org.apache.spark.ml.regression
AFTSurvivalRegressionModel
Companion object AFTSurvivalRegressionModel
class AFTSurvivalRegressionModel extends RegressionModel[Vector, AFTSurvivalRegressionModel] with AFTSurvivalRegressionParams with MLWritable
Model produced by AFTSurvivalRegression.
- Annotations
- @Since( "1.6.0" )
- Source
- AFTSurvivalRegression.scala
- Grouped
- Alphabetic
- By Inheritance
- AFTSurvivalRegressionModel
- MLWritable
- AFTSurvivalRegressionParams
- HasMaxBlockSizeInMB
- HasAggregationDepth
- HasFitIntercept
- HasTol
- HasMaxIter
- RegressionModel
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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- Public
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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
-
final
def
clear(param: Param[_]): AFTSurvivalRegressionModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
-
val
coefficients: Vector
- Annotations
- @Since( "2.0.0" )
-
def
copy(extra: ParamMap): AFTSurvivalRegressionModel
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
- AFTSurvivalRegressionModel → Model → Transformer → 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
-
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
-
def
hasParent: Boolean
Indicates whether this Model has a corresponding parent.
-
val
intercept: Double
- Annotations
- @Since( "1.6.0" )
-
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
-
def
numFeatures: Int
Returns the number of features the model was trained on.
Returns the number of features the model was trained on. If unknown, returns -1
- Definition Classes
- AFTSurvivalRegressionModel → PredictionModel
- Annotations
- @Since( "3.0.0" )
-
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.
-
var
parent: Estimator[AFTSurvivalRegressionModel]
The parent estimator that produced this model.
The parent estimator that produced this model.
- Definition Classes
- Model
- Note
For ensembles' component Models, this value can be null.
-
def
predict(features: Vector): Double
Predict label for the given features.
Predict label for the given features. This method is used to implement
transform()and output predictionCol.- Definition Classes
- AFTSurvivalRegressionModel → PredictionModel
- Annotations
- @Since( "2.0.0" )
-
def
predictQuantiles(features: Vector): Vector
- Annotations
- @Since( "2.0.0" )
-
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( ... )
-
val
scale: Double
- Annotations
- @Since( "1.6.0" )
-
final
def
set[T](param: Param[T], value: T): AFTSurvivalRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
setParent(parent: Estimator[AFTSurvivalRegressionModel]): AFTSurvivalRegressionModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
-
def
toString(): String
- Definition Classes
- AFTSurvivalRegressionModel → Identifiable → AnyRef → Any
- Annotations
- @Since( "3.0.0" )
-
def
transform(dataset: Dataset[_]): DataFrame
Transforms dataset by reading from featuresCol, calling
predict, and storing the predictions as a new column predictionCol.Transforms dataset by reading from featuresCol, calling
predict, and storing the predictions as a new column predictionCol.- dataset
input dataset
- returns
transformed dataset with predictionCol of type
Double
- Definition Classes
- AFTSurvivalRegressionModel → PredictionModel → Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
- dataset
input dataset
- paramMap
additional parameters, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
- dataset
input dataset
- firstParamPair
the first param pair, overwrite embedded params
- otherParamPairs
other param pairs, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
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
- AFTSurvivalRegressionModel → PredictionModel → 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
- AFTSurvivalRegressionModel → 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
- AFTSurvivalRegressionModel → MLWritable
- Annotations
- @Since( "1.6.0" )
Parameter setters
-
def
setFeaturesCol(value: String): AFTSurvivalRegressionModel
- Definition Classes
- PredictionModel
-
def
setPredictionCol(value: String): AFTSurvivalRegressionModel
- Definition Classes
- PredictionModel
-
def
setQuantileProbabilities(value: Array[Double]): AFTSurvivalRegressionModel.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setQuantilesCol(value: String): AFTSurvivalRegressionModel.this.type
- 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 getters
-
final
def
getAggregationDepth: Int
- Definition Classes
- HasAggregationDepth
-
final
def
getMaxBlockSizeInMB: Double
- Definition Classes
- HasMaxBlockSizeInMB