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

sealed abstract class Attribute extends Serializable

Abstract class for ML attributes.

Source
attributes.scala
Linear Supertypes
Serializable, AnyRef, Any
Known Subclasses
BinaryAttribute, NominalAttribute, NumericAttribute, UnresolvedAttribute
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. Attribute
  2. Serializable
  3. AnyRef
  4. Any
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Visibility
  1. Public
  2. Protected

Abstract Value Members

  1. abstract def attrType: AttributeType

    Attribute type.

  2. abstract def index: Option[Int]

    Index of the attribute.

    Index of the attribute. None if it is not set.

  3. abstract def isNominal: Boolean

    Tests whether this attribute is nominal, true for NominalAttribute and BinaryAttribute.

  4. abstract def isNumeric: Boolean

    Tests whether this attribute is numeric, true for NumericAttribute and BinaryAttribute.

  5. abstract def name: Option[String]

    Name of the attribute.

    Name of the attribute. None if it is not set.

  6. abstract def withIndex(index: Int): Attribute

    Copy with a new index.

  7. abstract def withName(name: String): Attribute

    Copy with a new name.

  8. abstract def withoutIndex: Attribute

    Copy without the index.

  9. abstract def withoutName: Attribute

    Copy without the name.

Concrete Value Members

  1. def toMetadata(): Metadata

    Converts to ML metadata

  2. def toMetadata(existingMetadata: Metadata): Metadata

    Converts to ML metadata with some existing metadata.

  3. def toString(): String
    Definition Classes
    Attribute → AnyRef → Any
  4. def toStructField(): StructField

    Converts to a StructField.

  5. def toStructField(existingMetadata: Metadata): StructField

    Converts to a StructField with some existing metadata.

    Converts to a StructField with some existing metadata.

    existingMetadata

    existing metadata to carry over