Saving/Resuming Study with RDB Backend¶

An RDB backend enables persistent experiments (i.e., to save and resume a study) as well as access to history of studies. In addition, we can run multi-node optimization tasks with this feature, which is described in Distributed Optimization.

In this section, let’s try simple examples running on a local environment with SQLite DB.

Note

You can also utilize other RDB backends, e.g., PostgreSQL or MySQL, by setting the storage argument to the DB’s URL. Please refer to SQLAlchemy’s document for how to set up the URL.

New Study¶

We can create a persistent study by calling create_study() function as follows. An SQLite file example.db is automatically initialized with a new study record.

import optuna
study_name = 'example-study'  # Unique identifier of the study.
study = optuna.create_study(study_name=study_name, storage='sqlite:///example.db')

To run a study, call optimize() method passing an objective function.

def objective(trial):
    x = trial.suggest_uniform('x', -10, 10)
    return (x - 2) ** 2

study.optimize(objective, n_trials=3)

Resume Study¶

To resume a study, instantiate a Study object passing the study name example-study and the DB URL sqlite:///example.db.

study = optuna.create_study(study_name='example-study', storage='sqlite:///example.db', load_if_exists=True)
study.optimize(objective, n_trials=3)

Experimental History¶

We can access histories of studies and trials via the Study class. For example, we can get all trials of example-study as:

import optuna
study = optuna.create_study(study_name='example-study', storage='sqlite:///example.db', load_if_exists=True)
df = study.trials_dataframe(attrs=('number', 'value', 'params', 'state'))

The method trials_dataframe() returns a pandas dataframe like:

print(df)

Out:

   number       value  params_x     state
     0   25.301959 -3.030105  COMPLETE
     1    1.406223  0.814157  COMPLETE
     2   44.010366 -4.634031  COMPLETE
     3   55.872181  9.474770  COMPLETE
     4  113.039223 -8.631991  COMPLETE
     5   57.319570  9.570969  COMPLETE

A Study object also provides properties such as trials, best_value, best_params (see also First Optimization).

study.best_params  # Get best parameters for the objective function.
study.best_value  # Get best objective value.
study.best_trial  # Get best trial's information.
study.trials  # Get all trials' information.