error_utils.py 18.2 KB
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#!/usr/bin/env python
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# Ivana Chingovska <ivana.chingovska@idiap.ch>
# Fri Dec  7 12:33:37 CET 2012
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"""Utility functions for computation of EPSC curve and related measurement"""

import bob.measure
import numpy
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from bob.measure import (
    far_threshold, eer_threshold, min_hter_threshold)


def calc_threshold(method, neg, pos):
    """Calculates the threshold based on the given method.
    The scores should be sorted!

    Parameters
    ----------
    method : str
        One of ``bpcer20``, ``eer``, ``min-hter``.
    neg : array_like
        The negative scores. They should be sorted!
    pos : array_like
        The positive scores. They should be sorted!

    Returns
    -------
    float
        The calculated threshold.

    Raises
    ------
    ValueError
        If method is unknown.
    """
    method = method.lower()
    if method == 'bpcer20':
        threshold = far_threshold(neg, pos, 0.05, True)
    elif method == 'eer':
        threshold = eer_threshold(neg, pos, True)
    elif method == 'min-hter':
        threshold = min_hter_threshold(neg, pos, True)
    else:
        raise ValueError("Unknown threshold criteria: {}".format(method))

    return threshold
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def calc_pass_rate(threshold, attacks):
    """Calculates the rate of successful spoofing attacks

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    Parameters
    ----------
    threshold :
      the threshold used for classification
    scores :
      numpy with the scores of the spoofing attacks

    Returns
    -------
    float
      rate of successful spoofing attacks
    """
    return (attacks >= threshold).mean()
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def weighted_neg_error_rate_criteria(data,
                                     weight,
                                     thres,
                                     beta=0.5,
                                     criteria='eer'):
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    """Given the single value for the weight parameter balancing between
    impostors and spoofing attacks and a threshold, calculates the error rates
    and their relationship depending on the criteria (difference in case of
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    'eer', hter in case of 'min-hter' criteria)
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    Keyword parameters:

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      - data - the development data used to determine the threshold. List on 4
      numpy.arrays containing: negatives (licit), positives (licit),
      negatives (spoof), positivies (spoof)
      - weight - the weight parameter balancing between impostors and spoofing
      attacks
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      - thres - the given threshold
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      - beta - the weight parameter balancing between real accesses and all the
      negative samples (impostors and spoofing attacks). Note that this
      parameter will be overriden and not considered if the selected criteria
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      is 'min-hter'.
      - criteria - 'eer', 'wer' or 'min-hter' criteria for decision threshold
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  """

    licit_neg = data[0]
    licit_pos = data[1]
    spoof_neg = data[2]
    spoof_pos = data[3]  # unpacking the data
    farfrr_licit = bob.measure.farfrr(licit_neg, licit_pos, thres)
    farfrr_spoof = bob.measure.farfrr(spoof_neg, spoof_pos, thres)

    frr = farfrr_licit[1]  # farfrr_spoof[1] should have the same value
    far_i = farfrr_licit[0]
    far_s = farfrr_spoof[0]

    far_w = (1 - weight) * far_i + weight * far_s

    if criteria == 'eer':
        if beta == 0.5:
            return abs(far_w - frr)
        else:
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            # return abs(far_w - frr)
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            return abs((1 - beta) * frr - beta * far_w)

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    elif criteria == 'min-hter':
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        return (far_w + frr) / 2

    else:
        return (1 - beta) * frr + beta * far_w


def recursive_thr_search(data,
                         span_min,
                         span_max,
                         weight,
                         beta=0.5,
                         criteria='eer'):
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    """Recursive search for the optimal threshold given a criteria. It
    evaluates the full range of thresholds at 100 points, and computes the one
    which optimizes the threshold. In the next search iteration, it examines
    the region around the point that optimizes the threshold. The procedure
    stops when the search range is smaller then 1e-10.
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  Keyword arguments:
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    - data - the development data used to determine the threshold. List on 4
    numpy.arrays containing: negatives (licit), positives (licit), negatives
    (spoof), positivies (spoof)
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    - span_min - the minimum of the search range
    - span_max - the maximum of the search range
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    - weight - the weight parameter balancing between impostors and spoofing
    attacks
    - beta - the weight parameter balancing between real accesses and all the
    negative samples (impostors and spoofing attacks). Note that methods called
    within this function will override this parameter and not considered if the
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    selected criteria is 'min-hter'.
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    - criteria - the decision threshold criteria ('eer' for EER, 'wer' for
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    Minimum WER or 'min-hter' for Minimum HTER criteria).
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  """

    quit_thr = 1e-10
    steps = 100
    if abs((span_max - span_min) / span_max) < quit_thr:
        return span_max  # or span_min, it doesn't matter
    else:
        step_size = (span_max - span_min) / steps
        thresholds = numpy.array(
            [(i * step_size) + span_min for i in range(steps + 1)])
        weighted_error_rates = numpy.array([
            weighted_neg_error_rate_criteria(data, weight, thr, beta, criteria)
            for thr in thresholds
        ])
        selected_thres = thresholds[numpy.where(
            weighted_error_rates == min(weighted_error_rates)
        )]  # all the thresholds which have minimum weighted error rate
        thr = selected_thres[int(
            selected_thres.size / 2
        )]  # choose the centrally positioned threshold
        return recursive_thr_search(data, thr - step_size, thr + step_size,
                                    weight, beta, criteria)


def weighted_negatives_threshold(licit_neg,
                                 licit_pos,
                                 spoof_neg,
                                 spoof_pos,
                                 weight,
                                 beta=0.5,
                                 criteria='eer'):
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    """Calculates the threshold for achieving the given criteria between the
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    FAR_w and the FRR, given the single value for the weight parameter
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    balancing between impostors and spoofing attacks and a single value for the
    parameter beta balancing between the real accesses and the negatives
    (impostors and spoofing attacks)
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  Keyword parameters:
    - licit_neg - numpy.array of scores for the negatives (licit scenario)
    - licit_pos - numpy.array of scores for the positives (licit scenario)
    - spoof_neg - numpy.array of scores for the negatives (spoof scenario)
    - spoof_pos - numpy.array of scores for the positives (spoof scenario)
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    - weight - the weight parameter balancing between impostors and spoofing
    attacks
    - beta - the weight parameter balancing between real accesses and all the
    negative samples (impostors and spoofing attacks). Note that methods called
    within this function will override this parameter and not considered if the
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    selected criteria is 'min-hter'.
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    - criteria - the decision threshold criteria ('eer' for EER, 'wer' for
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    Minimum WER or 'min-hter' for Minimum HTER criteria).
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  """
    span_min = min(
        numpy.append(licit_neg, spoof_neg)
    )  # the min of the span where we will search for the threshold
    span_max = max(
        numpy.append(licit_pos, spoof_pos)
    )  # the max of the span where we will search for the threshold
    data = (licit_neg, licit_pos, spoof_neg,
            spoof_pos)  # pack the data into a single list
    return recursive_thr_search(data, span_min, span_max, weight, beta,
                                criteria)


def epsc_weights(licit_neg, licit_pos, spoof_neg, spoof_pos, points=100):
    """Returns the weights for EPSC

  Keyword arguments:

    - points - number of points to calculate EPSC
  """
    step_size = 1 / float(points)
    weights = numpy.array([(i * step_size) for i in range(points + 1)])
    return weights


def epsc_thresholds(licit_neg,
                    licit_pos,
                    spoof_neg,
                    spoof_pos,
                    points=100,
                    criteria='eer',
                    omega=None,
                    beta=None):
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    """Calculates the optimal thresholds for EPSC, for a range of the weight
    parameter balancing between impostors and spoofing attacks, and for a range
    of the beta parameter balancing between real accesses and all the negatives
    (impostors and spoofing attacks)
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  Keyword arguments:

    - licit_neg - numpy.array of scores for the negatives (licit scenario)
    - licit_pos - numpy.array of scores for the positives (licit scenario)
    - spoof_neg - numpy.array of scores for the negatives (spoof scenario)
    - spoof_pos - numpy.array of scores for the positives (spoof scenario)
    - points - number of points to calculate EPSC
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    - criteria - the decision threshold criteria ('eer', 'wer' or 'min-hter')
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    - omega - the value of the parameter omega, balancing between impostors and
    spoofing attacks. If None, it is going to span the full range [0,1].
    Otherwise, can be set to a fixed value or a list of values.
    - beta - the value of the parameter beta, balancing between real accesses
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    and all the negatives (zero-effort impostors and spoofing attacks). If
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    None, it is going to span the full range [0,1]. Otherwise, can be set to a
    fixed value or a list of values.
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  """
    step_size = 1 / float(points)

