org.apache.spark.ml.classification

Class OneVsRest

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.classification.OneVsRest

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, ClassifierParams, ClassifierTypeTrait, OneVsRestParams, Params, HasFeaturesCol, HasLabelCol, HasParallelism, HasPredictionCol, HasRawPredictionCol, HasWeightCol, PredictorParams, Identifiable, MLWritable

public final class OneVsRest
extends Estimator<OneVsRestModel>
implements OneVsRestParams, HasParallelism, MLWritable

Reduction of Multiclass Classification to Binary Classification. Performs reduction using one against all strategy. For a multiclass classification with k classes, train k models (one per class). Each example is scored against all k models and the model with highest score is picked to label the example.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
OneVsRest()
OneVsRest(String uid)

Method Summary

Modifier and Type	Method and Description
Param<Classifier<?,? extends Classifier<Object,Classifier,ClassificationModel>,? extends ClassificationModel<Object,ClassificationModel>>>	classifier() param for the base binary classifier that we reduce multiclass classification into.
OneVsRest	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<String>	featuresCol() Param for features column name.
OneVsRestModel	fit(Dataset<?> dataset) Fits a model to the input data.
Param<String>	labelCol() Param for label column name.
static OneVsRest	load(String path)
IntParam	parallelism() The number of threads to use when running parallel algorithms.
Param<String>	predictionCol() Param for prediction column name.
Param<String>	rawPredictionCol() Param for raw prediction (a.k.a.
static MLReader<OneVsRest>	read()
OneVsRest	setClassifier(Classifier<?,?,?> value)
OneVsRest	setFeaturesCol(String value)
OneVsRest	setLabelCol(String value)
OneVsRest	setParallelism(int value) The implementation of parallel one vs.
OneVsRest	setPredictionCol(String value)
OneVsRest	setRawPredictionCol(String value)
OneVsRest	setWeightCol(String value) Sets the value of param weightCol.
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.
String	uid() An immutable unique ID for the object and its derivatives.
Param<String>	weightCol() Param for weight column name.
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 org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.ml.classification.OneVsRestParams

getClassifier

Methods inherited from interface org.apache.spark.ml.classification.ClassifierParams

extractInstances, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.PredictorParams

extractInstances, extractInstances

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol

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, onParamChange, 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.ml.param.shared.HasRawPredictionCol

getRawPredictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasParallelism

getExecutionContext, getParallelism

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

save

Methods inherited from interface org.apache.spark.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Constructor Detail

OneVsRest

public OneVsRest(String uid)

OneVsRest

public OneVsRest()

Method Detail

read

public static MLReader<OneVsRest> read()

load

public static OneVsRest load(String path)

parallelism

public IntParam parallelism()

Description copied from interface: HasParallelism

The number of threads to use when running parallel algorithms. Default is 1 for serial execution

Specified by:

parallelism in interface HasParallelism

Returns:

(undocumented)

classifier

public Param<Classifier<?,? extends Classifier<Object,Classifier,ClassificationModel>,? extends ClassificationModel<Object,ClassificationModel>>> classifier()

Description copied from interface: OneVsRestParams

param for the base binary classifier that we reduce multiclass classification into. The base classifier input and output columns are ignored in favor of the ones specified in OneVsRest.

Specified by:

classifier in interface OneVsRestParams

Returns:

(undocumented)

weightCol

public final Param<String> weightCol()

Description copied from interface: HasWeightCol

Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

Specified by:

weightCol in interface HasWeightCol

Returns:

(undocumented)

rawPredictionCol

public final Param<String> rawPredictionCol()

Description copied from interface: HasRawPredictionCol

Param for raw prediction (a.k.a. confidence) column name.

Specified by:

rawPredictionCol in interface HasRawPredictionCol

Returns:

(undocumented)

predictionCol

public final Param<String> predictionCol()

Description copied from interface: HasPredictionCol

Param for prediction column name.

Specified by:

predictionCol in interface HasPredictionCol

Returns:

(undocumented)

featuresCol

public final Param<String> featuresCol()

Description copied from interface: HasFeaturesCol

Param for features column name.

Specified by:

featuresCol in interface HasFeaturesCol

Returns:

(undocumented)

labelCol

public final Param<String> labelCol()

Description copied from interface: HasLabelCol

Param for label column name.

Specified by:

labelCol in interface HasLabelCol

Returns:

(undocumented)

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)

setClassifier

public OneVsRest setClassifier(Classifier<?,?,?> value)

setLabelCol

public OneVsRest setLabelCol(String value)

setFeaturesCol

public OneVsRest setFeaturesCol(String value)

setPredictionCol

public OneVsRest setPredictionCol(String value)

setRawPredictionCol

public OneVsRest setRawPredictionCol(String value)

setParallelism

public OneVsRest setParallelism(int value)

The implementation of parallel one vs. rest runs the classification for each class in a separate threads.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setWeightCol

public OneVsRest setWeightCol(String value)

Sets the value of param weightCol.

This is ignored if weight is not supported by classifier. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

Parameters:

value - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

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)

fit

public OneVsRestModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<OneVsRestModel>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

copy

public OneVsRest 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<OneVsRestModel>

Parameters:

extra - (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)