Comments (2)
Hi @turmeric-blend, good question. Yes, the multivariate implementation is quite a lot slower than the univariate implementation. (This is partly why, for now at least, they are separate, I have also separated MiniRocket and MiniRocketMultivariate in sktime.)
I am aware of the problem. Unfortunately, I haven't had the chance yet to work out whether there is a straightforward way to 'fix' it. There may be a way to avoid whatever is causing the problem by rearranging the oprerations a bit, or it might be that there is some issue with how numba handles the additional dimension.
Basically, if you have univariate data, just use the univaraite implementation. If you have multivariate data, unfortunately at the moment the speed issue is unavoidable.
There is a GPU implementation coming (this is not my work), which will most likely be a lot faster for most multivariate datasets, at least until I can work out how to fix this issue with the CPU implementation.
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ok, thanks for letting me know.
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Related Issues (20)
- Feature Size HOT 2
- Unlabeled data HOT 2
- Can't set random_state when doing a gridsearchCV HOT 3
- Are minirocket.py and minirocket_dv.py the implementation for the same Mini-Rocket algorithm? HOT 1
- is is necessary to normalize data when use minirocket multivariable version? HOT 1
- datatype HOT 13
- some question about multivarible version. HOT 3
- if the kernel numbers is much less than dataset sample numbers
- is the code handle the padding right?
- Port to R language
- Datatype Error HOT 2
- Need example to use variable ROCKET HOT 1
- Feature Transformation HOT 13
- pls help to understand how your this repo code related to sktime.transformers.series_as_features.rocket HOT 1
- Can't reproduce the accuracy of 'EOGHorizontalSignal' dataset in UCR109 based on original 'MINIROCKET' exmples HOT 7
- Accuracy problem HOT 3
- padding problem
- how to use minirocket in production HOT 1
- Channels get lost HOT 4
- Any help for minirocket on UEA multivariate time series classification HOT 1
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