Class/Object

org.apache.spark.ml

Pipeline

Related Docs: object Pipeline | package ml

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class Pipeline extends Estimator[PipelineModel] with MLWritable

A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each of which is either an Estimator or a Transformer. When Pipeline.fit is called, the stages are executed in order. If a stage is an Estimator, its Estimator.fit method will be called on the input dataset to fit a model. Then the model, which is a transformer, will be used to transform the dataset as the input to the next stage. If a stage is a Transformer, its Transformer.transform method will be called to produce the dataset for the next stage. The fitted model from a Pipeline is a PipelineModel, which consists of fitted models and transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as an identity transformer.

Annotations
@Since( "1.2.0" )
Linear Supertypes
MLWritable, Estimator[PipelineModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
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  2. Alphabetic
  3. By Inheritance
Inherited
  1. Pipeline
  2. MLWritable
  3. Estimator
  4. PipelineStage
  5. Logging
  6. Params
  7. Serializable
  8. Serializable
  9. Identifiable
  10. AnyRef
  11. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new Pipeline()

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    Annotations
    @Since( "1.4.0" )
  2. new Pipeline(uid: String)

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    Annotations
    @Since( "1.4.0" )

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
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  2. final def ##(): Int

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    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. final def clear(param: Param[_]): Pipeline.this.type

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    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  7. def clone(): AnyRef

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    protected[java.lang]
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    AnyRef
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    @throws( ... )
  8. def copy(extra: ParamMap): Pipeline

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    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
    PipelineEstimatorPipelineStageParams
    Annotations
    @Since( "1.4.0" )
  9. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    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

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    protected
    Definition Classes
    Params
  10. final def defaultCopy[T <: Params](extra: ParamMap): T

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    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
  11. final def eq(arg0: AnyRef): Boolean

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  12. def equals(arg0: Any): Boolean

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  13. def explainParam(param: Param[_]): String

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    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
  14. def explainParams(): String

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    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  15. final def extractParamMap(): ParamMap

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  16. final def extractParamMap(extra: ParamMap): ParamMap

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    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
  17. def finalize(): Unit

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    protected[java.lang]
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    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  18. def fit(dataset: Dataset[_]): PipelineModel

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    Fits the pipeline to the input dataset with additional parameters.

    Fits the pipeline to the input dataset with additional parameters. If a stage is an Estimator, its Estimator.fit method will be called on the input dataset to fit a model. Then the model, which is a transformer, will be used to transform the dataset as the input to the next stage. If a stage is a Transformer, its Transformer.transform method will be called to produce the dataset for the next stage. The fitted model from a Pipeline is an PipelineModel, which consists of fitted models and transformers, corresponding to the pipeline stages. If there are no stages, the output model acts as an identity transformer.

    dataset

    input dataset

    returns

    fitted pipeline

    Definition Classes
    PipelineEstimator
    Annotations
    @Since( "2.0.0" )
  19. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[PipelineModel]

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    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  20. def fit(dataset: Dataset[_], paramMap: ParamMap): PipelineModel

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    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  21. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): PipelineModel

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    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  22. final def get[T](param: Param[T]): Option[T]

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    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

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  23. final def getClass(): Class[_]

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  24. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  25. final def getOrDefault[T](param: Param[T]): T

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    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
  26. def getParam(paramName: String): Param[Any]

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  27. def getStages: Array[PipelineStage]

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    Annotations
    @Since( "1.2.0" )
  28. final def hasDefault[T](param: Param[T]): Boolean

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  29. def hasParam(paramName: String): Boolean

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    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
  30. def hashCode(): Int

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  31. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Attributes
    protected
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    Logging
  32. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Logging
  33. final def isDefined(param: Param[_]): Boolean

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    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

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    Params
  34. final def isInstanceOf[T0]: Boolean

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    Any
  35. final def isSet(param: Param[_]): Boolean

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  36. def isTraceEnabled(): Boolean

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    Logging
  37. def log: Logger

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    Logging
  38. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  39. def logDebug(msg: ⇒ String): Unit

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    Logging
  40. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  41. def logError(msg: ⇒ String): Unit

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    Logging
  42. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  43. def logInfo(msg: ⇒ String): Unit

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    Logging
  44. def logName: String

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    Logging
  45. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  46. def logTrace(msg: ⇒ String): Unit

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    Logging
  47. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  48. def logWarning(msg: ⇒ String): Unit

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    Logging
  49. final def ne(arg0: AnyRef): Boolean

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  50. final def notify(): Unit

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  51. final def notifyAll(): Unit

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  52. lazy val params: Array[Param[_]]

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    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.

  53. def save(path: String): Unit

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    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( ... )
  54. final def set(paramPair: ParamPair[_]): Pipeline.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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  55. final def set(param: String, value: Any): Pipeline.this.type

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    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

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    Params
  56. final def set[T](param: Param[T], value: T): Pipeline.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  57. final def setDefault(paramPairs: ParamPair[_]*): Pipeline.this.type

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    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
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    Definition Classes
    Params
  58. final def setDefault[T](param: Param[T], value: T): Pipeline.this.type

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    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
  59. def setStages(value: Array[_ <: PipelineStage]): Pipeline.this.type

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    @Since( "1.2.0" )
  60. val stages: Param[Array[PipelineStage]]

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    param for pipeline stages

    param for pipeline stages

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    @Since( "1.2.0" )
  61. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  62. def toString(): String

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    Identifiable → AnyRef → Any
  63. def transformSchema(schema: StructType): StructType

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    :: DeveloperApi ::

    :: DeveloperApi ::

    Check 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.

    Definition Classes
    PipelinePipelineStage
    Annotations
    @Since( "1.2.0" )
  64. def transformSchema(schema: StructType, logging: Boolean): StructType

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    :: 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.

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    PipelineStage
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    @DeveloperApi()
  65. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    PipelineIdentifiable
    Annotations
    @Since( "1.4.0" )
  66. final def wait(): Unit

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    @throws( ... )
  67. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  68. final def wait(arg0: Long): Unit

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    @throws( ... )
  69. def write: MLWriter

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    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    PipelineMLWritable
    Annotations
    @Since( "1.6.0" )

Inherited from MLWritable

Inherited from Estimator[PipelineModel]

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