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time-series-machine-learning / tsml-java

Java time series machine learning tools in a Weka compatible toolkit
GNU General Public License v3.0
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How to recreate 97,03% from ItalyPowerDemand, COTE, plus exception. #516

Open Blackisher opened 3 years ago

Blackisher commented 3 years ago

For jar - http://timeseriesclassification.com/Downloads/tsml11_3_2020.jar Running: java -jar tsml11_3_2020.jar -dp=C:\Recreate\Univariate_arff\ -rp=C:\Recreate\Temp\ -gtf=true -cn=FlatCote -dn=ItalyPowerDemand -f=100 --force=true -tb=true Getting: Raw args: -dp=C:\Recreate\Univariate_arff\ -rp=C:\Recreate\Temp\ -gtf=true -cn=FlatCote -dn=ItalyPowerDemand -f=100 --force=true -tb=true

Exception in thread "main" java.lang.NoSuchMethodError: weka.classifiers.functions.SMO.setBuildLogisticModels(Z)V at machine_learning.classifiers.ensembles.CAWPE.setupDefaultEnsembleSettings(CAWPE.java:138) at machine_learning.classifiers.ensembles.AbstractEnsemble.(AbstractEnsemble.java:148) at machine_learning.classifiers.ensembles.CAWPE.(CAWPE.java:111) at tsml.classifiers.legacy.COTE.FlatCote.buildClassifier(FlatCote.java:102) at evaluation.evaluators.SingleTestSetEvaluator.evaluate(SingleTestSetEvaluator.java:97) at evaluation.evaluators.CrossValidationEvaluator.lambda$crossValidateWithStats$0(CrossValidationEvaluator.java:145) at evaluation.evaluators.CrossValidationEvaluator.crossValidateWithStats(CrossValidationEvaluator.java:154) at evaluation.evaluators.CrossValidationEvaluator.crossValidateWithStats(CrossValidationEvaluator.java:87) at experiments.Experiments.findExternalTrainEstimate(Experiments.java:564) at experiments.Experiments.runExperiment(Experiments.java:356) at experiments.Experiments.setupAndRunExperiment(Experiments.java:293) at experiments.Experiments.main(Experiments.java:143)

Idea is: I want to recreate results of http://timeseriesclassification.com/description.php?Dataset=ItalyPowerDemand Meaning 97,03 % with COTE 1) from http://timeseriesclassification.com/Resamples.csv (from http://timeseriesclassification.com/results.php Legacy results) I think COTE is FlatCote. Right? 2) How program is getting 100 folds from 67 entries in ItalyPowerDemand Train set? I see I can run jar with "-f=100", when was running -cn=HiveCote, but how folds are created? Asking as in http://timeseriesclassification.com/AllSplits.zip for flatCote there are 100 columns with results for ItalyPowerDemand Or was program run 100 times on whole training set of 67 entries without any folds? How row 38 from Flat-COTE.csv meaning ItalyPowerDemand | 0,961127308 | 0,939747328 | .... and so on was created?

Basing on it statiscits statistics of row 38-ItalyPowerDemand sum | min | max 97,034985425 | 0,93877551 | 0,980563654

97,03 % was derived from here, but what are those 0,93877551, 0,980563654 ....

3) How to deal with this exception? or maybe I should have run jar in some other way?