Machine Learning Library (MLlib)

MLlib is a Spark implementation of some common machine learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives:

MLlib is a new component under active development. The APIs marked Experimental/DeveloperApi may change in future releases, and we will provide migration guide between releases.

Dependencies

MLlib uses linear algebra packages Breeze, which depends on netlib-java, and jblas. netlib-java and jblas depend on native Fortran routines. You need to install the gfortran runtime library if it is not already present on your nodes. MLlib will throw a linking error if it cannot detect these libraries automatically. Due to license issues, we do not include netlib-java’s native libraries in MLlib’s dependency set. If no native library is available at runtime, you will see a warning message. To use native libraries from netlib-java, please include artifact com.github.fommil.netlib:all:1.1.2 as a dependency of your project or build your own (see instructions).

To use MLlib in Python, you will need NumPy version 1.4 or newer.

Migration Guide

From 0.9 to 1.0

In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few breaking changes. If your data is sparse, please store it in a sparse format instead of dense to take advantage of sparsity in both storage and computation.

We used to represent a feature vector by Array[Double], which is replaced by Vector in v1.0. Algorithms that used to accept RDD[Array[Double]] now take RDD[Vector]. LabeledPoint is now a wrapper of (Double, Vector) instead of (Double, Array[Double]). Converting Array[Double] to Vector is straightforward:

import org.apache.spark.mllib.linalg.{Vector, Vectors}

val array: Array[Double] = ... // a double array
val vector: Vector = Vectors.dense(array) // a dense vector

Vectors provides factory methods to create sparse vectors.

Note. Scala imports scala.collection.immutable.Vector by default, so you have to import org.apache.spark.mllib.linalg.Vector explicitly to use MLlib’s Vector.

We used to represent a feature vector by double[], which is replaced by Vector in v1.0. Algorithms that used to accept RDD<double[]> now take RDD<Vector>. LabeledPoint is now a wrapper of (double, Vector) instead of (double, double[]). Converting double[] to Vector is straightforward:

import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;

double[] array = ... // a double array
Vector vector = Vectors.dense(array); // a dense vector

Vectors provides factory methods to create sparse vectors.

We used to represent a labeled feature vector in a NumPy array, where the first entry corresponds to the label and the rest are features. This representation is replaced by class LabeledPoint, which takes both dense and sparse feature vectors.

from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint

# Create a labeled point with a positive label and a dense feature vector.
pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])

# Create a labeled point with a negative label and a sparse feature vector.
neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))