humpday derivative-free optimizer Elo ratings 
Deriv-free optimizers from many packages in a common syntax, with evaluation
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There's a colab notebook that recommends a black-box derivative-free optimizer for your objective function.
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About fifty strategies drawn from various open source packages are assigned Elo ratings depending on dimension of the problem and number of function evaluations allowed.
Hello and welcome to HumpDay, a package that helps you choose a Python global optimizer package, and strategy therein, from Ax-Platform, bayesian-optimization, DLib, HyperOpt, NeverGrad, Optuna, Platypus, PyMoo, PySOT, Scipy classic and shgo, Skopt, nlopt, Py-Bobyaq, UltraOpt and maybe others by the time you read this. It also presents some of their functionality in a common calling syntax.
Cite or be cited
Pull requests at CITE.md are welcome. If your package is benchmarked here I'd like to get this bit right.
Install
Pick one of:
pip install humpday
pip install humpday[full]
The full option will try to install a slew of optim packages. You may prefer to do that piecemeal. See below.
Bleeding edge:
pip install git+https://github.com/microprediction/humpday
File an issue if you have problems. See this thread if you have issues on mac silicon M1.
This might help some of you sometimes
pip install cython pybind11
brew install openblas
export OPENBLAS=/opt/homebrew/opt/openblas/lib/
Installing one optimizer at a time
pip install scikit-optimize
pip install optuna
pip install platypus-opt
pip install poap
pip install pysot
Some of these are really good, but not 100% stable on all platforms we've used.
pip install cmake
pip install ultraopt
pip install dlib
pip install ax-platform
pip install py-bobyqa
pip install hebo
pip install nlopt
pip install freelunch
Broken pending issue:
pip install bayesian-optimization
pip install nevergrad
Recommendations
Pass the dimensions of the problem, function evaluation budget and time budget to receive suggestions that are independent of your problem set,
from pprint import pprint
from humpday import suggest
pprint(suggest(n_dim=5, n_trials=130,n_seconds=5*60))
where n_seconds is the total computation budget for the optimizer (not the objective function) over all 130 function evaluations. Or simply pass your objective function, and it will time it and do something sensible:
from humpday import recommend
def my_objective(u):
time.sleep(0.01)
return u[0]*math.sin(u[1])
recommendations = recommend(my_objective, n_dim=21, n_trials=130)
Points race
If you have more time, call points_race on a list of your own objective functions:
from humpday import points_race
points_race(objectives=[my_objective]*2,n_dim=5, n_trials=100)
See the colab notebook.
How it works
In the background, 50+ strategies are assigned Elo ratings by sister repo optimizer-elo-ratings. Oh I said that already. Never mind.
Contribute
By all means contribute more to optimizers.
