import numpy as np
import sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC


X, y = load_iris(return_X_y=True)

classifier = make_pipeline(
    StandardScaler(),
    SVC(kernel="linear", C=1.0, random_state=42),
)

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

metric = "accuracy"

scores = cross_val_score(
    classifier,
    X,
    y,
    cv=cv,
    scoring=metric,
)

print(f"scikit-learn {sklearn.__version__}")
print(f"Metric: {metric}")
print(f"Folds: {len(scores)}")
print(f"Fold {metric} scores:", np.array2string(scores, precision=3, floatmode="fixed"))
print(f"Mean {metric}: {scores.mean():.3f}")
print(f"Standard deviation: {scores.std():.3f}")