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https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15250825#comment-15250825
]
ASF GitHub Bot commented on FLINK-2157:
---------------------------------------
Github user rawkintrevo commented on the pull request:
https://github.com/apache/flink/pull/1849#issuecomment-212633912
Also two quick issues.
*pipelines*
```scala
val scaler = MinMaxScaler()
val pipeline = scaler.chainPredictor(mlr)
val evaluationDS = survivalLV.map(x => (x.vector, x.label))
pipeline.fit(survivalLV)
scorer.evaluate(evaluationDS, pipeline).collect().head
```
When using this with a ChainedPredictor as the predictor I get the
following error:
error: could not find implicit value for parameter evaluateOperation:
org.apache.flink.ml.pipeline.EvaluateDataSetOperation[org.apache.flink.ml.pipeline.ChainedPredictor[org.apache.flink.ml.preprocessing.MinMaxScaler,org.apache.flink.ml.regression.MultipleLinearRegression],(org.apache.flink.ml.math.Vector,
Double),Double]
*MinMaxScaler()*
Merging for me broke the following code:
```scala
val scaler = MinMaxScaler()
val scaledSurvivalLV = scaler.transform(survivalLV)
```
With the following error (omiting part of the stack trace)
Caused by: java.lang.NoSuchMethodError:
breeze.linalg.Vector$.scalarOf()Lbreeze/linalg/support/ScalarOf;
at
org.apache.flink.ml.preprocessing.MinMaxScaler$$anonfun$3.apply(MinMaxScaler.scala:156)
at
org.apache.flink.ml.preprocessing.MinMaxScaler$$anonfun$3.apply(MinMaxScaler.scala:154)
at org.apache.flink.api.scala.DataSet$$anon$7.reduce(DataSet.scala:584)
at
org.apache.flink.runtime.operators.chaining.ChainedAllReduceDriver.collect(ChainedAllReduceDriver.java:93)
at
org.apache.flink.runtime.operators.chaining.ChainedMapDriver.collect(ChainedMapDriver.java:78)
at org.apache.flink.runtime.operators.MapDriver.run(MapDriver.java:97)
at org.apache.flink.runtime.operators.BatchTask.run(BatchTask.java:480)
at
org.apache.flink.runtime.operators.BatchTask.invoke(BatchTask.java:345)
at org.apache.flink.runtime.taskmanager.Task.run(Task.java:559)
at java.lang.Thread.run(Thread.java:745)
I'm looking for a work around. Just saying I found a regression. Other
than that, looks/works AWESOME well done.
> Create evaluation framework for ML library
> ------------------------------------------
>
> Key: FLINK-2157
> URL: https://issues.apache.org/jira/browse/FLINK-2157
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Theodore Vasiloudis
> Labels: ML
> Fix For: 1.0.0
>
>
> Currently, FlinkML lacks means to evaluate the performance of trained models.
> It would be great to add some {{Evaluators}} which can calculate some score
> based on the information about true and predicted labels. This could also be
> used for the cross validation to choose the right hyper parameters.
> Possible scores could be F score [1], zero-one-loss score, etc.
> Resources
> [1] [http://en.wikipedia.org/wiki/F1_score]
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