lumin.inference package

Submodules

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)

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