couple minor things I noticed

[druce]

I've been using hyperopt and optuna for a while, very curious to see if any of the other optimizers do better, although honestly my use cases may be pretty simple and I suspect not that much difference.

would add requirements.txt or conda environment.yml to make it easier to set up, see below

in timemachines/optimizers/alloptimizers.py I see https://github.com/microprediction/timemachines/blob/main/timemachines/optimizers/alloptimizers.py#L40

print(optimizer.__name__,(optimizer.__name__,optimizer(objective, n_trials=50, n_dim=5, with_count=True)))

I think this should use the loop parameters per below?

print(optimizer.__name__,(optimizer.__name__,optimizer(objective, n_trials=n_trials, n_dim=n_dim, with_count=True)))

alloptimizers.py seems to run single_threaded, would consider pool.map to run many optimizations concurrently (but I'm not sure best way to do that). if that makes sense let me know and I can take a crack at a pull request. https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map

this is requirements.txt I have, can do a pull request, lmk, might need testing/adjusting,

# may want to install fbprophet via conda, needs compiler, pystan
ax-platform
deap
divinity
fbprophet
funcy
hyperopt
microconventions
momentum
nevergrad
numpy==1.19.5
optuna
platypus-opt
poap
pydlm
pymoo
pystan
pySOT
swarmlib

[microprediction]

Good points. I fixed the minor one (loop vars)

The other needs a cleanup, I agree. Right now I"m deliberately restricting to one thread to try to form an opinion about relative performance of the algorithms, but that should be clearer in the doc and perhaps there should be a flag for that.

Another fix would be to implement thread-safe counting of the function evals.

[microprediction]

I sorry I misread. Yes running one optim in each thread would make a lot of sense.

As for requirements, yes please go ahead. I don't use conda so you're better placed.

[microprediction]

You will be pleased (I think) to learn that I have separated out all the optim stuff into https://github.com/microprediction/humpday check it out!

[druce]

very cool!

could add scipy L-BFGS-B if you were inclined, quasi-Newton method that worked better for me than SLQSP which I think is similar , can do a pull request or below worked for me

-MINIMIZER_KWARGS = {'slqsp': {'method': 'SLQSP'}, +MINIMIZER_KWARGS = {'lbfgsb': {'method': 'L-BFGS-B'},

• 'slqsp': {'method': 'SLQSP'}, 'powell': {'method': 'Powell'}, 'nelder': {'method': 'Nelder-Mead'}, 'dogleg': {'method': 'dogleg'}

-SCIPY_OPTIMIZERS = [ scipy_slqsp_cube, scipy_powell_cube, scipy_nelder_cube, scipy_dogleg_cube ] +def scipy_lbfgsb_cube(objective, n_trials, n_dim, with_count=False):

• return scipy_cube(objective=objective, n_trials=n_trials, n_dim=n_dim, with_count=with_count, method='lbfgsb')

• +SCIPY_OPTIMIZERS = [ scipy_slqsp_cube, scipy_powell_cube, scipy_nelder_cube, scipy_dogleg_cube, scipy_lbfgsb_cube ]

[image: Screen Shot 2021-02-13 at 11.24.36 AM.png]

On Sat, Feb 13, 2021 at 12:45 AM Peter Cotton notifications@github.com wrote:

You will be pleased (I think) to learn that I have separated out all the optim stuff into https://github.com/microprediction/humpday check it out!

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[microprediction]

Great idea

[microprediction]

Druce, Would you mind doing the PR to humpday, not timemachines? Peter