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.

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

ALS

ALSModel

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

org.apache.spark.ml

recommendation

package recommendation

Ordering

Alphabetic

Visibility

Public
All

Type Members

class ALS extends Estimator[ALSModel] with ALSParams with DefaultParamsWritable
Alternating Least Squares (ALS) matrix factorization.
Alternating Least Squares (ALS) matrix factorization.
ALS attempts to estimate the ratings matrix R as the product of two lower-rank matrices, X and Y, i.e. X * Yt = R. Typically these approximations are called 'factor' matrices. The general approach is iterative. During each iteration, one of the factor matrices is held constant, while the other is solved for using least squares. The newly-solved factor matrix is then held constant while solving for the other factor matrix.
This is a blocked implementation of the ALS factorization algorithm that groups the two sets of factors (referred to as "users" and "products") into blocks and reduces communication by only sending one copy of each user vector to each product block on each iteration, and only for the product blocks that need that user's feature vector. This is achieved by pre-computing some information about the ratings matrix to determine the "out-links" of each user (which blocks of products it will contribute to) and "in-link" information for each product (which of the feature vectors it receives from each user block it will depend on). This allows us to send only an array of feature vectors between each user block and product block, and have the product block find the users' ratings and update the products based on these messages.
For implicit preference data, the algorithm used is based on "Collaborative Filtering for Implicit Feedback Datasets", available at https://doi.org/10.1109/ICDM.2008.22, adapted for the blocked approach used here.
Essentially instead of finding the low-rank approximations to the rating matrix R, this finds the approximations for a preference matrix P where the elements of P are 1 if r is greater than 0 and 0 if r is less than or equal to 0. The ratings then act as 'confidence' values related to strength of indicated user preferences rather than explicit ratings given to items.
Note: the input rating dataset to the ALS implementation should be deterministic. Nondeterministic data can cause failure during fitting ALS model. For example, an order-sensitive operation like sampling after a repartition makes dataset output nondeterministic, like dataset.repartition(2).sample(false, 0.5, 1618). Checkpointing sampled dataset or adding a sort before sampling can help make the dataset deterministic.

Annotations
@Since( "1.3.0" )
class ALSModel extends Model[ALSModel] with ALSModelParams with MLWritable
Model fitted by ALS.
Model fitted by ALS.

Annotations
@Since( "1.3.0" )

Value Members

object ALS extends DefaultParamsReadable[ALS] with Logging with Serializable
An implementation of ALS that supports generic ID types, specialized for Int and Long.
An implementation of ALS that supports generic ID types, specialized for Int and Long. This is exposed as a developer API for users who do need other ID types. But it is not recommended because it increases the shuffle size and memory requirement during training. For simplicity, users and items must have the same type. The number of distinct users/items should be smaller than 2 billion.
object ALSModel extends MLReadable[ALSModel] with Serializable

Annotations
@Since( "1.6.0" )

Packages

ML attributes

ML columns

Feature transformers

recommendation 

package recommendation

Type Members

Value Members

Members

recommendation