Optuna — Bayesian Hyperparameter Optimization

import optuna
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score, StratifiedKFold, train_test_split
from sklearn.datasets import load_breast_cancer
import matplotlib.pyplot as plt

optuna.logging.set_verbosity(optuna.logging.WARNING)

cancer = load_breast_cancer()
X, y = cancer.data, cancer.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

# OPTUNA OBJECTIVE FUNCTION
def objective(trial: optuna.Trial) -> float:
    params = {
        "n_estimators":     trial.suggest_int("n_estimators", 50, 500),
        "learning_rate":    trial.suggest_float("learning_rate", 0.005, 0.5, log=True),
        "max_depth":        trial.suggest_int("max_depth", 2, 8),
        "subsample":        trial.suggest_float("subsample", 0.6, 1.0),
        "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 20),
        "max_features":     trial.suggest_float("max_features", 0.4, 1.0),
        "random_state":     42,
    }
    pipe = Pipeline([
        ("scaler", StandardScaler()),
        ("model",  GradientBoostingClassifier(**params)),
    ])
    auc_scores = cross_val_score(pipe, X_train, y_train, cv=cv, scoring="roc_auc", n_jobs=-1)
    return auc_scores.mean()

# RUN STUDY (TPE sampler = Bayesian)
study = optuna.create_study(
    direction="maximize",
    sampler=optuna.samplers.TPESampler(seed=42),
    pruner=optuna.pruners.MedianPruner(n_startup_trials=10, n_warmup_steps=5),
)
study.optimize(objective, n_trials=50, show_progress_bar=True)

print(f"\nBest trial: #{study.best_trial.number}")
print(f"Best CV AUC: {study.best_value:.4f}")
print(f"Best params: {study.best_params}")

# COMPARE OPTUNA vs OTHER METHODS
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import loguniform, randint, uniform

rand_search = RandomizedSearchCV(
    Pipeline([("scaler", StandardScaler()), ("model", GradientBoostingClassifier(random_state=42))]),
    {"model__n_estimators": randint(50, 500), "model__learning_rate": loguniform(0.005, 0.5),
     "model__max_depth": randint(2, 8), "model__subsample": uniform(0.6, 0.4)},
    n_iter=50, cv=5, scoring="roc_auc", n_jobs=-1, random_state=42,
)
rand_search.fit(X_train, y_train)

print(f"\nComparison (50 trials each, 5-fold CV):")
print(f"  RandomizedSearchCV: {rand_search.best_score_:.4f}")
print(f"  Optuna (TPE):       {study.best_value:.4f}  (typically better in fewer trials)")

# TRAIN FINAL MODEL WITH OPTUNA PARAMS
best_pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("model",  GradientBoostingClassifier(**study.best_params, random_state=42)),
])
best_pipe.fit(X_train, y_train)
test_auc = cross_val_score(best_pipe, X_test, y_test, cv=5, scoring="roc_auc").mean()
print(f"  Test AUC: {test_auc:.4f}")

# VISUALIZE OPTIMIZATION HISTORY
try:
    fig = optuna.visualization.matplotlib.plot_optimization_history(study)
    plt.title("Optuna Optimization History -- AUC improves as trials progress")
    plt.tight_layout()
    plt.savefig("optuna_history.png", dpi=100, bbox_inches="tight")
    plt.show()
except Exception:
    # matplotlib backend issues -- plot manually
    aucs = [t.value for t in study.trials]
    best_so_far = np.maximum.accumulate(aucs)
    plt.figure(figsize=(9, 4))
    plt.plot(aucs, "o", alpha=0.4, color="steelblue", label="Trial AUC")
    plt.plot(best_so_far, "r-", linewidth=2, label="Best so far")
    plt.xlabel("Trial")
    plt.ylabel("AUC-ROC")
    plt.title("Optuna Optimization History")
    plt.legend()
    plt.tight_layout()
    plt.savefig("optuna_history.png", dpi=100, bbox_inches="tight")
    plt.show()