figure.py 25.5 KB
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'''Runs error analysis on score sets, outputs metrics and plots'''

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import pkg_resources  # to make sure bob gets imported properly
import logging
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import click
import numpy as np
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import matplotlib.pyplot as mpl
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import  bob.measure.script.figure as measure_figure
from tabulate import tabulate
from bob.extension.scripts.click_helper import verbosity_option
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from  bob.measure.utils import (get_fta, get_fta_list, get_thres)
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from bob.measure import (
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    far_threshold, eer_threshold, min_hter_threshold, farfrr, epc, ppndf
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)
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from bob.measure.plot import (det, det_axis)
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from . import error_utils

ALL_CRITERIA = ('bpcer20', 'eer', 'min-hter')

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

    Parameters
    ----------
    method : str
        One of ``bpcer201``, ``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

class Metrics(measure_figure.Metrics):
    def __init__(self, ctx, scores, evaluation, func_load):
        super(Metrics, self).__init__(ctx, scores, evaluation, func_load)

    ''' Compute metrics from score files'''
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    def compute(self, idx, input_scores, input_names):
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        ''' Compute metrics for the given criteria'''
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        neg_list, pos_list, _ = get_fta_list(input_scores)
        dev_neg, dev_pos = neg_list[0], pos_list[0]
        dev_file = input_names[0]
        if self._eval:
            eval_neg, eval_pos = neg_list[1], pos_list[1]
            eval_file = input_names[1]
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        title = self._legends[idx] if self._legends is not None else None
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        headers = ['' or title, 'Development %s' % dev_file]
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        if self._eval:
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            headers.append('Eval. % s' % eval_file)
        for m in ALL_CRITERIA:
            raws = []
            threshold = calc_threshold(m, dev_neg, dev_pos)
            click.echo("\nThreshold of %f selected with the %s criteria" % (
                threshold, m))
            apcer, bpcer = farfrr(dev_neg, dev_pos, threshold)
            raws.append(['BPCER20', '{:>5.1f}%'.format(apcer * 100)])
            raws.append(['EER', '{:>5.1f}%'.format(bpcer * 100)])
            raws.append(['min-HTER', '{:>5.1f}%'.format((apcer + bpcer) * 50)])
            if self._eval and eval_neg is not None:
                apcer, bpcer = farfrr(eval_neg, eval_pos, threshold)
                raws[0].append('{:>5.1f}%'.format(apcer * 100))
                raws[1].append('{:>5.1f}%'.format(bpcer * 100))
                raws[2].append('{:>5.1f}%'.format((apcer + bpcer) * 50))

            click.echo(
                tabulate(raws, headers, self._tablefmt),
                file=self.log_file
            )

class HistPad(measure_figure.Hist):
    ''' Histograms for PAD '''

    def _setup_hist(self, neg, pos):
        self._title_base = 'PAD'
        self._density_hist(
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            pos[0], n=0, label='Bona Fide', color='C1'
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        )
        self._density_hist(
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            neg[0], n=1, label='Presentation attack', alpha=0.4, color='C7',
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            hatch='\\\\'
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        )

def _calc_pass_rate(threshold, scores):
    return (scores >= threshold).mean()

def _iapmr_dot(threshold, iapmr, real_data, **kwargs):
    # plot a dot on threshold versus IAPMR line and show IAPMR as a number
    axlim = mpl.axis()
    mpl.plot(threshold, 100. * iapmr, 'o', color='C3', **kwargs)
    if not real_data:
        mpl.annotate(
            'IAPMR at\noperating point',
            xy=(threshold, 100. * iapmr),
            xycoords='data',
            xytext=(0.85, 0.6),
            textcoords='axes fraction',
            color='black',
            size='large',
            arrowprops=dict(facecolor='black', shrink=0.05, width=2),
            horizontalalignment='center',
            verticalalignment='top',
        )
    else:
        mpl.text(threshold + (threshold - axlim[0]) / 12, 100. * iapmr,
                 '%.1f%%' % (100. * iapmr,), color='C3')

def _iapmr_line_plot(scores, n_points=100, **kwargs):
    axlim = mpl.axis()
    step = (axlim[1] - axlim[0]) / float(n_points)
    thres = [(k * step) + axlim[0] for k in range(2, n_points - 1)]
    mix_prob_y = []
    for k in thres:
        mix_prob_y.append(100. * _calc_pass_rate(k, scores))

