org.apache.spark.ml.classification

Class LinearSVCModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.PredictionModel<FeaturesType,M>

org.apache.spark.ml.classification.ClassificationModel<Vector,LinearSVCModel>

org.apache.spark.ml.classification.LinearSVCModel

All Implemented Interfaces:

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

public class LinearSVCModel
extends ClassificationModel<Vector,LinearSVCModel>
implements MLWritable

:: Experimental :: Linear SVM Model trained by LinearSVC

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
static IntParam	aggregationDepth()
static Params	clear(Param<?> param)
Vector	coefficients()
LinearSVCModel	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()
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()
static boolean	getFitIntercept()
static String	getLabelCol()
static int	getMaxIter()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
static String	getRawPredictionCol()
static double	getRegParam()
static boolean	getStandardization()
static double	getThreshold()
static double	getTol()
static String	getWeightCol()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	hasParent()
double	intercept()
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Param<String>	labelCol()
static LinearSVCModel	load(String path)
static IntParam	maxIter()
int	numClasses() Number of classes (values which the label can take).
int	numFeatures() Returns the number of features the model was trained on.
static Param<?>[]	params()
static void	parent_$eq(Estimator<M> x$1)
static Estimator<M>	parent()
double	predict(Vector features) Predict label for the given features.
static Param<String>	predictionCol()
static Param<String>	rawPredictionCol()
static MLReader<LinearSVCModel>	read()
static DoubleParam	regParam()
static void	save(String path)
static <T> Params	set(Param<T> param, T value)
static M	setFeaturesCol(String value)
static M	setParent(Estimator<M> parent)
static M	setPredictionCol(String value)
static M	setRawPredictionCol(String value)
LinearSVCModel	setThreshold(double value)
LinearSVCModel	setWeightCol(double value)
static BooleanParam	standardization()
static DoubleParam	threshold()
DoubleParam	threshold() Param for threshold in binary classification prediction.
static DoubleParam	tol()
static String	toString()
static Dataset<Row>	transform(Dataset<?> dataset)
static Dataset<Row>	transform(Dataset<?> dataset, ParamMap paramMap)
static Dataset<Row>	transform(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
static Dataset<Row>	transform(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
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()
MLWriter	write() Returns an MLWriter instance for this ML instance.

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

setRawPredictionCol, transform

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

setFeaturesCol, setPredictionCol, transformSchema

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

hasParent, parent, setParent

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

transform, transform, transform

Methods inherited from class Object

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

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

getRawPredictionCol, rawPredictionCol

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.param.shared.HasRegParam

getRegParam, regParam

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

getMaxIter, maxIter

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

fitIntercept, getFitIntercept

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

getTol, tol

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

getStandardization, standardization

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

getWeightCol, weightCol

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

aggregationDepth, getAggregationDepth

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

getThreshold

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

save

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

getLabelCol, labelCol

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

featuresCol, getFeaturesCol

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

getPredictionCol, predictionCol

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

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

Method Detail

read

public static MLReader<LinearSVCModel> read()

load

public static LinearSVCModel 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()

transform

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

transform

public static Dataset<Row> transform(Dataset<?> dataset,
                                     ParamMap paramMap)

transform

public static Dataset<Row> transform(Dataset<?> dataset,
                                     ParamPair<?> firstParamPair,
                                     ParamPair<?>... otherParamPairs)

parent

public static Estimator<M> parent()

parent_$eq

public static void parent_$eq(Estimator<M> x$1)

setParent

public static M setParent(Estimator<M> parent)

hasParent

public static boolean hasParent()

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()

setFeaturesCol

public static M setFeaturesCol(String value)

setPredictionCol

public static M setPredictionCol(String value)

transformSchema

public static StructType transformSchema(StructType schema)

rawPredictionCol

public static final Param<String> rawPredictionCol()

getRawPredictionCol

public static final String getRawPredictionCol()

setRawPredictionCol

public static M setRawPredictionCol(String value)

transform

public static Dataset<Row> transform(Dataset<?> dataset)

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()

getThreshold

public static double getThreshold()

threshold

public static final DoubleParam threshold()

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)

coefficients

public Vector coefficients()

intercept

public double intercept()

numClasses

public int numClasses()

Description copied from class: ClassificationModel

Number of classes (values which the label can take).

Specified by:

numClasses in class ClassificationModel<Vector,LinearSVCModel>

numFeatures

public int numFeatures()

Description copied from class: PredictionModel

Returns the number of features the model was trained on. If unknown, returns -1

Overrides:

numFeatures in class PredictionModel<Vector,LinearSVCModel>

setThreshold

public LinearSVCModel setThreshold(double value)

setWeightCol

public LinearSVCModel setWeightCol(double value)

predict

public double predict(Vector features)

Description copied from class: ClassificationModel

Predict label for the given features. This method is used to implement transform() and output predictionCol.

This default implementation for classification predicts the index of the maximum value from predictRaw().

Overrides:

predict in class ClassificationModel<Vector,LinearSVCModel>

Parameters:

features - (undocumented)

Returns:

(undocumented)

copy

public LinearSVCModel 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 Model<LinearSVCModel>

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)

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

Specified by:

threshold in interface HasThreshold

Returns:

(undocumented)

validateAndTransformSchema

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

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