Packages

class AFTSurvivalRegressionModel extends RegressionModel[Vector, AFTSurvivalRegressionModel] with AFTSurvivalRegressionParams with MLWritable

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Inherited
  1. AFTSurvivalRegressionModel
  2. MLWritable
  3. AFTSurvivalRegressionParams
  4. HasMaxBlockSizeInMB
  5. HasAggregationDepth
  6. HasFitIntercept
  7. HasTol
  8. HasMaxIter
  9. RegressionModel
  10. PredictionModel
  11. PredictorParams
  12. HasPredictionCol
  13. HasFeaturesCol
  14. HasLabelCol
  15. Model
  16. Transformer
  17. PipelineStage
  18. Logging
  19. Params
  20. Serializable
  21. Serializable
  22. Identifiable
  23. AnyRef
  24. Any
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Parameters

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

  1. 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" )
  2. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  3. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  4. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  5. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  6. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  7. 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" )
  8. 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" )
  9. 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

Members

  1. final def clear(param: Param[_]): AFTSurvivalRegressionModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  2. val coefficients: Vector
    Annotations
    @Since( "2.0.0" )
  3. def copy(extra: ParamMap): AFTSurvivalRegressionModel

    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
    AFTSurvivalRegressionModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.6.0" )
  4. 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
  5. def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes
    Params
  6. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  7. 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
  8. 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
  9. 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
  10. 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
  11. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  12. 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
  13. 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
  14. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  15. val intercept: Double
    Annotations
    @Since( "1.6.0" )
  16. 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
  17. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  18. def numFeatures: Int

    Returns the number of features the model was trained on.

    Returns the number of features the model was trained on. If unknown, returns -1

    Definition Classes
    AFTSurvivalRegressionModelPredictionModel
    Annotations
    @Since( "3.0.0" )
  19. 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.

  20. var parent: Estimator[AFTSurvivalRegressionModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  21. def predict(features: Vector): Double

    Predict label for the given features.

    Predict label for the given features. This method is used to implement transform() and output predictionCol.

    Definition Classes
    AFTSurvivalRegressionModelPredictionModel
    Annotations
    @Since( "2.0.0" )
  22. def predictQuantiles(features: Vector): Vector
    Annotations
    @Since( "2.0.0" )
  23. 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( ... )
  24. val scale: Double
    Annotations
    @Since( "1.6.0" )
  25. final def set[T](param: Param[T], value: T): AFTSurvivalRegressionModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  26. def setParent(parent: Estimator[AFTSurvivalRegressionModel]): AFTSurvivalRegressionModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  27. def toString(): String
    Definition Classes
    AFTSurvivalRegressionModelIdentifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  28. def transform(dataset: Dataset[_]): DataFrame

    Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

    Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

    dataset

    input dataset

    returns

    transformed dataset with predictionCol of type Double

    Definition Classes
    AFTSurvivalRegressionModelPredictionModelTransformer
    Annotations
    @Since( "2.0.0" )
  29. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  30. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  31. def transformSchema(schema: StructType): StructType

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

    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
    AFTSurvivalRegressionModelPredictionModelPipelineStage
    Annotations
    @Since( "1.6.0" )
  32. 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
    AFTSurvivalRegressionModelIdentifiable
    Annotations
    @Since( "1.6.0" )
  33. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    AFTSurvivalRegressionModelMLWritable
    Annotations
    @Since( "1.6.0" )

Parameter setters

  1. def setFeaturesCol(value: String): AFTSurvivalRegressionModel

    Definition Classes
    PredictionModel
  2. def setPredictionCol(value: String): AFTSurvivalRegressionModel

    Definition Classes
    PredictionModel
  3. def setQuantileProbabilities(value: Array[Double]): AFTSurvivalRegressionModel.this.type

    Annotations
    @Since( "1.6.0" )
  4. def setQuantilesCol(value: String): AFTSurvivalRegressionModel.this.type

    Annotations
    @Since( "1.6.0" )

Parameter getters

  1. def getCensorCol: String

    Definition Classes
    AFTSurvivalRegressionParams
    Annotations
    @Since( "1.6.0" )
  2. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  3. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  4. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  5. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  6. final def getPredictionCol: String

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

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

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

    Definition Classes
    HasTol

(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.

  1. final val aggregationDepth: IntParam

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

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

    Definition Classes
    HasAggregationDepth
  2. final val maxBlockSizeInMB: DoubleParam

    Param for Maximum memory in MB for stacking input data into blocks.

    Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

    Definition Classes
    HasMaxBlockSizeInMB

(expert-only) Parameter getters

  1. final def getAggregationDepth: Int

    Definition Classes
    HasAggregationDepth
  2. final def getMaxBlockSizeInMB: Double

    Definition Classes
    HasMaxBlockSizeInMB