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import numpy as np |
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from hyperactive.optimizers import HillClimbingOptimizer |
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from hyperactive.experiment import BaseExperiment |
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from hyperactive.search_config import SearchConfig |
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def test_constr_opt_0(): |
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class Experiment(BaseExperiment): |
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def objective_function(self, para): |
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score = -para["x1"] * para["x1"] |
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return score |
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experiment = Experiment() |
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search_config = SearchConfig( |
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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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hyper = HillClimbingOptimizer() |
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hyper.add_search( |
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experiment, |
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search_config, |
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n_iter=50, |
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constraints=constraints_list, |
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) |
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hyper.run() |
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search_data = hyper.search_data(experiment) |
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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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class Experiment(BaseExperiment): |
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def objective_function(self, para): |
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score = -(para["x1"] * para["x1"] + para["x2"] * para["x2"]) |
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return score |
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experiment = Experiment() |
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search_config = SearchConfig( |
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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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hyper = HillClimbingOptimizer() |
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hyper.add_search( |
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experiment, |
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search_config, |
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n_iter=50, |
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constraints=constraints_list, |
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) |
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hyper.run() |
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search_data = hyper.search_data(experiment) |
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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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""" |
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def test_constr_opt_2(): |
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n_iter = 50 |
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class Experiment(BaseExperiment): |
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def objective_function(self, para): |
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score = -para["x1"] * para["x1"] |
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return score |
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experiment = Experiment() |
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search_config = SearchConfig( |
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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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hyper = HillClimbingOptimizer() |
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hyper.add_search( |
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experiment, |
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search_config, |
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n_iter=50, |
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constraints=constraints_list, |
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) |
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hyper.run() |
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search_data = hyper.search_data(experiment) |
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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_new_positions = 0 |
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n_new_scores = 0 |
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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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127
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128
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for hyper_optimizer in hyper.opt_pros.values(): |
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optimizer = hyper_optimizer.gfo_optimizer |
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n_new_positions = n_new_positions + len(optimizer.pos_new_list) |
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n_new_scores = n_new_scores + len(optimizer.score_new_list) |
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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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print("\n optimizer", optimizer) |
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print(" n_new_positions", optimizer.pos_new_list) |
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print(" n_new_scores", optimizer.score_new_list) |
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assert n_new_positions == n_iter |
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assert n_new_scores == n_iter |
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assert n_current_positions == n_current_scores |
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assert n_current_positions <= n_new_positions |
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150
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assert n_best_positions == n_best_scores |
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assert n_best_positions <= n_new_positions |
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153
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assert n_new_positions == n_new_scores |
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""" |
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SimonBlanke /
Hyperactive
