class IsotonicRegressionModel extends Model[IsotonicRegressionModel] with IsotonicRegressionBase with MLWritable
Model fitted by IsotonicRegression. Predicts using a piecewise linear function.
For detailed rules see org.apache.spark.mllib.regression.IsotonicRegressionModel.predict().
- Annotations
- @Since( "1.5.0" )
- Source
- IsotonicRegression.scala
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- IsotonicRegressionModel
- MLWritable
- IsotonicRegressionBase
- HasWeightCol
- HasPredictionCol
- HasLabelCol
- HasFeaturesCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
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final
def
!=(arg0: Any): Boolean
- Definition Classes
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final
def
##(): Int
- Definition Classes
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final
def
$[T](param: Param[T]): T
An alias for
getOrDefault().An alias for
getOrDefault().- Attributes
- protected
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final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
boundaries: Vector
Boundaries in increasing order for which predictions are known.
Boundaries in increasing order for which predictions are known.
- Annotations
- @Since( "2.0.0" )
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final
def
clear(param: Param[_]): IsotonicRegressionModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
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def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native() @IntrinsicCandidate()
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def
copy(extra: ParamMap): IsotonicRegressionModel
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
- IsotonicRegressionModel → Model → Transformer → PipelineStage → Params
- Annotations
- @Since( "1.5.0" )
-
def
copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to
defaultParamMap, and explicitly set Params are copied from and toparamMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.- to
the target instance, which should work with the same set of default Params as this source instance
- extra
extra params to be copied to the target's
paramMap- returns
the target instance with param values copied
- Attributes
- protected
- Definition Classes
- Params
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final
def
defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
- Attributes
- protected
- Definition Classes
- Params
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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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
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def
explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam().- Definition Classes
- Params
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final
def
extractParamMap(): ParamMap
extractParamMapwith no extra values.extractParamMapwith no extra values.- Definition Classes
- Params
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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
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def
extractWeightedLabeledPoints(dataset: Dataset[_]): RDD[(Double, Double, Double)]
Extracts (label, feature, weight) from input dataset.
Extracts (label, feature, weight) from input dataset.
- Attributes
- protected[ml]
- Definition Classes
- IsotonicRegressionBase
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final
val
featureIndex: IntParam
Param for the index of the feature if
featuresColis a vector column (default:0), no effect otherwise.Param for the index of the feature if
featuresColis a vector column (default:0), no effect otherwise.- Definition Classes
- IsotonicRegressionBase
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final
val
featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
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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
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final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @IntrinsicCandidate()
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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
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final
def
getFeatureIndex: Int
- Definition Classes
- IsotonicRegressionBase
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final
def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
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final
def
getIsotonic: Boolean
- Definition Classes
- IsotonicRegressionBase
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final
def
getLabelCol: String
- Definition Classes
- HasLabelCol
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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
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def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
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final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
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final
def
getWeightCol: String
- Definition Classes
- HasWeightCol
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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
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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
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def
hasParent: Boolean
Indicates whether this Model has a corresponding parent.
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def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @IntrinsicCandidate()
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
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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
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
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def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
final
val
isotonic: BooleanParam
Param for whether the output sequence should be isotonic/increasing (true) or antitonic/decreasing (false).
Param for whether the output sequence should be isotonic/increasing (true) or antitonic/decreasing (false). Default: true
- Definition Classes
- IsotonicRegressionBase
-
final
val
labelCol: Param[String]
Param for label column name.
Param for label column name.
- Definition Classes
- HasLabelCol
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def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logError(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
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def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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final
def
notify(): Unit
- Definition Classes
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- Annotations
- @native() @IntrinsicCandidate()
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final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @IntrinsicCandidate()
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val
numFeatures: Int
- 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.
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var
parent: Estimator[IsotonicRegressionModel]
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(value: Double): Double
- Annotations
- @Since( "3.0.0" )
-
final
val
predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
-
def
predictions: Vector
Predictions associated with the boundaries at the same index, monotone because of isotonic regression.
Predictions associated with the boundaries at the same index, monotone because of isotonic regression.
- 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( ... )
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final
def
set(paramPair: ParamPair[_]): IsotonicRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): IsotonicRegressionModel.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): IsotonicRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
final
def
setDefault(paramPairs: ParamPair[_]*): IsotonicRegressionModel.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter
setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.- paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): IsotonicRegressionModel.this.type
Sets a default value for a param.
-
def
setFeatureIndex(value: Int): IsotonicRegressionModel.this.type
- Annotations
- @Since( "1.5.0" )
-
def
setFeaturesCol(value: String): IsotonicRegressionModel.this.type
- Annotations
- @Since( "1.5.0" )
-
def
setParent(parent: Estimator[IsotonicRegressionModel]): IsotonicRegressionModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
-
def
setPredictionCol(value: String): IsotonicRegressionModel.this.type
- Annotations
- @Since( "1.5.0" )
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- IsotonicRegressionModel → Identifiable → AnyRef → Any
- Annotations
- @Since( "3.0.0" )
-
def
transform(dataset: Dataset[_]): DataFrame
Transforms the input dataset.
Transforms the input dataset.
- Definition Classes
- IsotonicRegressionModel → 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
- IsotonicRegressionModel → PipelineStage
- Annotations
- @Since( "1.5.0" )
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
-
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
- IsotonicRegressionModel → Identifiable
-
def
validateAndTransformSchema(schema: StructType, fitting: Boolean): StructType
Validates and transforms input schema.
Validates and transforms input schema.
- schema
input schema
- fitting
whether this is in fitting or prediction
- returns
output schema
- Attributes
- protected[ml]
- Definition Classes
- IsotonicRegressionBase
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
val
weightCol: Param[String]
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
- Definition Classes
- HasWeightCol
-
def
write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- IsotonicRegressionModel → MLWritable
- Annotations
- @Since( "1.6.0" )
Deprecated Value Members
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] ) @Deprecated
- Deprecated
Inherited from MLWritable
Inherited from IsotonicRegressionBase
Inherited from HasWeightCol
Inherited from HasPredictionCol
Inherited from HasLabelCol
Inherited from HasFeaturesCol
Inherited from Model[IsotonicRegressionModel]
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.