list_3_30_optimize_rf_in_parallel.py

import optuna
import pandas as pd
from sklearn.datasets import fetch_openml
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score


data = fetch_openml(name="adult")
X = pd.get_dummies(data["data"])
y = [1 if d == ">50K" else 0 for d in data["target"]]


def objective(trial):
    clf = RandomForestClassifier(
        max_depth=trial.suggest_int(
            "max_depth", 2, 32,
        ),
        min_samples_split=trial.suggest_float(
            "min_samples_split", 0, 1,
        ),
    )

    score = cross_val_score(clf, X, y, cv=3)
    accuracy = score.mean()
    return accuracy


study = optuna.load_study(
    study_name="ch3-parallel",
    storage="mysql+pymysql://user:test@localhost:3306/optunatest",
)
study.optimize(objective, n_trials=100)

print(f"Best objective value: {study.best_value}")
print(f"Best parameter: {study.best_params}")