Class

org.apache.spark.mllib.clustering

StreamingKMeansModel

Related Doc: package clustering

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class StreamingKMeansModel extends KMeansModel with Logging

StreamingKMeansModel extends MLlib's KMeansModel for streaming algorithms, so it can keep track of a continuously updated weight associated with each cluster, and also update the model by doing a single iteration of the standard k-means algorithm.

The update algorithm uses the "mini-batch" KMeans rule, generalized to incorporate forgetfullness (i.e. decay). The update rule (for each cluster) is:

$$ \begin{align} c_t+1 &= [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t] \\ n_t+t &= n_t * a + m_t \end{align} $$

Where c_t is the previously estimated centroid for that cluster, n_t is the number of points assigned to it thus far, x_t is the centroid estimated on the current batch, and m_t is the number of points assigned to that centroid in the current batch.

The decay factor 'a' scales the contribution of the clusters as estimated thus far, by applying a as a discount weighting on the current point when evaluating new incoming data. If a=1, all batches are weighted equally. If a=0, new centroids are determined entirely by recent data. Lower values correspond to more forgetting.

Decay can optionally be specified by a half life and associated time unit. The time unit can either be a batch of data or a single data point. Considering data arrived at time t, the half life h is defined such that at time t + h the discount applied to the data from t is 0.5. The definition remains the same whether the time unit is given as batches or points.

Annotations
@Since( "1.2.0" )
Linear Supertypes
Logging, KMeansModel, PMMLExportable, Serializable, Serializable, Saveable, AnyRef, Any
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  1. StreamingKMeansModel
  2. Logging
  3. KMeansModel
  4. PMMLExportable
  5. Serializable
  6. Serializable
  7. Saveable
  8. AnyRef
  9. Any
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Instance Constructors

  1. new StreamingKMeansModel(clusterCenters: Array[Vector], clusterWeights: Array[Double])

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    Annotations
    @Since( "1.2.0" )

Value Members

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

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  6. val clusterCenters: Array[Vector]

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    Definition Classes
    StreamingKMeansModelKMeansModel
    Annotations
    @Since( "1.2.0" )
  7. val clusterWeights: Array[Double]

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    Annotations
    @Since( "1.2.0" )
  8. def computeCost(data: RDD[Vector]): Double

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    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

    Definition Classes
    KMeansModel
    Annotations
    @Since( "0.8.0" )
  9. final def eq(arg0: AnyRef): Boolean

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

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  11. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  12. def formatVersion: String

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    Current version of model save/load format.

    Current version of model save/load format.

    Attributes
    protected
    Definition Classes
    KMeansModelSaveable
  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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

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    Logging
  17. final def isInstanceOf[T0]: Boolean

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  18. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  19. def k: Int

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    Total number of clusters.

    Total number of clusters.

    Definition Classes
    KMeansModel
    Annotations
    @Since( "0.8.0" )
  20. def log: Logger

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

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    Logging
  22. def logDebug(msg: ⇒ String): Unit

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

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

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

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

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    Logging
  27. def logName: String

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

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

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

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

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

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  33. final def notify(): Unit

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  34. final def notifyAll(): Unit

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  35. def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

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    Maps given points to their cluster indices.

    Maps given points to their cluster indices.

    Definition Classes
    KMeansModel
    Annotations
    @Since( "1.0.0" )
  36. def predict(points: RDD[Vector]): RDD[Int]

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    Maps given points to their cluster indices.

    Maps given points to their cluster indices.

    Definition Classes
    KMeansModel
    Annotations
    @Since( "1.0.0" )
  37. def predict(point: Vector): Int

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    Returns the cluster index that a given point belongs to.

    Returns the cluster index that a given point belongs to.

    Definition Classes
    KMeansModel
    Annotations
    @Since( "0.8.0" )
  38. def save(sc: SparkContext, path: String): Unit

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    Save this model to the given path.

    Save this model to the given path.

    This saves:

    • human-readable (JSON) model metadata to path/metadata/
    • Parquet formatted data to path/data/

    The model may be loaded using Loader.load.

    sc

    Spark context used to save model data.

    path

    Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

    Definition Classes
    KMeansModelSaveable
    Annotations
    @Since( "1.4.0" )
  39. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
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  40. def toPMML(): String

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    Export the model to a String in PMML format

    Export the model to a String in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Since( "1.4.0" )
  41. def toPMML(outputStream: OutputStream): Unit

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    Export the model to the OutputStream in PMML format

    Export the model to the OutputStream in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Since( "1.4.0" )
  42. def toPMML(sc: SparkContext, path: String): Unit

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    Export the model to a directory on a distributed file system in PMML format

    Export the model to a directory on a distributed file system in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Since( "1.4.0" )
  43. def toPMML(localPath: String): Unit

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    Export the model to a local file in PMML format

    Export the model to a local file in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Since( "1.4.0" )
  44. def toString(): String

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    Definition Classes
    AnyRef → Any
  45. def update(data: RDD[Vector], decayFactor: Double, timeUnit: String): StreamingKMeansModel

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    Perform a k-means update on a batch of data.

    Perform a k-means update on a batch of data.

    Annotations
    @Since( "1.2.0" )
  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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Inherited from Logging

Inherited from KMeansModel

Inherited from PMMLExportable

Inherited from Serializable

Inherited from Serializable

Inherited from Saveable

Inherited from AnyRef

Inherited from Any

Ungrouped