CombinedModel

Type Members

trait DirectPredictionModel[I, M <: DirectPredictionModel[I, M]] extends PredictionModel[I, M] with HasDirectTransformOption

Direct prediction model is an extension to a model with an ability to predict for single point instead of transforming dataset.
Direct prediction model is an extension to a model with an ability to predict for single point instead of transforming dataset. Used to improve performance of the combined models.
I
Type of the model input
class LinearCombinationModelLearner[M <: ModelWithSummary[M]] extends PerClassModelLearner[M, LinearCombinationModel[M]]

Trains a model which predicts a scalar of weighted classes or a vector which is a generalized linear combination of per-class predictions
class MultiClassModelLearner[M <: ModelWithSummary[M]] extends PerClassModelLearner[M, MultiClassCombinationModel[M]]

Trains a model which predicts vector (one element per class)
abstract class PerClassModelLearner[N <: ModelWithSummary[N], M <: MultiClassCombinationModelBase[N, M]] extends ForkedEstimator[N, String, M] with HasClassesCol with HasClassesWeights with HasLabelCol with HasPredictionCol

Collects all classes in the dataset and trains a model for each class.
Collects all classes in the dataset and trains a model for each class. Base class for multi-class and linear combination model.
class PerTypeModelLearner[M <: ModelWithSummary[M]] extends ForkedEstimator[M, String, SelectingModel[M]] with HasTypeCol with HasPredictionCol

Collects types, available in the dataset and trains one model per each type.
class Reader[M <: ModelWithSummary[M], C <: CombinedModel[M, C]] extends WithSummaryReader[C]
class ReaderUntyped extends WithSummaryReaderUntyped
class Writer[M <: ModelWithSummary[M], C <: CombinedModel[M, C]] extends WithSummaryWriter[C]

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
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final def ##(): Int

Definition Classes
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final def ==(arg0: Any): Boolean

Definition Classes
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final def asInstanceOf[T0]: T0

Definition Classes
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def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
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def equals(arg0: Any): Boolean

Definition Classes
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def finalize(): Unit

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

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def hashCode(): Int

Definition Classes
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final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def linearCombination[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], classesColumn: String = "classes", classesToIgnore: Seq[String] = Seq(), classesMap: Map[String, String] = Map(), parallel: Boolean = false, cacheForks: Boolean = false): LinearCombinationModelLearner[M]

Train models for all classes.
Train models for all classes. Result is a linear combination model. Summary blocks for combined model are merged from the nested model with extra class column.
M
Type of the model to train.
estimator
Estimator for training the nested models.
classesColumn
Column where the classes are recorded.
classesToIgnore
Which classes not to include into result.
classesMap
Mapping for merging/renaming classes.
parallel
Whenever to train classes in parallel.
cacheForks
Whenever to cache forks before iterating
returns
Linear combination model (given an instance coputes prediction of all the models and combines them linary with configured weights).
def linearCombinationReader[M <: ModelWithSummary[M]](nestedReader: WithSummaryReader[M]): WithSummaryReader[LinearCombinationModel[M]]

returns
Reader for linear combination combination model.
def load(path: String): PipelineStage

Definition Classes
MLReadable
Annotations
@Since( "1.6.0" )
def multiClass[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], classesColumn: String = "classes", classesToIgnore: Seq[String] = Seq(), classesMap: Map[String, String] = Map(), parallel: Boolean = false, cacheForks: Boolean = false): MultiClassModelLearner[M]

Train models for all classes.
Train models for all classes. Result is a vector with prediction for each class. Summary blocks for combined model are merged from the nested model with extra class column.
M
Type of the model to train.
estimator
Estimator for training the nested models.
classesColumn
Column where the classes are recorded.
classesToIgnore
Which classes not to include into result.
classesMap
Mapping for merging/renaming classes.
parallel
Whenever to train classes in parallel.
cacheForks
Whenever to cache forks before iterating
returns
Linear combination model (given an instance coputes prediction of all the models and combines them linary with configured weights).
def multiClassReader[M <: ModelWithSummary[M]](nestedReader: WithSummaryReader[M]): WithSummaryReader[LinearCombinationModel[M]]

returns
Reader for multi class combination model.
final def ne(arg0: AnyRef): Boolean

