Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    Definition Classes
    spark
  • package attribute

    The ML pipeline API uses DataFrames as ML datasets.

    ML attributes

    The ML pipeline API uses DataFrames as ML datasets. Each dataset consists of typed columns, e.g., string, double, vector, etc. However, knowing only the column type may not be sufficient to handle the data properly. For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices, which cannot be treated as numeric values in ML algorithms, and, for another instance, we may want to know the names and types of features stored in a vector column. ML attributes are used to provide additional information to describe columns in a dataset.

    ML columns

    A column with ML attributes attached is called an ML column. The data in ML columns are stored as double values, i.e., an ML column is either a scalar column of double values or a vector column. Columns of other types must be encoded into ML columns using transformers. We use Attribute to describe a scalar ML column, and AttributeGroup to describe a vector ML column. ML attributes are stored in the metadata field of the column schema.

    Definition Classes
    ml
  • package classification
    Definition Classes
    ml
  • package clustering
    Definition Classes
    ml
  • package evaluation
    Definition Classes
    ml
  • package feature

    The ml.feature package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting.

    Feature transformers

    The ml.feature package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting. Most feature transformers are implemented as Transformers, which transform one DataFrame into another, e.g., HashingTF. Some feature transformers are implemented as Estimators, because the transformation requires some aggregated information of the dataset, e.g., document frequencies in IDF. For those feature transformers, calling Estimator.fit is required to obtain the model first, e.g., IDFModel, in order to apply transformation. The transformation is usually done by appending new columns to the input DataFrame, so all input columns are carried over.

    We try to make each transformer minimal, so it becomes flexible to assemble feature transformation pipelines. Pipeline can be used to chain feature transformers, and VectorAssembler can be used to combine multiple feature transformations, for example:

    import org.apache.spark.ml.feature._
import org.apache.spark.ml.Pipeline

// a DataFrame with three columns: id (integer), text (string), and rating (double).
val df = spark.createDataFrame(Seq(
  (0, "Hi I heard about Spark", 3.0),
  (1, "I wish Java could use case classes", 4.0),
  (2, "Logistic regression models are neat", 4.0)
)).toDF("id", "text", "rating")

// define feature transformers
val tok = new RegexTokenizer()
  .setInputCol("text")
  .setOutputCol("words")
val sw = new StopWordsRemover()
  .setInputCol("words")
  .setOutputCol("filtered_words")
val tf = new HashingTF()
  .setInputCol("filtered_words")
  .setOutputCol("tf")
  .setNumFeatures(10000)
val idf = new IDF()
  .setInputCol("tf")
  .setOutputCol("tf_idf")
val assembler = new VectorAssembler()
  .setInputCols(Array("tf_idf", "rating"))
  .setOutputCol("features")

// assemble and fit the feature transformation pipeline
val pipeline = new Pipeline()
  .setStages(Array(tok, sw, tf, idf, assembler))
val model = pipeline.fit(df)

// save transformed features with raw data
model.transform(df)
  .select("id", "text", "rating", "features")
  .write.format("parquet").save("/output/path")

    Some feature transformers implemented in MLlib are inspired by those implemented in scikit-learn. The major difference is that most scikit-learn feature transformers operate eagerly on the entire input dataset, while MLlib's feature transformers operate lazily on individual columns, which is more efficient and flexible to handle large and complex datasets.

    Definition Classes
    ml
    See also

    scikit-learn.preprocessing

  • package fpm
    Definition Classes
    ml
  • package image
    Definition Classes
    ml
  • package linalg
    Definition Classes
    ml
  • package param
    Definition Classes
    ml
  • package recommendation
    Definition Classes
    ml
  • package regression
    Definition Classes
    ml
  • package source
    Definition Classes
    ml
  • package stat
    Definition Classes
    ml
  • package tree
    Definition Classes
    ml
  • package tuning
    Definition Classes
    ml
  • package util
    Definition Classes
    ml
  • Estimator
  • FitEnd
  • FitStart
  • LoadInstanceEnd
  • LoadInstanceStart
  • MLEvent
  • Model
  • Pipeline
  • PipelineModel
  • PipelineStage
  • PredictionModel
  • Predictor
  • SaveInstanceEnd
  • SaveInstanceStart
  • TransformEnd
  • TransformStart
  • Transformer
  • UnaryTransformer
  • functions

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" )
Source
Pipeline.scala
Linear Supertypes
MLWritable, Estimator[PipelineModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  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
  1. Hide All
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Visibility
  1. Public
  2. All

Instance Constructors

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

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final def clear(param: Param[_]): Pipeline.this.type

    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
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. def copy(extra: ParamMap): Pipeline

    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
    Pipeline → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.4.0" )
  9. 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
  10. 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
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. 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
  14. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  16. 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
  17. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  18. def fit(dataset: Dataset[_]): PipelineModel

    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
    Pipeline → Estimator
    Annotations
    @Since( "2.0.0" )
  19. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[PipelineModel]

    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

    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

    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]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  23. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  24. 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
  25. 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
  26. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

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

    Annotations
    @Since( "1.2.0" )
  28. 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
  29. 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
  30. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  31. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  32. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  33. 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
  34. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  35. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  36. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  37. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  38. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  39. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  40. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  41. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  42. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  43. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  44. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  45. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  46. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  47. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  48. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  49. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  50. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  51. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  52. 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.

  53. 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( ... )
  54. final def set(paramPair: ParamPair[_]): Pipeline.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  55. final def set(param: String, value: Any): Pipeline.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
  56. final def set[T](param: Param[T], value: T): Pipeline.this.type

    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

    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
  58. final def setDefault[T](param: Param[T], value: T): Pipeline.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
  59. def setStages(value: Array[_ <: PipelineStage]): Pipeline.this.type

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

    param for pipeline stages

    param for pipeline stages

    Annotations
    @Since( "1.2.0" )
  61. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  62. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  63. 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 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
    Pipeline → PipelineStage
    Annotations
    @Since( "1.2.0" )
  64. 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()
  65. 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
    Pipeline → Identifiable
    Annotations
    @Since( "1.4.0" )
  66. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  67. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  68. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  69. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    Pipeline → MLWritable
    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