    mpl.plot(thres, mix_prob_y, label='IAPMR', color='C3', **kwargs)

def _iapmr_plot(scores, threshold, iapmr, real_data, **kwargs):
    _iapmr_dot(threshold, iapmr, real_data, **kwargs)
    _iapmr_line_plot(scores, n_points=100, **kwargs)

class HistVuln(measure_figure.Hist):
    ''' Histograms for vulnerability '''

    def _setup_hist(self, neg, pos):
        self._title_base = 'Vulnerability'
        self._density_hist(
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            pos[0], n=0, label='Genuine', color='C1'
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        )
        self._density_hist(
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            neg[0], n=1, label='Zero-effort impostors', alpha=0.8, color='C0'
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        )
        self._density_hist(
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            neg[1], n=2, label='Presentation attack', alpha=0.4, color='C7',
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            hatch='\\\\'
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        )

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    def _lines(self, threshold, label, neg, pos, idx, **kwargs):
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        if 'iapmr_line' not in self._ctx.meta or self._ctx.meta['iapmr_line']:
            #plot vertical line
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            super(HistVuln, self)._lines(threshold, label, neg, pos, idx)
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            #plot iapmr_line
            iapmr, _ = farfrr(neg[1], pos[0], threshold)
            ax2 = mpl.twinx()
            # we never want grid lines on axis 2
            ax2.grid(False)
            real_data = True if 'real_data' not in self._ctx.meta else \
                    self._ctx.meta['real_data']
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            far, frr = farfrr(neg[0], pos[0], threshold)
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            _iapmr_plot(neg[1], threshold, iapmr, real_data=real_data)
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            click.echo(
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                '%s (t=%.2g) = %.2f%%; IAPMR = %.2f%%' % (
                    self._criterion.upper(),
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                    threshold,
                    50*(far+frr), 100*iapmr
                )
            )
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            n = idx % self._step_print
            col = n % self._ncols
            rest_print = self.n_systems - int(idx / self._step_print) * self._step_print
            if col == self._ncols - 1 or n == rest_print - 1:
                ax2.set_ylabel("IAPMR (%)", color='C3')
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            ax2.tick_params(axis='y', colors='red')
            ax2.yaxis.label.set_color('red')
            ax2.spines['right'].set_color('red')

class PadPlot(measure_figure.PlotBase):
    '''Base class for PAD plots'''
    def __init__(self, ctx, scores, evaluation, func_load):
        super(PadPlot, self).__init__(ctx, scores, evaluation, func_load)
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        mpl.rcParams['figure.constrained_layout.use'] = self._clayout
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    def end_process(self):
        '''Close pdf '''
        #do not want to close PDF when running evaluate
        if 'PdfPages' in self._ctx.meta and \
           ('closef' not in self._ctx.meta or self._ctx.meta['closef']):
            self._pdf_page.close()

    def _plot_legends(self):
        #legends for all axes
        lines = []
        labels = []
        for ax in mpl.gcf().get_axes():
            li, la = ax.get_legend_handles_labels()
            lines += li
            labels += la
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        mpl.gca().legend(lines, labels, loc=0, fancybox=True, framealpha=0.5)
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class Epc(PadPlot):
    ''' Handles the plotting of EPC '''
    def __init__(self, ctx, scores, evaluation, func_load):
        super(Epc, self).__init__(ctx, scores, evaluation, func_load)
        self._iapmr = True if 'iapmr' not in self._ctx.meta else \
                self._ctx.meta['iapmr']
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        self._title = self._title or ('EPC and IAPMR' if self._iapmr else
                                      'EPC')
        self._x_label = self._x_label or r"Weight $\beta$"
        self._y_label = self._y_label or "WER (%)"
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        self._eval = True #always eval data with EPC
        self._split = False
        self._nb_figs = 1