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    if omega is None:
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        omega = numpy.array([(i * step_size) for i in range(points + 1)])
    elif not isinstance(omega, list) and not isinstance(
            omega, tuple) and not isinstance(omega, numpy.ndarray):
        omega = numpy.array([omega])
    else:
        omega = numpy.array(omega)

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    if beta is None:
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        beta = numpy.array([(i * step_size) for i in range(points + 1)])
    elif not isinstance(beta, list) and not isinstance(
            beta, tuple) and not isinstance(beta, numpy.ndarray):
        beta = numpy.array([beta])
    else:
        beta = numpy.array(beta)

    thresholds = numpy.ndarray([beta.size, omega.size], 'float64')
    for bindex, b in enumerate(beta):
        thresholds[bindex, :] = numpy.array([
            weighted_negatives_threshold(
                licit_neg,
                licit_pos,
                spoof_neg,
                spoof_pos,
                w,
                b,
                criteria=criteria) for w in omega
        ], 'float64')

    return omega, beta, thresholds


def weighted_err(error_1, error_2, weight):
    """Calculates the weighted error rate between the two input parameters

  Keyword arguments:
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    - error_1 - the first input error rate (FAR for zero effort impostors
    usually)
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    - error_2 - the second input error rate (SFAR)
    - weight - the given weight
  """
    return (1 - weight) * error_1 + weight * error_2


def error_rates_at_weight(licit_neg,
                          licit_pos,
                          spoof_neg,
                          spoof_pos,
                          omega,
                          threshold,
                          beta=0.5):
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    """Calculates several error rates: FRR, FAR (zero-effort impostors), SFAR,
    FAR_w, HTER_w for a given value of w. It returns the calculated threshold
    as a last argument
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  Keyword arguments:

    - licit_neg - numpy.array of scores for the negatives (licit scenario)
    - licit_pos - numpy.array of scores for the positives (licit scenario)
    - spoof_neg - numpy.array of scores for the negatives (spoof scenario)
    - spoof_pos - numpy.array of scores for the positives (spoof scenario)
    - threshold - the given threshold
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    - omega - the omega parameter balancing between impostors and spoofing
    attacks
    - beta - the weight parameter balancing between real accesses and all the

negative samples (impostors and spoofing attacks).
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  """

    farfrr_licit = bob.measure.farfrr(
        licit_neg, licit_pos,
        threshold)  # calculate test frr @ threshold (licit scenario)
    farfrr_spoof = bob.measure.farfrr(
        spoof_neg, spoof_pos,
        threshold)  # calculate test frr @ threshold (spoof scenario)

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    # we can take this value from farfrr_spoof as well, it doesn't matter
    frr = farfrr_licit[1]
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    far = farfrr_licit[0]
    sfar = farfrr_spoof[0]

    far_w = weighted_err(far, sfar, omega)
    hter_w = (far_w + frr) / 2
    wer_wb = weighted_err(frr, far_w, beta)

    return (frr, far, sfar, far_w, wer_wb, hter_w, threshold)


def epsc_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos, thresholds,
                     omega, beta):
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    """Calculates several error rates: FAR_w and WER_wb for the given weights
    (omega and beta) and thresholds (the thresholds need to be computed first
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    using the method: epsc_thresholds() before passing to this method)
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    Parameters
    ----------
    licit_neg : array_like
        array of scores for the negatives (licit scenario)
    licit_pos : array_like
        array of scores for the positives (licit scenario)
    spoof_neg : array_like
        array of scores for the negatives (spoof scenario)
    spoof_pos : array_like
        array of scores for the positives (spoof scenario)
    thresholds : array_like
        ndarray with threshold values
    omega : array_like
        array of the omega parameter balancing between impostors
        and spoofing attacks
    beta : array_like
        array of the beta parameter balancing between real accesses
        and all negatives (impostors and spoofing attacks)

