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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import math
from ._affiliation_zone import (
get_all_E_gt_func,
affiliation_partition)
from ._integral_interval import (
integral_interval_distance,
integral_interval_probaCDF_precision,
integral_interval_probaCDF_recall,
interval_length,
sum_interval_lengths)
def affiliation_precision_distance(Is = [(1,2),(3,4),(5,6)], J = (2,5.5)):
"""
Compute the individual average distance from Is to a single ground truth J
:param Is: list of predicted events within the affiliation zone of J
:param J: couple representating the start and stop of a ground truth interval
:return: individual average precision directed distance number
"""
if all([I is None for I in Is]): # no prediction in the current area
return(math.nan) # undefined
return(sum([integral_interval_distance(I, J) for I in Is]) / sum_interval_lengths(Is))
def affiliation_precision_proba(Is = [(1,2),(3,4),(5,6)], J = (2,5.5), E = (0,8)):
"""
Compute the individual precision probability from Is to a single ground truth J
:param Is: list of predicted events within the affiliation zone of J
:param J: couple representating the start and stop of a ground truth interval
:param E: couple representing the start and stop of the zone of affiliation of J
:return: individual precision probability in [0, 1], or math.nan if undefined
"""
if all([I is None for I in Is]): # no prediction in the current area
return(math.nan) # undefined
return(sum([integral_interval_probaCDF_precision(I, J, E) for I in Is]) / sum_interval_lengths(Is))
def affiliation_recall_distance(Is = [(1,2),(3,4),(5,6)], J = (2,5.5)):
"""
Compute the individual average distance from a single J to the predictions Is
:param Is: list of predicted events within the affiliation zone of J
:param J: couple representating the start and stop of a ground truth interval
:return: individual average recall directed distance number
"""
Is = [I for I in Is if I is not None] # filter possible None in Is
if len(Is) == 0: # there is no prediction in the current area
return(math.inf)
E_gt_recall = get_all_E_gt_func(Is, (-math.inf, math.inf)) # here from the point of view of the predictions
Js = affiliation_partition([J], E_gt_recall) # partition of J depending of proximity with Is
return(sum([integral_interval_distance(J[0], I) for I, J in zip(Is, Js)]) / interval_length(J))
def affiliation_recall_proba(Is = [(1,2),(3,4),(5,6)], J = (2,5.5), E = (0,8)):
"""
Compute the individual recall probability from a single ground truth J to Is
:param Is: list of predicted events within the affiliation zone of J
:param J: couple representating the start and stop of a ground truth interval
:param E: couple representing the start and stop of the zone of affiliation of J
:return: individual recall probability in [0, 1]
"""
Is = [I for I in Is if I is not None] # filter possible None in Is
if len(Is) == 0: # there is no prediction in the current area
return(0)
E_gt_recall = get_all_E_gt_func(Is, E) # here from the point of view of the predictions
Js = affiliation_partition([J], E_gt_recall) # partition of J depending of proximity with Is
return(sum([integral_interval_probaCDF_recall(I, J[0], E) for I, J in zip(Is, Js)]) / interval_length(J))
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