ranking_HM.Rank.score_bg_fg_lists#

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Summary#

Compute quantities needed to assign scores to the triggers Responsible for populating: 1. coherent scores 2. rank functions (CDF of P(SNR^2\|H0) in each detector) If we are rerunning with a different subset of vetoes, update the desired set in self.mask_veto_criteria and, if apply_veto_before_scoring is True, set redo_bg = True

Signature#

def score_bg_fg_lists(self, redo_bg = False, redo_fg = False, apply_veto_before_scoring = True, score_triggers = True, coherent_score_func = utils.coherent_score, include_vetoed_triggers = False, safety_factor = 4, matching_point = None, scoring_method = 'old', downsampling_correction = True, min_trigs_per_grp = 500, p_veto_real_event = (DEFAULT_P_VETO, DEFAULT_P_VETO), **ranking_kwargs)

Input variables#
Name	Type	Default	Description
`redo_bg`		False	Flag whether to override the saved coherent scores for background triggers and recompute them (also recomputes for the foreground)
`redo_fg`		False	Flag whether to override the saved coherent scores for foreground triggers and recompute them
`apply_veto_before_scoring`		True	Flag whether to apply the vetoes before scoring. The vetoes to apply are set by self.mask_veto_criteria on self.veto_metadata_keys (we apply them after scoring anyway)
`score_triggers`		True	Boolean flag to compute the scores
`coherent_score_func`		utils.coherent_score	Function that accepts the coherent terms for a trigger and returns the coherent score
`include_vetoed_triggers`		False	Flag to include vetoed triggers with a penalty in the final list
`safety_factor`		4	Add this to threshold_chi2 before estimating the rank functions to account for incompleteness related to optimization
`matching_point`		None	Set SNR^2 at which the rank functions are matched, the default is threshold network SNR^2/2
`scoring_method`		‘old’	Flag to indicate whether we’re using the old (mz/cdf) way of ranking vs new way (fitting for the pdf)
`downsampling_correction`		True	If the triggers were downsampled compared to a chi-sq distribution because of an additional cut (e.g., based on whether the mode ratios A33/A22 or A44/A22 are physical). This flag corrects the rank function so that it follows the chi-sq behavior again. This flag needs a file downsamp_corr_path.npy to be input when creating Rank class object
`min_trigs_per_grp`		500	To avoid pathologies with making the rank functions, we require that the templates in each group have more than a particular number of background triggers associated to them
`p_veto_real_event`		(DEFAULT_P_VETO, DEFAULT_P_VETO)	Tuple with functions for the probability that a real event fails the vetoes in each detector, which in the most general case can be a function of all properties of the trigger. They should accept a list of entries of scores_(non)vetoed_max and yield an array of probabilities.
`\\ranking_kwargs`			Any extra arguments we want to pass to the ranking function

Output variables#

Return annotation	Docstring type	Description
`None`

Docstring#

Compute quantities needed to assign scores to the triggers
Responsible for populating:
 1. coherent scores
 2. rank functions (CDF of P(SNR^2|H0) in each detector)
If we are rerunning with a different subset of vetoes, update the
desired set in self.mask_veto_criteria and, if
apply_veto_before_scoring is True, set redo_bg = True
:param redo_bg:
    Flag whether to override the saved coherent scores for background
    triggers and recompute them (also recomputes for the foreground)
:param redo_fg:
    Flag whether to override the saved coherent scores for foreground
    triggers and recompute them
:param apply_veto_before_scoring:
    Flag whether to apply the vetoes before scoring. The vetoes to
    apply are set by self.mask_veto_criteria on self.veto_metadata_keys
    (we apply them after scoring anyway)
:param score_triggers: Boolean flag to compute the scores
:param coherent_score_func:
    Function that accepts the coherent terms for a trigger and returns
    the coherent score
:param include_vetoed_triggers:
    Flag to include vetoed triggers with a penalty in the final list
:param safety_factor:
    Add this to threshold_chi2 before estimating the rank functions
    to account for incompleteness related to optimization
:param matching_point:
    Set SNR^2 at which the rank functions are matched, the default
    is threshold network SNR^2/2
:param scoring_method:
    Flag to indicate whether we're using the old (mz/cdf) way of ranking
    vs new way (fitting for the pdf)
:param downsampling_correction:
    If the triggers were downsampled compared to a chi-sq distribution
    because of an additional cut (e.g., based on whether the mode ratios
    A33/A22 or A44/A22 are physical). This flag corrects the rank
    function so that it follows the chi-sq behavior again. This flag
    needs a file downsamp_corr_path.npy to be input when creating
    Rank class object
:param min_trigs_per_grp:
    To avoid pathologies with making the rank functions, we require that
    the templates in each group have more than a particular
    number of background triggers associated to them
:param p_veto_real_event:
    Tuple with functions for the probability that a real event fails the
    vetoes in each detector, which in the most general case can be a
    function of all properties of the trigger. They should accept a list
    of entries of scores_(non)vetoed_max and yield an array of
    probabilities.
:param ranking_kwargs:
    Any extra arguments we want to pass to the ranking function
:return: