tests.optimizer_parameter.TPE

Complexity

Total Complexity

6

Size/Duplication

Total Lines	43
Duplicated Lines	0 %

Importance

Changes

0

3 Functions

Rating	Name	Duplication	Size	Complexity
A	test_warm_start_smbo()	0	4	1
A	sphere_function()	0	8	3
A	test_start_up_evals()	0	7	2

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

import numpy as np

from hyperactive import Hyperactive

X, y = np.array([0]), np.array([0])
memory = False
n_iter = 25


def sphere_function(para, X_train, y_train):
    loss = []
    for key in para.keys():
        if key == "iteration":
            continue
        loss.append(para[key] * para[key])

    return -np.array(loss).sum()


search_config = {
    sphere_function: {"x1": np.arange(-3, 3, 0.1), "x2": np.arange(-3, 3, 0.1)}
}


def test_start_up_evals():
    for start_up_evals in [1, 100]:
        opt = Hyperactive(X, y, memory=memory)
        opt.search(
            search_config,
            n_iter=n_iter,
            optimizer={"TPE": {"start_up_evals": start_up_evals}},
        )


def test_warm_start_smbo():
    opt = Hyperactive(X, y, memory="long")
    opt.search(
        search_config, n_iter=n_iter, optimizer={"TPE": {"warm_start_smbo": True}}
    )