I went over the entry diagram and the documentation again. Let me know if this is the proper workflow:
Using included detectors: 1- Format new dataset appropriately and add it to a folder under data/. 2- Add ground truth labels to combined_labels.json under the dataset name. (Do I need to also include the original user labeled data like AG_labels_v1.json under labels/raw/ or will NAB skip the combining algorithm?) 3- use ‘python run.py’ to run the detectors and get score and results files. Now for the DUT, I don’t have it written as a class like the inbuilt detectors so I wanted to use its anomaly output using path III. Will that work? I can’t figure out how to have NAB run on that file exclusively since run.py requires a detector specified. Also, I tried using a result file with the format included in appendix F (timestamp, value, label) but that produced an error when running ‘python run.py —score’ that was only resolved when I retained the format of the result files already in the folder (including scores and raw anomaly fields). Thanks of the help Alex! Nick > On Apr 18, 2015, at 6:02 PM, Alex Lavin <[email protected]> wrote: > > Yes this should work, as long as you follow the datafile naming conventions > in NAB, the results files are as specified in the whitepaper [1], and the > labels are in the correct format of the current json file [2]. > > Cheers, > Alex > > [1] https://github.com/numenta/NAB/wiki#nab-whitepaper > <https://github.com/numenta/NAB/wiki#nab-whitepaper> > [2] https://github.com/numenta/NAB/blob/master/labels/combined_labels.json > <https://github.com/numenta/NAB/blob/master/labels/combined_labels.json> > > Alexander Lavin > Software Engineer > Numenta
