##### Site index · List index
Message view
Top
From fhue...@apache.org
Date Fri, 12 Jun 2015 13:33:08 GMT
[FLINK-2072] [ml] Adds quickstart guide

This closes #792.

Commit: 40e2df5acf9385cc3c6e3a947b4bf6cd2bd375b3
Parents: af0fee5
Author: Theodore Vasiloudis <tvas@sics.se>
Authored: Fri Jun 5 11:09:11 2015 +0200
Committer: Fabian Hueske <fhueske@apache.org>
Committed: Fri Jun 12 14:27:29 2015 +0200

----------------------------------------------------------------------
docs/libs/ml/contribution_guide.md |  10 +-
docs/libs/ml/index.md              |  27 ++--
docs/libs/ml/quickstart.md         | 216 +++++++++++++++++++++++++++++++-
3 files changed, 235 insertions(+), 18 deletions(-)
----------------------------------------------------------------------

----------------------------------------------------------------------
diff --git a/docs/libs/ml/contribution_guide.md b/docs/libs/ml/contribution_guide.md
index 89f05c0..f0754cb 100644
--- a/docs/libs/ml/contribution_guide.md
+++ b/docs/libs/ml/contribution_guide.md
@@ -36,7 +36,7 @@ Everything from this guide also applies to FlinkML.

## Pick a Topic

-If you are looking for some new ideas, then you should check out the list of [unresolved
then you should check out the list of [unresolved issues on JIRA](https://issues.apache.org/jira/issues/?jql=component%20%3D%20%22Machine%20Learning%20Library%22%20AND%20project%20%3D%20FLINK%20AND%20resolution%20%3D%20Unresolved%20ORDER%20BY%20priority%20DESC).
Once you decide to contribute to one of these issues, you should take ownership of it and
track your progress with this issue.
That way, the other contributors know the state of the different issues and redundant work
is avoided.

@@ -61,7 +61,7 @@ Thus, an integration test could look the following:
{% highlight scala %}
class ExampleITSuite extends FlatSpec with FlinkTestBase {
behavior of "An example algorithm"
-
+
it should "do something" in {
...
}
@@ -81,12 +81,12 @@ Every new algorithm is described by a single markdown file.
This file should contain at least the following points:

1. What does the algorithm do
-2. How does the algorithm work (or reference to description)
+2. How does the algorithm work (or reference to description)
3. Parameter description with default values
4. Code snippet showing how the algorithm is used

In order to use latex syntax in the markdown file, you have to include mathjax: include
in the YAML front matter.
-
+
{% highlight java %}
---
mathjax: include
@@ -103,4 +103,4 @@ See docs/_include/latex_commands.html for the complete list of predefined
late
## Contributing

Once you have implemented the algorithm with adequate test coverage and added documentation,
you are ready to open a pull request.
-Details of how to open a pull request can be found [here](http://flink.apache.org/how-to-contribute.html#contributing-code--documentation).

+Details of how to open a pull request can be found [here](http://flink.apache.org/how-to-contribute.html#contributing-code--documentation).

----------------------------------------------------------------------
diff --git a/docs/libs/ml/index.md b/docs/libs/ml/index.md
index de9137d..9ff7a4b 100644
--- a/docs/libs/ml/index.md
+++ b/docs/libs/ml/index.md
@@ -21,9 +21,9 @@ under the License.
-->

FlinkML is the Machine Learning (ML) library for Flink. It is a new effort in the Flink community,
-with a growing list of algorithms and contributors. With FlinkML we aim to provide
-scalable ML algorithms, an intuitive API, and tools that help minimize glue code in end-to-end
ML
-systems. You can see more details about our goals and where the library is headed in our
[vision
+with a growing list of algorithms and contributors. With FlinkML we aim to provide
+scalable ML algorithms, an intuitive API, and tools that help minimize glue code in end-to-end
ML
+systems. You can see more details about our goals and where the library is headed in our
[vision

* This will be replaced by the TOC
@@ -55,10 +55,13 @@ FlinkML currently supports the following algorithms:

## Getting Started

-Next, you have to add the FlinkML dependency to the pom.xml of your project.
+You can check out our [quickstart guide](quickstart.html) for a comprehensive getting started
+example.

-{% highlight bash %}
+Next, you have to add the FlinkML dependency to the pom.xml of your project.
+
+{% highlight xml %}
<dependency>
@@ -85,12 +88,11 @@ mlr.fit(trainingData, parameters)
val predictions: DataSet[LabeledVector] = mlr.predict(testingData)
{% endhighlight %}

-For a more comprehensive guide, please check out our [quickstart guide](quickstart.html)
-
## Pipelines

A key concept of FlinkML is its [scikit-learn](http://scikit-learn.org) inspired pipelining
mechanism.
It allows you to quickly build complex data analysis pipelines how they appear in every data
scientist's daily work.
+An in-depth description of FlinkML's pipelines and their internal workings can be found [here](pipelines.html).

