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import numpy as np |
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from hyperactive import Hyperactive |
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from hyperactive.data_tools import DataCollector |
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View Code Duplication |
def test_data_collector_0(): |
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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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hyper = Hyperactive() |
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hyper.add_search(objective_function, search_space, n_iter=20) |
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hyper.run() |
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search_data = hyper.results(objective_function) |
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data_c = DataCollector("./search_data.csv") |
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data_c.save(search_data) |
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search_data_ = data_c.load() |
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search_data.equals(search_data_) |
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View Code Duplication |
def test_data_collector_1(): |
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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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hyper = Hyperactive() |
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hyper.add_search(objective_function, search_space, n_iter=20) |
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hyper.run() |
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search_data = hyper.results(objective_function) |
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data_c = DataCollector("./search_data.csv") |
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data_c.save(search_data, replace_existing=True) |
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search_data_ = data_c.load() |
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search_data.equals(search_data_) |
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def test_data_collector_2(): |
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data_c = DataCollector("./search_data.csv") |
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def objective_function(opt): |
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score = -opt["x1"] * opt["x1"] |
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para_dict = { |
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"x1": opt["x1"], |
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"score": score, |
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} |
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data_c.append(para_dict) |
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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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hyper = Hyperactive() |
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hyper.add_search(objective_function, search_space, n_iter=20) |
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hyper.run() |
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search_data_ = data_c.load() |
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SimonBlanke /
Hyperactive
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