org.apache.spark.mllib.api.python

PythonMLLibAPI

class PythonMLLibAPI extends Serializable

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

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@DeveloperApi()
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Instance Constructors

  1. new PythonMLLibAPI()

Value Members

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

    Definition Classes
    AnyRef
  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

    Definition Classes
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  7. def chiSqTest(data: JavaRDD[LabeledPoint]): Array[ChiSqTestResult]

    Java stub for mllib Statistics.

    Java stub for mllib Statistics.chiSqTest(RDD[LabelPoint])

  8. def chiSqTest(observed: Matrix): ChiSqTestResult

    Java stub for mllib Statistics.

    Java stub for mllib Statistics.chiSqTest(observed: Matrix)

  9. def chiSqTest(observed: Vector, expected: Vector): ChiSqTestResult

    Java stub for mllib Statistics.

    Java stub for mllib Statistics.chiSqTest()

  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )
  11. def colStats(rdd: JavaRDD[Vector]): MultivariateStatisticalSummary

    Java stub for mllib Statistics.

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

  12. def corr(x: JavaRDD[Double], y: JavaRDD[Double], method: String): Double

    Java stub for mllib Statistics.

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

  13. def corr(x: JavaRDD[Vector], method: String): Matrix

    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.

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

    Definition Classes
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  15. def equals(arg0: Any): Boolean

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

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  17. def fitIDF(minDocFreq: Int, dataset: JavaRDD[Vector]): IDFModel

    Java stub for IDF.

    Java stub for IDF.fit(). 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.

  18. def fitStandardScaler(withMean: Boolean, withStd: Boolean, data: JavaRDD[Vector]): StandardScalerModel

    Java stub for IDF.

    Java stub for IDF.fit(). 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.

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

    Definition Classes
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  20. def getUpdaterFromString(regType: String): Updater

    Return the Updater from string

  21. def hashCode(): Int

    Definition Classes
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  22. final def isInstanceOf[T0]: Boolean

    Definition Classes
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  23. def loadLabeledPoints(jsc: JavaSparkContext, path: String, minPartitions: Int): JavaRDD[LabeledPoint]

    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

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

    Definition Classes
    AnyRef
  25. def normalRDD(jsc: JavaSparkContext, size: Long, numPartitions: Integer, seed: Long): JavaRDD[Double]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.normalRDD()

  26. def normalVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Integer, seed: Long): JavaRDD[Vector]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.normalVectorRDD()

  27. def normalizeVector(p: Double, rdd: JavaRDD[Vector]): JavaRDD[Vector]

    Java stub for Normalizer.

    Java stub for Normalizer.transform()

  28. def normalizeVector(p: Double, vector: Vector): Vector

    Java stub for Normalizer.

    Java stub for Normalizer.transform()

  29. final def notify(): Unit

    Definition Classes
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  30. final def notifyAll(): Unit

    Definition Classes
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  31. def poissonRDD(jsc: JavaSparkContext, mean: Double, size: Long, numPartitions: Integer, seed: Long): JavaRDD[Double]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.poissonRDD()

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

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.poissonVectorRDD()

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

    Definition Classes
    AnyRef
  34. def toString(): String

    Definition Classes
    AnyRef → Any
  35. def trainALSModel(ratingsJRDD: JavaRDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, nonnegative: Boolean, seed: Long): 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.

  36. def trainDecisionTreeModel(data: JavaRDD[LabeledPoint], algoStr: String, numClasses: Int, categoricalFeaturesInfo: Map[Int, Int], impurityStr: String, maxDepth: Int, maxBins: Int, minInstancesPerNode: Int, minInfoGain: Double): 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.

    data

    Training data

    categoricalFeaturesInfo

    Categorical features info, as Java map

  37. def trainImplicitALSModel(ratingsJRDD: JavaRDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, alpha: Double, nonnegative: Boolean, seed: Long): 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.

  38. def trainKMeansModel(data: JavaRDD[Vector], k: Int, maxIterations: Int, runs: Int, initializationMode: String): KMeansModel

    Java stub for Python mllib KMeans.

    Java stub for Python mllib KMeans.train()

  39. def trainLassoModelWithSGD(data: JavaRDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double, initialWeights: Vector): List[AnyRef]

    Java stub for Python mllib LassoWithSGD.

    Java stub for Python mllib LassoWithSGD.train()

  40. def trainLinearRegressionModelWithSGD(data: JavaRDD[LabeledPoint], numIterations: Int, stepSize: Double, miniBatchFraction: Double, initialWeights: Vector, regParam: Double, regType: String, intercept: Boolean): List[AnyRef]

    Java stub for Python mllib LinearRegressionWithSGD.

    Java stub for Python mllib LinearRegressionWithSGD.train()

  41. def trainLogisticRegressionModelWithLBFGS(data: JavaRDD[LabeledPoint], numIterations: Int, initialWeights: Vector, regParam: Double, regType: String, intercept: Boolean, corrections: Int, tolerance: Double): List[AnyRef]

    Java stub for Python mllib LogisticRegressionWithLBFGS.

    Java stub for Python mllib LogisticRegressionWithLBFGS.train()

  42. def trainLogisticRegressionModelWithSGD(data: JavaRDD[LabeledPoint], numIterations: Int, stepSize: Double, miniBatchFraction: Double, initialWeights: Vector, regParam: Double, regType: String, intercept: Boolean): List[AnyRef]

    Java stub for Python mllib LogisticRegressionWithSGD.

    Java stub for Python mllib LogisticRegressionWithSGD.train()

  43. def trainNaiveBayes(data: JavaRDD[LabeledPoint], lambda: Double): List[AnyRef]

    Java stub for NaiveBayes.

    Java stub for NaiveBayes.train()

  44. def trainRandomForestModel(data: JavaRDD[LabeledPoint], algoStr: String, numClasses: Int, categoricalFeaturesInfo: Map[Int, Int], numTrees: Int, featureSubsetStrategy: String, impurityStr: String, maxDepth: Int, maxBins: Int, seed: Int): RandomForestModel

    Java stub for Python mllib RandomForest.

    Java stub for Python mllib RandomForest.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.

  45. def trainRidgeModelWithSGD(data: JavaRDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double, initialWeights: Vector): List[AnyRef]

    Java stub for Python mllib RidgeRegressionWithSGD.

    Java stub for Python mllib RidgeRegressionWithSGD.train()

  46. def trainSVMModelWithSGD(data: JavaRDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double, initialWeights: Vector, regType: String, intercept: Boolean): List[AnyRef]

    Java stub for Python mllib SVMWithSGD.

    Java stub for Python mllib SVMWithSGD.train()

  47. def trainWord2Vec(dataJRDD: JavaRDD[ArrayList[String]], vectorSize: Int, learningRate: Double, numPartitions: Int, numIterations: Int, seed: Long): Word2VecModelWrapper

    Java stub for Python mllib Word2Vec fit().

    Java stub for Python mllib Word2Vec fit(). 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.

    dataJRDD

    input JavaRDD

    vectorSize

    size of vector

    learningRate

    initial learning rate

    numPartitions

    number of partitions

    numIterations

    number of iterations

    seed

    initial seed for random generator

    returns

    A handle to java Word2VecModelWrapper instance at python side

  48. def uniformRDD(jsc: JavaSparkContext, size: Long, numPartitions: Integer, seed: Long): JavaRDD[Double]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.uniformRDD()

  49. def uniformVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Integer, seed: Long): JavaRDD[Vector]

    Java stub for Python mllib RandomRDDGenerators.

    Java stub for Python mllib RandomRDDGenerators.uniformVectorRDD()

  50. final def wait(): Unit

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

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

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