public class AFTSurvivalRegressionModel extends Model<AFTSurvivalRegressionModel> implements AFTSurvivalRegressionParams, MLWritable
AFTSurvivalRegression.| Modifier and Type | Method and Description |
|---|---|
IntParam |
aggregationDepth()
Param for suggested depth for treeAggregate (>= 2).
|
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.
|
Param<String> |
featuresCol()
Param for features column name.
|
BooleanParam |
fitIntercept()
Param for whether to fit an intercept term.
|
double |
intercept() |
Param<String> |
labelCol()
Param for label column name.
|
static AFTSurvivalRegressionModel |
load(String path) |
IntParam |
maxIter()
Param for maximum number of iterations (>= 0).
|
double |
predict(Vector features) |
Param<String> |
predictionCol()
Param for prediction column name.
|
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) |
DoubleParam |
tol()
Param for the convergence tolerance for iterative algorithms (>= 0).
|
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
StructType |
transformSchema(StructType schema)
:: DeveloperApi ::
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transformparamsequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetCensorCol, getQuantileProbabilities, getQuantilesCol, hasQuantilesCol, validateAndTransformSchemagetFeaturesColgetLabelColgetPredictionColgetMaxItergetFitInterceptgetAggregationDepthclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringinitializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarningsavepublic static MLReader<AFTSurvivalRegressionModel> read()
public static AFTSurvivalRegressionModel load(String path)
public final Param<String> censorCol()
AFTSurvivalRegressionParamscensorCol in interface AFTSurvivalRegressionParamspublic final DoubleArrayParam quantileProbabilities()
AFTSurvivalRegressionParamsquantileProbabilities in interface AFTSurvivalRegressionParamspublic final Param<String> quantilesCol()
AFTSurvivalRegressionParamsquantilesCol in interface AFTSurvivalRegressionParamspublic final IntParam aggregationDepth()
HasAggregationDepthaggregationDepth in interface HasAggregationDepthpublic final BooleanParam fitIntercept()
HasFitInterceptfitIntercept in interface HasFitInterceptpublic final DoubleParam tol()
HasTolpublic final IntParam maxIter()
HasMaxItermaxIter in interface HasMaxIterpublic final Param<String> predictionCol()
HasPredictionColpredictionCol in interface HasPredictionColpublic final Param<String> labelCol()
HasLabelCollabelCol in interface HasLabelColpublic final Param<String> featuresCol()
HasFeaturesColfeaturesCol in interface HasFeaturesColpublic String uid()
Identifiableuid in interface Identifiablepublic Vector coefficients()
public double intercept()
public double scale()
public AFTSurvivalRegressionModel setFeaturesCol(String value)
public AFTSurvivalRegressionModel setPredictionCol(String value)
public AFTSurvivalRegressionModel setQuantileProbabilities(double[] value)
public AFTSurvivalRegressionModel setQuantilesCol(String value)
public double predict(Vector features)
public Dataset<Row> transform(Dataset<?> dataset)
Transformertransform in class Transformerdataset - (undocumented)public StructType transformSchema(StructType schema)
PipelineStageCheck 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.
transformSchema in class PipelineStageschema - (undocumented)public AFTSurvivalRegressionModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<AFTSurvivalRegressionModel>extra - (undocumented)public MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritable