Refactor feature time series. #18
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Although I've just seen that another PR already did something similar =) |
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Hi Daniel, |
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Hi Maarten, this one: #8 . I guess it's probably the big one you mean |
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This is addressed in the 0.4.4-beta release. Many thanks for the suggestion Daniel, this indeed results in a massive speed-up! |
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I noticed that the computation of the feature time series was a bit of a bottleneck of AROMA.
I've refactored the code for a >300x speedup, i.e., from over 50min to less than 10s in an example with 350 components and 1000 timepoints.
I should note there is 1 slight difference from my code to the original. I use
np.rollto generate the shifted realignment parameters. That, by design, does not set the edges to 0, as in the original code. I didn't see this as a relevant issue since the difference in feature estimates is minimal ( <0.01% on avg -- <0.3% on max), as seen in the figure below: