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lumin.inference package

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lumin.inference.summary_stat module

lumin.inference.summary_stat.bin_binary_class_pred(df, max_unc, consider_samples=None, step_sz=0.001, pred_name='pred', sample_name='gen_sample', compact_samples=False, class_name='gen_target', add_pure_signal_bin=False, max_unc_pure_signal=0.1, verbose=True)[source]

Define bin-edges for binning particle process samples as a function of event class prediction (signal | background) such that the statistical uncertainties on per bin yields are below max_unc for each considered sample.

Parameters
  • df (DataFrame) – DataFrame containing the data

  • max_unc (float) – maximum fractional statisitcal uncertainty to allow when defining bins

  • consider_samples (Optional[List[str]]) – if set, only listed samples are considered when defining bins

  • step_sz (float) – resolution of scan along event prediction

  • pred_name (str) – column to use as event class prediction

  • sample_name (str) – column to use as particle process fo reach event

  • compact_samples (bool) – if true, will not consider samples when computing bin edges, only the class

  • class_name (str) – name of column to use as class indicator

  • add_pure_signal_bin (bool) – if true will attempt to add a bin which oonly contains signal (class 1) if the fractional bin-fill uncertainty would be less than max_unc_pure_signal

  • max_unc_pure_signal (float) – maximum fractional statisitcal uncertainty to allow when defining pure-signal bins

  • verbose (bool) – whether to show progress bar

Return type

List[float]

Returns

list of bin edges

Module contents

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