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Problem: self.inputMin, inputMax are equal on some dataset #13

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breznak opened this issue Jul 25, 2019 · 2 comments
Open

Problem: self.inputMin, inputMax are equal on some dataset #13

breznak opened this issue Jul 25, 2019 · 2 comments
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bug Something isn't working

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@breznak
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breznak commented Jul 25, 2019

A possible bug, the self.inputMin, inputMax is provided by the NAB API.

  • one some dataset (I don't know which) the values are all the same (ie the dataset is just a flat line)

57: Results have been written to /mnt/store/devel/HTM/NAB/results/htmcore/realTweets/htmcore_Twitter_volume_UPS.csv
multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/usr/lib/python3.6/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.6/multiprocessing/pool.py", line 44, in mapstar
return list(map(*args))
File "/mnt/store/devel/HTM/NAB/nab/detectors/base.py", line 147, in detectDataSet
detectorInstance.initialize()
File "/mnt/store/devel/HTM/NAB/nab/detectors/htmcore/htmcore_detector.py", line 284, in initialize
assert self.inputMin < self.inputMax, "FAILED "+str(self.inputMin)+" "+str(self.inputMax)
AssertionError: FAILED 45.0 45.0
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "run.py", line 247, in
main(args)
File "run.py", line 96, in main
runner.detect(detectorConstructors)
File "/mnt/store/devel/HTM/NAB/nab/runner.py", line 134, in detect
self.pool.map_async(detectDataSet, args).get(99999999)
File "/usr/lib/python3.6/multiprocessing/pool.py", line 644, in get
raise self._value
AssertionError: FAILED 45.0 45.0

  • our c++ ScalarEncoder has a check that min < max

    • which is reasonable, for otherwise the encoder would use 0 for some internal sizes.
  • I don't know if the problem is a bug in how this NAB implementation computes/deals with setting the min.max,

  • or if we have to workaround that in c++ ScalarEncoder?

@breznak breznak added the bug Something isn't working label Jul 25, 2019
@ctrl-z-9000-times
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I think that this is a bug in NAB, or maybe the data set is a flat line at 45.0?

@markNZed
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One of the timeseries https://github.com/htm-community/NAB/blob/master/data/artificialNoAnomaly/art_flatline.csv is a flat line - I guess to check that the detector does not create false positives.

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