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
  • package classification
    Definition Classes
    ml
  • package clustering
    Definition Classes
    ml
  • package evaluation
    Definition Classes
    ml
  • package feature

    The ml.feature package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting.

    Feature transformers

    The ml.feature package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting. Most feature transformers are implemented as Transformers, which transform one DataFrame into another, e.g., HashingTF. Some feature transformers are implemented as Estimators, because the transformation requires some aggregated information of the dataset, e.g., document frequencies in IDF. For those feature transformers, calling Estimator.fit is required to obtain the model first, e.g., IDFModel, in order to apply transformation. The transformation is usually done by appending new columns to the input DataFrame, so all input columns are carried over.

    We try to make each transformer minimal, so it becomes flexible to assemble feature transformation pipelines. Pipeline can be used to chain feature transformers, and VectorAssembler can be used to combine multiple feature transformations, for example:

    import org.apache.spark.ml.feature._
    import org.apache.spark.ml.Pipeline
    
    // a DataFrame with three columns: id (integer), text (string), and rating (double).
    val df = spark.createDataFrame(Seq(
      (0, "Hi I heard about Spark", 3.0),
      (1, "I wish Java could use case classes", 4.0),
      (2, "Logistic regression models are neat", 4.0)
    )).toDF("id", "text", "rating")
    
    // define feature transformers
    val tok = new RegexTokenizer()
      .setInputCol("text")
      .setOutputCol("words")
    val sw = new StopWordsRemover()
      .setInputCol("words")
      .setOutputCol("filtered_words")
    val tf = new HashingTF()
      .setInputCol("filtered_words")
      .setOutputCol("tf")
      .setNumFeatures(10000)
    val idf = new IDF()
      .setInputCol("tf")
      .setOutputCol("tf_idf")
    val assembler = new VectorAssembler()
      .setInputCols(Array("tf_idf", "rating"))
      .setOutputCol("features")
    
    // assemble and fit the feature transformation pipeline
    val pipeline = new Pipeline()
      .setStages(Array(tok, sw, tf, idf, assembler))
    val model = pipeline.fit(df)
    
    // save transformed features with raw data
    model.transform(df)
      .select("id", "text", "rating", "features")
      .write.format("parquet").save("/output/path")

    Some feature transformers implemented in MLlib are inspired by those implemented in scikit-learn. The major difference is that most scikit-learn feature transformers operate eagerly on the entire input dataset, while MLlib's feature transformers operate lazily on individual columns, which is more efficient and flexible to handle large and complex datasets.

    Definition Classes
    ml
    See also

    scikit-learn.preprocessing

  • package fpm
    Definition Classes
    ml
  • package image
    Definition Classes
    ml
  • package linalg
    Definition Classes
    ml
  • package param
    Definition Classes
    ml
  • package recommendation
    Definition Classes
    ml
  • package regression
    Definition Classes
    ml
  • package source
    Definition Classes
    ml
  • package stat
    Definition Classes
    ml
  • package tree
    Definition Classes
    ml
  • package tuning
    Definition Classes
    ml
  • package util
    Definition Classes
    ml
  • DefaultParamsReadable
  • DefaultParamsWritable
  • GeneralMLWritable
  • GeneralMLWriter
  • Identifiable
  • MLFormatRegister
  • MLReadable
  • MLReader
  • MLWritable
  • MLWriter
  • MLWriterFormat

package util

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Type Members

  1. trait DefaultParamsReadable[T] extends MLReadable[T]

    :: DeveloperApi ::

    :: DeveloperApi ::

    Helper trait for making simple Params types readable. If a Params class stores all data as org.apache.spark.ml.param.Param values, then extending this trait will provide a default implementation of reading saved instances of the class. This only handles simple org.apache.spark.ml.param.Param types; e.g., it will not handle org.apache.spark.sql.Dataset.

    T

    ML instance type

    Annotations
    @DeveloperApi()
    See also

    DefaultParamsWritable, the counterpart to this trait

  2. trait DefaultParamsWritable extends MLWritable

    :: DeveloperApi ::

    :: DeveloperApi ::

    Helper trait for making simple Params types writable. If a Params class stores all data as org.apache.spark.ml.param.Param values, then extending this trait will provide a default implementation of writing saved instances of the class. This only handles simple org.apache.spark.ml.param.Param types; e.g., it will not handle org.apache.spark.sql.Dataset.

    Annotations
    @DeveloperApi()
    See also

    DefaultParamsReadable, the counterpart to this trait

  3. trait GeneralMLWritable extends MLWritable

    Trait for classes that provide GeneralMLWriter.

    Trait for classes that provide GeneralMLWriter.

    Annotations
    @Since( "2.4.0" ) @Unstable()
  4. class GeneralMLWriter extends MLWriter with Logging

    A ML Writer which delegates based on the requested format.

    A ML Writer which delegates based on the requested format.

    Annotations
    @Unstable() @Since( "2.4.0" )
  5. trait Identifiable extends AnyRef

    :: DeveloperApi ::

    :: DeveloperApi ::

    Trait for an object with an immutable unique ID that identifies itself and its derivatives.

    WARNING: There have not yet been final discussions on this API, so it may be broken in future releases.

    Annotations
    @DeveloperApi()
  6. trait MLFormatRegister extends MLWriterFormat

    ML export formats for should implement this trait so that users can specify a shortname rather than the fully qualified class name of the exporter.

    ML export formats for should implement this trait so that users can specify a shortname rather than the fully qualified class name of the exporter.

    A new instance of this class will be instantiated each time a save call is made.

    Annotations
    @Unstable() @Since( "2.4.0" )
    Since

    2.4.0

  7. trait MLReadable[T] extends AnyRef

    Trait for objects that provide MLReader.

    Trait for objects that provide MLReader.

    T

    ML instance type

    Annotations
    @Since( "1.6.0" )
  8. abstract class MLReader[T] extends BaseReadWrite

    Abstract class for utility classes that can load ML instances.

    Abstract class for utility classes that can load ML instances.

    T

    ML instance type

    Annotations
    @Since( "1.6.0" )
  9. trait MLWritable extends AnyRef

    Trait for classes that provide MLWriter.

    Trait for classes that provide MLWriter.

    Annotations
    @Since( "1.6.0" )
  10. abstract class MLWriter extends BaseReadWrite with Logging

    Abstract class for utility classes that can save ML instances in Spark's internal format.

    Abstract class for utility classes that can save ML instances in Spark's internal format.

    Annotations
    @Since( "1.6.0" )
  11. trait MLWriterFormat extends AnyRef

    Abstract class to be implemented by objects that provide ML exportability.

    Abstract class to be implemented by objects that provide ML exportability.

    A new instance of this class will be instantiated each time a save call is made.

    Must have a valid zero argument constructor which will be called to instantiate.

    Annotations
    @Unstable() @Since( "2.4.0" )
    Since

    2.4.0

Value Members

  1. object Identifiable

    :: DeveloperApi ::

    :: DeveloperApi ::

    Annotations
    @DeveloperApi()

Members