tests.test_optimizers.test_best_results

Complexity

Total Complexity

5

Size/Duplication

Total Lines	115
Duplicated Lines	0 %

Importance

Changes

0

5 Functions

Rating	Name	Duplication	Size	Complexity
A	test_best_results_1()	0	23	1
A	objective_function()	0	4	1
A	test_best_results_0()	0	23	1
A	objective_function_m5()	0	4	1
A	objective_function_p5()	0	4	1

"""Test module for best results optimizer functionality."""

import numpy as np
import pytest

from hyperactive import Hyperactive

from ._parametrize import optimizers


def objective_function(opt):
    """Return standard quadratic objective function."""
    score = -opt["x1"] * opt["x1"]
    return score


def objective_function_m5(opt):
    """Quadratic objective function shifted by -5."""
    score = -(opt["x1"] - 5) * (opt["x1"] - 5)
    return score


def objective_function_p5(opt):
    """Quadratic objective function shifted by +5."""
    score = -(opt["x1"] + 5) * (opt["x1"] + 5)
    return score


search_space_0 = {"x1": list(np.arange(-100, 101, 1))}
search_space_1 = {"x1": list(np.arange(0, 101, 1))}
search_space_2 = {"x1": list(np.arange(-100, 1, 1))}

search_space_3 = {"x1": list(np.arange(-10, 11, 0.1))}
search_space_4 = {"x1": list(np.arange(0, 11, 0.1))}
search_space_5 = {"x1": list(np.arange(-10, 1, 0.1))}

search_space_6 = {"x1": list(np.arange(-0.0000000003, 0.0000000003, 0.0000000001))}
search_space_7 = {"x1": list(np.arange(0, 0.0000000003, 0.0000000001))}
search_space_8 = {"x1": list(np.arange(-0.0000000003, 0, 0.0000000001))}

objective_para = (
    "objective",
    [
        (objective_function),
        (objective_function_m5),
        (objective_function_p5),
    ],
)

search_space_para = (
    "search_space",
    [
        (search_space_0),
        (search_space_1),
        (search_space_2),
        (search_space_3),
        (search_space_4),
        (search_space_5),
        (search_space_6),
        (search_space_7),
        (search_space_8),
    ],
)


@pytest.mark.parametrize(*objective_para)
@pytest.mark.parametrize(*search_space_para)
@pytest.mark.parametrize(*optimizers)
def test_best_results_0(Optimizer, search_space, objective):
    """Test best score consistency with best parameters."""
    search_space = search_space
    objective_function = objective

    initialize = {"vertices": 2}

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        optimizer=Optimizer(),
        n_iter=10,
        memory=False,
        initialize=initialize,
    )
    hyper.run()

    assert hyper.best_score(objective_function) == objective_function(
        hyper.best_para(objective_function)
    )


@pytest.mark.parametrize(*objective_para)
@pytest.mark.parametrize(*search_space_para)
@pytest.mark.parametrize(*optimizers)
def test_best_results_1(Optimizer, search_space, objective):
    """Test best parameters are present in search data."""
    search_space = search_space
    objective_function = objective

    initialize = {"vertices": 2}

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        optimizer=Optimizer(),
        n_iter=10,
        memory=False,
        initialize=initialize,
    )
    hyper.run()

    assert hyper.best_para(objective_function)["x1"] in list(
        hyper.search_data(objective_function)["x1"]
    )