org.apache.spark.ml.classification

Class LinearSVC

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.Predictor<FeaturesType,E,M>

org.apache.spark.ml.classification.Classifier<Vector,LinearSVC,LinearSVCModel>

org.apache.spark.ml.classification.LinearSVC

All Implemented Interfaces:

java.io.Serializable, Logging, Params, DefaultParamsWritable, Identifiable, MLWritable

public class LinearSVC
extends Classifier<Vector,LinearSVC,LinearSVCModel>
implements DefaultParamsWritable

:: Experimental ::

Linear SVM Classifier

This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LinearSVC()
LinearSVC(String uid)

Method Summary

Modifier and Type	Method and Description
static IntParam	aggregationDepth()
static Params	clear(Param<?> param)
LinearSVC	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)
static Param<String>	featuresCol()
Param<String>	featuresCol() Param for features column name.
static M	fit(Dataset<?> dataset)
static M	fit(Dataset<?> dataset, ParamMap paramMap)
static scala.collection.Seq<M>	fit(Dataset<?> dataset, ParamMap[] paramMaps)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
static BooleanParam	fitIntercept()
static <T> scala.Option<T>	get(Param<T> param)
static int	getAggregationDepth()
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getFeaturesCol()
String	getFeaturesCol()
static boolean	getFitIntercept()
static String	getLabelCol()
String	getLabelCol()
static int	getMaxIter()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
String	getPredictionCol()
static String	getRawPredictionCol()
String	getRawPredictionCol()
static double	getRegParam()
static boolean	getStandardization()
static double	getThreshold()
double	getThreshold()
static double	getTol()
static String	getWeightCol()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Param<String>	labelCol()
Param<String>	labelCol() Param for label column name.
static LinearSVC	load(String path)
static IntParam	maxIter()
static Param<?>[]	params()
static Param<String>	predictionCol()
Param<String>	predictionCol() Param for prediction column name.
static Param<String>	rawPredictionCol()
Param<String>	rawPredictionCol() Param for raw prediction (a.k.a.
static DoubleParam	regParam()
static void	save(String path)
static <T> Params	set(Param<T> param, T value)
LinearSVC	setAggregationDepth(int value) Suggested depth for treeAggregate (greater than or equal to 2).
static Learner	setFeaturesCol(String value)
LinearSVC	setFitIntercept(boolean value) Whether to fit an intercept term.
static Learner	setLabelCol(String value)
LinearSVC	setMaxIter(int value) Set the maximum number of iterations.
static Learner	setPredictionCol(String value)
static E	setRawPredictionCol(String value)
LinearSVC	setRegParam(double value) Set the regularization parameter.
LinearSVC	setStandardization(boolean value) Whether to standardize the training features before fitting the model.
LinearSVC	setThreshold(double value) Set threshold in binary classification.
LinearSVC	setTol(double value) Set the convergence tolerance of iterations.
LinearSVC	setWeightCol(String value) Set the value of param weightCol.
static BooleanParam	standardization()
static DoubleParam	threshold()
DoubleParam	threshold() Param for threshold in binary classification prediction.
static DoubleParam	tol()
static String	toString()
static StructType	transformSchema(StructType schema)
String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.
static Param<String>	weightCol()
static MLWriter	write()

Methods inherited from class org.apache.spark.ml.classification.Classifier

setRawPredictionCol

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

fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

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.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.ml.util.DefaultParamsWritable

write

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

save

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

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

Constructor Detail

LinearSVC

public LinearSVC(String uid)

LinearSVC

public LinearSVC()

Method Detail

load

public static LinearSVC 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()

fit

public static M fit(Dataset<?> dataset,
                    ParamPair<?> firstParamPair,
                    scala.collection.Seq<ParamPair<?>> otherParamPairs)

fit

public static M fit(Dataset<?> dataset,
                    ParamMap paramMap)

fit

public static scala.collection.Seq<M> fit(Dataset<?> dataset,
                                          ParamMap[] paramMaps)

fit

public static M fit(Dataset<?> dataset,
                    ParamPair<?> firstParamPair,
                    ParamPair<?>... otherParamPairs)

labelCol

public static final Param<String> labelCol()

getLabelCol

public static final String getLabelCol()

featuresCol

public static final Param<String> featuresCol()

getFeaturesCol

public static final String getFeaturesCol()

predictionCol

public static final Param<String> predictionCol()

getPredictionCol

public static final String getPredictionCol()

setLabelCol

public static Learner setLabelCol(String value)

setFeaturesCol

public static Learner setFeaturesCol(String value)

setPredictionCol

public static Learner setPredictionCol(String value)

fit

public static M fit(Dataset<?> dataset)

transformSchema

public static StructType transformSchema(StructType schema)

rawPredictionCol

public static final Param<String> rawPredictionCol()

getRawPredictionCol

public static final String getRawPredictionCol()

setRawPredictionCol

public static E setRawPredictionCol(String value)

regParam

public static final DoubleParam regParam()

getRegParam

public static final double getRegParam()

maxIter

public static final IntParam maxIter()

getMaxIter

public static final int getMaxIter()

fitIntercept

public static final BooleanParam fitIntercept()

getFitIntercept

public static final boolean getFitIntercept()

tol

public static final DoubleParam tol()

getTol

public static final double getTol()

standardization

public static final BooleanParam standardization()

getStandardization

public static final boolean getStandardization()

weightCol

public static final Param<String> weightCol()

getWeightCol

public static final String getWeightCol()

aggregationDepth

public static final IntParam aggregationDepth()

getAggregationDepth

public static final int getAggregationDepth()

threshold

public static final DoubleParam threshold()

getThreshold

public static double getThreshold()

save

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

Throws:

java.io.IOException

write

public static MLWriter write()

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)

setRegParam

public LinearSVC setRegParam(double value)

Set the regularization parameter. Default is 0.0.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMaxIter

public LinearSVC setMaxIter(int value)

Set the maximum number of iterations. Default is 100.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setFitIntercept

public LinearSVC setFitIntercept(boolean value)

Whether to fit an intercept term. Default is true.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setTol

public LinearSVC setTol(double value)

Set the convergence tolerance of iterations. Smaller values will lead to higher accuracy at the cost of more iterations. Default is 1E-6.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setStandardization

public LinearSVC setStandardization(boolean value)

Whether to standardize the training features before fitting the model. Default is true.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setWeightCol

public LinearSVC setWeightCol(String value)

Set the value of param weightCol. 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)

setThreshold

public LinearSVC setThreshold(double value)

Set threshold in binary classification.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setAggregationDepth

public LinearSVC setAggregationDepth(int value)

Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.

Parameters:

value - (undocumented)

Returns:

(undocumented)

copy

public LinearSVC 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 Predictor<Vector,LinearSVC,LinearSVCModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

threshold

public DoubleParam threshold()

Param for threshold in binary classification prediction. For LinearSVC, this threshold is applied to the rawPrediction, rather than a probability. This threshold can be any real number, where Inf will make all predictions 0.0 and -Inf will make all predictions 1.0. Default: 0.0

Returns:

(undocumented)

getThreshold

public double getThreshold()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                             boolean fitting,
                                             DataType featuresDataType)

rawPredictionCol

public Param<String> rawPredictionCol()

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

Returns:

(undocumented)

getRawPredictionCol

public String getRawPredictionCol()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                             boolean fitting,
                                             DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema

labelCol

public Param<String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public String getLabelCol()

featuresCol

public Param<String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public String getFeaturesCol()

predictionCol

public Param<String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public String getPredictionCol()