Class/Object

org.apache.spark.mllib.classification

LogisticRegressionWithSGD

Related Docs: object LogisticRegressionWithSGD | package classification

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class LogisticRegressionWithSGD extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

Train a classification model for Binary Logistic Regression using Stochastic Gradient Descent. By default L2 regularization is used, which can be changed via LogisticRegressionWithSGD.optimizer.

Using LogisticRegressionWithLBFGS is recommended over this.

Annotations
@Since( "0.8.0" )
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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Inherited
  1. LogisticRegressionWithSGD
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
  5. Logging
  6. AnyRef
  7. Any
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Visibility
  1. Public
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Instance Constructors

  1. new LogisticRegressionWithSGD()

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    Construct a LogisticRegression object with default parameters: {stepSize: 1.0, numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.

    Construct a LogisticRegression object with default parameters: {stepSize: 1.0, numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.

    Annotations
    @Since( "0.8.0" ) @deprecated
    Deprecated

    (Since version 2.0.0) Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS

Value Members

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

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

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  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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    Definition Classes
    Any
  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @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[org.apache.spark.mllib]
    Definition Classes
    LogisticRegressionWithSGDGeneralizedLinearAlgorithm
  8. final def eq(arg0: AnyRef): Boolean

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

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    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

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    Attributes
    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
    AnyRef → Any
  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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    Definition Classes
    AnyRef → Any
  15. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  16. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  17. 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" )
  18. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  19. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  20. def log: Logger

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    protected
    Definition Classes
    Logging
  21. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  22. def logDebug(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  23. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  24. def logError(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  25. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  26. def logInfo(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  27. def logName: String

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    protected
    Definition Classes
    Logging
  28. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  29. def logTrace(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  30. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  31. def logWarning(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  32. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  33. final def notify(): Unit

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    Definition Classes
    AnyRef
  34. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  35. var numFeatures: Int

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    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  36. 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
  37. val optimizer: GradientDescent

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    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    LogisticRegressionWithSGDGeneralizedLinearAlgorithm
    Annotations
    @Since( "0.8.0" )
  38. def run(input: RDD[LabeledPoint], initialWeights: Vector): LogisticRegressionModel

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    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.0.0" )
  39. def run(input: RDD[LabeledPoint]): LogisticRegressionModel

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    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "0.8.0" )
  40. def setIntercept(addIntercept: Boolean): LogisticRegressionWithSGD.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" )
  41. def setValidateData(validateData: Boolean): LogisticRegressionWithSGD.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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    Definition Classes
    AnyRef
  43. def toString(): String

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    AnyRef → Any
  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
    LogisticRegressionWithSGDGeneralizedLinearAlgorithm
  46. final def wait(): Unit

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

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

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    Definition Classes
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    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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