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        if self._min_arg != 4:
            raise click.BadParameter("You must provide 4 scores files:{licit,"
                                     "spoof}/{dev,eval}")

    def compute(self, idx, input_scores, input_names):
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        ''' Plot EPC for PAD'''
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        licit_dev_neg = input_scores[0][0]
        licit_dev_pos = input_scores[0][1]
        licit_eval_neg = input_scores[1][0]
        licit_eval_pos = input_scores[1][1]
        spoof_eval_neg = input_scores[3][0]
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        mpl.gcf().clear()
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        epc_baseline = epc(
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            licit_dev_neg, licit_dev_pos, licit_eval_neg,
            licit_eval_pos, 100
        )
        mpl.plot(
            epc_baseline[:, 0], [100. * k for k in epc_baseline[:, 1]],
            color='C0',
            label=self._label(
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                'WER', '%s-%s' % (input_names[0], input_names[1]), idx
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            ),
            linestyle='-'
        )
        mpl.xlabel(self._x_label)
        mpl.ylabel(self._y_label)
        if self._iapmr:
            mix_prob_y = []
            for k in epc_baseline[:, 2]:
                prob_attack = sum(
                    1 for i in spoof_eval_neg if i >= k
                ) / float(spoof_eval_neg.size)
                mix_prob_y.append(100. * prob_attack)

            mpl.gca().set_axisbelow(True)
            prob_ax = mpl.gca().twinx()
            mpl.plot(
                epc_baseline[:, 0],
                mix_prob_y,
                color='C3',
                linestyle='-',
                label=self._label(
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                    'IAPMR', '%s-%s' % (input_names[0], input_names[1]), idx
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                )
            )
            prob_ax.set_yticklabels(prob_ax.get_yticks())
            prob_ax.tick_params(axis='y', colors='red')
            prob_ax.yaxis.label.set_color('red')
            prob_ax.spines['right'].set_color('red')
            ylabels = prob_ax.get_yticks()
            prob_ax.yaxis.set_ticklabels(["%.0f" % val for val in ylabels])
            prob_ax.set_axisbelow(True)
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        title = self._legends[idx] if self._legends is not None else self._title
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        mpl.title(title)
        #legends for all axes
        self._plot_legends()
        mpl.xticks(rotation=self._x_rotation)
        self._pdf_page.savefig(mpl.gcf())

class Epsc(PadPlot):
    ''' Handles the plotting of EPSC '''
    def __init__(self, ctx, scores, evaluation, func_load,
                 criteria, var_param, fixed_param):
        super(Epsc, self).__init__(ctx, scores, evaluation, func_load)
        self._iapmr = False if 'iapmr' not in self._ctx.meta else \
                self._ctx.meta['iapmr']
        self._wer = True if 'wer' not in self._ctx.meta else \
                self._ctx.meta['wer']
        self._criteria = 'eer' if criteria is None else criteria
        self._var_param = "omega" if var_param is None else var_param
        self._fixed_param = 0.5 if fixed_param is None else fixed_param
        self._eval = True #always eval data with EPC
        self._split = False
        self._nb_figs = 1
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        self._title = ''

        if self._min_arg != 4:
            raise click.BadParameter("You must provide 4 scores files:{licit,"
                                     "spoof}/{dev,eval}")
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    def compute(self, idx, input_scores, input_names):
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        ''' Plot EPSC for PAD'''
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        licit_dev_neg = input_scores[0][0]
        licit_dev_pos = input_scores[0][1]
        licit_eval_neg = input_scores[1][0]
        licit_eval_pos = input_scores[1][1]
        spoof_dev_neg = input_scores[2][0]
        spoof_dev_pos = input_scores[2][1]
        spoof_eval_neg = input_scores[3][0]
        spoof_eval_pos = input_scores[3][1]
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        title = self._legends[idx] if self._legends is not None else None
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        mpl.gcf().clear()
        points = 10

        if self._var_param == 'omega':
            omega, beta, thrs = error_utils.epsc_thresholds(
                licit_dev_neg,
                licit_dev_pos,
                spoof_dev_neg,
                spoof_dev_pos,
                points=points,
                criteria=self._criteria,
                beta=self._fixed_param)
        else:
            omega, beta, thrs = error_utils.epsc_thresholds(
                licit_dev_neg,
                licit_dev_pos,
                spoof_dev_neg,
                spoof_dev_pos,
                points=points,
                criteria= self._criteria,
                omega=self._fixed_param
            )

        errors = error_utils.all_error_rates(
            licit_eval_neg, licit_eval_pos, spoof_eval_neg,
            spoof_eval_pos, thrs, omega, beta
        )  # error rates are returned in a list in the
           # following order: frr, far, IAPMR, far_w, wer_w

