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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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class Experiment(BaseExperiment): |
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def objective_function(self, opt): |
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score = -opt["x1"] * opt["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(-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 = 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=1, |
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initialize=initialize, |
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) |
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hyper.run() |
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assert abs(hyper.best_score(experiment)) < 0.001 |
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def test_initialize_warm_start_1(): |
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search_space = { |
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"x1": list(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 = 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=1, |
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initialize=initialize, |
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) |
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hyper.run() |
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assert hyper.best_para(experiment) == init |
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def test_initialize_vertices(): |
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initialize = {"vertices": 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=2, |
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initialize=initialize, |
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) |
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hyper.run() |
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assert abs(hyper.best_score(experiment)) - 10000 < 0.001 |
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77
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View Code Duplication |
def test_initialize_grid_0(): |
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search_space = { |
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"x1": list(np.arange(-1, 2, 1)), |
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} |
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initialize = {"grid": 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=1, |
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initialize=initialize, |
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) |
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hyper.run() |
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assert abs(hyper.best_score(experiment)) < 0.001 |
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95
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View Code Duplication |
def test_initialize_grid_1(): |
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search_space = { |
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"x1": list(np.arange(-2, 3, 1)), |
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} |
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initialize = {"grid": 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=1, |
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initialize=initialize, |
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) |
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hyper.run() |
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assert abs(hyper.best_score(experiment)) - 1 < 0.001 |
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def test_initialize_all_0(): |
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search_space = { |
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"x1": list(np.arange(-2, 3, 1)), |
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} |
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initialize = {"grid": 100, "vertices": 100, "random": 100} |
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121
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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=300, |
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initialize=initialize, |
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) |
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hyper.run() |
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SimonBlanke /
Hyperactive
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