org.apache.spark.mllib.api.python

PythonMLLibAPI

class PythonMLLibAPI extends Serializable

:: DeveloperApi :: The Java stubs necessary for the Python mllib bindings.

See python/pyspark/mllib/_common.py for the mutually agreed upon data format.

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@DeveloperApi()
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Instance Constructors

  1. new PythonMLLibAPI()

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  8. def colStats(X: JavaRDD[Array[Byte]]): MultivariateStatisticalSummarySerialized

    Java stub for mllib Statistics.

    Java stub for mllib Statistics.colStats(X: RDD[Vector]). TODO figure out return type.

  9. def corr(x: JavaRDD[Array[Byte]], y: JavaRDD[Array[Byte]], method: String): Double

    Java stub for mllib Statistics.

    Java stub for mllib Statistics.corr(x: RDD[Double], y: RDD[Double], method: String).

  10. def corr(X: JavaRDD[Array[Byte]], method: String): Array[Byte]

    Java stub for mllib Statistics.

    Java stub for mllib Statistics.corr(X: RDD[Vector], method: String). Returns the correlation matrix serialized into a byte array understood by deserializers in pyspark.

  11. final def eq(arg0: AnyRef): Boolean

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  12. def equals(arg0: Any): Boolean

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  13. def finalize(): Unit

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  14. final def getClass(): Class[_]

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. def loadLabeledPoints(jsc: JavaSparkContext, path: String, minPartitions: Int): JavaRDD[Array[Byte]]

    Loads and serializes labeled points saved with RDD#saveAsTextFile.

    Loads and serializes labeled points saved with RDD#saveAsTextFile.

    jsc

    Java SparkContext

    path

    file or directory path in any Hadoop-supported file system URI

    minPartitions

    min number of partitions

    returns

    serialized labeled points stored in a JavaRDD of byte array

  18. final def ne(arg0: AnyRef): Boolean

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  19. def normalRDD(jsc: JavaSparkContext, size: Long, numPartitions: Integer, seed: Long): JavaRDD[Array[Byte]]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.normalRDD()

  20. def normalVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Integer, seed: Long): JavaRDD[Array[Byte]]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.normalVectorRDD()

  21. final def notify(): Unit

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  22. final def notifyAll(): Unit

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  23. def poissonRDD(jsc: JavaSparkContext, mean: Double, size: Long, numPartitions: Integer, seed: Long): JavaRDD[Array[Byte]]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.poissonRDD()

  24. def poissonVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int, numPartitions: Integer, seed: Long): JavaRDD[Array[Byte]]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.poissonVectorRDD()

  25. def predictDecisionTreeModel(model: DecisionTreeModel, dataJRDD: JavaRDD[Array[Byte]]): JavaRDD[Array[Byte]]

    Predict the labels of the given data points.

    Predict the labels of the given data points. This is a Java stub for python DecisionTreeModel.predict()

    dataJRDD

    A JavaRDD with serialized feature vectors

    returns

    JavaRDD of serialized predictions

  26. def predictDecisionTreeModel(model: DecisionTreeModel, featuresBytes: Array[Byte]): Double

    Predict the label of the given data point.

    Predict the label of the given data point. This is a Java stub for python DecisionTreeModel.predict()

    featuresBytes

    Serialized feature vector for data point

    returns

    predicted label

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

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  28. def toString(): String

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  29. def trainALSModel(ratingsBytesJRDD: JavaRDD[Array[Byte]], rank: Int, iterations: Int, lambda: Double, blocks: Int): MatrixFactorizationModel

    Java stub for Python mllib ALS.

    Java stub for Python mllib ALS.train(). This stub returns a handle to the Java object instead of the content of the Java object. Extra care needs to be taken in the Python code to ensure it gets freed on exit; see the Py4J documentation.

  30. def trainDecisionTreeModel(dataBytesJRDD: JavaRDD[Array[Byte]], algoStr: String, numClasses: Int, categoricalFeaturesInfoJMap: Map[Int, Int], impurityStr: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

    Java stub for Python mllib DecisionTree.

    Java stub for Python mllib DecisionTree.train(). This stub returns a handle to the Java object instead of the content of the Java object. Extra care needs to be taken in the Python code to ensure it gets freed on exit; see the Py4J documentation.

    dataBytesJRDD

    Training data

    categoricalFeaturesInfoJMap

    Categorical features info, as Java map

  31. def trainImplicitALSModel(ratingsBytesJRDD: JavaRDD[Array[Byte]], rank: Int, iterations: Int, lambda: Double, blocks: Int, alpha: Double): MatrixFactorizationModel

    Java stub for Python mllib ALS.

    Java stub for Python mllib ALS.trainImplicit(). This stub returns a handle to the Java object instead of the content of the Java object. Extra care needs to be taken in the Python code to ensure it gets freed on exit; see the Py4J documentation.

  32. def trainKMeansModel(dataBytesJRDD: JavaRDD[Array[Byte]], k: Int, maxIterations: Int, runs: Int, initializationMode: String): List[AnyRef]

    Java stub for Python mllib KMeans.

    Java stub for Python mllib KMeans.train()

  33. def trainLassoModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double, initialWeightsBA: Array[Byte]): List[AnyRef]

    Java stub for Python mllib LassoWithSGD.

    Java stub for Python mllib LassoWithSGD.train()

  34. def trainLinearRegressionModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, stepSize: Double, miniBatchFraction: Double, initialWeightsBA: Array[Byte], regParam: Double, regType: String, intercept: Boolean): List[AnyRef]

    Java stub for Python mllib LinearRegressionWithSGD.

    Java stub for Python mllib LinearRegressionWithSGD.train()

  35. def trainLogisticRegressionModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, stepSize: Double, miniBatchFraction: Double, initialWeightsBA: Array[Byte], regParam: Double, regType: String, intercept: Boolean): List[AnyRef]

    Java stub for Python mllib LogisticRegressionWithSGD.

    Java stub for Python mllib LogisticRegressionWithSGD.train()

  36. def trainNaiveBayes(dataBytesJRDD: JavaRDD[Array[Byte]], lambda: Double): List[AnyRef]

    Java stub for NaiveBayes.

    Java stub for NaiveBayes.train()

  37. def trainRidgeModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double, initialWeightsBA: Array[Byte]): List[AnyRef]

    Java stub for Python mllib RidgeRegressionWithSGD.

    Java stub for Python mllib RidgeRegressionWithSGD.train()

  38. def trainSVMModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double, initialWeightsBA: Array[Byte], regType: String, intercept: Boolean): List[AnyRef]

    Java stub for Python mllib SVMWithSGD.

    Java stub for Python mllib SVMWithSGD.train()

  39. def uniformRDD(jsc: JavaSparkContext, size: Long, numPartitions: Integer, seed: Long): JavaRDD[Array[Byte]]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.uniformRDD()

  40. def uniformVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Integer, seed: Long): JavaRDD[Array[Byte]]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.uniformVectorRDD()

  41. final def wait(): Unit

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  42. final def wait(arg0: Long, arg1: Int): Unit

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

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