MOL-2607/ feat: implement random-k selector #10
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padix-key
reviewed
Apr 9, 2025
src/peppr/selector.py
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| The best value is chosen from *k* randomly chosen predictions. | ||
| """ | ||
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| def __init__(self, k: int) -> None: |
| Check that the :class:`RandomSelector`, when ran multiple times, selects approximately the expected value for known | ||
| examples. | ||
| """ | ||
| selector = peppr.RandomSelector(k=5) |
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The random seed could be used in this test to make it deterministic
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Addressed in af4a29a.
tests/test_selectors.py
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| selector.select(values, smaller_is_better=False) for _ in range(20) | ||
| ] | ||
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| assert np.isclose(np.mean(selected_values), 5.0, rtol=0.5) |
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Currently this test fails, probably because sometimes the mean is randomly not within the tolerance. Using a much larger k (and more values to select from) would solve this, right?
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Addressed in af4a29a. I was actually computing the wrong expected value.
padix-key
approved these changes
Apr 9, 2025
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Looks good to me now, thanks! |
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Summary
Implements a new
RandomSelectorclass that selects the best value from k randomly chosen values in the input array. This allows for easier benchmarking of confidence model, since the perfomance obtained with aRandomSelectoris the same that would be obtained with aTopSelectorby a random confidence model.Changes
RandomSelectorclass toselector.pytest_selectors.pyImplementation Details
RandomSelectorrandomly selects k indices from the input arraysmaller_is_betterparameter