from sklearn.datasets import load_wine
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

wine = load_wine()
X_train, X_test, y_train, y_test = train_test_split(
    wine.data,
    wine.target,
    test_size=0.30,
    stratify=wine.target,
    random_state=7,
)

model = DecisionTreeClassifier(max_depth=3, random_state=7)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

print(
    classification_report(
        y_test,
        y_pred,
        target_names=wine.target_names,
        digits=3,
        zero_division=0,
    )
)

metrics = classification_report(
    y_test,
    y_pred,
    target_names=wine.target_names,
    output_dict=True,
    zero_division=0,
)

print(f"macro avg f1-score: {metrics['macro avg']['f1-score']:.3f}")