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, org.apache.spark.internal.Logging, ClassifierParams, LinearSVCParams, Params, HasAggregationDepth, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasMaxBlockSizeInMB, HasMaxIter, HasPredictionCol, HasRawPredictionCol, HasRegParam, HasStandardization, HasThreshold, HasTol, HasWeightCol, PredictorParams, HasTrainingSummary<LinearSVCTrainingSummary>, Identifiable, MLWritable

public class LinearSVCModel
extends ClassificationModel<Vector,LinearSVCModel>
implements LinearSVCParams, MLWritable, HasTrainingSummary<LinearSVCTrainingSummary>

Linear SVM Model trained by LinearSVC

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

Modifier and Type	Method and Description
IntParam	aggregationDepth() Param for suggested depth for treeAggregate (>= 2).
Vector	coefficients()
LinearSVCModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
LinearSVCSummary	evaluate(Dataset<?> dataset) Evaluates the model on a test dataset.
BooleanParam	fitIntercept() Param for whether to fit an intercept term.
double	intercept()
static LinearSVCModel	load(String path)
DoubleParam	maxBlockSizeInMB() Param for Maximum memory in MB for stacking input data into blocks.
IntParam	maxIter() Param for maximum number of iterations (&gt;= 0).
int	numClasses() Number of classes (values which the label can take).
int	numFeatures() Returns the number of features the model was trained on.
double	predict(Vector features) Predict label for the given features.
Vector	predictRaw(Vector features) Raw prediction for each possible label.
static MLReader<LinearSVCModel>	read()
DoubleParam	regParam() Param for regularization parameter (&gt;= 0).
LinearSVCModel	setThreshold(double value)
BooleanParam	standardization() Param for whether to standardize the training features before fitting the model.
LinearSVCTrainingSummary	summary() Gets summary of model on training set.
DoubleParam	threshold() Param for threshold in binary classification prediction.
DoubleParam	tol() Param for the convergence tolerance for iterative algorithms (&gt;= 0).
String	toString()
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.classification.ClassificationModel

rawPredictionCol, setRawPredictionCol, transform, transformImpl, transformSchema

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

featuresCol, labelCol, predictionCol, setFeaturesCol, setPredictionCol

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 org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

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

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

validateAndTransformSchema

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.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.param.shared.HasRawPredictionCol

getRawPredictionCol, rawPredictionCol

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

getRegParam

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

getMaxIter

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

getFitIntercept

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

getTol

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

getStandardization

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

getWeightCol

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

getAggregationDepth

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

getThreshold

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

getMaxBlockSizeInMB

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

save

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

hasSummary, setSummary

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

Method Detail

read

public static MLReader<LinearSVCModel> read()

load

public static LinearSVCModel load(String path)

threshold

public final DoubleParam threshold()

Description copied from interface: LinearSVCParams

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 LinearSVCParams

Specified by:

threshold in interface HasThreshold

Returns:

(undocumented)

maxBlockSizeInMB

public final DoubleParam maxBlockSizeInMB()

Description copied from interface: HasMaxBlockSizeInMB

Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be &gt;= 0..

Specified by:

maxBlockSizeInMB in interface HasMaxBlockSizeInMB

Returns:

(undocumented)

aggregationDepth

public final IntParam aggregationDepth()

Description copied from interface: HasAggregationDepth

Param for suggested depth for treeAggregate (&gt;= 2).

Specified by:

aggregationDepth in interface HasAggregationDepth

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)

standardization

public final BooleanParam standardization()

Description copied from interface: HasStandardization

Param for whether to standardize the training features before fitting the model.

Specified by:

standardization in interface HasStandardization

Returns:

(undocumented)

tol

public final DoubleParam tol()

Description copied from interface: HasTol

Param for the convergence tolerance for iterative algorithms (&gt;= 0).

Specified by:

tol in interface HasTol

Returns:

(undocumented)

fitIntercept

public final BooleanParam fitIntercept()

Description copied from interface: HasFitIntercept

Param for whether to fit an intercept term.

Specified by:

fitIntercept in interface HasFitIntercept

Returns:

(undocumented)

maxIter

public final IntParam maxIter()

Description copied from interface: HasMaxIter

Param for maximum number of iterations (&gt;= 0).

Specified by:

maxIter in interface HasMaxIter

Returns:

(undocumented)

regParam

public final DoubleParam regParam()

Description copied from interface: HasRegParam

Param for regularization parameter (&gt;= 0).

Specified by:

regParam in interface HasRegParam

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)

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)

summary

public LinearSVCTrainingSummary summary()

Gets summary of model on training set. An exception is thrown if hasSummary is false.

Specified by:

summary in interface HasTrainingSummary<LinearSVCTrainingSummary>

Returns:

(undocumented)

evaluate

public LinearSVCSummary evaluate(Dataset<?> dataset)

Evaluates the model on a test dataset.

Parameters:

dataset - Test dataset to evaluate model on.

Returns:

(undocumented)

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)

predictRaw

public Vector predictRaw(Vector features)

Description copied from class: ClassificationModel

Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

Specified by:

predictRaw in class ClassificationModel<Vector,LinearSVCModel>

Parameters:

features - (undocumented)

Returns:

vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

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)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object