AFTSurvivalRegression (Spark 2.1.0 JavaDoc)

Object
- org.apache.spark.ml.PipelineStage
- - org.apache.spark.ml.Estimator<AFTSurvivalRegressionModel>
  - - org.apache.spark.ml.regression.AFTSurvivalRegression

All Implemented Interfaces:

java.io.Serializable, Params, DefaultParamsWritable, Identifiable, MLWritable
```
public class AFTSurvivalRegression
extends Estimator<AFTSurvivalRegressionModel>
implements DefaultParamsWritable
```
:: 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)

Constructors
Constructor and Description
`AFTSurvivalRegression()`
`AFTSurvivalRegression(String uid)`

Method Summary

Methods
Modifier and Type	Method and Description
`static IntParam`	`aggregationDepth()`
`static Param<String>`	`censorCol()`
`Param<String>`	`censorCol()` Param for censor column name.
`static Params`	`clear(Param<?> param)`
`AFTSurvivalRegression`	`copy(ParamMap extra)` Creates a copy of this instance with the same UID and some extra params.
`static String`	`explainParam(Param<?> param)`
`static String`	`explainParams()`
`static ParamMap`	`extractParamMap()`
`static ParamMap`	`extractParamMap(ParamMap extra)`
`static Param<String>`	`featuresCol()`
`AFTSurvivalRegressionModel`	`fit(Dataset<?> dataset)` Fits a model to the input data.
`static BooleanParam`	`fitIntercept()`
`static <T> scala.Option<T>`	`get(Param<T> param)`
`static int`	`getAggregationDepth()`
`static String`	`getCensorCol()`
`String`	`getCensorCol()`
`static <T> scala.Option<T>`	`getDefault(Param<T> param)`
`static String`	`getFeaturesCol()`
`static boolean`	`getFitIntercept()`
`static String`	`getLabelCol()`
`static int`	`getMaxIter()`
`static <T> T`	`getOrDefault(Param<T> param)`
`static Param<Object>`	`getParam(String paramName)`
`static String`	`getPredictionCol()`
`static double[]`	`getQuantileProbabilities()`
`double[]`	`getQuantileProbabilities()`
`static String`	`getQuantilesCol()`
`String`	`getQuantilesCol()`
`static double`	`getTol()`
`static <T> boolean`	`hasDefault(Param<T> param)`
`static boolean`	`hasParam(String paramName)`
`boolean`	`hasQuantilesCol()` Checks whether the input has quantiles column name.
`static boolean`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static Param<String>`	`labelCol()`
`static AFTSurvivalRegression`	`load(String path)`
`static IntParam`	`maxIter()`
`static Param<?>[]`	`params()`
`static Param<String>`	`predictionCol()`
`static DoubleArrayParam`	`quantileProbabilities()`
`DoubleArrayParam`	`quantileProbabilities()` Param for quantile probabilities array.
`static Param<String>`	`quantilesCol()`
`Param<String>`	`quantilesCol()` Param for quantiles column name.
`static void`	`save(String path)`
`static <T> Params`	`set(Param<T> param, T value)`
`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.
`static DoubleParam`	`tol()`
`static String`	`toString()`
`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.
`static MLWriter`	`write()`

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.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.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)
```
  - toString
```
public static String toString()
```
  - params
```
public static Param<?>[] params()
```
  - explainParam
```
public static String explainParam(Param<?> param)
```
  - explainParams
```
public static String explainParams()
```
  - isSet
```
public static final boolean isSet(Param<?> param)
```
  - isDefined
```
public static final boolean isDefined(Param<?> param)
```
  - hasParam
```
public static boolean hasParam(String paramName)
```
  - getParam
```
public static Param<Object> getParam(String paramName)
```
  - set
```
public static final <T> Params set(Param<T> param,
             T value)
```
  - get
```
public static final <T> scala.Option<T> get(Param<T> param)
```
  - clear
```
public static final Params clear(Param<?> param)
```
  - getOrDefault
```
public static final <T> T getOrDefault(Param<T> param)
```
  - getDefault
```
public static final <T> scala.Option<T> getDefault(Param<T> param)
```
  - hasDefault
```
public static final <T> boolean hasDefault(Param<T> param)
```
  - extractParamMap
```
public static final ParamMap extractParamMap(ParamMap extra)
```
  - extractParamMap
```
public static final ParamMap extractParamMap()
```
  - featuresCol
```
public static final Param<String> featuresCol()
```
  - getFeaturesCol
```
public static final String getFeaturesCol()
```
  - labelCol
```
public static final Param<String> labelCol()
```
  - getLabelCol
```
public static final String getLabelCol()
```
  - predictionCol
```
public static final Param<String> predictionCol()
```
  - getPredictionCol
```
public static final String getPredictionCol()
```
  - maxIter
```
public static final IntParam maxIter()
```
  - getMaxIter
```
public static final int getMaxIter()
```
  - tol
```
public static final DoubleParam tol()
```
  - getTol
```
public static final double getTol()
```
  - fitIntercept
```
public static final BooleanParam fitIntercept()
```
  - getFitIntercept
```
public static final boolean getFitIntercept()
```
  - aggregationDepth
```
public static final IntParam aggregationDepth()
```
  - getAggregationDepth
```
public static final int getAggregationDepth()
```
  - censorCol
```
public static final Param<String> censorCol()
```
  - getCensorCol
```
public static String getCensorCol()
```
  - quantileProbabilities
```
public static final DoubleArrayParam quantileProbabilities()
```
  - getQuantileProbabilities
```
public static double[] getQuantileProbabilities()
```
  - quantilesCol
```
public static final Param<String> quantilesCol()
```
  - getQuantilesCol
```
public static String getQuantilesCol()
```
  - save
```
public static void save(String path)
                 throws java.io.IOException
```
    Throws:
    
    java.io.IOException
  - write
```
public static MLWriter write()
```
  - 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)
  - 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

Class AFTSurvivalRegression

Constructor Summary

Method Summary

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

Methods inherited from class Object

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

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

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

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

Constructor Detail

AFTSurvivalRegression

AFTSurvivalRegression

Method Detail

load

toString

params

explainParam

explainParams

isSet

isDefined

hasParam

getParam

set

get

clear

getOrDefault

getDefault

hasDefault

extractParamMap

extractParamMap

featuresCol

getFeaturesCol

labelCol

getLabelCol

predictionCol

getPredictionCol

maxIter

getMaxIter

tol

getTol

fitIntercept

getFitIntercept

aggregationDepth

getAggregationDepth

censorCol

getCensorCol

quantileProbabilities

getQuantileProbabilities

quantilesCol

getQuantilesCol

save

write

uid

setFeaturesCol

setLabelCol

setCensorCol

setPredictionCol

setQuantileProbabilities

setQuantilesCol

setFitIntercept

setMaxIter

setTol

setAggregationDepth

fit

transformSchema

copy

censorCol

getCensorCol

quantileProbabilities

getQuantileProbabilities

quantilesCol

getQuantilesCol

hasQuantilesCol

validateAndTransformSchema