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update docs for optuna sampler

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viotemp1
2025-03-26 08:44:46 +02:00
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docs/advanced-hyperopt.md
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@@ -161,30 +161,20 @@ class MyAwesomeStrategy(IStrategy):
### Overriding Base estimator ### Overriding Base estimator
You can define your own estimator for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass. You can define your own optuna sampler for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.
```python ```python
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 "RF" return "TPESampler"
``` ```
Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the [scikit-optimize documentation](https://scikit-optimize.github.io/)), or "an instance of a class that inherits from `RegressorMixin` (from sklearn) and where the `predict` method has an optional `return_std` argument, which returns `std(Y | x)` along with `E[Y | x]`". Possible values are either one of "NSGAIISampler", "TPESampler", "GPSampler", "CmaEsSampler", "NSGAIIISampler", "QMCSampler" (Details can be found in the [optuna-samplers documentation](https://optuna.readthedocs.io/en/stable/reference/samplers/index.html)), or "an instance of a class that inherits from `optuna.samplers.BaseSampler`".
Some research will be necessary to find additional Regressors. Some research will be necessary to find additional Regressors.
Example for `ExtraTreesRegressor` ("ET") with additional parameters:
```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
from skopt.learning import ExtraTreesRegressor
# Corresponds to "ET" - but allows additional parameters.
return ExtraTreesRegressor(n_estimators=100)
``` ```
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: 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: