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03:57
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test_exploration_1()   A

Complexity

Conditions 1

Size

Total Lines 39
Code Lines 27

Duplication

Lines 39
Ratio 100 %

Importance

Changes 0
Metric Value
cc 1
eloc 27
nop 1
dl 39
loc 39
rs 9.232
c 0
b 0
f 0
1
import pytest
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from tqdm import tqdm
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import numpy as np
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from ._parametrize import pytest_parameter
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8 View Code Duplication
@pytest.mark.parametrize(*pytest_parameter)
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9
def test_exploration_0(Optimizer):
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    def objective_function(para):
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        score = -(para["x1"] * para["x1"] + para["x2"] * para["x2"])
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        return score
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    search_space = {
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        "x1": np.arange(-50, 1, 1),
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        "x2": np.arange(0, 10, 1),
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    }
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    init1 = {
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        "x1": -50,
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        "x2": 1,
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    }
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    init2 = {
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        "x1": -49,
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        "x2": 2,
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    }
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    opt = Optimizer(search_space)
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    opt.search(
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        objective_function,
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        n_iter=50,
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        memory=False,
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        verbosity={"print_results": False, "progress_bar": False,},
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        initialize={"warm_start": [init1, init2]},
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    )
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    uniques_2nd_dim = list(opt.results["x2"].values)
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    print("\n uniques_2nd_dim \n", uniques_2nd_dim, "\n")
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    print("\n Results head \n", opt.results.head())
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    print("\n Results tail \n", opt.results.tail())
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    print("\nN iter:", len(opt.results))
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    assert 0 in uniques_2nd_dim
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@pytest.mark.parametrize(*pytest_parameter)
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50
def test_exploration_1(Optimizer):
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    def objective_function(para):
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        score = -(para["x1"] * para["x1"] + para["x2"] * para["x2"])
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        return score
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    search_space = {
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        "x1": np.arange(-50, 1, 1),
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        "x2": np.arange(-10, 1, 1),
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    }
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    init1 = {
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        "x1": -50,
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        "x2": -1,
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    }
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    init2 = {
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        "x1": -49,
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        "x2": -2,
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    }
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    opt = Optimizer(search_space)
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    opt.search(
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        objective_function,
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        n_iter=50,
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        memory=False,
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        verbosity={"print_results": False, "progress_bar": False,},
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        initialize={"warm_start": [init1]},
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    )
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    uniques_2nd_dim = list(opt.results["x2"].values)
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    print("\n uniques_2nd_dim \n", uniques_2nd_dim, "\n")
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    print("\n Results head \n", opt.results.head())
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    print("\n Results tail \n", opt.results.tail())
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    print("\nN iter:", len(opt.results))
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    assert 0 in uniques_2nd_dim
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