Definition Classes
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final def notify(): Unit

Definition Classes
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final def notifyAll(): Unit

Definition Classes
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def perType[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], typeColumn: String = "type", parallel: Boolean = false, cacheForks: Boolean = false): PerTypeModelLearner[M]

Train dedicated model for a certain type.
Train dedicated model for a certain type. Result is a selector model. Summary blocks for combined model are merged from the nested model with extra type column.
M
Type of the model to train.
estimator
Estimator for training the nested models.
typeColumn
Column where the type label stored.
parallel
Whenever to train types in parallel.
cacheForks
Whenever to cache forks before iterating
returns
Selector model (given instance type applies ONE of the models)
def perTypeReader[M <: ModelWithSummary[M]](nestedReader: WithSummaryReader[M]): WithSummaryReader[SelectingModel[M]]

returns
Reader for type selecting model.
def read: MLReader[PipelineStage]

Definition Classes
CombinedModel → MLReadable
Annotations
@Since( "1.6.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
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final def wait(): Unit

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

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

Definition Classes
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@throws( ... )

Related Docs: class CombinedModel | package odkl

object CombinedModel extends MLReadable[PipelineStage] with Serializable

Type Members

trait DirectPredictionModel[I, M <: DirectPredictionModel[I, M]] extends PredictionModel[I, M] with HasDirectTransformOption

class LinearCombinationModelLearner[M <: ModelWithSummary[M]] extends PerClassModelLearner[M, LinearCombinationModel[M]]

class MultiClassModelLearner[M <: ModelWithSummary[M]] extends PerClassModelLearner[M, MultiClassCombinationModel[M]]

abstract class PerClassModelLearner[N <: ModelWithSummary[N], M <: MultiClassCombinationModelBase[N, M]] extends ForkedEstimator[N, String, M] with HasClassesCol with HasClassesWeights with HasLabelCol with HasPredictionCol

class PerTypeModelLearner[M <: ModelWithSummary[M]] extends ForkedEstimator[M, String, SelectingModel[M]] with HasTypeCol with HasPredictionCol

class Reader[M <: ModelWithSummary[M], C <: CombinedModel[M, C]] extends WithSummaryReader[C]

class ReaderUntyped extends WithSummaryReaderUntyped

class Writer[M <: ModelWithSummary[M], C <: CombinedModel[M, C]] extends WithSummaryWriter[C]

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def linearCombination[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], classesColumn: String = "classes", classesToIgnore: Seq[String] = Seq(), classesMap: Map[String, String] = Map(), parallel: Boolean = false, cacheForks: Boolean = false): LinearCombinationModelLearner[M]

def linearCombinationReader[M <: ModelWithSummary[M]](nestedReader: WithSummaryReader[M]): WithSummaryReader[LinearCombinationModel[M]]

def load(path: String): PipelineStage

def multiClass[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], classesColumn: String = "classes", classesToIgnore: Seq[String] = Seq(), classesMap: Map[String, String] = Map(), parallel: Boolean = false, cacheForks: Boolean = false): MultiClassModelLearner[M]

def multiClassReader[M <: ModelWithSummary[M]](nestedReader: WithSummaryReader[M]): WithSummaryReader[LinearCombinationModel[M]]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def perType[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], typeColumn: String = "type", parallel: Boolean = false, cacheForks: Boolean = false): PerTypeModelLearner[M]

def perTypeReader[M <: ModelWithSummary[M]](nestedReader: WithSummaryReader[M]): WithSummaryReader[SelectingModel[M]]

def read: MLReader[PipelineStage]

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

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from MLReadable[PipelineStage]

Inherited from AnyRef

Inherited from Any

Ungrouped