org.apache.spark.ml.regression

Class AFTSurvivalRegression

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<AFTSurvivalRegressionModel>

org.apache.spark.ml.regression.AFTSurvivalRegression

All Implemented Interfaces:

java.io.Serializable, Logging, Params, HasAggregationDepth, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasMaxIter, HasPredictionCol, HasTol, AFTSurvivalRegressionParams, DefaultParamsWritable, Identifiable, MLWritable

public class AFTSurvivalRegression
extends Estimator<AFTSurvivalRegressionModel>
implements AFTSurvivalRegressionParams, DefaultParamsWritable, 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.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
AFTSurvivalRegression()
AFTSurvivalRegression(String uid)

Method Summary

Modifier and Type	Method and Description
AFTSurvivalRegression	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
AFTSurvivalRegressionModel	fit(Dataset<?> dataset) Fits a model to the input data.
static AFTSurvivalRegression	load(String path)
static MLReader<T>	read()
AFTSurvivalRegression	setAggregationDepth(int value) Suggested depth for treeAggregate (greater than or equal to 2).
AFTSurvivalRegression	setCensorCol(String value)
AFTSurvivalRegression	setFeaturesCol(String value)
AFTSurvivalRegression	setFitIntercept(boolean value) Set if we should fit the intercept Default is true.
AFTSurvivalRegression	setLabelCol(String value)
AFTSurvivalRegression	setMaxIter(int value) Set the maximum number of iterations.
AFTSurvivalRegression	setPredictionCol(String value)
AFTSurvivalRegression	setQuantileProbabilities(double[] value)
AFTSurvivalRegression	setQuantilesCol(String value)
AFTSurvivalRegression	setTol(double value) Set the convergence tolerance of iterations.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.regression.AFTSurvivalRegressionParams

censorCol, getCensorCol, getQuantileProbabilities, getQuantilesCol, hasQuantilesCol, quantileProbabilities, quantilesCol, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

featuresCol, getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol, labelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol, predictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter

getMaxIter, maxIter

Methods inherited from interface org.apache.spark.ml.param.shared.HasTol

getTol, tol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFitIntercept

fitIntercept, getFitIntercept

Methods inherited from interface org.apache.spark.ml.param.shared.HasAggregationDepth

aggregationDepth, getAggregationDepth

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.internal.Logging

initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable

write

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Constructor Detail

AFTSurvivalRegression

public AFTSurvivalRegression(String uid)

AFTSurvivalRegression

public AFTSurvivalRegression()

Method Detail

load

public static AFTSurvivalRegression load(String path)

read

public static MLReader<T> read()

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setFeaturesCol

public AFTSurvivalRegression setFeaturesCol(String value)

setLabelCol

public AFTSurvivalRegression setLabelCol(String value)

setCensorCol

public AFTSurvivalRegression setCensorCol(String value)

setPredictionCol

public AFTSurvivalRegression setPredictionCol(String value)

setQuantileProbabilities

public AFTSurvivalRegression setQuantileProbabilities(double[] value)

setQuantilesCol

public AFTSurvivalRegression setQuantilesCol(String value)

setFitIntercept

public AFTSurvivalRegression setFitIntercept(boolean value)

Set if we should fit the intercept Default is true.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMaxIter

public AFTSurvivalRegression setMaxIter(int value)

Set the maximum number of iterations. Default is 100.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setTol

public AFTSurvivalRegression setTol(double value)

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

Parameters:

value - (undocumented)

Returns:

(undocumented)

setAggregationDepth

public AFTSurvivalRegression setAggregationDepth(int value)

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.

Parameters:

value - (undocumented)

Returns:

(undocumented)

fit

public AFTSurvivalRegressionModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<AFTSurvivalRegressionModel>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: 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.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public AFTSurvivalRegression copy(ParamMap extra)

Description copied from interface: 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().

Specified by:

copy in interface Params

Specified by:

copy in class Estimator<AFTSurvivalRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)