You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@flink.apache.org by tr...@apache.org on 2016/11/02 17:13:14 UTC
flink git commit: [FLINK-4850] [ml] FlinkML - SVM predict Operation
for Vector and not LaveledVector
Repository: flink
Updated Branches:
refs/heads/master 30a53ef69 -> da991aebb
[FLINK-4850] [ml] FlinkML - SVM predict Operation for Vector and not LaveledVector
This closes #2658.
Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/da991aeb
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/da991aeb
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/da991aeb
Branch: refs/heads/master
Commit: da991aebb038b13a2d34344cf456c32feb4222dd
Parents: 30a53ef
Author: Theodore Vasiloudis <tv...@sics.se>
Authored: Wed Oct 19 14:09:09 2016 +0200
Committer: Till Rohrmann <tr...@apache.org>
Committed: Wed Nov 2 18:12:53 2016 +0100
----------------------------------------------------------------------
docs/dev/libs/ml/quickstart.md | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/flink/blob/da991aeb/docs/dev/libs/ml/quickstart.md
----------------------------------------------------------------------
diff --git a/docs/dev/libs/ml/quickstart.md b/docs/dev/libs/ml/quickstart.md
index 50ca08a..e4c6962 100644
--- a/docs/dev/libs/ml/quickstart.md
+++ b/docs/dev/libs/ml/quickstart.md
@@ -135,12 +135,13 @@ We can simply import the dataset then using:
import org.apache.flink.ml.MLUtils
-val astroTrain: DataSet[LabeledVector] = MLUtils.readLibSVM("/path/to/svmguide1")
-val astroTest: DataSet[LabeledVector] = MLUtils.readLibSVM("/path/to/svmguide1.t")
+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))
{% endhighlight %}
-This gives us two `DataSet[LabeledVector]` objects that we will use in the following section to
+This gives us two `DataSet` objects that we will use in the following section to
create a classifier.
## Classification
@@ -167,11 +168,11 @@ svm.fit(astroTrain)
{% endhighlight %}
-We can now make predictions on the test set.
+We can now make predictions on the test set, and use the `evaluate` function to create (truth, prediction) pairs.
{% highlight scala %}
-val predictionPairs = svm.predict(astroTest)
+val evaluationPairs: DataSet[(Double, Double)] = svm.evaluate(astroTest)
{% endhighlight %}
@@ -210,12 +211,11 @@ make predictions.
scaledSVM.fit(astroTrain)
-val predictionPairsScaled: DataSet[(Double, Double)] = scaledSVM.predict(astroTest)
+val evaluationPairsScaled: DataSet[(Double, Double)] = scaledSVM.evaluate(astroTest)
{% endhighlight %}
The scaled inputs should give us better prediction performance.
-The result of the prediction on `LabeledVector`s 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