        ax1 = mpl.subplot(
            111
        )  # EPC like curves for FVAS fused scores for weighted error rates
           # between the negatives (impostors and Presentation attacks)
        if self._wer:
            if self._var_param == 'omega':
                mpl.plot(
                    omega,
                    100. * errors[4].flatten(),
                    color='C0',
                    linestyle='-',
                    label=r"WER$_{\omega,\beta}$")
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                mpl.xlabel(self._x_label or r"Weight $\omega$")
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            else:
                mpl.plot(
                    beta,
                    100. * errors[4].flatten(),
                    color='C0',
                    linestyle='-',
                    label=r"WER$_{\omega,\beta}$")
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                mpl.xlabel(self._x_label or r"Weight $\beta$")
            mpl.ylabel(self._y_label or r"WER$_{\omega,\beta}$ (%)")
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        if self._iapmr:
            axis = mpl.gca()
            if self._wer:
                axis = mpl.twinx()
                axis.grid(False)
            if self._var_param == 'omega':
                mpl.plot(
                    omega,
                    100. * errors[2].flatten(),
                    color='C3',
                    linestyle='-',
                    label='IAPMR')
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                mpl.xlabel(self._x_label or r"Weight $\omega$")
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            else:
                mpl.plot(
                    beta,
                    100. * errors[2].flatten(),
                    color='C3',
                    linestyle='-',
                    label='IAPMR')
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                mpl.xlabel(self._x_label or r"Weight $\beta$")
            mpl.ylabel(self._y_label or r"IAPMR  (%)")
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            if self._wer:
                axis.set_yticklabels(axis.get_yticks())
                axis.tick_params(axis='y', colors='red')
                axis.yaxis.label.set_color('red')
                axis.spines['right'].set_color('red')

        if self._var_param == 'omega':
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            mpl.title(title or (r"EPSC with $\beta$ = %.2f" %\
                                self._fixed_param))
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        else:
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            mpl.title(title or (r"EPSC with $\omega$ = %.2f" %\
                                self._fixed_param))
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        mpl.grid()
        self._plot_legends()
        ax1.set_xticklabels(ax1.get_xticks())
        ax1.set_yticklabels(ax1.get_yticks())
        mpl.xticks(rotation=self._x_rotation)
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        self._pdf_page.savefig()
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class Epsc3D(Epsc):
    ''' 3D EPSC plots for PAD'''
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    def compute(self, idx, input_scores, input_names):
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        ''' Implements plots'''
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        licit_dev_neg = input_scores[0][0]
        licit_dev_pos = input_scores[0][1]
        licit_eval_neg = input_scores[1][0]
        licit_eval_pos = input_scores[1][1]
        spoof_dev_neg = input_scores[2][0]
        spoof_dev_pos = input_scores[2][1]
        spoof_eval_neg = input_scores[3][0]
        spoof_eval_pos = input_scores[3][1]
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        title = self._legends[idx] if self._legends is not None else None
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        mpl.rcParams.pop('key', None)

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        mpl.gcf().clear()
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        mpl.gcf().set_constrained_layout(self._clayout)
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        from mpl_toolkits.mplot3d import Axes3D
        from matplotlib import cm

        points = 10

        omega, beta, thrs = error_utils.epsc_thresholds(
            licit_dev_neg,
            licit_dev_pos,
            spoof_dev_neg,
            spoof_dev_pos,
            points=points,
            criteria=self._criteria)

        errors = error_utils.all_error_rates(
            licit_eval_neg, licit_eval_pos, spoof_eval_neg, spoof_eval_pos,
            thrs, omega, beta
        )
        # error rates are returned in a list as 2D numpy.ndarrays in
        # the following order: frr, far, IAPMR, far_w, wer_wb, hter_wb
        wer_errors = 100 * errors[2 if self._iapmr else 4]

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        ax1 = mpl.gcf().add_subplot(111, projection='3d')
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        W, B = np.meshgrid(omega, beta)

        ax1.plot_wireframe(
            W, B, wer_errors, cmap=cm.coolwarm, antialiased=False
        )  # surface

        if self._iapmr:
            ax1.azim = -30
            ax1.elev = 50

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        ax1.set_xlabel(self._x_label or r"Weight $\omega$")
        ax1.set_ylabel(self._y_label or r"Weight $\beta$")
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        ax1.set_zlabel(
            r"WER$_{\omega,\beta}$ (%)" if self._wer else "IAPMR (%)"
        )

