Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Use P2 for calc_p_safe and optimization metric #190

Merged
merged 2 commits into from
Dec 10, 2018
Merged

Use P2 for calc_p_safe and optimization metric #190

merged 2 commits into from
Dec 10, 2018

Conversation

taldcroft
Copy link
Member

I've been thinking about acq catalog optimization and realizing (after the acq model update issues) that the current p_safe definition (as probability of 1 or fewer stars) is dominated by the few brightest stars and the "safety" stars can have little or no influence.

Changing to "probability of 2 or fewer stars" makes the algorithm dig one star deeper. It also has the good feature of optimizing for exactly the metric that is used in catalog evaluation.

The regression diffs introduced here are not that substantial and in line with typical churn from other changes.

proseco/acq.py Outdated Show resolved Hide resolved
@taldcroft taldcroft merged commit 52145e0 into master Dec 10, 2018
@taldcroft taldcroft deleted the use-p2 branch December 10, 2018 17:10
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

2 participants