org.apache.spark.ml.regression

IsotonicRegressionModel

class IsotonicRegressionModel extends Model[IsotonicRegressionModel] with IsotonicRegressionBase

:: Experimental :: Model fitted by IsotonicRegression. Predicts using a piecewise linear function.

For detailed rules see org.apache.spark.mllib.regression.IsotonicRegressionModel.predict().

Annotations
@Experimental()
Linear Supertypes
IsotonicRegressionBase, HasWeightCol, HasPredictionCol, HasLabelCol, HasFeaturesCol, Model[IsotonicRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. IsotonicRegressionModel
  2. IsotonicRegressionBase
  3. HasWeightCol
  4. HasPredictionCol
  5. HasLabelCol
  6. HasFeaturesCol
  7. Model
  8. Transformer
  9. PipelineStage
  10. Logging
  11. Params
  12. Serializable
  13. Serializable
  14. Identifiable
  15. AnyRef
  16. Any
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  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  5. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  6. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def boundaries: Vector

    Boundaries in increasing order for which predictions are known.

  9. 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.

    Attributes
    protected
    Definition Classes
    Params
  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  11. 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.

    Definition Classes
    IsotonicRegressionModelModelTransformerPipelineStageParams
    See also

    defaultCopy()

  12. 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 to paramMap. 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
  13. 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
  14. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  16. 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
  17. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance.

    Definition Classes
    Params
    See also

    explainParam()

  18. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  19. 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.

    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 < user-supplied values < extra.

    Definition Classes
    Params
  20. def extractWeightedLabeledPoints(dataset: DataFrame): RDD[(Double, Double, Double)]

    Extracts (label, feature, weight) from input dataset.

    Extracts (label, feature, weight) from input dataset.

    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    IsotonicRegressionBase
  21. final val featureIndex: IntParam

    Param for the index of the feature if featuresCol is a vector column (default: 0), no effect otherwise.

    Param for the index of the feature if featuresCol is a vector column (default: 0), no effect otherwise.

    Definition Classes
    IsotonicRegressionBase
  22. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. 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
  25. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  26. 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
  27. final def getFeatureIndex: Int

    Definition Classes
    IsotonicRegressionBase
  28. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  29. final def getIsotonic: Boolean

    Definition Classes
    IsotonicRegressionBase
  30. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  31. 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
  32. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  33. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  34. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  35. 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
  36. 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
  37. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  38. def hasWeightCol: Boolean

    Checks whether the input has weight column.

    Checks whether the input has weight column.

    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    IsotonicRegressionBase
  39. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  40. 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
  41. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  42. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  43. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  44. 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
  45. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  46. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  47. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  48. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  49. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  50. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  51. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  52. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  53. def logName: String

    Attributes
    protected
    Definition Classes
    Logging
  54. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  55. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  56. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  57. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  58. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  59. final def notify(): Unit

    Definition Classes
    AnyRef
  60. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  61. 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.

    Note: Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

    Definition Classes
    Params
  62. var parent: Estimator[IsotonicRegressionModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model. Note: For ensembles' component Models, this value can be null.

    Definition Classes
    Model
  63. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  64. def predictions: Vector

    Predictions associated with the boundaries at the same index, monotone because of isotonic regression.

  65. 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
  66. 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
  67. 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.

    Attributes
    protected
    Definition Classes
    Params
  68. 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
  69. final def setDefault[T](param: Param[T], value: T): IsotonicRegressionModel.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  70. def setFeatureIndex(value: Int): IsotonicRegressionModel.this.type

  71. def setFeaturesCol(value: String): IsotonicRegressionModel.this.type

  72. 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
  73. def setPredictionCol(value: String): IsotonicRegressionModel.this.type

  74. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  75. def toString(): String

    Definition Classes
    Identifiable → AnyRef → Any
  76. def transform(dataset: DataFrame): DataFrame

    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes
    IsotonicRegressionModelTransformer
  77. def transform(dataset: DataFrame, 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
  78. def transform(dataset: DataFrame, 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
    @varargs()
  79. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema.

    Definition Classes
    IsotonicRegressionModelPipelineStage
  80. 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()
  81. 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
    IsotonicRegressionModelIdentifiable
  82. 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[org.apache.spark.ml]
    Definition Classes
    IsotonicRegressionBase
  83. def validateParams(): Unit

    Validates parameter values stored internally.

    Validates parameter values stored internally. Raise an exception if any parameter value is invalid.

    This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.

    Definition Classes
    Params
  84. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  85. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  86. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  87. 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

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.

Members

Parameter setters

Parameter getters