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https://issues.apache.org/jira/browse/FLINK-9664?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16573225#comment-16573225
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ASF GitHub Bot commented on FLINK-9664:
---------------------------------------
azagrebin commented on a change in pull request #6425: [FLINK-9664][Doc] fixing
documentation in ML quick start
URL: https://github.com/apache/flink/pull/6425#discussion_r208576999
##########
File path: docs/dev/libs/ml/quickstart.md
##########
@@ -129,6 +129,10 @@ 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 their
practical Support Vector Machine (SVM) guide. It contains 4 numerical
features, and the class label.
+Before importing the traning and test dataset, Flink SVM only supports
threshold binary values of
+`+1.0` and `-1.0`. Thus a conversion is needed upon downloading the svmguide1
dataset since it is
+labelled using `1`s and `0`s.
+
Review comment:
I think this section belongs to the beginning of the next one
`Classification`, because this one is about LibSVM format.
The code example of conversion could be also provided to make the example
fully 'copy-paste' runnable.
Small thing is also typo in `traning` -> `training`.
I would suggest to modify the code example in this `LibSVM files` section
like this:
```
val astroTrainLibSVM: DataSet[LabeledVector] = MLUtils.readLibSVM(env,
"/path/to/svmguide1")
val astroTestLibSVM: DataSet[LabeledVector] = MLUtils.readLibSVM(env,
"/path/to/svmguide1.t")
```
to have no SVM training specifics here, and add something like this to the
beginning of the `Classification` section:
_... After importing the training and test dataset, the data needs to be
prepared for the classification, because Flink SVM only supports ... conversion
is needed after downloading ..._
And then the code example:
```
def svmNormaliser : LabeledVector => LabeledVector =
lv => LabeledVector(if (lv.label > 0.0) 1.0 else -1.0, lv.vector)
val astroTrain: DataSet[LabeledVector] = astroTrainLibSVM.map(svmNormaliser)
val astroTest: DataSet[(Vector, Double)] =
astroTestLibSVM.map(svmNormaliser).map(x => (x.vector, x.label))
```
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> FlinkML Quickstart Loading Data section example doesn't work as described
> -------------------------------------------------------------------------
>
> Key: FLINK-9664
> URL: https://issues.apache.org/jira/browse/FLINK-9664
> Project: Flink
> Issue Type: Bug
> Components: Documentation, Machine Learning Library
> Affects Versions: 1.5.0
> Reporter: Mano Swerts
> Assignee: Rong Rong
> Priority: Major
> Labels: documentation-update, machine_learning, ml,
> pull-request-available
> Original Estimate: 1h
> Remaining Estimate: 1h
>
> The ML documentation example isn't complete:
> [https://ci.apache.org/projects/flink/flink-docs-release-1.5/dev/libs/ml/quickstart.html#loading-data]
> The referred section loads data from an astroparticle binary classification
> dataset to showcase SVM. The dataset uses 0 and 1 as labels, which doesn't
> produce correct results. The SVM predictor expects -1 and 1 labels to
> correctly predict the label. The documentation, however, doesn't mention
> that. The example therefore doesn't work without a clue why.
> The documentation should be updated with an explicit mention to -1 and 1
> labels and a mapping function that shows the conversion of the labels.
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