Introduce basic experiment visualization module mlos_viz via dabl #624
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Co-authored-by: Sergiy Matusevych <[email protected]>
Co-authored-by: Sergiy Matusevych <[email protected]>
…-mlos-viz-with-dabl
bpkroth
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Jan 16, 2024
…experiment (#628) This PR is useful for mlos-viz and dabl wrapper (#624) to be able to automatically graph the results for a given optimization target, for instance via something like the following: ```python for opt_target in exp.objectives: dabl.plot(exp.results, opt_target) ``` Since the prior efforts on capturing this data in the Trial metadata are somewhat problematic (allow conflicting changes between runs of an experiment, don't support multi-objective), we extend them to also store values directly as a part of the Experiment, which is a somewhat more appropriate location. Upon retrieval, an attempt is also made to merge the two data sources for backwards compatibility. This PR does not enforce strictness on that metadata, but future versions could (e.g., disallow resuming an Experiment if it looks like the objective targets have changed. In that case the prior Trial results can potentially still be used to prewarm a new Experiment's optimizer). --------- Co-authored-by: Sergiy Matusevych <[email protected]>
motus
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Jan 16, 2024
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Adds a basic
mlos_viz.plot(exp)style API for simple visualizations ofExperimentDataresults relative to the experiment's objectives (building off of #628 and dabl/dabl#335).Note: this PR currently omits unit tests for the new module due to the complexity of testing visualizations. We intend to add this in future PRs. There is however, a working example of its use here right now:
Microsoft-CISL/sqlite-autotuning#41