> For the complete documentation index, see [llms.txt](https://gilad-rubin.gitbook.io/hypster/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gilad-rubin.gitbook.io/hypster/integrations/optuna.md).

# Optuna

Optuna is the first supported HPO backend for Hypster. The integration lives in `hypster.hpo.optuna`.

## Install

{% code overflow="wrap" %}

```bash
uv add 'hypster[optuna]'
```

{% endcode %}

or:

{% code overflow="wrap" %}

```bash
pip install 'hypster[optuna]'
```

{% endcode %}

## Basic Pattern

{% code overflow="wrap" %}

```python
import optuna
from sklearn.base import ClassifierMixin
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression

from hypster import HP, instantiate
from hypster.hpo.optuna import suggest_values
from hypster.hpo.types import HpoFloat, HpoInt

def linear_model(hp: HP) -> LogisticRegression:
    C = hp.float(
        1.0,
        name="C",
        min=1e-4,
        max=10.0,
        hpo_spec=HpoFloat(scale="log"),
    )
    return LogisticRegression(C=C, max_iter=1000)

def forest_model(hp: HP) -> RandomForestClassifier:
    n_estimators = hp.int(
        200,
        name="n_estimators",
        min=50,
        max=1000,
        hpo_spec=HpoInt(step=50),
    )
    return RandomForestClassifier(n_estimators=n_estimators, random_state=42)

model_options = {
    "linear": linear_model,
    "forest": forest_model,
}

def model_config(hp: HP) -> ClassifierMixin:
    selected_config = hp.select(model_options, name="model_family", default="forest", options_only=True)
    return hp.nest(selected_config, name="model")

def objective(trial: optuna.Trial) -> float:
    values = suggest_values(trial, model_config)
    model = instantiate(model_config, values=values)
    return train_and_score(model)

study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=30)
```

{% endcode %}

## What Is Supported

* `hp.int`, backed by `trial.suggest_int`
* `hp.float`, backed by `trial.suggest_float`
* `hp.select`, backed by `trial.suggest_categorical`
* `hp.nest`, which prefixes nested parameter paths

Multi-value HP calls are not expanded by the current adapter.

The adapter only accepts HPO spec fields that Optuna can represent. Supported fields include `HpoInt(step=..., scale=..., include_max=...)`, `HpoFloat(step=..., scale=...)`, `HpoFloat(distribution="uniform"|"loguniform")`, and `HpoCategorical(ordered=False, weights=None)`. Unsupported fields such as custom `base=...`, normal/lognormal float distributions, `center=...`, `spread=...`, ordered categoricals, and categorical weights raise instead of being ignored.

Nested explicit overrides passed through `hp.nest(..., values=...)` are validated before `suggest_values()` returns.

## More

* [Perform Hyperparameter Optimization](/hypster/how-to-guides/perform-hyperparameter-optimization.md)
* [Optuna HPO API](/hypster/reference/optuna-hpo.md)


---

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