"""
.. _reuse_best_trial:

Re-use the best values
==========================

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.

:class:`~optuna.study.Study.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_loguniform("C", 1e-7, 10.0)

    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.

###################################################################################################
# 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_loguniform("C", 1e-7, 10.0)

    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

###################################################################################################
# The difference between :class:`~optuna.study.Study.best_trial` and ordinal trials
# ----------------------------------------------------------------------------------
#
# This uses :class:`~optuna.study.Study.best_trial`, which returns the `best_trial` as a
# :class:`~optuna.trial.FrozenTrial`.
# The :class:`~optuna.trial.FrozenTrial` is different from an active trial
# and behaves differently from :class:`~optuna.trial.Trial` in some situations.
# For example, pruning does not work because :class:`~optuna.trial.FrozenTrial.should_prune`
# always returns ``False``.