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import numpy as np |
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from hyperactive import Hyperactive |
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def objective_function(opt): |
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score = -opt["x1"] * opt["x1"] |
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return score |
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search_space = { |
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"x1": np.arange(-100, 101, 1), |
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} |
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def test_initialize_warm_start_0(): |
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init = { |
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"x1": 0, |
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} |
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initialize = {"warm_start": [init]} |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, search_space, n_iter=1, initialize=initialize, |
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) |
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hyper.run() |
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assert abs(hyper.best_score(objective_function)) < 0.001 |
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def test_initialize_warm_start_1(): |
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search_space = { |
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"x1": np.arange(-10, 10, 1), |
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} |
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init = { |
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"x1": -10, |
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} |
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initialize = {"warm_start": [init]} |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, search_space, n_iter=1, initialize=initialize, |
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) |
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hyper.run() |
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assert hyper.best_para(objective_function) == init |
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def test_initialize_vertices(): |
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initialize = {"vertices": 2} |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, search_space, n_iter=2, initialize=initialize, |
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) |
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hyper.run() |
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assert abs(hyper.best_score(objective_function)) - 10000 < 0.001 |
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View Code Duplication |
def test_initialize_grid_0(): |
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search_space = { |
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"x1": np.arange(-1, 2, 1), |
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} |
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initialize = {"grid": 1} |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, search_space, n_iter=1, initialize=initialize, |
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) |
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hyper.run() |
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assert abs(hyper.best_score(objective_function)) < 0.001 |
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View Code Duplication |
def test_initialize_grid_1(): |
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search_space = { |
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"x1": np.arange(-2, 3, 1), |
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} |
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initialize = {"grid": 1} |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, search_space, n_iter=1, initialize=initialize, |
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) |
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hyper.run() |
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assert abs(hyper.best_score(objective_function)) - 1 < 0.001 |
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def test_initialize_all_0(): |
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search_space = { |
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"x1": np.arange(-2, 3, 1), |
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} |
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initialize = {"grid": 100, "vertices": 100, "random": 100} |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, search_space, n_iter=300, initialize=initialize, |
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) |
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hyper.run() |
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