BEAT - plot/isoroc/1

Parameters 23

Parameters allow users to change the configuration of an plotter when scheduling an experiment

Name	Description	Type	Default
xlabel	The label of the X-axis (horizontal)	string	False Positives (False Match Rate), in %
ylabel	The label of the Y-axis (vertical)	string	True Positives (1 - False Non-Match Rate), in %
xlim-left		float64	0.0
xlim-right		float64	100.0
ylim-bottom		float64	0.0
ylim-top		float64	100.0
title	The title for this plot	string	ISO/IEC 19795-1:2006(E) ROC
title-fontsize	Controls the title font size	uint16	10
xaxis_multiplier	The multiplication factor for the X-axis (horizontal)	float64	100.0
yaxis_multiplier	The multiplication factor for the Y-axis (vertical)	float64	100.0
axis-fontsize	Controls the axis font size (labels and values)	uint16	10
legend	Short description of the data, to be added to the plot	string
legend-fontsize	Controls the font size of the legend	uint16	12
legend-loc	The location of the legend	string	best
grid	If we should draw grid lines or not for the plot	bool	True
xaxis_log	If X-axis (horizontal) should be in log-scale	bool	False
yaxis_log	If Y-axis (vertical) should be in log-scale	bool	False
dpi	Dots-per-inch in raster image formats	uint16	60
width	Width of the resulting image in pixels	uint16	400
height	Height of the resulting image in pixels	uint16	300
content_type	The type of image returned	string	image/png
line_attributes	Scatter/Line attributes passed directly to Matplotlib	string
det	If set, plot a DET curve instead of a ROC	bool	False

###############################################################################
#                                                                             #
# Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/           #
# Contact: beat.support@idiap.ch                                              #
#                                                                             #
# This file is part of the beat.examples module of the BEAT platform.         #
#                                                                             #
# Commercial License Usage                                                    #
# Licensees holding valid commercial BEAT licenses may use this file in       #
# accordance with the terms contained in a written agreement between you      #
# and Idiap. For further information contact tto@idiap.ch                     #
#                                                                             #
# Alternatively, this file may be used under the terms of the GNU Affero      #
# Public License version 3 as published by the Free Software and appearing    #
# in the file LICENSE.AGPL included in the packaging of this file.            #
# The BEAT platform is distributed in the hope that it will be useful, but    #
# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY  #
# or FITNESS FOR A PARTICULAR PURPOSE.                                        #
#                                                                             #
# You should have received a copy of the GNU Affero Public License along      #
# with the BEAT platform. If not, see http://www.gnu.org/licenses/.           #
#                                                                             #
###############################################################################

# Makes sure we won't require an X11 connection
import matplotlib
matplotlib.use('Agg')

import numpy
import itertools

class Plotter(baselib.Plotter):

def setup(self, parameters):

super(Plotter, self).setup(parameters)
        self.line_attributes = parameters.get('line_attributes', '').split('&')
        self.loc = parameters.get('legend-loc', '')

self.title_fontsize    = parameters.get('title-fontsize', '')

self.xlim_left   = parameters.get('xlim-left', '')
        self.xlim_right  = parameters.get('xlim-right', '')
        self.ylim_bottom = parameters.get('ylim-bottom', '')
        self.ylim_top    = parameters.get('ylim-top', '')

self.axis_fontsize    = parameters.get('axis-fontsize', '')

self.legend_fontsize = parameters.get('legend-fontsize', '')
        self.line_attributes = [k for k in self.line_attributes if k]
        self.det = parameters.get('det', False)

if self.det:
            self.ylabel = "False Negatives (False Non-Match Rate), in %"

# These are the "Tableau 20" colors as RGB.
        self.tableau20 = [(31,119,180), (152,223,138), (140,86,75), (199,199,199), 
                          (174,199,232), (214,39,40), (196,156,148), (188,189,34), 
                          (255,127,14), (255,152,150), (227,119,194), (219,219,141), 
                          (255,187,120), (148,103,189), (247,182,210), (23,190,207), 
                          (44,160,44), (197,176,213), (127,127,127), (158,218,229)]
        # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.
        for i in range(len(self.tableau20)):
            r, g, b = self.tableau20[i]
            self.tableau20[i] = (r / 255., g / 255., b / 255.)

