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import numpy as np |
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from hyperactive import Hyperactive |
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from hyperactive.optimizers.strategies import CustomOptimizationStrategy |
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from hyperactive.optimizers import HillClimbingOptimizer, RandomSearchOptimizer |
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def test_constr_opt_0(): |
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def objective_function(para): |
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score = -para["x1"] * para["x1"] |
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return score |
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search_space = { |
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"x1": list(np.arange(-15, 15, 1)), |
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} |
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def constraint_1(para): |
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print(" para", para) |
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return para["x1"] > -5 |
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constraints_list = [constraint_1] |
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optimizer1 = RandomSearchOptimizer() |
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optimizer2 = HillClimbingOptimizer() |
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opt_strat = CustomOptimizationStrategy() |
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opt_strat.add_optimizer(optimizer1, duration=0.7) |
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opt_strat.add_optimizer(optimizer2, duration=0.3) |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, |
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search_space, |
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n_iter=50, |
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constraints=constraints_list, |
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optimizer=opt_strat, |
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) |
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hyper.run() |
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search_data = hyper.search_data(objective_function) |
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x0_values = search_data["x1"].values |
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print("\n search_data \n", search_data, "\n") |
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assert np.all(x0_values > -5) |
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def test_constr_opt_1(): |
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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": list(np.arange(-10, 10, 1)), |
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"x2": list(np.arange(-10, 10, 1)), |
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} |
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def constraint_1(para): |
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return para["x1"] > -5 |
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constraints_list = [constraint_1] |
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optimizer1 = RandomSearchOptimizer() |
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optimizer2 = HillClimbingOptimizer() |
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opt_strat = CustomOptimizationStrategy() |
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opt_strat.add_optimizer(optimizer1, duration=0.7) |
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opt_strat.add_optimizer(optimizer2, duration=0.3) |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, |
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search_space, |
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n_iter=50, |
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constraints=constraints_list, |
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optimizer=opt_strat, |
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) |
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hyper.run() |
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search_data = hyper.search_data(objective_function) |
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x0_values = search_data["x1"].values |
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print("\n search_data \n", search_data, "\n") |
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assert np.all(x0_values > -5) |
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def test_constr_opt_2(): |
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n_iter = 50 |
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def objective_function(para): |
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score = -para["x1"] * para["x1"] |
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return score |
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search_space = { |
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"x1": list(np.arange(-10, 10, 0.1)), |
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} |
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def constraint_1(para): |
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return para["x1"] > -5 |
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def constraint_2(para): |
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return para["x1"] < 5 |
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constraints_list = [constraint_1, constraint_2] |
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optimizer1 = RandomSearchOptimizer() |
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optimizer2 = HillClimbingOptimizer() |
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opt_strat = CustomOptimizationStrategy() |
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opt_strat.add_optimizer(optimizer1, duration=0.7) |
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opt_strat.add_optimizer(optimizer2, duration=0.3) |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, |
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search_space, |
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n_iter=50, |
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constraints=constraints_list, |
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optimizer=opt_strat, |
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) |
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hyper.run() |
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search_data = hyper.search_data(objective_function) |
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x0_values = search_data["x1"].values |
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print("\n search_data \n", search_data, "\n") |
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assert np.all(x0_values > -5) |
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assert np.all(x0_values < 5) |
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n_current_positions = 0 |
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n_current_scores = 0 |
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n_best_positions = 0 |
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n_best_scores = 0 |
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139
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for optimizer_setup in list(hyper.opt_pros.values())[0].optimizer_setup_l: |
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optimizer = optimizer_setup["optimizer"].gfo_optimizer |
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duration = optimizer_setup["duration"] |
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143
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duration_sum = 1 |
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n_iter_expected = round(n_iter * duration / duration_sum) |
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n_current_positions = n_current_positions + len(optimizer.pos_current_list) |
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n_current_scores = n_current_scores + len(optimizer.score_current_list) |
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n_best_positions = n_best_positions + len(optimizer.pos_best_list) |
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n_best_scores = n_best_scores + len(optimizer.score_best_list) |
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152
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print("\n optimizer", optimizer) |
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print(" n_new_positions", optimizer.pos_new_list, len(optimizer.pos_new_list)) |
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print(" n_new_scores", optimizer.score_new_list, len(optimizer.score_new_list)) |
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print(" n_iter_expected", n_iter_expected) |
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assert len(optimizer.pos_new_list) == n_iter_expected |
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assert len(optimizer.score_new_list) == n_iter_expected |
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SimonBlanke /
Hyperactive
