org.apache.spark.ml

Class Pipeline

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<PipelineModel>

org.apache.spark.ml.Pipeline

All Implemented Interfaces:

java.io.Serializable, Params, Identifiable, MLWritable

public class Pipeline
extends Estimator<PipelineModel>
implements 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 fit(org.apache.spark.sql.Dataset<?>) is called, the stages are executed in order. If a stage is an Estimator, its Estimator.fit(org.apache.spark.sql.Dataset<?>, org.apache.spark.ml.param.ParamPair<?>, org.apache.spark.ml.param.ParamPair<?>...) 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(org.apache.spark.sql.Dataset<?>, org.apache.spark.ml.param.ParamPair<?>, org.apache.spark.ml.param.ParamPair<?>...) 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.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Pipeline.SharedReadWrite$ Methods for MLReader and MLWriter shared between Pipeline and PipelineModel

Constructor Summary

Constructors

Constructor and Description
Pipeline()
Pipeline(String uid)

Method Summary

Methods

Modifier and Type	Method and Description
static Params	clear(Param<?> param)
Pipeline	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
PipelineModel	fit(Dataset<?> dataset) Fits the pipeline to the input dataset with additional parameters.
static <T> scala.Option<T>	get(Param<T> param)
static <T> scala.Option<T>	getDefault(Param<T> param)
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
PipelineStage[]	getStages()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Pipeline	load(String path)
static Param<?>[]	params()
static MLReader<Pipeline>	read()
static void	save(String path)
static <T> Params	set(Param<T> param, T value)
Pipeline	setStages(PipelineStage[] value)
Param<PipelineStage[]>	stages() param for pipeline stages
static String	toString()
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.
MLWriter	write() Returns an MLWriter instance for this ML instance.

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Constructor Detail

Pipeline

public Pipeline(String uid)

Pipeline

public Pipeline()

Method Detail

read

public static MLReader<Pipeline> read()

load

public static Pipeline load(String path)

toString

public static String toString()

params

public static Param<?>[] params()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
             T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

save

public static void save(String path)
                 throws java.io.IOException

Throws:

java.io.IOException

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

stages

public Param<PipelineStage[]> stages()

param for pipeline stages

Returns:

(undocumented)

setStages

public Pipeline setStages(PipelineStage[] value)

getStages

public PipelineStage[] getStages()

fit

public PipelineModel fit(Dataset<?> dataset)

Fits the pipeline to the input dataset with additional parameters. If a stage is an Estimator, its Estimator.fit(org.apache.spark.sql.Dataset<?>, org.apache.spark.ml.param.ParamPair<?>, org.apache.spark.ml.param.ParamPair<?>...) 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(org.apache.spark.sql.Dataset<?>, org.apache.spark.ml.param.ParamPair<?>, org.apache.spark.ml.param.ParamPair<?>...) 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.

Specified by:

fit in class Estimator<PipelineModel>

Parameters:

dataset - input dataset

Returns:

fitted pipeline

copy

public Pipeline copy(ParamMap extra)

Description copied from interface: 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().

Specified by:

copy in interface Params

Specified by:

copy in class Estimator<PipelineModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: 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.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)