return True

def process(self, inputs):
        fig, ax = super(Plotter, self).prepare_canvas()
        super(Plotter, self).apply_parameters(ax)

ax.set_title(self.title, fontdict={'fontsize':self.title_fontsize})

ax.set_xlabel(self.xlabel, fontdict={'fontsize':self.axis_fontsize})
        ax.set_ylabel(self.ylabel, fontdict={'fontsize':self.axis_fontsize})

ax.set_xlim((self.xlim_left,self.xlim_right))
        ax.set_ylim((self.ylim_bottom,self.ylim_top))

label = ""
        kwargs = {}
        single_experiment = len(inputs) == 1

number_of_positives = []
        number_of_negatives = []
        n_lines = 0

for xp_label, xp_data in inputs:
            for scatter in xp_data.data:
                args = []
                # the data
                x = scatter.false_positives
                if self.det:
                    y = scatter.false_negatives
                else:
                    y = 1.0 - scatter.false_negatives

# accumulate counts (useful in log plots)
                number_of_positives.append(scatter.number_of_positives)
                number_of_negatives.append(scatter.number_of_negatives)

args.append(x * self.xaxis_multiplier)
                args.append(y * self.yaxis_multiplier)

# the label
                if self.label: #gets the label from user overwritten input
                    label = self.label[n_lines%len(self.label)]
                else: #make-up label
                    if single_experiment:
                        label = scatter.label
                    else:
                        text = [k for k in (xp_label, scatter.label) if k]
                        label = '-'.join(text)

# the line attributes
                if self.line_attributes:
                    args.append(self.line_attributes[n_lines%len(self.line_attributes)])
                else:
                    kwargs['color'] = self.tableau20[n_lines % len(self.tableau20)]
                    
                kwargs['label'] = label
                n_lines += 1
                
                ax.plot(*args, **kwargs)
                
        if n_lines > 1:
            ax.legend(loc=self.loc, fontsize=self.legend_fontsize, fancybox=True, framealpha=0.5)

# reset log axis
        if self.xaxis_log:
            N = min([k for k in number_of_positives if k > 0]) or 100
            ax.set_xscale('symlog', linthreshx=1.0/N)
        if self.yaxis_log:
            N = min([k for k in number_of_negatives if k > 0]) or 100
            ax.set_yscale('symlog', linthreshy=1.0/N)

return super(Plotter, self).encode_figure(fig)

This is a plotter for ISO/IEC 19795-1:2006(E) standard ROC and DET curves. It is based on the description on the standard, section 10.6 "Graphical presentation of results", pages 35 and 36.

Reports

Updated	Name	Actions
Jan. 5, 2017	sbhatta/replay_antispoofing (PAD experiments using ReplayAttack database)	2
July 16, 2016	pkorshunov/joint-ASV-PAD-systems (Performance of joint ASV-PAD systems)	2
July 15, 2016	pkorshunov/ISV-based-ASV-system-licit-scenario (Performance of ISV-based ASV system on data with no attacks)	1
July 15, 2016	pkorshunov/ISV-based-ASV-system-spoof-scenario (Performance of ISV-based ASV system under spoofing attacks)	1
July 15, 2016	pkorshunov/MFCC-GMM-based-PAD-system (Performance of MFCC-GMM-based PAD system on presentation attacks)	1
July 15, 2016	pkorshunov/Compare-PAD-systems (Performance of MFCC-GMM-based and LBP-LR-based PAD systems)	2
Feb. 14, 2016	robertodaza/KBOC16_COMPETITION_REPORT (KBOC16: Three baseline systems compared with results and plot.)	3
Sept. 9, 2015	tpereira/btas2015_cpqd-smartphone_female (Periocular Biometrics in Mobile Environment - Table 1 and Figure 5(b))	4
Sept. 9, 2015	tpereira/btas2015_mobio_male (Periocular Biometrics in Mobile Environment - Table 1 and Figure 4(a))	4
Sept. 9, 2015	tpereira/btas2015_cpqd-smartphone_male (Periocular Biometrics in Mobile Environment - Table 1 and Figure 5(a))	4
Sept. 8, 2015	tpereira/btas2015_mobio_female (Periocular Biometrics in Mobile Environment - Table 1 and Figure 4(b))	4

plotters plot isoroc 1

Parameters 23

Reports

plotters

plot

isoroc

1