tests.test_optimizer_parameter.test_DecisionTree

Complexity

Total Complexity

6

Size/Duplication

Total Lines	42
Duplicated Lines	35.71 %

Importance

Changes

0

3 Functions

Rating	Name	Duplication	Size	Complexity
A	test_warm_start_smbo()	15	15	3
A	test_max_sample_size()	0	4	2
A	get_score()	0	2	1

# Author: Simon Blanke
# Email: simon.blanke@yahoo.com
# License: MIT License

import numpy as np

from gradient_free_optimizers import DecisionTreeOptimizer
from ._base_test import _base_test

n_iter = 33
opt = DecisionTreeOptimizer


def get_score(para):
    return -(para["x1"] * para["x1"])


def test_warm_start_smbo():

This code seems to be duplicated in your project.

    gpr_X, gpr_y = [], []
    for _ in range(10):
        pos_ = np.random.randint(0, high=9)
        pos = np.array([pos_])

        para = {
            "x1": pos_,
        }
        gpr_X.append(pos)
        gpr_y.append(get_score(para))

    for warm_start_smbo in [None, (gpr_X, gpr_y)]:
        opt_para = {"warm_start_smbo": warm_start_smbo}
        _base_test(opt, n_iter, opt_para=opt_para)


def test_max_sample_size():
    for max_sample_size in [10, 100, 10000, 10000000000]:
        opt_para = {"max_sample_size": max_sample_size}
        _base_test(opt, n_iter, opt_para=opt_para)


"""
def test_gpr():
    opt = Hyperactive(X, y, memory=memory)
    opt.search(
        search_config,
        n_iter=n_iter,
        optimizer={"DecisionTree": {"tree_regressor": "random_forest"}},
    )
"""