dataset_classify.py

import numpy as np
from pysvm import LinearSVC, KernelSVC, NuSVC
from sklearn.datasets import load_iris, load_breast_cancer, load_digits, load_wine
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

np.random.seed(2022)

score = np.zeros((3, 4))
for i, load_dataset in enumerate(
    [load_iris, load_wine, load_breast_cancer, load_digits]):
    X, y = load_dataset(return_X_y=True)
    train_X, test_X, train_y, test_y = train_test_split(X, y)
    stder = StandardScaler().fit(train_X)
    train_X = stder.transform(train_X)
    test_X = stder.transform(test_X)

    for j, model in enumerate([LinearSVC, KernelSVC, NuSVC]):
        score[j, i] = model(
            n_jobs=6,
            max_iter=1000,
        ).fit(train_X, train_y).score(test_X, test_y)

print(score)