General toyMC unbinned method plus max-slice method #49
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As an example, |
…ing very simple rules via "computer says no" rather than just putting in the extra line itself.
…stEqual (places=2 instead of decimals=2?)
hammannr
reviewed
Dec 15, 2023
GOFevaluation/evaluator_base.py
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| self.distance_measure = distance_measure | ||
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| def get_pvalue(self, n_toys=10000, generator_kwargs={}, distance_measure_kwargs={}): | ||
| self.gof = self.distance_measure(self.data, **distance_measure_kwargs) |
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I would suggest defining a trivial method get_gof to get the test statistic value so that it has the same interface as the other tests in this package.
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oh. so this is the reason why people call the test statistic gof.
| return np.abs((t0 - t1 + np.pi) % (2 * np.pi) - np.pi) | ||
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| @staticmethod | ||
| def get_best_partition(data_t, opening_angle=np.pi, test_angles=None, return_best_angle=False): |
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please add a short description of all parameters (with types) to the docstring :)
GOFevaluation/evaluators_1d.py
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| an equal or greater portion in a slice set in the generator | ||
| angles in radians | ||
| """ | ||
| def __init__(self, data, opening_angle=np.pi, fixed_length=True): |
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opening_angle is not used in the init and fixed_length should be briefly explained in the docstring
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This PR implements a generalised GOF where you need to provide a toyMC generator and distance measure given your model, which then runs toyMCs, computes the distance measure for each and returns the p-value based on that.
In particular, it also provides a circular max-slice GOF evaluators to compute how asymmetrical a theta-distribution is.