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        mpl.title(title or "3D EPSC")
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        ax1.set_xticklabels(ax1.get_xticks())
        ax1.set_yticklabels(ax1.get_yticks())
        ax1.set_zticklabels(ax1.get_zticks())

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        self._pdf_page.savefig()

class Det(PadPlot):
    '''DET for PAD'''
    def __init__(self, ctx, scores, evaluation, func_load, criteria, real_data):
        super(Det, self).__init__(ctx, scores, evaluation, func_load)
        self._no_spoof = False if 'no_spoof' not in ctx.meta else\
        ctx.meta['no_spoof']
        self._criteria = criteria
        self._real_data = True if real_data is None else real_data

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    def compute(self, idx, input_scores, input_names):
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        ''' Implements plots'''
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        licit_dev_neg = input_scores[0][0]
        licit_dev_pos = input_scores[0][1]
        licit_eval_neg = input_scores[1][0]
        licit_eval_pos = input_scores[1][1]
        spoof_eval_neg = input_scores[3][0] if len(input_scores) > 2 else None
        spoof_eval_pos = input_scores[3][1] if len(input_scores) > 2 else None
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        det(
            licit_eval_neg,
            licit_eval_pos,
            self._points,
            color=self._colors[idx],
            linestyle='-',
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            label=self._label("licit", input_names[0], idx)
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        )
        if not self._no_spoof and spoof_eval_neg is not None:
            det(
                spoof_eval_neg,
                spoof_eval_pos,
                self._points,
                color=self._colors[idx],
                linestyle='--',
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                label=self._label("spoof", input_names[3], idx)
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            )

        if self._criteria is None:
            return

        thres_baseline = calc_threshold(
            self._criteria, licit_dev_neg, licit_dev_pos
        )

        axlim = mpl.axis()

        farfrr_licit = farfrr(
            licit_eval_neg, licit_eval_pos,
            thres_baseline)  # calculate test frr @ EER (licit scenario)
        farfrr_spoof = farfrr(
            spoof_eval_neg, spoof_eval_pos,
            thres_baseline)  # calculate test frr @ EER (spoof scenario)
        farfrr_licit_det = [
            ppndf(i) for i in farfrr_licit
        ]
        # find the FAR and FRR values that need to be plotted on normal deviate
        # scale
        farfrr_spoof_det = [
            ppndf(i) for i in farfrr_spoof
        ]
        # find the FAR and FRR values that need to be plotted on normal deviate
        # scale
        if not self._real_data:
            mpl.axhline(
                y=farfrr_licit_det[1],
                xmin=axlim[2],
                xmax=axlim[3],
                color='k',
                linestyle='--',
                label="FRR @ EER")  # vertical FRR threshold
        else:
            mpl.axhline(
                y=farfrr_licit_det[1],
                xmin=axlim[0],
                xmax=axlim[1],
                color='k',
                linestyle='--',
                label="FRR = %.2f%%" %
                (farfrr_licit[1] * 100))  # vertical FRR threshold

        mpl.plot(
            farfrr_licit_det[0],
            farfrr_licit_det[1],
            'o',
            color=self._colors[idx],
            markersize=9)  # FAR point, licit scenario
        mpl.plot(
            farfrr_spoof_det[0],
            farfrr_spoof_det[1],
            'o',
            color=self._colors[idx],
            markersize=9)  # FAR point, spoof scenario

        # annotate the FAR points
        xyannotate_licit = [
            ppndf(0.7 * farfrr_licit[0]),
            ppndf(1.8 * farfrr_licit[1])
        ]
        xyannotate_spoof = [
            ppndf(0.95 * farfrr_spoof[0]),
            ppndf(1.8 * farfrr_licit[1])
        ]

        if not self._real_data:
            mpl.annotate(
                'FMR @\noperating point',
                xy=(farfrr_licit_det[0], farfrr_licit_det[1]),
                xycoords='data',
                xytext=(xyannotate_licit[0], xyannotate_licit[1]),
                color=self._colors[idx])
            mpl.annotate(
                'IAPMR @\noperating point',
                xy=(farfrr_spoof_det[0], farfrr_spoof_det[1]),
                xycoords='data',
                xytext=(xyannotate_spoof[0], xyannotate_spoof[1]),
                color=self._colors[idx])
        else:
            mpl.annotate(
                'FAR=%.2f%%' % (farfrr_licit[0] * 100),
                xy=(farfrr_licit_det[0], farfrr_licit_det[1]),
                xycoords='data',
                xytext=(xyannotate_licit[0], xyannotate_licit[1]),
                color=self._colors[idx],
                size='large')
            mpl.annotate(
                'IAPMR=\n%.2f%%' % (farfrr_spoof[0] * 100),
                xy=(farfrr_spoof_det[0], farfrr_spoof_det[1]),
                xycoords='data',
                xytext=(xyannotate_spoof[0], xyannotate_spoof[1]),
                color=self._colors[idx],
                size='large')

