spark-instrumented-optimizer/docs/mllib-guide.md

layout	title
global	Machine Learning Library (MLlib)

MLlib is Spark's scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below:

• Data types
• Basic statistics
  • data generators
  • stratified sampling
  • summary statistics
  • hypothesis testing
• Classification and regression
  • linear models (SVMs, logistic regression, linear regression)
  • decision trees
  • naive Bayes
• Collaborative filtering
  • alternating least squares (ALS)
• Clustering
  • k-means
• Dimensionality reduction
  • singular value decomposition (SVD)
  • principal component analysis (PCA)
• Feature extraction and transformation
• Optimization (developer)
  • stochastic gradient descent
  • limited-memory BFGS (L-BFGS)

MLlib is under active development. The APIs marked Experimental/DeveloperApi may change in future releases, and the migration guide below will explain all changes between releases.

Dependencies

MLlib uses the linear algebra package 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.

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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. Details are described below.

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:

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

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

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:

{% highlight java %} 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 {% endhighlight %}

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.

{% highlight python %} 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])) {% endhighlight %}