jsonify-optuna 0.2.0

Convert an Optuna Study into a JSON-serializable dict.

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)

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")

[!NOTE] When the storage contains exactly one study, study_name can be omitted.

API Reference

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

Parameter	Type	Description
study	optuna.Study	The study to convert.
states	list[TrialState] | None	Filter trials by state. None includes all states.
deepcopy	bool	Whether to deep-copy trials from storage. Default True.

Returns a dict with:

Key	Type	Description
directions	list[str]	"minimize" or "maximize" per objective.
user_attrs	dict	Study-level user attributes.
metric_names	list[str] | None	Metric names if set, else None.
trials	list[dict]	Trial dicts (see below).
best_trial_indices	list[int]	Indices into trials for Pareto-front trials.

Each trial dict contains:

Key	Type	Description
state	str	"complete", "pruned", "fail", "running", or "waiting".
values	list[float] | None	Objective values. None if not complete.
params	dict	Parameter name-value pairs.
user_attrs	dict	Trial-level user attributes.
intermediate_values	dict[int, float]	Step-value pairs from trial.report().
distributions	dict	Parameter distributions (see below).

Distribution formats:

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

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

Parameter	Type	Description
study_name	str | None	Study name. Optional when storage has exactly one study.
storage	BaseStorage | None	An Optuna storage object.
journal_path	str | None	Path to a journal log file.
rdb_url	str | None	SQLAlchemy-style database URL.

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