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:
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:
-
Nested Class Summary
Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
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Method Summary
Modifier and TypeMethodDescriptionfinal IntParam
Param for suggested depth for treeAggregate (>= 2).Creates a copy of this instance with the same UID and some extra params.Evaluates the model on a test dataset.final BooleanParam
Param for whether to fit an intercept term.double
static LinearSVCModel
final DoubleParam
Param for Maximum memory in MB for stacking input data into blocks.final IntParam
maxIter()
Param for maximum number of iterations (>= 0).int
Number of classes (values which the label can take).int
Returns the number of features the model was trained on.double
Predict label for the given features.predictRaw
(Vector features) Raw prediction for each possible label.static MLReader<LinearSVCModel>
read()
final DoubleParam
regParam()
Param for regularization parameter (>= 0).setThreshold
(double value) final BooleanParam
Param for whether to standardize the training features before fitting the model.summary()
Gets summary of model on training set.final DoubleParam
Param for threshold in binary classification prediction.final DoubleParam
tol()
Param for the convergence tolerance for iterative algorithms (>= 0).toString()
uid()
An immutable unique ID for the object and its derivatives.Param for weight column name.write()
Returns anMLWriter
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.Transformer
transform, transform, transform
Methods inherited from class org.apache.spark.ml.PipelineStage
params
Methods inherited from class java.lang.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.HasAggregationDepth
getAggregationDepth
Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol
featuresCol, getFeaturesCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasFitIntercept
getFitIntercept
Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol
getLabelCol, labelCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxBlockSizeInMB
getMaxBlockSizeInMB
Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter
getMaxIter
Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol
getPredictionCol, predictionCol
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.HasStandardization
getStandardization
Methods inherited from interface org.apache.spark.ml.param.shared.HasThreshold
getThreshold
Methods inherited from interface org.apache.spark.ml.util.HasTrainingSummary
hasSummary, setSummary
Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol
getWeightCol
Methods inherited from interface org.apache.spark.internal.Logging
initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
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, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
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Method Details
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read
-
load
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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 interfaceHasThreshold
- Specified by:
threshold
in interfaceLinearSVCParams
- Returns:
- (undocumented)
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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 >= 0..- Specified by:
maxBlockSizeInMB
in interfaceHasMaxBlockSizeInMB
- Returns:
- (undocumented)
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aggregationDepth
Description copied from interface:HasAggregationDepth
Param for suggested depth for treeAggregate (>= 2).- Specified by:
aggregationDepth
in interfaceHasAggregationDepth
- Returns:
- (undocumented)
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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 interfaceHasWeightCol
- Returns:
- (undocumented)
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standardization
Description copied from interface:HasStandardization
Param for whether to standardize the training features before fitting the model.- Specified by:
standardization
in interfaceHasStandardization
- Returns:
- (undocumented)
-
tol
Description copied from interface:HasTol
Param for the convergence tolerance for iterative algorithms (>= 0). -
fitIntercept
Description copied from interface:HasFitIntercept
Param for whether to fit an intercept term.- Specified by:
fitIntercept
in interfaceHasFitIntercept
- Returns:
- (undocumented)
-
maxIter
Description copied from interface:HasMaxIter
Param for maximum number of iterations (>= 0).- Specified by:
maxIter
in interfaceHasMaxIter
- Returns:
- (undocumented)
-
regParam
Description copied from interface:HasRegParam
Param for regularization parameter (>= 0).- Specified by:
regParam
in interfaceHasRegParam
- Returns:
- (undocumented)
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uid
Description copied from interface:Identifiable
An immutable unique ID for the object and its derivatives.- Specified by:
uid
in interfaceIdentifiable
- Returns:
- (undocumented)
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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 classClassificationModel<Vector,
LinearSVCModel>
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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 classPredictionModel<Vector,
LinearSVCModel>
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setThreshold
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summary
Gets summary of model on training set. An exception is thrown ifhasSummary
is false.- Specified by:
summary
in interfaceHasTrainingSummary<LinearSVCTrainingSummary>
- Returns:
- (undocumented)
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evaluate
Evaluates the model on a test dataset.- Parameters:
dataset
- Test dataset to evaluate model on.- Returns:
- (undocumented)
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predict
Description copied from class:ClassificationModel
Predict label for the given features. This method is used to implementtransform()
and outputPredictionModel.predictionCol()
.This default implementation for classification predicts the index of the maximum value from
predictRaw()
.- Overrides:
predict
in classClassificationModel<Vector,
LinearSVCModel> - Parameters:
features
- (undocumented)- Returns:
- (undocumented)
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predictRaw
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 implementtransform()
and outputClassificationModel.rawPredictionCol()
.- Specified by:
predictRaw
in classClassificationModel<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.
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copy
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. SeedefaultCopy()
.- Specified by:
copy
in interfaceParams
- Specified by:
copy
in classModel<LinearSVCModel>
- Parameters:
extra
- (undocumented)- Returns:
- (undocumented)
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write
Description copied from interface:MLWritable
Returns anMLWriter
instance for this ML instance.- Specified by:
write
in interfaceMLWritable
- Returns:
- (undocumented)
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toString
- Specified by:
toString
in interfaceIdentifiable
- Overrides:
toString
in classObject
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