Increase training and inference performance for GAK kernel #77
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rtavenar
reviewed
Oct 22, 2018
rtavenar
reviewed
Oct 22, 2018
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Woops sorry for double posting my review... |
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@rtavenar anything else? |
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Hi @lejafar Sorry it took me so long to get back to you. Your suggestion is still welcome but the codebase has changed quite a lot since you posted it. Specifically, we are working on a future release in the dev branch. So, my question is : would you be willing to close this PR and open a new one based on the current dev branch ? The place where your hack could be inserted is probably around: |
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Hi! I've been using the
TimeSeriesSVCwith thegakkernel for a while now and I've made some changes that increased the performance quite a bit, the most time consuming part in my case was computing the kernel and its there where I've made some changesChanges that apply to training:
To compute the self-similarity, scikit-learn evaluates the callable kernel using a reference of X as the second argument, in the current implementation will check if the second argument is
Nonewhich it isn't, hence it computes everything twiceChanges that apply to inference:
When doing prediction, scikit-learn evaluates the callable kernel for every point (
X_fit) seen up until then, but the only thing libsvm really needs is the distance to its support vectors, hence we can omit computing the others.