'''Plots and measures for bob.bio.base'''
import math
import click
import matplotlib.pyplot as mpl
import bob.measure.script.figure as measure_figure
import bob.measure
from bob.measure import (plot, utils)
from tabulate import tabulate
import logging
LOGGER = logging.getLogger("bob.bio.base")
[docs]class Roc(measure_figure.Roc):
def __init__(self, ctx, scores, evaluation, func_load):
super(Roc, self).__init__(ctx, scores, evaluation, func_load)
self._x_label = ctx.meta.get('x_label') or 'FMR'
default_y_label = '1 - FNMR' if self._semilogx \
else 'False Non Match Rate'
self._y_label = ctx.meta.get('y_label') or default_y_label
[docs]class Det(measure_figure.Det):
def __init__(self, ctx, scores, evaluation, func_load):
super(Det, self).__init__(ctx, scores, evaluation, func_load)
self._x_label = ctx.meta.get('x_label') or 'FMR (%)'
self._y_label = ctx.meta.get('y_label') or 'FNMR (%)'
[docs]class Cmc(measure_figure.PlotBase):
''' Handles the plotting of Cmc '''
def __init__(self, ctx, scores, evaluation, func_load):
super(Cmc, self).__init__(ctx, scores, evaluation, func_load)
self._semilogx = ctx.meta.get('semilogx', True)
self._titles = self._titles or ['CMC dev.', 'CMC eval.']
self._x_label = self._x_label or 'Rank'
self._y_label = self._y_label or 'Identification rate'
self._max_R = 0
[docs] def compute(self, idx, input_scores, input_names):
''' Plot CMC for dev and eval data using
:py:func:`bob.measure.plot.cmc`'''
mpl.figure(1)
if self._eval:
linestyle = '-' if not self._split else self._linestyles[idx]
LOGGER.info("CMC dev. curve using %s", input_names[0])
rank = plot.cmc(
input_scores[0], logx=self._semilogx,
color=self._colors[idx], linestyle=linestyle,
label=self._label('dev.', idx)
)
self._max_R = max(rank, self._max_R)
linestyle = '--'
if self._split:
mpl.figure(2)
linestyle = self._linestyles[idx]
LOGGER.info("CMC eval. curve using %s", input_names[1])
rank = plot.cmc(
input_scores[1], logx=self._semilogx,
color=self._colors[idx], linestyle=linestyle,
label=self._label('eval.', idx)
)
self._max_R = max(rank, self._max_R)
else:
LOGGER.info("CMC dev. curve using %s", input_names[0])
rank = plot.cmc(
input_scores[0], logx=self._semilogx,
color=self._colors[idx], linestyle=self._linestyles[idx],
label=self._label('dev.', idx)
)
self._max_R = max(rank, self._max_R)
[docs]class Dir(measure_figure.PlotBase):
''' Handles the plotting of DIR curve'''
def __init__(self, ctx, scores, evaluation, func_load):
super(Dir, self).__init__(ctx, scores, evaluation, func_load)
self._semilogx = ctx.meta.get('semilogx', True)
self._rank = ctx.meta.get('rank', 1)
self._titles = self._titles or ['DIR curve'] * 2
self._x_label = self._x_label or 'False Alarm Rate'
self._y_label = self._y_label or 'DIR'
[docs] def compute(self, idx, input_scores, input_names):
''' Plot DIR for dev and eval data using
:py:func:`bob.measure.plot.detection_identification_curve`'''
mpl.figure(1)
if self._eval:
linestyle = '-' if not self._split else self._linestyles[idx]
LOGGER.info("DIR dev. curve using %s", input_names[0])
plot.detection_identification_curve(
input_scores[0], rank=self._rank, logx=self._semilogx,
color=self._colors[idx], linestyle=linestyle,
label=self._label('dev', idx)
)
linestyle = '--'
if self._split:
mpl.figure(2)
linestyle = self._linestyles[idx]
LOGGER.info("DIR eval. curve using %s", input_names[1])
plot.detection_identification_curve(
input_scores[1], rank=self._rank, logx=self._semilogx,
color=self._colors[idx], linestyle=linestyle,
label=self._label('eval', idx)
)
else:
LOGGER.info("DIR dev. curve using %s", input_names[0])
plot.detection_identification_curve(
input_scores[0], rank=self._rank, logx=self._semilogx,
color=self._colors[idx], linestyle=self._linestyles[idx],
label=self._label('dev', idx)
)
if self._min_dig is not None:
mpl.xlim(xmin=math.pow(10, self._min_dig))
[docs]class Metrics(measure_figure.Metrics):
''' Compute metrics from score files'''
