test.py

from evolutionary_search import EvolutionaryAlgorithmSearchCV, maximize
import sklearn.datasets
import numpy as np
from sklearn.model_selection import StratifiedKFold
from sklearn.svm import SVC
import unittest
import random


def func(x, y, m=1.0, z=False):
    return m * (np.exp(-(x ** 2 + y ** 2)) + float(z))


def readme():
    data = sklearn.datasets.load_digits()
    X = data["data"]
    y = data["target"]

    paramgrid = {
        "kernel": ["rbf"],
        "C": np.logspace(-9, 9, num=25, base=10),
        "gamma": np.logspace(-9, 9, num=25, base=10),
    }

    random.seed(1)

    cv = EvolutionaryAlgorithmSearchCV(
        estimator=SVC(),
        params=paramgrid,
        scoring="accuracy",
        cv=StratifiedKFold(n_splits=4),
        verbose=1,
        population_size=10,
        gene_mutation_prob=0.10,
        gene_crossover_prob=0.5,
        tournament_size=3,
        generations_number=5,
    )
    cv.fit(X, y)
    return cv


class TestEvolutionarySearch(unittest.TestCase):
    def test_cv(self):
        def try_with_params(**kwargs):
            cv = readme()
            cv_results_ = cv.cv_results_
            print("CV Results:\n{}".format(cv_results_))
            self.assertIsNotNone(cv_results_, msg="cv_results is None.")
            self.assertNotEqual(cv_results_, {}, msg="cv_results is empty.")
            self.assertAlmostEqual(
                cv.best_score_,
                1.0,
                delta=0.05,
                msg="Did not find the best score. Returned: {}".format(cv.best_score_),
            )

        try_with_params()

    def test_optimize(self):
        """ Simple hill climbing optimization with some twists. """

        param_grid = {"x": [-1.0, 0.0, 1.0], "y": [-1.0, 0.0, 1.0], "z": [True, False]}
        args = {"m": 1.0}

        best_params, best_score, score_results, _, _ = maximize(
            func, param_grid, args, verbose=True
        )
        print("Score Results:\n{}".format(score_results))
        self.assertEqual(best_params, {"x": 0.0, "y": 0.0, "z": True})
        self.assertEqual(best_score, 2.0)


if __name__ == "__main__":
    unittest.main()