jsonify-optuna

Convert an Optuna Study into a plain, JSON-serializable Python dict.

Installation

pip install jsonify-optuna

Usage

Convert an in-memory study to a JSON-friendly dict:

import optuna
from jsonify_optuna import jsonify
from optuna.trial import TrialState

def objective(trial):
    x = trial.suggest_float("x", -10.0, 10.0)
    return x ** 2

study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=20)
result = jsonify(study, states=[TrialState.COMPLETE])
# This includes all the trials.
result = jsonify(study)

Reconstruct an optuna.Study from a JSON dict:

from jsonify_optuna import json_to_optuna_study

study = json_to_optuna_study(result)

If you have a study stored in a backend, you can load it as follows:

from jsonify_optuna import load_study

# From an RDB (SQLite, PostgreSQL, etc.)
study = load_study(rdb_url="sqlite:///example.db", study_name="my_study")

# From a journal file
study = load_study(journal_path="journal.log", study_name="my_study")

# From a storage object directly
study = load_study(storage=my_storage, study_name="my_study")

API Reference

jsonify(study, *, states=None, deepcopy=True)

Returns a dict with:

Each trial dict contains:

Distribution formats:

• Int/Float: {"low": ..., "high": ..., "step": ..., "log": ...}
• Categorical: {"choices": [...]}

json_to_optuna_study(study_json)

Returns an in-memory optuna.Study with directions, user attributes, metric names, and all trials reconstructed from the JSON dict.

load_study(*, study_name=None, storage=None, journal_path=None, rdb_url=None)

Exactly one of storage, journal_path, or rdb_url must be provided.