Packages

  • package root
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    root
  • package org
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    root
  • package apache
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    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    Definition Classes
    spark
  • package stat
    Definition Classes
    ml
  • package distribution
    Definition Classes
    stat
  • ChiSquareTest
  • Correlation
  • KolmogorovSmirnovTest
  • Summarizer
  • SummaryBuilder

object Summarizer extends Logging

Tools for vectorized statistics on MLlib Vectors.

The methods in this package provide various statistics for Vectors contained inside DataFrames.

This class lets users pick the statistics they would like to extract for a given column. Here is an example in Scala:

import org.apache.spark.ml.linalg._
import org.apache.spark.sql.Row
val dataframe = ... // Some dataframe containing a feature column and a weight column
val multiStatsDF = dataframe.select(
    Summarizer.metrics("min", "max", "count").summary($"features", $"weight")
val Row(minVec, maxVec, count) = multiStatsDF.first()

If one wants to get a single metric, shortcuts are also available:

val meanDF = dataframe.select(Summarizer.mean($"features"))
val Row(meanVec) = meanDF.first()

Note: Currently, the performance of this interface is about 2x~3x slower than using the RDD interface.

Annotations
@Since("2.3.0")
Source
Summarizer.scala
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  1. implicit class LogStringContext extends AnyRef
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  4. def MDC(key: LogKey, value: Any): MDC
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  7. def count(col: Column): Column
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    @Since("2.3.0")
  8. def count(col: Column, weightCol: Column): Column
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  9. final def eq(arg0: AnyRef): Boolean
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  13. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  14. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  15. final def isInstanceOf[T0]: Boolean
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  16. def isTraceEnabled(): Boolean
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  17. def log: Logger
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  23. def logError(msg: => String, throwable: Throwable): Unit
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  26. def logError(msg: => String): Unit
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  27. def logInfo(msg: => String, throwable: Throwable): Unit
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  28. def logInfo(entry: LogEntry, throwable: Throwable): Unit
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  29. def logInfo(entry: LogEntry): Unit
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  30. def logInfo(msg: => String): Unit
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  31. def logName: String
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  32. def logTrace(msg: => String, throwable: Throwable): Unit
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  33. def logTrace(entry: LogEntry, throwable: Throwable): Unit
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  34. def logTrace(entry: LogEntry): Unit
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  35. def logTrace(msg: => String): Unit
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  39. def logWarning(msg: => String): Unit
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  40. def max(col: Column): Column
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  41. def max(col: Column, weightCol: Column): Column
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  42. def mean(col: Column): Column
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  43. def mean(col: Column, weightCol: Column): Column
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  44. def metrics(metrics: String*): SummaryBuilder

    Given a list of metrics, provides a builder that it turns computes metrics from a column.

    Given a list of metrics, provides a builder that it turns computes metrics from a column.

    See the documentation of Summarizer for an example.

    The following metrics are accepted (case sensitive):

    • mean: a vector that contains the coefficient-wise mean.
    • sum: a vector that contains the coefficient-wise sum.
    • variance: a vector that contains the coefficient-wise variance.
    • std: a vector that contains the coefficient-wise standard deviation.
    • count: the count of all vectors seen.
    • numNonzeros: a vector with the number of non-zeros for each coefficients
    • max: the maximum for each coefficient.
    • min: the minimum for each coefficient.
    • normL2: the Euclidean norm for each coefficient.
    • normL1: the L1 norm of each coefficient (sum of the absolute values).
    metrics

    metrics that can be provided.

    returns

    a builder.

    Annotations
    @Since("2.3.0") @varargs()
    Exceptions thrown

    IllegalArgumentException if one of the metric names is not understood. Note: Currently, the performance of this interface is about 2x~3x slower than using the RDD interface.

  45. def min(col: Column): Column
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  46. def min(col: Column, weightCol: Column): Column
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  47. final def ne(arg0: AnyRef): Boolean
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  48. def normL1(col: Column): Column
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  49. def normL1(col: Column, weightCol: Column): Column
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  50. def normL2(col: Column): Column
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  51. def normL2(col: Column, weightCol: Column): Column
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  54. def numNonZeros(col: Column): Column
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  55. def numNonZeros(col: Column, weightCol: Column): Column
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  56. def std(col: Column): Column
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  57. def std(col: Column, weightCol: Column): Column
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  58. def sum(col: Column): Column
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  59. def sum(col: Column, weightCol: Column): Column
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  60. final def synchronized[T0](arg0: => T0): T0
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  61. def toString(): String
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  62. def variance(col: Column): Column
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  63. def variance(col: Column, weightCol: Column): Column
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    (Since version 9)

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