Comments (2)
Hello David,
I am glad to see, that you are still using Hyperactive.
In the current version of v2.x Hyperactive cannot do an early stop. This feature will be implemented in v3.0 coming in a few weeks.
However, part of the current Hyperactive development is the separation of the optimization algorithms into a simpler api. It is called Gradient-Free-Optimizers and is basically Hyperactive-lite. It will be the optimization-back-end of Hyperactive in the future. But it can also be used by itself, without Hyperactive:
https://github.com/SimonBlanke/Gradient-Free-Optimizers
Your code would convert into the following with Gradient-Free-Optimizers:
import time
import numpy as np
from gradient_free_optimizers import BayesianOptimizer
def my_model(para):
time.sleep(3)
return 0.9
search_space = {"n_estimators": np.arange(10, 200, 10)}
c_time = time.time()
opt = BayesianOptimizer(search_space)
opt.search(my_model, n_iter=8, max_score=0.9)
diff_time = time.time() - c_time
print("\n Optimization time:", diff_time)
Gradient Free Optimizers is well tested and very easy to use, but it has a few restrictions compared to Hyperactive:
- The search space must be numeric (numpy arrays)
- No build-in multiprocessing
- No access to optimizer-parameters at run time. (In Hyperactive 3.0)
To make the relationship between Hyperactive and Gradient Free Optimizers clear, I will add a section into the readme about the differences and purposes of both packages.
I hope I was able to help you. If you have additional questions or suggestions please let me know.
from hyperactive.
Hi Simon,
Thank you for your fast and in depth response!
I appreciate the work-around code you have provided - it is working great.
We will use Gradient Free Optimizers
package and wait for hyperactive 3.x to be released, your approach makes a lot of sense :)
Thanks,
David
from hyperactive.
Related Issues (20)
- ValueError: assignment destination is read-only HOT 3
- Dynamic inertia in ParticleSwarmOptimizer HOT 4
- Feature: Passing extra parameters to the optimization function HOT 5
- Optimization in serial? HOT 4
- New feature: save optimizer object to continue optimization run at a later time.
- hyper.results(model) HOT 1
- New feature: Optimization Strategies HOT 1
- add ray multiprocessing support
- Change Optimization paramters at runtime
- Show speed difference between python version
- Question of Particle Swarm Optimizer HOT 6
- Progress Bar visual error when running in parallel HOT 2
- Error when creating shared memory HOT 1
- TypeError: cannot pickle '_thread.RLock' object HOT 5
- Redesign command-line output of optimization run HOT 1
- Add early stopping feature to custom optimization strategies HOT 1
- Add type hints to hyperactive-api HOT 1
- add `prune_search_space`-method to optimization strategies HOT 1
- add constrained optimization to API HOT 1
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from hyperactive.