pydantic-optuna-bridge 0.1.1

Derive Optuna distributions from constrained Pydantic models.

pydantic-optuna-bridge

Bridge constrained Pydantic models directly into Optuna search spaces. The package extracts the metadata helpers originally built for kaggle-map so that tightly scoped hyperparameter schemas can be reused across projects.

Installation

uv add pydantic-optuna-bridge
# or
pip install pydantic-optuna-bridge

# Optional CLI extras
uv add pydantic-optuna-bridge[cli]

End-to-End Usage with Optuna

1. Describe the search space in Pydantic.
2. Decorate the model to wire Optuna metadata and helpers.
3. Sample configurations inside an Optuna objective.

Install Optuna alongside the bridge helpers:

uv add optuna

from enum import Enum
from typing import Annotated

import optuna
from annotated_types import Ge, Gt, Le, Lt
from pydantic import BaseModel

from pydantic_optuna_bridge import optuna_config


class Optimizer(str, Enum):
    ADAM = "adam"
    SGD = "sgd"
    RMSPROP = "rmsprop"


@optuna_config(
    log_scale_fields={"learning_rate"},
    categorical_field_weights={"optimizer": [0.5, 0.3, 0.2]},
)
class TrainingConfig(BaseModel):
    optimizer: Optimizer
    learning_rate: Annotated[float, Gt(1e-5), Lt(1.0)]
    hidden_units: Annotated[int, Ge(32), Le(256)]

def objective(trial: optuna.trial.Trial) -> float:
    config = TrainingConfig.from_optuna_trial(trial)

    # Replace with real training logic, keeping a toy signal for documentation.
    score = (
        0.1 if config.optimizer == Optimizer.ADAM else 0.2
    ) + 0.05 * abs(config.learning_rate - 1e-2) + config.hidden_units / 1_024
    return score


study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=5)
print(study.best_params)

Optuna now drives the search space defined by the Pydantic model. Because the decorator attaches metadata to json_schema_extra, downstream tools can inspect the schema without re-deriving it:

schema_payload = {
    name: field.json_schema_extra["optuna"]
    for name, field in TrainingConfig.model_fields.items()
}
assert schema_payload == TrainingConfig.optuna_metadata()

CLI Demo

The optional CLI showcases the metadata derivation and prints a formatted table for inspection:

uv run -m pydantic_optuna_bridge --help
uv run -m pydantic_optuna_bridge

Development

uv run pytest
uv build

Issues and pull requests are welcome.