flink-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From GitBox <...@apache.org>
Subject [GitHub] zentol closed pull request #6425: [FLINK-9664][Doc] fixing documentation in ML quick start
Date Tue, 14 Aug 2018 10:13:36 GMT
zentol closed pull request #6425: [FLINK-9664][Doc] fixing documentation in ML quick start
URL: https://github.com/apache/flink/pull/6425

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/docs/dev/libs/ml/quickstart.md b/docs/dev/libs/ml/quickstart.md
index ea6f8049755..e056b28b505 100644
--- a/docs/dev/libs/ml/quickstart.md
+++ b/docs/dev/libs/ml/quickstart.md
@@ -129,15 +129,14 @@ and the [test set here](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/b
 This is an astroparticle binary classification dataset, used by Hsu et al. [[3]](#hsu) in
 practical Support Vector Machine (SVM) guide. It contains 4 numerical features, and the class
-We can simply import the dataset then using:
+We can simply import the dataset using:
 {% highlight scala %}
 import org.apache.flink.ml.MLUtils
-val astroTrain: DataSet[LabeledVector] = MLUtils.readLibSVM(env, "/path/to/svmguide1")
-val astroTest: DataSet[(Vector, Double)] = MLUtils.readLibSVM(env, "/path/to/svmguide1.t")
-      .map(x => (x.vector, x.label))
+val astroTrainLibSVM: DataSet[LabeledVector] = MLUtils.readLibSVM(env, "/path/to/svmguide1")
+val astroTestLibSVM: DataSet[LabeledVector] = MLUtils.readLibSVM(env, "/path/to/svmguide1.t")
 {% endhighlight %}
@@ -146,7 +145,23 @@ create a classifier.
 ## Classification
-Once we have imported the dataset we can train a `Predictor` such as a linear SVM classifier.
+After importing the training and test dataset, they need to be prepared for the classification.

+Since Flink SVM only supports threshold binary values of `+1.0` and `-1.0`, a conversion
+needed after loading the LibSVM dataset because it is labelled using `1`s and `0`s.
+A conversion can be done using a simple normalizer mapping function:
+{% highlight scala %}
+def normalizer : LabeledVector => LabeledVector = { 
+    lv => LabeledVector(if (lv.label > 0.0) 1.0 else -1.0, lv.vector)
+val astroTrain: DataSet[LabeledVector] = astroTrainLibSVM.map(normalizer)
+val astroTest: DataSet[(Vector, Double)] = astroTestLibSVM.map(normalizer).map(x => (x.vector,
+{% endhighlight %}
+Once we have converted 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


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:

With regards,
Apache Git Services

View raw message