Re-use the best trial

In some cases, you may want to re-evaluate the objective function with the best hyperparameters again after the hyperparameter optimization.

For example,

• You have found good hyperparameters with Optuna and want to run a similar objective function using the best hyperparameters found so far to further analyze the results, or

• You have optimized with Optuna using a partial dataset to reduce training time. After the hyperparameter tuning, you want to train the model using the whole dataset with the best hyperparameter values found.

best_trial provides an interface to re-evaluate the objective function with the current best hyperparameter values.

This tutorial shows an example of how to re-run a different objective function with the current best values, like the first example above.

Investigating the best model further

Let’s consider a classical supervised classification problem with Optuna as follows:

from sklearn import metrics
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split


import optuna


def objective(trial):
    X, y = make_classification(n_features=10, random_state=1)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

    C = trial.suggest_float("C", 1e-7, 10.0, log=True)

    clf = LogisticRegression(C=C)
    clf.fit(X_train, y_train)

    return clf.score(X_test, y_test)


study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=10)

print(study.best_trial.value)  # Show the best value.

0.92

Suppose after the hyperparameter optimization, you want to calculate other evaluation metrics such as recall, precision, and f1-score on the same dataset. You can define another objective function that shares most of the objective function to reproduce the model with the best hyperparameters.

def detailed_objective(trial):
    # Use same code objective to reproduce the best model
    X, y = make_classification(n_features=10, random_state=1)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

    C = trial.suggest_float("C", 1e-7, 10.0, log=True)

    clf = LogisticRegression(C=C)
    clf.fit(X_train, y_train)

    # calculate more evaluation metrics
    pred = clf.predict(X_test)

    acc = metrics.accuracy_score(pred, y_test)
    recall = metrics.recall_score(pred, y_test)
    precision = metrics.precision_score(pred, y_test)
    f1 = metrics.f1_score(pred, y_test)

    return acc, f1, recall, precision

Pass study.best_trial as the argument of detailed_objective.

detailed_objective(study.best_trial)  # calculate acc, f1, recall, and precision

(0.92, np.float64(0.9285714285714286), np.float64(0.9285714285714286), np.float64(0.9285714285714286))

The difference between best_trial and ordinal trials

This uses best_trial, which returns the best_trial as a FrozenTrial. The FrozenTrial is different from an active trial and behaves differently from Trial in some situations. For example, pruning does not work because should_prune always returns False.

Note

For multi-objective optimization as demonstrated by Multi-objective Optimization with Optuna, best_trials returns a list of FrozenTrial on Pareto front. So we can re-use each trial in the list by the similar way above.