Source code for bob.measure.calibration

#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Thu May 16 11:41:49 CEST 2013

"""Measures for calibration"""

import math

import numpy


[docs]def cllr(negatives, positives): """Cost of log likelihood ratio as defined by the Bosaris toolkit Computes the 'cost of log likelihood ratio' (:math:`C_{llr}`) measure as given in the Bosaris toolkit Parameters: negatives (array): 1D float array that contains the scores of the "negative" (noise, non-class) samples of your classifier. positives (array): 1D float array that contains the scores of the "positive" (signal, class) samples of your classifier. Returns: float: The computed :math:`C_{llr}` value. """ sum_pos, sum_neg = 0.0, 0.0 for pos in positives: sum_pos += math.log(1.0 + math.exp(-pos), 2.0) for neg in negatives: sum_neg += math.log(1.0 + math.exp(neg), 2.0) return (sum_pos / len(positives) + sum_neg / len(negatives)) / 2.0
[docs]def min_cllr(negatives, positives): """Minimum cost of log likelihood ratio as defined by the Bosaris toolkit Computes the 'minimum cost of log likelihood ratio' (:math:`C_{llr}^{min}`) measure as given in the bosaris toolkit Parameters: negatives (array): 1D float array that contains the scores of the "negative" (noise, non-class) samples of your classifier. positives (array): 1D float array that contains the scores of the "positive" (signal, class) samples of your classifier. Returns: float: The computed :math:`C_{llr}^{min}` value. """ # first, sort both scores neg = sorted(negatives) pos = sorted(positives) N = len(neg) P = len(pos) II = N + P # now, iterate through both score sets and add a 0 for negative and 1 for # positive scores n, p = 0, 0 ideal = numpy.zeros(II) neg_indices = [0] * N pos_indices = [0] * P for i in range(II): if p < P and (n == N or neg[n] > pos[p]): pos_indices[p] = i p += 1 ideal[i] = 1 else: neg_indices[n] = i n += 1 # compute the pool adjacent violaters method on the ideal LLR scores ghat = numpy.ndarray(ideal.shape, dtype=numpy.float) raise NotImplementedError("No pavx implementation") pavx(ideal, ghat) # noqa: F821 # disable runtime warnings for a short time since log(0) will raise a warning old_warn_setup = numpy.seterr(divide="ignore") # ... compute logs posterior_log_odds = numpy.log(ghat) - numpy.log(1.0 - ghat) log_prior_odds = math.log(float(P) / float(N)) # ... activate old warnings numpy.seterr(**old_warn_setup) llrs = posterior_log_odds - log_prior_odds # some weired addition # for i in range(II): # llrs[i] += float(i)*1e-6/float(II) # unmix positive and negative scores new_neg = numpy.zeros(N) for n in range(N): new_neg[n] = llrs[neg_indices[n]] new_pos = numpy.zeros(P) for p in range(P): new_pos[p] = llrs[pos_indices[p]] # compute cllr of these new 'optimal' LLR scores return cllr(new_neg, new_pos)