The following example code shows how easy it is to set up an analysis pipeline with FlinkML.

@@ -110,13 +112,14 @@ pipeline.fit(trainingData)

// Calculate predictions
val predictions: DataSet[LabeledVector] = pipeline.predict(testingData)
-{% endhighlight %}
+{% endhighlight %}

One can chain a Transformer to another Transformer or a set of chained Transformers
by calling the method chainTransformer.
-If one wants to chain a Predictor to a Transformer or a set of chained Transformers,
one has to call the method chainPredictor.
-An in-depth description of FlinkML's pipelines and their internal workings can be found [here](pipelines.html).
+If one wants to chain a Predictor to a Transformer or a set of chained Transformers,
one has to call the method chainPredictor.
+

## How to contribute

The Flink community welcomes all contributors who want to get involved in the development
[contribution guide]({{site.baseurl}}/libs/ml/contribution_guide.html).
\ No newline at end of file
+[contribution guide]({{site.baseurl}}/libs/ml/contribution_guide.html).

----------------------------------------------------------------------
diff --git a/docs/libs/ml/quickstart.md b/docs/libs/ml/quickstart.md
index b8501f8..f5d7451 100644
--- a/docs/libs/ml/quickstart.md
+++ b/docs/libs/ml/quickstart.md
@@ -1,4 +1,5 @@
---
+mathjax: include
title: <a href="../ml">FlinkML</a> - Quickstart Guide
---
@@ -24,4 +25,217 @@ under the License.
* This will be replaced by the TOC
{:toc}

-Coming soon.
+## Introduction
+
+FlinkML is designed to make learning from your data a straight-forward process, abstracting
away
+the complexities that usually come with big data learning tasks. In this
+quick-start guide we will show just how easy it is to solve a simple supervised learning
problem
+using FlinkML. But first some basics, feel free to skip the next few lines if you're already
+familiar with Machine Learning (ML).
+
+As defined by Murphy [[1]](#murphy) ML deals with detecting patterns in data, and using those
+learned patterns to make predictions about the future. We can categorize most ML algorithms
into
+two major categories: Supervised and Unsupervised Learning.
+
+* **Supervised Learning** deals with learning a function (mapping) from a set of inputs
+(features) to a set of outputs. The learning is done using a *training set* of (input,
+output) pairs that we use to approximate the mapping function. Supervised learning problems
are
+further divided into classification and regression problems. In classification problems we
try to
+predict the *class* that an example belongs to, for example whether a user is going to click
on
+an ad or not. Regression problems one the other hand, are about predicting (real) numerical
+values, often called the dependent variable, for example what the temperature will be tomorrow.
+
+* **Unsupervised Learning** deals with discovering patterns and regularities in the data.
An example
+of this would be *clustering*, where we try to discover groupings of the data from the
+descriptive features. Unsupervised learning can also be used for feature selection, for example
+through [principal components analysis](https://en.wikipedia.org/wiki/Principal_component_analysis).
+
+
+In order to use FlinkML in your project, first you have to
+Next, you have to add the FlinkML dependency to the pom.xml of your project:
+
+{% highlight xml %}
+<dependency>
+  <version>{{site.version }}</version>
+</dependency>
+{% endhighlight %}
+
+
+To load data to be used with FlinkML we can use the ETL capabilities of Flink, or specialized
+functions for formatted data, such as the LibSVM format. For supervised learning problems
it is
+common to use the LabeledVector class to represent the (label, features) examples. A
LabeledVector
+object will have a FlinkML Vector member representing the features of the example and a
Double
+member which represents the label, which could be the class in a classification problem,
or the dependent
+variable for a regression problem.
+
+As an example, we can use Haberman's Survival Data Set , which you can
+This dataset *"contains cases from a study conducted on the survival of patients who had
undergone
+surgery for breast cancer"*. The data comes in a comma-separated file, where the first 3
columns
+are the features and last column is the class, and the 4th column indicates whether the patient
+survived 5 years or longer (label 1), or died within 5 years (label 2). You can check the
[UCI
on the data.
+
+We can load the data as a DataSet[String] first:
+
+{% highlight scala %}
+
+
+val env = ExecutionEnvironment.getExecutionEnvironment
+
+val survival = env.readCsvFile[(String, String, String, String)]("/path/to/haberman.data")
+
+{% endhighlight %}
+
+We can now transform the data into a DataSet[LabeledVector]. This will allow us to use
the
+dataset with the FlinkML classification algorithms. We know that the 4th element of the dataset
+is the class label, and the rest are features, so we can build LabeledVector elements like
this:
+
+{% highlight scala %}
+
+
+val survivalLV = survival
+  .map{tuple =>
+    val list = tuple.productIterator.toList
+    val numList = list.map(_.asInstanceOf[String].toDouble)
+    LabeledVector(numList(3), DenseVector(numList.take(3).toArray))
+  }
+
+{% endhighlight %}
+
+We can then use this data to train a learner. We will however use another dataset to exemplify
+building a learner; that will allow us to show how we can import other dataset formats.
+
+**LibSVM files**
+
+A common format for ML datasets is the LibSVM format and a number of datasets using that
format can be
+found [in the LibSVM datasets website](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/).
+datasets using the LibSVM format through the readLibSVM function available through the
MLUtils
+object.
+You can also save datasets in the LibSVM format using the writeLibSVM function.
+[training set here](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/svmguide1)
+and the [test set here](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/svmguide1.t).
+This is an astroparticle binary classification dataset, used by Hsu et al. [[3]](#hsu) in
their
+practical Support Vector Machine (SVM) guide. It contains 4 numerical features, and the class
label.
+
+We can simply import the dataset then using:
+
+{% highlight scala %}
+
+
+
+{% endhighlight %}
+
+This gives us two DataSet[LabeledVector] objects that we will use in the following section
to
+create a classifier.
+
+## Classification
+
+Once we have imported the dataset we can train a Predictor such as a linear SVM classifier.
+We can set a number of parameters for the classifier. Here we set the Blocks parameter,
+which is used to split the input by the underlying CoCoA algorithm [[2]](#jaggi) uses. The

