import optuna
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Load the iris dataset from Seaborn
iris = sns.load_dataset('iris')

# Split the dataset into features and labels
X = iris.drop('species', axis=1)
y = iris['species']

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

def objective(trial):
    """
    Objective function for Optuna. 
    """
    # Define the range of hyperparameters to search over
    n_estimators = trial.suggest_int('n_estimators', 10, 100)
    max_depth = trial.suggest_int('max_depth', 2, 10)
    min_samples_split = trial.suggest_int('min_samples_split', 2, 10)
    min_samples_leaf = trial.suggest_int('min_samples_leaf', 1, 10)

    # Create a random forest classifier with the specified hyperparameters
    clf = RandomForestClassifier(n_estimators=n_estimators,
                                 max_depth=max_depth,
                                 min_samples_split=min_samples_split,
                                 min_samples_leaf=min_samples_leaf)

    # Fit the classifier to the training data
    clf.fit(X_train, y_train)

    # Evaluate the classifier on the testing data
    score = clf.score(X_test, y_test)

    # Report the score back to Optuna
    return score

# Define the Optuna study
study = optuna.create_study(direction='maximize')

# Run the study
study.optimize(objective, n_trials=100)
    
# Print the best hyperparameters found by Optuna
print(f"Best hyperparameters: {study.best_params}")

# Create a final classifier using the best hyperparameters
best_clf = RandomForestClassifier(**study.best_params)

# Fit the final classifier to the training data
best_clf.fit(X_train, y_train)

# Evaluate the final classifier on the testing data
final_score = best_clf.score(X_test, y_test)