Package org.apache.spark.ml.regression

Class AFTSurvivalRegressionModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.PredictionModel<FeaturesType,M>

org.apache.spark.ml.regression.RegressionModel<Vector,AFTSurvivalRegressionModel>

org.apache.spark.ml.regression.AFTSurvivalRegressionModel

All Implemented Interfaces:

Serializable, org.apache.spark.internal.Logging, Params, HasAggregationDepth, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasMaxBlockSizeInMB, HasMaxIter, HasPredictionCol, HasTol, PredictorParams, AFTSurvivalRegressionParams, Identifiable, MLWritable

public class AFTSurvivalRegressionModel
extends RegressionModel<Vector,AFTSurvivalRegressionModel>
implements AFTSurvivalRegressionParams, MLWritable

Model produced by AFTSurvivalRegression.

See Also:

• Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	AFTSurvivalRegressionModel.Data$

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

Modifier and Type	Method	Description
final IntParam	aggregationDepth()	Param for suggested depth for treeAggregate (>= 2).
final 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.
final BooleanParam	fitIntercept()	Param for whether to fit an intercept term.
double	intercept()
static AFTSurvivalRegressionModel	load(String path)
final DoubleParam	maxBlockSizeInMB()	Param for Maximum memory in MB for stacking input data into blocks.
final IntParam	maxIter()	Param for maximum number of iterations (&gt;= 0).
int	numFeatures()	Returns the number of features the model was trained on.
double	predict(Vector features)	Predict label for the given features.
Vector	predictQuantiles(Vector features)
final DoubleArrayParam	quantileProbabilities()	Param for quantile probabilities array.
final Param<String>	quantilesCol()	Param for quantiles column name.
static MLReader<AFTSurvivalRegressionModel>	read()
double	scale()
AFTSurvivalRegressionModel	setQuantileProbabilities(double[] value)
AFTSurvivalRegressionModel	setQuantilesCol(String value)
final DoubleParam	tol()	Param for the convergence tolerance for iterative algorithms (&gt;= 0).
String	toString()
Dataset<Row>	transform(Dataset<?> dataset)	Transforms dataset by reading from PredictionModel.featuresCol(), calling predict, and storing the predictions as a new column PredictionModel.predictionCol().
StructType	transformSchema(StructType schema)	Check transform validity and derive the output schema from the input schema.
String	uid()	An immutable unique ID for the object and its derivatives.
MLWriter	write()	Returns an MLWriter instance for this ML instance.

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

featuresCol, labelCol, predictionCol, setFeaturesCol, setPredictionCol

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 org.apache.spark.ml.PipelineStage

params

Methods inherited from class java.lang.Object

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

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

getCensorCol, getQuantileProbabilities, getQuantilesCol, hasQuantilesCol, validateAndTransformSchema

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

getAggregationDepth

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

featuresCol, getFeaturesCol

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

getFitIntercept

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

getLabelCol, labelCol

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

getMaxBlockSizeInMB

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

getMaxIter

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

getPredictionCol, predictionCol

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

getTol

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

initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logBasedOnLevel, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, MDC, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

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

save

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

clear, copyValues, defaultCopy, defaultParamMap, estimateMatadataSize, 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.PredictorParams

validateAndTransformSchema

Method Details

read

public static MLReader<AFTSurvivalRegressionModel> read()

load

public static AFTSurvivalRegressionModel load(String path)

censorCol

public final Param<String> censorCol()

Description copied from interface: AFTSurvivalRegressionParams

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.

Specified by:

censorCol in interface AFTSurvivalRegressionParams

Returns:

(undocumented)

quantileProbabilities

public final DoubleArrayParam quantileProbabilities()

Description copied from interface: AFTSurvivalRegressionParams

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.

Specified by:

quantileProbabilities in interface AFTSurvivalRegressionParams

Returns:

(undocumented)

quantilesCol

public final Param<String> quantilesCol()

Description copied from interface: AFTSurvivalRegressionParams

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

Specified by:

quantilesCol in interface AFTSurvivalRegressionParams

Returns:

(undocumented)

maxBlockSizeInMB

public final DoubleParam maxBlockSizeInMB()

Description copied from interface: HasMaxBlockSizeInMB

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 &gt;= 0..

Specified by:

maxBlockSizeInMB in interface HasMaxBlockSizeInMB

Returns:

(undocumented)

aggregationDepth

public final IntParam aggregationDepth()

Description copied from interface: HasAggregationDepth

Param for suggested depth for treeAggregate (&gt;= 2).

Specified by:

aggregationDepth in interface HasAggregationDepth

Returns:

(undocumented)

fitIntercept

public final BooleanParam fitIntercept()

Description copied from interface: HasFitIntercept

Param for whether to fit an intercept term.

Specified by:

fitIntercept in interface HasFitIntercept

Returns:

(undocumented)

tol

public final DoubleParam tol()

Description copied from interface: HasTol

Param for the convergence tolerance for iterative algorithms (&gt;= 0).

Specified by:

tol in interface HasTol

Returns:

(undocumented)

maxIter

public final IntParam maxIter()

Description copied from interface: HasMaxIter

Param for maximum number of iterations (&gt;= 0).

Specified by:

maxIter in interface HasMaxIter

Returns:

(undocumented)

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()

numFeatures

public int numFeatures()

Description copied from class: PredictionModel

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

Overrides:

numFeatures in class PredictionModel<Vector,AFTSurvivalRegressionModel>

setQuantileProbabilities

public AFTSurvivalRegressionModel setQuantileProbabilities(double[] value)

setQuantilesCol

public AFTSurvivalRegressionModel setQuantilesCol(String value)

predictQuantiles

public Vector predictQuantiles(Vector features)

predict

public double predict(Vector features)

Description copied from class: PredictionModel

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

Specified by:

predict in class PredictionModel<Vector,AFTSurvivalRegressionModel>

Parameters:

features - (undocumented)

Returns:

(undocumented)

transform

public Dataset<Row> transform(Dataset<?> dataset)

Description copied from class: PredictionModel

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

Overrides:

transform in class PredictionModel<Vector,AFTSurvivalRegressionModel>

Parameters:

dataset - input dataset

Returns:

transformed dataset with PredictionModel.predictionCol() of type Double

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

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.

Overrides:

transformSchema in class PredictionModel<Vector,AFTSurvivalRegressionModel>

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. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Model<AFTSurvivalRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

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)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object