from sklearn.datasets import load_breast_cancer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

cancer = load_breast_cancer()
X = cancer.data
y = cancer.target
target_names = cancer.target_names

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.25,
    random_state=42,
    stratify=y,
)

model = make_pipeline(
    StandardScaler(),
    SVC(kernel="rbf", C=1.0, gamma="scale"),
)
model.fit(X_train, y_train)

predictions = model.predict(X_test)
svc = model.named_steps["svc"]

print(f"train rows: {X_train.shape[0]}")
print(f"test rows: {X_test.shape[0]}")
print(f"kernel: {svc.kernel}")
print(f"support vectors per class: {svc.n_support_.tolist()}")
print(f"held-out accuracy: {accuracy_score(y_test, predictions):.3f}")
print(f"first predictions: {target_names[predictions[:5]].tolist()}")
print(f"first actual labels: {target_names[y_test[:5]].tolist()}")