class LogisticRegressionWithLBFGS extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable
Train a classification model for Multinomial/Binary Logistic Regression using Limitedmemory BFGS. Standard feature scaling and L2 regularization are used by default.
Earlier implementations of LogisticRegressionWithLBFGS applies a regularization penalty to all elements including the intercept. If this is called with one of standard updaters (L1Updater, or SquaredL2Updater) this is translated into a call to ml.LogisticRegression, otherwise this will use the existing mllib GeneralizedLinearAlgorithm trainer, resulting in a regularization penalty to the intercept.
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 @Since( "1.1.0" )
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 LogisticRegression.scala
 Note
Labels used in Logistic Regression should be {0, 1, ..., k  1} for k classes multilabel classification problem.
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 LogisticRegressionWithLBFGS
 GeneralizedLinearAlgorithm
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 new LogisticRegressionWithLBFGS()
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final
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!=(arg0: Any): Boolean
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##(): Int
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var
addIntercept: Boolean
Whether to add intercept (default: false).
Whether to add intercept (default: false).
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 protected
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 GeneralizedLinearAlgorithm

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def
createModel(weights: Vector, intercept: Double): LogisticRegressionModel
Create a model given the weights and intercept
Create a model given the weights and intercept
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 LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm

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finalize(): Unit
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def
generateInitialWeights(input: RDD[LabeledPoint]): Vector
Generate the initial weights when the user does not supply them
Generate the initial weights when the user does not supply them
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 GeneralizedLinearAlgorithm

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getClass(): Class[_]
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def
getNumFeatures: Int
The dimension of training features.
The dimension of training features.
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 GeneralizedLinearAlgorithm
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 @Since( "1.4.0" )

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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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def
isAddIntercept: Boolean
Get if the algorithm uses addIntercept
Get if the algorithm uses addIntercept
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 GeneralizedLinearAlgorithm
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 @Since( "1.4.0" )

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log: Logger
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logDebug(msg: ⇒ String): Unit
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logError(msg: ⇒ String): Unit
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logName: String
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logTrace(msg: ⇒ String): Unit
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logWarning(msg: ⇒ String, throwable: Throwable): Unit
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logWarning(msg: ⇒ String): Unit
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var
numFeatures: Int
The dimension of training features.
The dimension of training features.
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 protected
 Definition Classes
 GeneralizedLinearAlgorithm

var
numOfLinearPredictor: Int
In
GeneralizedLinearModel
, only single linear predictor is allowed for both weights and intercept.In
GeneralizedLinearModel
, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K1 independent binary logistic regression models which requires K1 sets of linear predictor.As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger
weights
vector which can hold both weights and intercepts. If the intercepts are added, the dimension ofweights
will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension ofweights
will be (numOfLinearPredictor) * numFeatures.Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.
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 protected
 Definition Classes
 GeneralizedLinearAlgorithm

val
optimizer: LBFGS
The optimizer to solve the problem.
The optimizer to solve the problem.
 Definition Classes
 LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
 Annotations
 @Since( "1.1.0" )

def
run(input: RDD[LabeledPoint], initialWeights: Vector): LogisticRegressionModel
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
If a known updater is used calls the ml implementation, to avoid applying a regularization penalty to the intercept, otherwise defaults to the mllib implementation. If more than two classes or feature scaling is disabled, always uses mllib implementation. Uses user provided weights.
In the ml LogisticRegression implementation, the number of corrections used in the LBFGS update can not be configured. So
optimizer.setNumCorrections()
will have no effect if we fall into that route. Definition Classes
 LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm

def
run(input: RDD[LabeledPoint]): LogisticRegressionModel
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries.
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries.
If a known updater is used calls the ml implementation, to avoid applying a regularization penalty to the intercept, otherwise defaults to the mllib implementation. If more than two classes or feature scaling is disabled, always uses mllib implementation. If using ml implementation, uses ml code to generate initial weights.
 Definition Classes
 LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm

def
setIntercept(addIntercept: Boolean): LogisticRegressionWithLBFGS.this.type
Set if the algorithm should add an intercept.
Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.
 Definition Classes
 GeneralizedLinearAlgorithm
 Annotations
 @Since( "0.8.0" )

def
setNumClasses(numClasses: Int): LogisticRegressionWithLBFGS.this.type
Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression.
Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so k will be set to 2.
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 @Since( "1.3.0" )

def
setValidateData(validateData: Boolean): LogisticRegressionWithLBFGS.this.type
Set if the algorithm should validate data before training.
Set if the algorithm should validate data before training. Default true.
 Definition Classes
 GeneralizedLinearAlgorithm
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 @Since( "0.8.0" )

final
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synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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var
validateData: Boolean
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 protected
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 GeneralizedLinearAlgorithm

val
validators: List[(RDD[LabeledPoint]) ⇒ Boolean]
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 protected
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 LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm

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wait(): Unit
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