org.apache.spark.ml.regression

Class AFTSurvivalRegressionModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.regression.AFTSurvivalRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable, MLWritable

public class AFTSurvivalRegressionModel
extends Model<AFTSurvivalRegressionModel>
implements MLWritable

:: Experimental :: Model produced by AFTSurvivalRegression.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
Param<String>	censorCol() Param for censor column name.
Vector	coefficients()
AFTSurvivalRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
String	getCensorCol()
double[]	getQuantileProbabilities()
String	getQuantilesCol()
boolean	hasQuantilesCol() Checks whether the input has quantiles column name.
double	intercept()
static AFTSurvivalRegressionModel	load(String path)
double	predict(Vector features)
Vector	predictQuantiles(Vector features)
DoubleArrayParam	quantileProbabilities() Param for quantile probabilities array.
Param<String>	quantilesCol() Param for quantiles column name.
static MLReader<AFTSurvivalRegressionModel>	read()
double	scale()
AFTSurvivalRegressionModel	setFeaturesCol(String value)
AFTSurvivalRegressionModel	setPredictionCol(String value)
AFTSurvivalRegressionModel	setQuantileProbabilities(double[] value)
AFTSurvivalRegressionModel	setQuantilesCol(String value)
DataFrame	transform(DataFrame dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema, boolean fitting) Validates and transforms the input schema with the provided param map.
MLWriter	write() Returns an MLWriter instance for this ML instance.

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

hasParent, parent, setParent

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

transform, transform, transform

Methods inherited from class Object

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

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, validateParams

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

toString

Methods inherited from interface org.apache.spark.Logging

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

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

save

Method Detail

read

public static MLReader<AFTSurvivalRegressionModel> read()

load

public static AFTSurvivalRegressionModel load(String path)

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)

coefficients

public Vector coefficients()

intercept

public double intercept()

scale

public double scale()

setFeaturesCol

public AFTSurvivalRegressionModel setFeaturesCol(String value)

setPredictionCol

public AFTSurvivalRegressionModel setPredictionCol(String value)

setQuantileProbabilities

public AFTSurvivalRegressionModel setQuantileProbabilities(double[] value)

setQuantilesCol

public AFTSurvivalRegressionModel setQuantilesCol(String value)

predictQuantiles

public Vector predictQuantiles(Vector features)

predict

public double predict(Vector features)

transform

public DataFrame transform(DataFrame dataset)

Description copied from class: Transformer

Transforms the input dataset.

Specified by:

transform in class Transformer

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

Derives the output schema from the input schema.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public AFTSurvivalRegressionModel 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.

Specified by:

copy in interface Params

Specified by:

copy in class Model<AFTSurvivalRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

censorCol

public Param<String> censorCol()

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.

Returns:

(undocumented)

getCensorCol

public String getCensorCol()

quantileProbabilities

public DoubleArrayParam quantileProbabilities()

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.

Returns:

(undocumented)

getQuantileProbabilities

public double[] getQuantileProbabilities()

quantilesCol

public Param<String> quantilesCol()

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

Returns:

(undocumented)

getQuantilesCol

public String getQuantilesCol()

hasQuantilesCol

public boolean hasQuantilesCol()

Checks whether the input has quantiles column name.

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting)

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

Parameters:

schema - input schema

fitting - whether this is in fitting or prediction

Returns:

output schema