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update docs/advanced-hyperopt.md

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viotemp1
2025-04-13 11:27:33 +03:00
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docs/advanced-hyperopt.md
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@@ -167,7 +167,7 @@ You can define your own optuna sampler for Hyperopt by implementing `generate_es
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
return "TPESampler"
return "NSGAIIISampler"
```
@@ -175,32 +175,10 @@ Possible values are either one of "NSGAIISampler", "TPESampler", "GPSampler", "C
Some research will be necessary to find additional Samplers (from optunahub) for example.
```
The `dimensions` parameter is the list of `skopt.space.Dimension` objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the `skopt.learning.GaussianProcessRegressor` estimator. Here's an example:
```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
from skopt.utils import cook_estimator
from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
kernel_bounds = (0.0001, 10000)
kernel = (
ConstantKernel(1.0, kernel_bounds) *
Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
)
kernel += (
ConstantKernel(1.0, kernel_bounds) *
Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
)
return cook_estimator("GP", space=dimensions, kernel=kernel, n_restarts_optimizer=2)
```
!!! Note
While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.
If you're unsure about this, best use one of the Defaults (`"NSGAIIISampler"` has proven to be the most versatile) without further parameters.
## Space options