class AFTSurvivalRegression extends Regressor[Vector, AFTSurvivalRegression, AFTSurvivalRegressionModel] with AFTSurvivalRegressionParams with DefaultParamsWritable with Logging
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.
Since 3.1.0, it supports stacking instances into blocks and using GEMV for better performance. The block size will be 1.0 MB, if param maxBlockSizeInMB is set 0.0 by default.
- Annotations
- @Since("1.6.0")
- Source
- AFTSurvivalRegression.scala
- Grouped
- Alphabetic
- By Inheritance
- AFTSurvivalRegression
- DefaultParamsWritable
- MLWritable
- AFTSurvivalRegressionParams
- HasMaxBlockSizeInMB
- HasAggregationDepth
- HasFitIntercept
- HasTol
- HasMaxIter
- Regressor
- Predictor
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Identifiable
- AnyRef
- Any
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- Public
- Protected
Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
- 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")
- final val featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
- final val fitIntercept: BooleanParam
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.
- Definition Classes
- HasFitIntercept
- final val labelCol: Param[String]
Param for label column name.
Param for label column name.
- Definition Classes
- HasLabelCol
- final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
- Definition Classes
- HasMaxIter
- final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
- 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")
- 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")
- 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
- implicit class LogStringContext extends AnyRef
- Definition Classes
- Logging
- 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
- 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 → Predictor → Estimator → PipelineStage → Params
- Annotations
- @Since("1.6.0")
- 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
- def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam().- Definition Classes
- Params
- final def extractParamMap(): ParamMap
extractParamMapwith no extra values.extractParamMapwith no extra values.- Definition Classes
- Params
- 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
- def fit(dataset: Dataset[_]): AFTSurvivalRegressionModel
Fits a model to the input data.
- def fit(dataset: Dataset[_], paramMaps: Seq[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")
- 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")
- 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()
- 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
- 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
- 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
- def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
- 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
- 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
- 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
- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
- 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.
- 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("If the input path already exists but overwrite is not enabled.")
- 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
- def toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
- 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
transformSchemaand raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate().Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- AFTSurvivalRegression → Predictor → PipelineStage
- Annotations
- @Since("1.6.0")
- 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")
- def write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Parameter setters
- def setCensorCol(value: String): AFTSurvivalRegression.this.type
- Annotations
- @Since("1.6.0")
- def setFeaturesCol(value: String): AFTSurvivalRegression
- Definition Classes
- Predictor
- 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")
- def setLabelCol(value: String): AFTSurvivalRegression
- Definition Classes
- Predictor
- 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")
- def setPredictionCol(value: String): AFTSurvivalRegression
- Definition Classes
- Predictor
- def setQuantileProbabilities(value: Array[Double]): AFTSurvivalRegression.this.type
- Annotations
- @Since("1.6.0")
- def setQuantilesCol(value: String): AFTSurvivalRegression.this.type
- Annotations
- @Since("1.6.0")
- 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")
Parameter getters
- def getCensorCol: String
- Definition Classes
- AFTSurvivalRegressionParams
- Annotations
- @Since("1.6.0")
- final def getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
- final def getFitIntercept: Boolean
- Definition Classes
- HasFitIntercept
- final def getLabelCol: String
- Definition Classes
- HasLabelCol
- final def getMaxIter: Int
- Definition Classes
- HasMaxIter
- final def getPredictionCol: String
- Definition Classes
- HasPredictionCol
- def getQuantileProbabilities: Array[Double]
- Definition Classes
- AFTSurvivalRegressionParams
- Annotations
- @Since("1.6.0")
- def getQuantilesCol: String
- Definition Classes
- AFTSurvivalRegressionParams
- Annotations
- @Since("1.6.0")
- 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.
- final val aggregationDepth: IntParam
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).
- Definition Classes
- HasAggregationDepth
- 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 setters
- 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")
- def setMaxBlockSizeInMB(value: Double): AFTSurvivalRegression.this.type
Sets the value of param maxBlockSizeInMB.
Sets the value of param maxBlockSizeInMB. Default is 0.0, then 1.0 MB will be chosen.
- Annotations
- @Since("3.1.0")
(expert-only) Parameter getters
- final def getAggregationDepth: Int
- Definition Classes
- HasAggregationDepth
- final def getMaxBlockSizeInMB: Double
- Definition Classes
- HasMaxBlockSizeInMB