simple_dask

simple_dask.py

import optuna   
                                            
from dask.distributed import Client
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split


def objective(trial):
    X, y = load_digits(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2)

    max_depth = trial.suggest_int("max_depth", 2, 10)
    n_estimators = trial.suggest_int("n_estimators", 1, 100)

    clf = RandomForestClassifier(max_depth=max_depth, n_estimators=n_estimators)
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    score = accuracy_score(y_test, y_pred)
    return score


if __name__ == "__main__":
    storages = [
        None,
        "sqlite:///example.db",
    ]

    for s in storages:
        with Client() as client:
            print(f"Dask dashboard is available at {client.dashboard_link}")
            study_name = "burabura"
            storage = optuna.integration.dask.DaskStorage(storage=s)
            study = optuna.create_study(
                study_name=study_name,
                storage=storage,
                direction="maximize",
            )
            study.optimize(objective, n_trials=100)

            print(f"Best params: {study.best_params}")

            if s is None:
                continue

            study = optuna.integration.dask.DaskStudy(study_name, storage=storage)
            print(f"Best params: {study.best_params}")
            study.optimize(objective, n_trials=10)