Class/Object

org.apache.spark.mllib.classification

LogisticRegressionWithSGD

Related Docs: object LogisticRegressionWithSGD | package classification

Permalink

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. NOTE: Labels used in Logistic Regression should be {0, 1, ..., k - 1} for k classes multi-label classification problem. Using LogisticRegressionWithLBFGS is recommended over this.

Annotations
@Since( "0.8.0" )
Source
LogisticRegression.scala
Linear Supertypes
GeneralizedLinearAlgorithm[LogisticRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. LogisticRegressionWithSGD
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
  5. Logging
  6. AnyRef
  7. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new LogisticRegressionWithSGD()

    Permalink

    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

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

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

    Permalink
    Definition Classes
    AnyRef → Any
  4. var addIntercept: Boolean

    Permalink

    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

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

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

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

    Permalink

    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

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

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

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

    Permalink

    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[_]

    Permalink
    Definition Classes
    AnyRef → Any
  13. def getNumFeatures: Int

    Permalink

    The dimension of training features.

    The dimension of training features.

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

    Permalink
    Definition Classes
    AnyRef → Any
  15. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  16. def isAddIntercept: Boolean

    Permalink

    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

    Permalink
    Definition Classes
    Any
  18. def isTraceEnabled(): Boolean

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  19. def log: Logger

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

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

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  23. def logError(msg: ⇒ String): Unit

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

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  26. def logName: String

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  28. def logTrace(msg: ⇒ String): Unit

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  30. def logWarning(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  31. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  32. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  33. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  34. var numFeatures: Int

    Permalink

    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  35. var numOfLinearPredictor: Int

    Permalink

    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: GradientDescent

    Permalink

    The optimizer to solve the problem.

    The optimizer to solve the problem.

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

    Permalink

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

    Permalink

    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" )
  39. def setIntercept(addIntercept: Boolean): LogisticRegressionWithSGD.this.type

    Permalink

    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 setValidateData(validateData: Boolean): LogisticRegressionWithSGD.this.type

    Permalink

    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" )
  41. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  42. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  43. var validateData: Boolean

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

    Permalink
    Attributes
    protected
    Definition Classes
    LogisticRegressionWithSGDGeneralizedLinearAlgorithm
  45. final def wait(): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  46. final def wait(arg0: Long, arg1: Int): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  47. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped