Python: How to retrive the best model from Optuna LightGBM study?

Question

I would like to get the best model to use later in the notebook to predict using a different test batch.

reproducible example (taken from Optuna Github) :

import lightgbm as lgb
import numpy as np
import sklearn.datasets
import sklearn.metrics
from sklearn.model_selection import train_test_split

import optuna


# FYI: Objective functions can take additional arguments
# (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).
def objective(trial):
    data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
    train_x, valid_x, train_y, valid_y = train_test_split(data, target, test_size=0.25)
    dtrain = lgb.Dataset(train_x, label=train_y)
    dvalid = lgb.Dataset(valid_x, label=valid_y)

    param = {
        "objective": "binary",
        "metric": "auc",
        "verbosity": -1,
        "boosting_type": "gbdt",
        "lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 10.0),
        "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 10.0),
        "num_leaves": trial.suggest_int("num_leaves", 2, 256),
        "feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0),
        "bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.4, 1.0),
        "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
        "min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
    }

    # Add a callback for pruning.
    pruning_callback = optuna.integration.LightGBMPruningCallback(trial, "auc")
    gbm = lgb.train(
        param, dtrain, valid_sets=[dvalid], verbose_eval=False, callbacks=[pruning_callback]
    )

    preds = gbm.predict(valid_x)
    pred_labels = np.rint(preds)
    accuracy = sklearn.metrics.accuracy_score(valid_y, pred_labels)
    return accuracy

my understanding is that the study below will tune for accuracy. I would like to somehow retrieve the best model from the study (not just the parameters) without saving it as a pickle, I just want to use the model somewhere else in my notebook.

if __name__ == "__main__":
    study = optuna.create_study(
        pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction="maximize"
    )
    study.optimize(objective, n_trials=100)

    print("Best trial:")
    trial = study.best_trial

    print("  Params: ")
    for key, value in trial.params.items():
        print("    {}: {}".format(key, value))

desired output would be

best_model = ~model from above~
new_target_pred = best_model.predict(new_data_test)
metrics.accuracy_score(new_target_test, new__target_pred)

Answer 1

Short addition to @Toshihiko Yanase's answer, because the condition study.best_trial==trial was never True for me. This was even the case when both (Frozen)Trial objects had the same content, so it is likely a bug in Optuna. Changing the condition to study.best_trial.number==trial.number solves the problem for me.

Also if you prefer to not use globals in Python, you can use the study and trial user attributes

def objective(trial):
    gmb = ...
    trial.set_user_attr(key="best_booster", value=gbm)

def callback(study, trial):
    if study.best_trial.number == trial.number:
        study.set_user_attr(key="best_booster", value=trial.user_attrs["best_booster"])


if __name__ == "__main__":
    study = optuna.create_study(
        pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction="maximize"
    )
    study.optimize(objective, n_trials=100, callbacks=[callback])
    best_model=study.user_attrs["best_booster"]