25ad8f9301
While play-testing the Scala and Java code examples in the MLlib docs, I noticed a number of small compile errors, and some typos. This led to finding and fixing a few similar items in other docs. Then in the course of building the site docs to check the result, I found a few small suggestions for the build instructions. I also found a few more formatting and markdown issues uncovered when I accidentally used maruku instead of kramdown. Author: Sean Owen <sowen@cloudera.com> Closes #653 from srowen/SPARK-1727 and squashes the following commits: 6e7c38a [Sean Owen] Final doc updates - one more compile error, and use of mean instead of sum and count 8f5e847 [Sean Owen] Fix markdown syntax issues that maruku flags, even though we use kramdown (but only those that do not affect kramdown's output) 99966a9 [Sean Owen] Update issue tracker URL in docs 23c9ac3 [Sean Owen] Add Scala Naive Bayes example, to use existing example data file (whose format needed a tweak) 8c81982 [Sean Owen] Fix small compile errors and typos across MLlib docs
124 lines
5.2 KiB
Markdown
124 lines
5.2 KiB
Markdown
---
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layout: global
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title: Machine Learning Library (MLlib)
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---
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MLlib is a Spark implementation of some common machine learning algorithms and utilities,
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including classification, regression, clustering, collaborative
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filtering, dimensionality reduction, as well as underlying optimization primitives:
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* [Basics](mllib-basics.html)
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* data types
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* summary statistics
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* Classification and regression
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* [linear support vector machine (SVM)](mllib-linear-methods.html#linear-support-vector-machine-svm)
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* [logistic regression](mllib-linear-methods.html#logistic-regression)
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* [linear least squares, Lasso, and ridge regression](mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression)
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* [decision tree](mllib-decision-tree.html)
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* [naive Bayes](mllib-naive-bayes.html)
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* [Collaborative filtering](mllib-collaborative-filtering.html)
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* alternating least squares (ALS)
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* [Clustering](mllib-clustering.html)
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* k-means
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* [Dimensionality reduction](mllib-dimensionality-reduction.html)
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* singular value decomposition (SVD)
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* principal component analysis (PCA)
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* [Optimization](mllib-optimization.html)
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* stochastic gradient descent
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* limited-memory BFGS (L-BFGS)
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MLlib is currently a *beta* component under active development.
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The APIs may change in the future releases, and we will provide migration guide between releases.
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## Dependencies
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MLlib uses linear algebra packages [Breeze](http://www.scalanlp.org/), which depends on
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[netlib-java](https://github.com/fommil/netlib-java), and
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[jblas](https://github.com/mikiobraun/jblas).
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`netlib-java` and `jblas` depend on native Fortran routines.
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You need to install the
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[gfortran runtime library](https://github.com/mikiobraun/jblas/wiki/Missing-Libraries) if it is not
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already present on your nodes. MLlib will throw a linking error if it cannot detect these libraries
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automatically. Due to license issues, we do not include `netlib-java`'s native libraries in MLlib's
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dependency set. If no native library is available at runtime, you will see a warning message. To
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use native libraries from `netlib-java`, please include artifact
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`com.github.fommil.netlib:all:1.1.2` as a dependency of your project or build your own (see
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[instructions](https://github.com/fommil/netlib-java/blob/master/README.md#machine-optimised-system-libraries)).
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To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer.
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---
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## Migration guide
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### From 0.9 to 1.0
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In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
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breaking changes. If your data is sparse, please store it in a sparse format instead of dense to
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take advantage of sparsity in both storage and computation.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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We used to represent a feature vector by `Array[Double]`, which is replaced by
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[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
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to accept `RDD[Array[Double]]` now take
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`RDD[Vector]`. [`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint)
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is now a wrapper of `(Double, Vector)` instead of `(Double, Array[Double])`. Converting
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`Array[Double]` to `Vector` is straightforward:
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{% highlight scala %}
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import org.apache.spark.mllib.linalg.{Vector, Vectors}
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val array: Array[Double] = ... // a double array
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val vector: Vector = Vectors.dense(array) // a dense vector
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{% endhighlight %}
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[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.
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*Note*. Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`.
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</div>
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<div data-lang="java" markdown="1">
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We used to represent a feature vector by `double[]`, which is replaced by
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[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
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to accept `RDD<double[]>` now take
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`RDD<Vector>`. [`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint)
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is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to
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`Vector` is straightforward:
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{% highlight java %}
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import org.apache.spark.mllib.linalg.Vector;
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import org.apache.spark.mllib.linalg.Vectors;
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double[] array = ... // a double array
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Vector vector = Vectors.dense(array); // a dense vector
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{% endhighlight %}
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[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to
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create sparse vectors.
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</div>
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<div data-lang="python" markdown="1">
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We used to represent a labeled feature vector in a NumPy array, where the first entry corresponds to
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the label and the rest are features. This representation is replaced by class
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[`LabeledPoint`](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html), which takes both
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dense and sparse feature vectors.
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{% highlight python %}
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from pyspark.mllib.linalg import SparseVector
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from pyspark.mllib.regression import LabeledPoint
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# Create a labeled point with a positive label and a dense feature vector.
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pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])
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# Create a labeled point with a negative label and a sparse feature vector.
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neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))
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{% endhighlight %}
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</div>
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</div>
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