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, Logging, Params, Identifiable, MLWritable

public class Pipeline
extends Estimator<PipelineModel>
implements MLWritable

:: Experimental :: 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.DataFrame) is called, the stages are executed in order. If a stage is an Estimator, its Estimator.fit(org.apache.spark.sql.DataFrame, 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.DataFrame, 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 pipeline acts as an identity transformer.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Pipeline()
Pipeline(String uid)

Method Summary

Methods

Modifier and Type	Method and Description
Pipeline	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
PipelineModel	fit(DataFrame dataset) Fits the pipeline to the input dataset with additional parameters.
PipelineStage[]	getStages()
static Pipeline	load(String path)
static MLReader<Pipeline>	read()
Pipeline	setStages(PipelineStage[] value)
Param<PipelineStage[]>	stages() param for pipeline stages
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.
void	validateParams()
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

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

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)

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()

validateParams

public void validateParams()

Specified by:

validateParams in interface Params

fit

public PipelineModel fit(DataFrame dataset)

Fits the pipeline to the input dataset with additional parameters. If a stage is an Estimator, its Estimator.fit(org.apache.spark.sql.DataFrame, 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.DataFrame, 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.

Specified by:

copy in interface Params

Specified by:

copy in class Estimator<PipelineModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

Derives the output schema from the input schema.

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)