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 shared
  • BooleanParam
  • DoubleArrayArrayParam
  • DoubleArrayParam
  • DoubleParam
  • FloatParam
  • IntArrayParam
  • IntParam
  • JavaParams
  • LongParam
  • Param
  • ParamMap
  • ParamPair
  • ParamValidators
  • Params
  • StringArrayParam
  • 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

package param

Ordering
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Visibility
  1. Public
  2. All

Type Members

  1. class BooleanParam extends Param[Boolean]

    :: DeveloperApi :: Specialized version of Param[Boolean] for Java.

    :: DeveloperApi :: Specialized version of Param[Boolean] for Java.

    Annotations
    @DeveloperApi()
  2. class DoubleArrayArrayParam extends Param[Array[Array[Double]]]

    :: DeveloperApi :: Specialized version of Param[Array[Array[Double]]] for Java.

    :: DeveloperApi :: Specialized version of Param[Array[Array[Double]]] for Java.

    Annotations
    @DeveloperApi()
  3. class DoubleArrayParam extends Param[Array[Double]]

    :: DeveloperApi :: Specialized version of Param[Array[Double]] for Java.

    :: DeveloperApi :: Specialized version of Param[Array[Double]] for Java.

    Annotations
    @DeveloperApi()
  4. class DoubleParam extends Param[Double]

    :: DeveloperApi :: Specialized version of Param[Double] for Java.

    :: DeveloperApi :: Specialized version of Param[Double] for Java.

    Annotations
    @DeveloperApi()
  5. class FloatParam extends Param[Float]

    :: DeveloperApi :: Specialized version of Param[Float] for Java.

    :: DeveloperApi :: Specialized version of Param[Float] for Java.

    Annotations
    @DeveloperApi()
  6. class IntArrayParam extends Param[Array[Int]]

    :: DeveloperApi :: Specialized version of Param[Array[Int]] for Java.

    :: DeveloperApi :: Specialized version of Param[Array[Int]] for Java.

    Annotations
    @DeveloperApi()
  7. class IntParam extends Param[Int]

    :: DeveloperApi :: Specialized version of Param[Int] for Java.

    :: DeveloperApi :: Specialized version of Param[Int] for Java.

    Annotations
    @DeveloperApi()
  8. abstract class JavaParams extends Params

    :: DeveloperApi :: Java-friendly wrapper for Params.

    :: DeveloperApi :: Java-friendly wrapper for Params. Java developers who need to extend Params should use this class instead. If you need to extend an abstract class which already extends Params, then that abstract class should be Java-friendly as well.

    Annotations
    @DeveloperApi()
  9. class LongParam extends Param[Long]

    :: DeveloperApi :: Specialized version of Param[Long] for Java.

    :: DeveloperApi :: Specialized version of Param[Long] for Java.

    Annotations
    @DeveloperApi()
  10. class Param[T] extends Serializable

    :: DeveloperApi :: A param with self-contained documentation and optionally default value.

    :: DeveloperApi :: A param with self-contained documentation and optionally default value. Primitive-typed param should use the specialized versions, which are more friendly to Java users.

    T

    param value type

    Annotations
    @DeveloperApi()
  11. final class ParamMap extends Serializable

    A param to value map.

    A param to value map.

    Annotations
    @Since( "1.2.0" )
  12. case class ParamPair[T](param: Param[T], value: T) extends Product with Serializable

    A param and its value.

    A param and its value.

    Annotations
    @Since( "1.2.0" )
  13. trait Params extends Identifiable with Serializable

    :: DeveloperApi :: Trait for components that take parameters.

    :: DeveloperApi :: Trait for components that take parameters. This also provides an internal param map to store parameter values attached to the instance.

    Annotations
    @DeveloperApi()
  14. class StringArrayParam extends Param[Array[String]]

    :: DeveloperApi :: Specialized version of Param[Array[String]] for Java.

    :: DeveloperApi :: Specialized version of Param[Array[String]] for Java.

    Annotations
    @DeveloperApi()

Value Members

  1. object ParamMap extends Serializable
    Annotations
    @Since( "1.2.0" )
  2. object ParamValidators

    :: DeveloperApi :: Factory methods for common validation functions for Param.isValid.

    :: DeveloperApi :: Factory methods for common validation functions for Param.isValid. The numerical methods only support Int, Long, Float, and Double.

    Annotations
    @DeveloperApi()

Members