opt.py

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import optuna


X, y = load_iris(return_X_y=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.20, stratify=y)

def objective(trial: optuna.Trial):

    params = {
        'max_depth': trial.suggest_int('max_depth', 1, 20),
        'criterion': trial.suggest_categorical('crterion', ['gini', 'entropy']),
        'min_samples_split': trial.suggest_float('min_samples_split', 0.1, 1.0,log=True)
    }

    clf = DecisionTreeClassifier(**params)

    clf.fit(X_train, y_train)

    score = clf.score(X_test, y_test) # acc

    return score


study = optuna.create_study(study_name='irysy', storage='sqlite:///irysy.db', direction='maximize', load_if_exists=True)
study.optimize(objective, n_trials=20)

opt2.py

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVR
import optuna


X, y = load_digits(return_X_y=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.20, stratify=y)

def objective(trial: optuna.Trial):

    type_ = trial.suggest_categorical('type_', ['svr', 'rf'])

    if type_ == 'rf':
        params = {
            'max_depth': trial.suggest_int('max_depth', 1, 20),
            'criterion': trial.suggest_categorical('crterion', ['gini', 'entropy']),
            'min_samples_split': trial.suggest_float('min_samples_split', 0.1, 1.0,log=True),
            'max_features': trial.suggest_categorical('max_features', ["auto", "sqrt", "log2"])
        }
        clf = DecisionTreeClassifier(**params)
        clf.fit(X_train, y_train)
        score = clf.score(X_test, y_test) # acc
        return score
    elif type_ == 'svr':
        params = {
            'kernel': trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf', 'sigmoid']),
            'degree': trial.suggest_int('degree', 1, 3),
            'C': trial.suggest_float('C', 0.01, 10, log=True)
        }
        clf = SVR(**params)
        clf.fit(X_train, y_train)
        score = clf.score(X_test, y_test) # acc
        return score



study = optuna.create_study(study_name='digits2', storage='sqlite:///digits2.db', direction='maximize', load_if_exists=True)
study.optimize(objective, n_trials=200)