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"""Test module for max score functionality.""" |
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import time |
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import numpy as np |
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7
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from hyperactive import Hyperactive |
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from hyperactive.optimizers import ( |
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HillClimbingOptimizer, |
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) |
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11
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12
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13
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def objective_function(para): |
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"""Return simple quadratic objective function for testing.""" |
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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(0, 100000, 0.1)), |
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} |
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def test_max_score_0(): |
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"""Test max_score termination with hill climbing optimizer.""" |
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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(0, 100, 0.1)), |
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} |
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35
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max_score = -9999 |
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37
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opt = HillClimbingOptimizer( |
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epsilon=0.01, |
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rand_rest_p=0, |
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) |
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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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optimizer=opt, |
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n_iter=100000, |
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initialize={"warm_start": [{"x1": 99}]}, |
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max_score=max_score, |
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) |
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hyper.run() |
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print("\n Results head \n", hyper.search_data(objective_function).head()) |
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print("\n Results tail \n", hyper.search_data(objective_function).tail()) |
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print("\nN iter:", len(hyper.search_data(objective_function))) |
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assert -100 > hyper.best_score(objective_function) > max_score |
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60
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61
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def test_max_score_1(): |
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"""Test max_score termination with time constraint.""" |
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64
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def objective_function(para): |
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score = -para["x1"] * para["x1"] |
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time.sleep(0.01) |
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return score |
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69
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search_space = { |
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"x1": list(np.arange(0, 100, 0.1)), |
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} |
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73
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max_score = -9999 |
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75
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c_time = time.perf_counter() |
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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=100000, |
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initialize={"warm_start": [{"x1": 99}]}, |
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max_score=max_score, |
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) |
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hyper.run() |
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diff_time = time.perf_counter() - c_time |
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86
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87
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print("\n Results head \n", hyper.search_data(objective_function).head()) |
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print("\n Results tail \n", hyper.search_data(objective_function).tail()) |
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90
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print("\nN iter:", len(hyper.search_data(objective_function))) |
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92
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assert diff_time < 1 |
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93
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SimonBlanke /
Hyperactive
