The API is still not very Java-friendly because `Array[Item]` in `freqItemsets` is recognized as `Object` in Java. We might want to define a case class to wrap the return pair to make it Java friendly. Author: Xiangrui Meng <meng@databricks.com> Closes #4661 from mengxr/SPARK-5519 and squashes the following commits: 58ccc25 [Xiangrui Meng] add user guide with example code for fp-growth
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layout | title | displayTitle | description |
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global | MLlib | Machine Learning Library (MLlib) Guide | MLlib machine learning library overview for Spark SPARK_VERSION_SHORT |
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
- summary statistics
- correlations
- stratified sampling
- hypothesis testing
- random data generation
- Classification and regression
- linear models (SVMs, logistic regression, linear regression)
- naive Bayes
- decision trees
- ensembles of trees (Random Forests and Gradient-Boosted Trees)
- isotonic regression
- Collaborative filtering
- alternating least squares (ALS)
- Clustering
- Dimensionality reduction
- singular value decomposition (SVD)
- principal component analysis (PCA)
- Feature extraction and transformation
- Frequent pattern mining
- FP-growth
- 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.
spark.ml: high-level APIs for ML pipelines
Spark 1.2 includes a new package called spark.ml
, which aims to provide a uniform set of
high-level APIs that help users create and tune practical machine learning pipelines.
It is currently an alpha component, and we would like to hear back from the community about
how it fits real-world use cases and how it could be improved.
Note that we will keep supporting and adding features to spark.mllib
along with the
development of spark.ml
.
Users should be comfortable using spark.mllib
features and expect more features coming.
Developers should contribute new algorithms to spark.mllib
and can optionally contribute
to spark.ml
.
See the spark.ml programming guide for more information on this package.
Dependencies
MLlib uses the linear algebra package Breeze, which depends on netlib-java for optimised numerical processing. If natives are not available at runtime, you will see a warning message and a pure JVM implementation will be used instead.
To learn more about the benefits and background of system optimised natives, you may wish to watch Sam Halliday's ScalaX talk on High Performance Linear Algebra in Scala).
Due to licensing issues with runtime proprietary binaries, we do not
include netlib-java
's native proxies by default. To configure
netlib-java
/ Breeze to use system optimised binaries, include
com.github.fommil.netlib:all:1.1.2
(or build Spark with
-Pnetlib-lgpl
) as a dependency of your project and read the
netlib-java documentation for
your platform's additional installation instructions.
MLlib also uses jblas which will require you to install the gfortran runtime library if it is not already present on your nodes.
To use MLlib in Python, you will need NumPy version 1.4 or newer.
Migration Guide
From 1.1 to 1.2
The only API changes in MLlib v1.2 are in
DecisionTree
,
which continues to be an experimental API in MLlib 1.2:
-
(Breaking change) The Scala API for classification takes a named argument specifying the number of classes. In MLlib v1.1, this argument was called
numClasses
in Python andnumClassesForClassification
in Scala. In MLlib v1.2, the names are both set tonumClasses
. ThisnumClasses
parameter is specified either viaStrategy
or viaDecisionTree
statictrainClassifier
andtrainRegressor
methods. -
(Breaking change) The API for
Node
has changed. This should generally not affect user code, unless the user manually constructs decision trees (instead of using thetrainClassifier
ortrainRegressor
methods). The treeNode
now includes more information, including the probability of the predicted label (for classification). -
Printing methods' output has changed. The
toString
(Scala/Java) and__repr__
(Python) methods used to print the full model; they now print a summary. For the full model, usetoDebugString
.
Examples in the Spark distribution and examples in the Decision Trees Guide have been updated accordingly.
From 1.0 to 1.1
The only API changes in MLlib v1.1 are in
DecisionTree
,
which continues to be an experimental API in MLlib 1.1:
-
(Breaking change) The meaning of tree depth has been changed by 1 in order to match the implementations of trees in scikit-learn and in rpart. In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes. In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes. This depth is specified by the
maxDepth
parameter inStrategy
or viaDecisionTree
statictrainClassifier
andtrainRegressor
methods. -
(Non-breaking change) We recommend using the newly added
trainClassifier
andtrainRegressor
methods to build aDecisionTree
, rather than using the old parameter classStrategy
. These new training methods explicitly separate classification and regression, and they replace specialized parameter types with simpleString
types.
Examples of the new, recommended trainClassifier
and trainRegressor
are given in the
Decision Trees 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 %}