    def end_process(self):
        ''' Set title, legend, axis labels, grid colors, save figures and
        close pdf is needed '''
        #only for plots
        add = ''
        if not self._no_spoof:
            add = " and overlaid SPOOF scenario"
        title = self._title if self._title is not None else \
                ('DET: LICIT' + add)
        mpl.title(title)
        mpl.xlabel(self._x_label or "False Acceptance Rate (%)")
        mpl.ylabel(self._y_label or "False Rejection Rate (%)")
        mpl.grid(True, color=self._grid_color)
        mpl.legend(loc='best')
        self._set_axis()
        fig = mpl.gcf()
        mpl.xticks(rotation=self._x_rotation)
        mpl.tick_params(axis='both', which='major', labelsize=4)
        for tick in mpl.gca().xaxis.get_major_ticks():
            tick.label.set_fontsize(6)
        for tick in mpl.gca().yaxis.get_major_ticks():
            tick.label.set_fontsize(6)

        self._pdf_page.savefig(fig)

        #do not want to close PDF when running evaluate
        if 'PdfPages' in self._ctx.meta and \
            ('closef' not in self._ctx.meta or self._ctx.meta['closef']):
            self._pdf_page.close()

    def _set_axis(self):
        if self._axlim is not None and None not in self._axlim:
            det_axis(self._axlim)
        else:
            det_axis([0.01, 99, 0.01, 99])
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class FmrIapmr(PadPlot):
    '''FMR vs IAPMR'''
    def __init__(self, ctx, scores, evaluation, func_load):
        super(FmrIapmr, self).__init__(ctx, scores, evaluation, func_load)
        self._eval = True #always eval data with EPC
        self._split = False
        self._nb_figs = 1
        self._semilogx = False if 'semilogx' not in ctx.meta else\
        ctx.meta['semilogx']
        if self._min_arg != 4:
            raise click.BadParameter("You must provide 4 scores files:{licit,"
                                     "spoof}/{dev,eval}")

    def compute(self, idx, input_scores, input_names):
        ''' Implements plots'''
        licit_eval_neg = input_scores[1][0]
        licit_eval_pos = input_scores[1][1]
        spoof_eval_neg = input_scores[3][0]
        fmr_list = np.linspace(0, 1, 100)
        iapmr_list = []
        for i, fmr in enumerate(fmr_list):
            thr = far_threshold(licit_eval_neg, licit_eval_pos, fmr, True)
            iapmr_list.append(farfrr(spoof_eval_neg, licit_eval_pos, thr)[0])
            # re-calculate fmr since threshold might give a different result
            # for fmr.
            fmr_list[i] = farfrr(licit_eval_neg, licit_eval_pos, thr)[0]
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        label = self._legends[idx] if self._legends is not None else \
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                '(%s/%s)' % (input_names[1], input_names[3])
        if self._semilogx:
            mpl.semilogx(fmr_list, iapmr_list, label=label)
        else:
            mpl.plot(fmr_list, iapmr_list, label=label)

    def end_process(self):
        ''' Set title, legend, axis labels, grid colors, save figures and
        close pdf is needed '''
        #only for plots
        title = self._title if self._title is not None else "FMR vs IAPMR"
        mpl.title(title)
        mpl.xlabel(self._x_label or "False Match Rate (%)")
        mpl.ylabel(self._y_label or "IAPMR (%)")
        mpl.grid(True, color=self._grid_color)
        mpl.legend(loc='best')
        self._set_axis()
        fig = mpl.gcf()
        mpl.xticks(rotation=self._x_rotation)
        mpl.tick_params(axis='both', which='major', labelsize=4)

        self._pdf_page.savefig(fig)

        #do not want to close PDF when running evaluate
        if 'PdfPages' in self._ctx.meta and \
            ('closef' not in self._ctx.meta or self._ctx.meta['closef']):
            self._pdf_page.close()