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63 lines
1.4 KiB
Python
63 lines
1.4 KiB
Python
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import numpy as np
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from flaml import tune
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from flaml import BlendSearch, CFO
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def _invalid_objective(config):
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# DragonFly uses `point`
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metric = "point" if "point" in config else "report"
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if config[metric] > 4:
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tune.report(float("inf"))
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elif config[metric] > 3:
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tune.report(float("-inf"))
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elif config[metric] > 2:
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tune.report(np.nan)
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else:
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tune.report(float(config[metric]) or 0.1)
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config = {"report": tune.uniform(0.0, 5.0)}
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def test_blendsearch():
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out = tune.run(
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_invalid_objective,
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search_alg=BlendSearch(
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points_to_evaluate=[
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{"report": 1.0},
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{"report": 2.1},
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{"report": 3.1},
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{"report": 4.1},
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]
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),
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config=config,
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metric="_metric",
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mode="max",
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num_samples=16,
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)
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best_trial = out.best_trial
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assert best_trial.config["report"] <= 2.0
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def test_cfo():
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out = tune.run(
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_invalid_objective,
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search_alg=CFO(
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points_to_evaluate=[
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{"report": 1.0},
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{"report": 2.1},
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{"report": 3.1},
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{"report": 4.1},
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]
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),
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config=config,
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metric="_metric",
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mode="max",
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num_samples=16,
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)
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best_trial = out.best_trial
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assert best_trial.config["report"] <= 2.0
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