Since( "1.6.0" )
  2. new AFTSurvivalRegression(uid: String)
    Annotations
    @Since( "1.6.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final val aggregationDepth: IntParam

    Param for suggested depth for treeAggregate (>= 2).

    Param for suggested depth for treeAggregate (>= 2).

    Definition Classes
    HasAggregationDepth
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. final val censorCol: Param[String]

    Param for censor column name.

    Param for censor column name. The value of this column could be 0 or 1. If the value is 1, it means the event has occurred i.e. uncensored; otherwise censored.

    Definition Classes
    AFTSurvivalRegressionParams
    Annotations
    @Since( "1.6.0" )
  8. final def clear(param: Param[_]): AFTSurvivalRegression.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  9. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  10. def copy(extra: ParamMap): AFTSurvivalRegression

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes
    AFTSurvivalRegression → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.6.0" )
  11. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  12. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  16. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  17. def extractAFTPoints(dataset: Dataset[_]): RDD[AFTPoint]

    Extract featuresCol, labelCol and censorCol from input dataset, and put it in an RDD with strong types.

    Extract featuresCol, labelCol and censorCol from input dataset, and put it in an RDD with strong types.

    Attributes
    protected[org.apache.spark.ml]
  18. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  19. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  20. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  21. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. def fit(dataset: Dataset[_]): AFTSurvivalRegressionModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    AFTSurvivalRegression → Estimator
    Annotations
    @Since( "2.0.0" )
  23. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[AFTSurvivalRegressionModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  24. def fit(dataset: Dataset[_], paramMap: ParamMap): AFTSurvivalRegressionModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  25. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): AFTSurvivalRegressionModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  26. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  27. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  28. final def getAggregationDepth: Int

    Definition Classes
    HasAggregationDepth
  29. def getCensorCol: String

    Definition Classes
    AFTSurvivalRegressionParams
    Annotations
    @Since( "1.6.0" )
  30. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  31. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  32. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  33. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  34. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  35. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  36. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  37. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  38. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  39. def getQuantileProbabilities: Array[Double]

    Definition Classes
    AFTSurvivalRegressionParams
    Annotations
    @Since( "1.6.0" )
  40. def getQuantilesCol: String

    Definition Classes
    AFTSurvivalRegressionParams
    Annotations
    @Since( "1.6.0" )
  41. final def getTol: Double

    Definition Classes
    HasTol
  42. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  43. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  44. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  45. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  46. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  47. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  48. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  49. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  50. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  52. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  53. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  60. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  65. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  66. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  67. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  68. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  69. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  70. final val quantileProbabilities: DoubleArrayParam

    Param for quantile probabilities array.

    Param for quantile probabilities array. Values of the quantile probabilities array should be in the range (0, 1) and the array should be non-empty.

    Definition Classes
    AFTSurvivalRegressionParams
    Annotations
    @Since( "1.6.0" )
  71. final val quantilesCol: Param[String]

    Param for quantiles column name.

    Param for quantiles column name. This column will output quantiles of corresponding quantileProbabilities if it is set.

    Definition Classes
    AFTSurvivalRegressionParams
    Annotations
    @Since( "1.6.0" )
  72. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  73. final def set(paramPair: ParamPair[_]): AFTSurvivalRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  74. final def set(param: String, value: Any): AFTSurvivalRegression.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  75. final def set[T](param: Param[T], value: T): AFTSurvivalRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  76. def setAggregationDepth(value: Int): AFTSurvivalRegression.this.type

    Suggested depth for treeAggregate (greater than or equal to 2).

    Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.

    Annotations
    @Since( "2.1.0" )
  77. def setCensorCol(value: String): AFTSurvivalRegression.this.type

    Annotations
    @Since( "1.6.0" )
  78. final def setDefault(paramPairs: ParamPair[_]*): AFTSurvivalRegression.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  79. final def setDefault[T](param: Param[T], value: T): AFTSurvivalRegression.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  80. def setFeaturesCol(value: String): AFTSurvivalRegression.this.type

    Annotations
    @Since( "1.6.0" )
  81. def setFitIntercept(value: Boolean): AFTSurvivalRegression.this.type

    Set if we should fit the intercept Default is true.

    Set if we should fit the intercept Default is true.

    Annotations
    @Since( "1.6.0" )
  82. def setLabelCol(value: String): AFTSurvivalRegression.this.type

    Annotations
    @Since( "1.6.0" )
  83. def setMaxIter(value: Int): AFTSurvivalRegression.this.type

    Set the maximum number of iterations.

    Set the maximum number of iterations. Default is 100.

    Annotations
    @Since( "1.6.0" )
  84. def setPredictionCol(value: String): AFTSurvivalRegression.this.type

    Annotations
    @Since( "1.6.0" )
  85. def setQuantileProbabilities(value: Array[Double]): AFTSurvivalRegression.this.type

    Annotations
    @Since( "1.6.0" )
  86. def setQuantilesCol(value: String): AFTSurvivalRegression.this.type

    Annotations
    @Since( "1.6.0" )
  87. def setTol(value: Double): AFTSurvivalRegression.this.type

    Set the convergence tolerance of iterations.

    Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

    Annotations
    @Since( "1.6.0" )
  88. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  89. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  90. final val tol: DoubleParam

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Definition Classes
    HasTol
  91. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    AFTSurvivalRegression → PipelineStage
    Annotations
    @Since( "1.6.0" )
  92. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  93. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    AFTSurvivalRegression → Identifiable
    Annotations
    @Since( "1.6.0" )
  94. def validateAndTransformSchema(schema: StructType, fitting: Boolean): StructType

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    fitting

    whether this is in fitting or prediction

    returns

    output schema

    Attributes
    protected
    Definition Classes
    AFTSurvivalRegressionParams
  95. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  96. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  97. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  98. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from AFTSurvivalRegressionParams

Inherited from HasAggregationDepth

Inherited from HasFitIntercept

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasPredictionCol

Inherited from HasLabelCol

Inherited from HasFeaturesCol

Inherited from Estimator[AFTSurvivalRegressionModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

(expert-only) Parameter setters

(expert-only) Parameter getters