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

package regression

Definition Classes: ml

package source

Definition Classes: ml

package stat

Definition Classes: ml

package distribution

ChiSquareTest

Correlation

KolmogorovSmirnovTest

Summarizer

SummaryBuilder

package tree

Definition Classes: ml

package tuning

Definition Classes: ml

package util

Definition Classes: ml

org.apache.spark.ml

stat

package stat

Ordering

Alphabetic

Visibility

Public
All

Type Members

sealed abstract class SummaryBuilder extends AnyRef
A builder object that provides summary statistics about a given column.
A builder object that provides summary statistics about a given column.
Users should not directly create such builders, but instead use one of the methods in Summarizer.

Annotations
@Since( "2.3.0" )

Value Members

object ChiSquareTest
Chi-square hypothesis testing for categorical data.
Chi-square hypothesis testing for categorical data.
See Wikipedia for more information on the Chi-squared test.

Annotations
@Since( "2.2.0" )
object Correlation
API for correlation functions in MLlib, compatible with DataFrames and Datasets.
API for correlation functions in MLlib, compatible with DataFrames and Datasets.
The functions in this package generalize the functions in org.apache.spark.sql.Dataset#stat to spark.ml's Vector types.

Annotations
@Since( "2.2.0" )
object KolmogorovSmirnovTest
Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a continuous distribution.
Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a continuous distribution. By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution. For more information on KS Test:

Annotations
@Since( "2.4.0" )
See also
Kolmogorov-Smirnov test (Wikipedia)
object Summarizer extends Logging
Tools for vectorized statistics on MLlib Vectors.
Tools for vectorized statistics on MLlib Vectors.
The methods in this package provide various statistics for Vectors contained inside DataFrames.
This class lets users pick the statistics they would like to extract for a given column. Here is an example in Scala:
```
import org.apache.spark.ml.linalg._
import org.apache.spark.sql.Row
val dataframe = ... // Some dataframe containing a feature column and a weight column
val multiStatsDF = dataframe.select(
    Summarizer.metrics("min", "max", "count").summary($"features", $"weight")
val Row(minVec, maxVec, count) = multiStatsDF.first()
```
If one wants to get a single metric, shortcuts are also available:
```
val meanDF = dataframe.select(Summarizer.mean($"features"))
val Row(meanVec) = meanDF.first()
```
Note: Currently, the performance of this interface is about 2x~3x slower than using the RDD interface.
Annotations
@Since( "2.3.0" )

Packages

ML attributes

ML columns

Feature transformers

stat 

package stat

Type Members

Value Members

Members

stat