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

layout	title
global	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:

• Basics
  • data types
  • summary statistics
• Classification and regression
  • linear support vector machine (SVM)
  • logistic regression
  • linear least squares, Lasso, and ridge regression
  • decision tree
  • naive Bayes
• Collaborative filtering
  • alternating least squares (ALS)
• Clustering
  • k-means
• Dimensionality reduction
  • singular value decomposition (SVD)
  • principal component analysis (PCA)
• Optimization
  • stochastic gradient descent
  • limited-memory BFGS (L-BFGS)

MLlib is currently a beta component under active development. The APIs may change in the 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:

{% 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 %}