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https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15251513#comment-15251513
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ASF GitHub Bot commented on FLINK-2157:
---------------------------------------
Github user thvasilo commented on the pull request:
https://github.com/apache/flink/pull/1849#issuecomment-212793430
Hello Trevor,
Thanks for taking the time to look at this, I'll investigate these issues
today hopefully.
--
Sent from a mobile device. May contain autocorrect errors.
On Apr 21, 2016 12:16 AM, "Trevor Grant" <[email protected]> wrote:
> Also two quick issues.
>
> *pipelines*
>
> 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:
>
> 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.
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly or view it on GitHub
> <https://github.com/apache/flink/pull/1849#issuecomment-212633912>
>
> 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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