Class

org.apache.spark.mllib.classification

LogisticRegressionWithLBFGS

Related Doc: package classification

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class LogisticRegressionWithLBFGS extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory 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.

Annotations
@Since( "1.1.0" )
Source
LogisticRegression.scala
Note

Labels used in Logistic Regression should be {0, 1, ..., k - 1} for k classes multi-label classification problem.

Linear Supertypes
GeneralizedLinearAlgorithm[LogisticRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
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  1. LogisticRegressionWithLBFGS
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
  5. Logging
  6. AnyRef
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Instance Constructors

  1. new LogisticRegressionWithLBFGS()

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. var addIntercept: Boolean

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    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  5. final def asInstanceOf[T0]: T0

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    Any
  6. def clone(): AnyRef

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    protected[java.lang]
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    AnyRef
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    @throws( ... )
  7. def createModel(weights: Vector, intercept: Double): LogisticRegressionModel

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    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected
    Definition Classes
    LogisticRegressionWithLBFGSGeneralizedLinearAlgorithm
  8. final def eq(arg0: AnyRef): Boolean

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    AnyRef
  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. def generateInitialWeights(input: RDD[LabeledPoint]): Vector

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    Generate the initial weights when the user does not supply them

    Generate the initial weights when the user does not supply them

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  12. final def getClass(): Class[_]

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    Definition Classes
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  13. def getNumFeatures: Int

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    The dimension of training features.

    The dimension of training features.

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.4.0" )
  14. def hashCode(): Int

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  15. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    protected
    Definition Classes
    Logging
  16. def isAddIntercept: Boolean

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    Get if the algorithm uses addIntercept

    Get if the algorithm uses addIntercept

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.4.0" )
  17. final def isInstanceOf[T0]: Boolean

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    Any
  18. def isTraceEnabled(): Boolean

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    protected
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    Logging
  19. def log: Logger

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    Logging
  20. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logDebug(msg: ⇒ String): Unit

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    Logging
  22. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  23. def logError(msg: ⇒ String): Unit

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  24. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  25. def logInfo(msg: ⇒ String): Unit

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    Logging
  26. def logName: String

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    protected
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    Logging
  27. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  28. def logTrace(msg: ⇒ String): Unit

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    Logging
  29. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  30. def logWarning(msg: ⇒ String): Unit

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    Logging
  31. final def ne(arg0: AnyRef): Boolean

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  32. final def notify(): Unit

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  33. final def notifyAll(): Unit

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  34. var numFeatures: Int

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    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  35. var numOfLinearPredictor: Int

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    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 K-1 independent binary logistic regression models which requires K-1 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 of weights will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension of weights will be (numOfLinearPredictor) * numFeatures.

    Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  36. val optimizer: LBFGS

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    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    LogisticRegressionWithLBFGSGeneralizedLinearAlgorithm
    Annotations
    @Since( "1.1.0" )
  37. def run(input: RDD[LabeledPoint], initialWeights: Vector): LogisticRegressionModel

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    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
    LogisticRegressionWithLBFGSGeneralizedLinearAlgorithm
  38. def run(input: RDD[LabeledPoint]): LogisticRegressionModel

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    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
    LogisticRegressionWithLBFGSGeneralizedLinearAlgorithm
  39. def setIntercept(addIntercept: Boolean): LogisticRegressionWithLBFGS.this.type

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    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" )
  40. def setNumClasses(numClasses: Int): LogisticRegressionWithLBFGS.this.type

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    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.

    Annotations
    @Since( "1.3.0" )
  41. def setValidateData(validateData: Boolean): LogisticRegressionWithLBFGS.this.type

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    Set if the algorithm should validate data before training.

    Set if the algorithm should validate data before training. Default true.

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "0.8.0" )
  42. final def synchronized[T0](arg0: ⇒ T0): T0

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  43. def toString(): String

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  44. var validateData: Boolean

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    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  45. val validators: List[(RDD[LabeledPoint]) ⇒ Boolean]

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    Attributes
    protected
    Definition Classes
    LogisticRegressionWithLBFGSGeneralizedLinearAlgorithm
  46. final def wait(): Unit

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    @throws( ... )
  47. final def wait(arg0: Long, arg1: Int): Unit

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  48. final def wait(arg0: Long): Unit

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