+regularization parameter determines the amount of $l_2$ regularization applied, which is
used
+to avoid overfitting. The step size determines the contribution of the weight vector updates
to
+the next weight vector value. This parameter sets the initial step size.
+
+{% highlight scala %}
+
+
+val svm = SVM()
+  .setBlocks(env.getParallelism)
+  .setIterations(100)
+  .setRegularization(0.001)
+  .setStepsize(0.1)
+  .setSeed(42)
+
+svm.fit(astroTrain)
+
+{% endhighlight %}
+
+We can now make predictions on the test set.
+
+{% highlight scala %}
+
+val predictionPairs = svm.predict(astroTest)
+
+{% endhighlight %}
+
+Next we will see how we can pre-process our data, and use the ML pipelines capabilities of
+
+## Data pre-processing and pipelines
+
+A pre-processing step that is often encouraged [[3]](#hsu) when using SVM classification
is scaling
+the input features to the [0, 1] range, in order to avoid features with extreme values
+dominating the rest.
+FlinkML has a number of Transformers such as MinMaxScaler that are used to pre-process
data,
+and a key feature is the ability to chain Transformers and Predictors together. This
allows
+us to run the same pipeline of transformations and make predictions on the train and test
data in
+a straight-forward and type-safe manner. You can read more on the pipeline system of FlinkML
+[in the pipelines documentation](pipelines.html).
+
+Let us first create a normalizing transformer for the features in our dataset, and chain
it to a
+new SVM classifier.
+
+{% highlight scala %}
+
+
+val scaler = MinMaxScaler()
+
+val scaledSVM = scaler.chainPredictor(svm)
+
+{% endhighlight %}
+
+We can now use our newly created pipeline to make predictions on the test set.
+First we call fit again, to train the scaler and the SVM classifier.
+The data of the test set will then be automatically scaled before being passed on to the
SVM to
+make predictions.
+
+{% highlight scala %}
+
+scaledSVM.fit(astroTrain)
+
+val predictionPairsScaled: DataSet[(Double, Double)] = scaledSVM.predict(astroTest)
+
+{% endhighlight %}
+
+The scaled inputs should give us better prediction performance.
+The result of the prediction on LabeledVectors is a data set of tuples where the first
entry denotes the true label value and the second entry is the predicted label value.
+
+## Where to go from here
+
+This quickstart guide can act as an introduction to the basic concepts of FlinkML, but there's
a lot
+more you can do.
+We recommend going through the [FlinkML documentation](index.html), and trying out the different
+algorithms.
+A very good way to get started is to play around with interesting datasets from the UCI ML
+repository and the LibSVM datasets.
+Tackling an interesting problem from a website like [Kaggle](https://www.kaggle.com) or
+[DrivenData](http://www.drivendata.org/) is also a great way to learn by competing with other
+data scientists.
+If you would like to contribute some new algorithms take a look at our
+[contribution guide](contribution_guide.html).
+
+**References**
+
+<a name="murphy"></a>[1] Murphy, Kevin P. *Machine learning: a probabilistic
perspective.* MIT
+press, 2012.
+
+<a name="jaggi"></a>[2] Jaggi, Martin, et al. *Communication-efficient distributed
dual
+coordinate ascent.* Advances in Neural Information Processing Systems. 2014.
+
+<a name="hsu"></a>[3] Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen Lin.
+ *A practical guide to support vector classification.* 2003.


Mime
View raw message