Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    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 attribute

    The ML pipeline API uses DataFrames as ML datasets.

    ML attributes

    The ML pipeline API uses DataFrames as ML datasets. Each dataset consists of typed columns, e.g., string, double, vector, etc. However, knowing only the column type may not be sufficient to handle the data properly. For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices, which cannot be treated as numeric values in ML algorithms, and, for another instance, we may want to know the names and types of features stored in a vector column. ML attributes are used to provide additional information to describe columns in a dataset.

    ML columns

    A column with ML attributes attached is called an ML column. The data in ML columns are stored as double values, i.e., an ML column is either a scalar column of double values or a vector column. Columns of other types must be encoded into ML columns using transformers. We use Attribute to describe a scalar ML column, and AttributeGroup to describe a vector ML column. ML attributes are stored in the metadata field of the column schema.

    Definition Classes
    ml
  • Attribute
  • AttributeGroup
  • AttributeType
  • BinaryAttribute
  • NominalAttribute
  • NumericAttribute
  • UnresolvedAttribute

class AttributeGroup extends Serializable

Attributes that describe a vector ML column.

Source
AttributeGroup.scala
Linear Supertypes
Serializable, Serializable, AnyRef, Any
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Inherited
  1. AttributeGroup
  2. Serializable
  3. Serializable
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  5. Any
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Visibility
  1. Public
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Instance Constructors

  1. new AttributeGroup(name: String, attrs: Array[Attribute])

    Creates an attribute group with attributes.

    Creates an attribute group with attributes.

    name

    name of the attribute group

    attrs

    array of attributes. Attributes will be copied with their corresponding indices in the array.

  2. new AttributeGroup(name: String, numAttributes: Int)

    Creates an attribute group knowing only the number of attributes.

    Creates an attribute group knowing only the number of attributes.

    name

    name of the attribute group

    numAttributes

    number of attributes

  3. new AttributeGroup(name: String)

    Creates an attribute group without attribute info.

    Creates an attribute group without attribute info.

    name

    name of the attribute group

Value Members

  1. def apply(attrIndex: Int): Attribute

    Gets an attribute by its index.

  2. def apply(attrName: String): Attribute

    Gets an attribute by its name.

  3. val attributes: Option[Array[Attribute]]

    Optional array of attributes.

    Optional array of attributes. At most one of numAttributes and attributes can be defined.

  4. def equals(other: Any): Boolean
    Definition Classes
    AttributeGroup → AnyRef → Any
  5. def getAttr(attrIndex: Int): Attribute

    Gets an attribute by its index.

  6. def getAttr(attrName: String): Attribute

    Gets an attribute by its name.

  7. def hasAttr(attrName: String): Boolean

    Test whether this attribute group contains a specific attribute.

  8. def hashCode(): Int
    Definition Classes
    AttributeGroup → AnyRef → Any
  9. def indexOf(attrName: String): Int

    Index of an attribute specified by name.

  10. val name: String
  11. val numAttributes: Option[Int]
  12. def size: Int

    Size of the attribute group.

    Size of the attribute group. Returns -1 if the size is unknown.

  13. def toMetadata(): Metadata

    Converts to ML metadata

  14. def toMetadata(existingMetadata: Metadata): Metadata

    Converts to ML metadata with some existing metadata.

  15. def toString(): String
    Definition Classes
    AttributeGroup → AnyRef → Any
  16. def toStructField(): StructField

    Converts to a StructField.

  17. def toStructField(existingMetadata: Metadata): StructField

    Converts to a StructField with some existing metadata.