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7b24662dca
* update tune function * pass incumbent result to the training function * Update test/tune/test_record_incumbent.py * Update flaml/searcher/search_thread.py * Update flaml/searcher/blendsearch.py * Update flaml/tune/tune.py * add constant variable Co-authored-by: 张少坤 <zhangshaokun@fuzhi.ai> Co-authored-by: Chi Wang <wang.chi@microsoft.com>
86 lines
2.4 KiB
Python
86 lines
2.4 KiB
Python
import numpy as np
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from flaml import tune
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from flaml.tune import INCUMBENT_RESULT
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def rosenbrock_function(config: dict):
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funcLoss = 50
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for key, value in config.items():
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if key in ["x1", "x2", "x3", "x4", "x5"]:
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funcLoss += value ** 2 - 10 * np.cos(2 * np.pi * value)
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if INCUMBENT_RESULT in config.keys():
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print("----------------------------------------------")
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print("incumbent result", config[INCUMBENT_RESULT])
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print("----------------------------------------------")
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return {"funcLoss": funcLoss}
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def test_record_incumbent(method="BlendSearch"):
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if method != "CFOCat":
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search_space = {
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"x1": tune.randint(1, 9),
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"x2": tune.randint(1, 9),
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"x3": tune.randint(1, 9),
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"x4": tune.randint(1, 9),
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"x5": tune.randint(1, 9),
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}
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else:
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search_space = {
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"x1": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
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"x2": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
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"x3": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
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"x4": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
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"x5": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
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}
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max_iter = 100
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num_samples = 128
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time_budget_s = 1
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n_cpu = 1
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if method == "BlendSearch":
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tune.run(
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evaluation_function=rosenbrock_function,
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config=search_space,
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verbose=0,
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metric="funcLoss",
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mode="min",
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max_resource=max_iter,
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min_resource=1,
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local_dir="logs/",
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num_samples=num_samples * n_cpu,
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time_budget_s=time_budget_s,
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use_incumbent_result_in_evaluation=True,
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)
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return
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elif method == "CFO":
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from flaml import CFO
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algo = CFO(
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use_incumbent_result_in_evaluation=True,
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)
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elif method == "CFOCat":
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from flaml.searcher.cfo_cat import CFOCat
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algo = CFOCat(
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use_incumbent_result_in_evaluation=True,
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)
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else:
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raise NotImplementedError
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tune.run(
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evaluation_function=rosenbrock_function,
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metric="funcLoss",
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mode="min",
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config=search_space,
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local_dir="logs/",
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num_samples=num_samples * n_cpu,
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time_budget_s=time_budget_s,
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search_alg=algo,
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)
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if __name__ == "__main__":
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test_record_incumbent(method="BlendSearch")
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