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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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@@ -167,7 +167,7 @@ You can define your own optuna sampler for Hyperopt by implementing `generate_es
class MyAwesomeStrategy(IStrategy): class MyAwesomeStrategy(IStrategy):
class HyperOpt: class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs): 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. 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 !!! 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. 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 ## Space options