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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" <notificati...@github.com> 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] -- This message was sent by Atlassian JIRA (v6.3.4#6332)