    Returns
    -------
    far_w_errors: array_like
        FAR_w
    wer_wb_errors: array_like
        WER_wb
    """
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    far_w_errors = numpy.ndarray((beta.size, omega.size), 'float64')
    wer_wb_errors = numpy.ndarray((beta.size, omega.size), 'float64')

    for bindex, b in enumerate(beta):
        errors = [
            error_rates_at_weight(licit_neg, licit_pos, spoof_neg, spoof_pos,
                                  w, thresholds[bindex, windex], b)
            for windex, w in enumerate(omega)
        ]
        far_w_errors[bindex, :] = [errors[i][3] for i in range(len(errors))]
        wer_wb_errors[bindex, :] = [errors[i][4] for i in range(len(errors))]

    return far_w_errors, wer_wb_errors


def all_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos, thresholds,
                    omega, beta):
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    """Calculates several error rates: FAR_w and WER_wb for the given weights
    (omega and beta) and thresholds (the thresholds need to be computed first
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    using the method: epsc_thresholds() before passing to this method)
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    Parameters
    ----------
    licit_neg : array_like
        array of scores for the negatives (licit scenario)
    licit_pos : array_like
        array of scores for the positives (licit scenario)
    spoof_neg : array_like
        array of scores for the negatives (spoof scenario)
    spoof_pos : array_like
        array of scores for the positives (spoof scenario)
    thresholds : array_like
        ndarray with threshold values
    omega : array_like
        array of the omega parameter balancing between impostors
        and spoofing attacks
    beta : array_like
        array of the beta parameter balancing between real accesses
        and all negatives (impostors and spoofing attacks)

    Returns
    -------
    far_w_errors: array_like
        FAR_w
    wer_wb_errors: array_like
        WER_wb
    """
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    frr_errors = numpy.ndarray((beta.size, omega.size), 'float64')
    far_errors = numpy.ndarray((beta.size, omega.size), 'float64')
    sfar_errors = numpy.ndarray((beta.size, omega.size), 'float64')
    far_w_errors = numpy.ndarray((beta.size, omega.size), 'float64')
    wer_wb_errors = numpy.ndarray((beta.size, omega.size), 'float64')
    hter_wb_errors = numpy.ndarray((beta.size, omega.size), 'float64')

    for bindex, b in enumerate(beta):
        errors = [
            error_rates_at_weight(licit_neg, licit_pos, spoof_neg, spoof_pos,
                                  w, thresholds[bindex, windex], b)
            for windex, w in enumerate(omega)
        ]
        frr_errors[bindex, :] = [errors[i][0] for i in range(len(errors))]
        far_errors[bindex, :] = [errors[i][1] for i in range(len(errors))]
        sfar_errors[bindex, :] = [errors[i][2] for i in range(len(errors))]
        far_w_errors[bindex, :] = [errors[i][3] for i in range(len(errors))]
        wer_wb_errors[bindex, :] = [errors[i][4] for i in range(len(errors))]
        hter_wb_errors[bindex, :] = [errors[i][5] for i in range(len(errors))]

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    return (frr_errors, far_errors, sfar_errors, far_w_errors, wer_wb_errors,
            hter_wb_errors)
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def calc_aue(licit_neg,
             licit_pos,
             spoof_neg,
             spoof_pos,
             thresholds,
             omega,
             beta,
             l_bound=0,
             h_bound=1,
             var_param='omega'):
    """Calculates AUE of EPSC for the given thresholds and weights

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    Keyword arguments:
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    - licit_neg - numpy.array of scores for the negatives (licit scenario)
    - licit_pos - numpy.array of scores for the positives (licit scenario)
    - spoof_neg - numpy.array of scores for the negatives (spoof scenario)
    - spoof_pos - numpy.array of scores for the positives (spoof scenario)
    - l_bound - lower bound of integration
    - h_bound - higher bound of integration
    - points - number of points to calculate EPSC
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    - criteria - the decision threshold criteria ('eer', 'wer' or 'min-hter')
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    - var_param - name of the parameter which is varied on the abscissa
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    ('omega' or 'beta')
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  """

    from scipy import integrate

    if var_param == 'omega':
        errors = all_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos,
                                 thresholds, omega, beta)
        weights = omega  # setting the weights to the varying parameter
    else:
        errors = all_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos,
                                 thresholds, omega, beta)
        weights = beta  # setting the weights to the varying parameter

    wer_errors = errors[4].reshape(1, errors[4].size)

    l_ind = numpy.where(weights >= l_bound)[0][0]
    h_ind = numpy.where(weights <= h_bound)[0][-1]
    aue = integrate.cumtrapz(wer_errors, weights)
    aue = numpy.append(
        [0], aue)  # for indexing purposes, aue is cumulative integration
    aue = aue[h_ind] - aue[l_ind]

    return aue