def __init__(self, ctx, scores, evaluation, func_load,
names=('Failure to Acquire', 'False Match Rate',
'False Non Match Rate', 'False Accept Rate',
'False Reject Rate', 'Half Total Error Rate')):
super(Metrics, self).__init__(
ctx, scores, evaluation, func_load, names
)
[docs] def init_process(self):
if self._criterion == 'rr':
self._thres = [None] * self.n_systems if self._thres is None else \
self._thres
[docs] def compute(self, idx, input_scores, input_names):
''' Compute metrics for the given criteria'''
title = self._legends[idx] if self._legends is not None else None
headers = ['' or title, 'Dev. %s' % input_names[0]]
if self._eval and input_scores[1] is not None:
headers.append('eval % s' % input_names[1])
if self._criterion == 'rr':
rr = bob.measure.recognition_rate(
input_scores[0], self._thres[idx])
dev_rr = "%.1f%%" % (100 * rr)
raws = [['RR', dev_rr]]
if self._eval and input_scores[1] is not None:
rr = bob.measure.recognition_rate(
input_scores[1], self._thres[idx])
eval_rr = "%.1f%%" % (100 * rr)
raws[0].append(eval_rr)
click.echo(
tabulate(raws, headers, self._tablefmt), file=self.log_file
)
elif self._criterion == 'mindcf':
if 'cost' in self._ctx.meta:
cost = self._ctx.meta.get('cost', 0.99)
threshold = bob.measure.min_weighted_error_rate_threshold(
input_scores[0][0], input_scores[0][1], cost
) if self._thres is None else self._thres[idx]
if self._thres is None:
click.echo(
"[minDCF - Cost:%f] Threshold on Development set `%s`: %e"
% (cost, input_names[0], threshold),
file=self.log_file
)
else:
click.echo(
"[minDCF] User defined Threshold: %e" % threshold,
file=self.log_file
)
# apply threshold to development set
far, frr = bob.measure.farfrr(
input_scores[0][0], input_scores[0][1], threshold
)
dev_far_str = "%.1f%%" % (100 * far)
dev_frr_str = "%.1f%%" % (100 * frr)
dev_mindcf_str = "%.1f%%" % (
(cost * far + (1 - cost) * frr) * 100.)
raws = [['FAR', dev_far_str],
['FRR', dev_frr_str],
['minDCF', dev_mindcf_str]]
if self._eval and input_scores[1] is not None:
# apply threshold to development set
far, frr = bob.measure.farfrr(
input_scores[1][0], input_scores[1][1], threshold
)
eval_far_str = "%.1f%%" % (100 * far)
eval_frr_str = "%.1f%%" % (100 * frr)
eval_mindcf_str = "%.1f%%" % (
(cost * far + (1 - cost) * frr) * 100.)
raws[0].append(eval_far_str)
raws[1].append(eval_frr_str)
raws[2].append(eval_mindcf_str)
click.echo(
tabulate(raws, headers, self._tablefmt), file=self.log_file
)
elif self._criterion == 'cllr':
cllr = bob.measure.calibration.cllr(input_scores[0][0],
input_scores[0][1])
min_cllr = bob.measure.calibration.min_cllr(
input_scores[0][0], input_scores[0][1]
)
dev_cllr_str = "%.1f%%" % cllr
dev_min_cllr_str = "%.1f%%" % min_cllr
raws = [['Cllr', dev_cllr_str],
['minCllr', dev_min_cllr_str]]
if self._eval and input_scores[1] is not None:
cllr = bob.measure.calibration.cllr(input_scores[1][0],
input_scores[1][1])
min_cllr = bob.measure.calibration.min_cllr(
input_scores[1][0], input_scores[1][1]
)
eval_cllr_str = "%.1f%%" % cllr
eval_min_cllr_str = "%.1f%%" % min_cllr
raws[0].append(eval_cllr_str)
raws[1].append(eval_min_cllr_str)
click.echo(
tabulate(raws, headers, self._tablefmt), file=self.log_file
)
else:
title = self._legends[idx] if self._legends is not None else None
all_metrics = self._get_all_metrics(idx, input_scores, input_names)
headers = [' ' or title, 'Development']
rows = [[self.names[0], all_metrics[0][0]],
[self.names[1], all_metrics[0][1]],
[self.names[2], all_metrics[0][2]],
[self.names[3], all_metrics[0][3]],
[self.names[4], all_metrics[0][4]],
[self.names[5], all_metrics[0][5]]]
if self._eval:
# computes statistics for the eval set based on the threshold a
# priori
headers.append('Evaluation')
rows[0].append(all_metrics[1][0])
rows[1].append(all_metrics[1][1])
rows[2].append(all_metrics[1][2])
rows[3].append(all_metrics[1][3])
rows[4].append(all_metrics[1][4])
rows[5].append(all_metrics[1][5])
click.echo(tabulate(rows, headers, self._tablefmt), file=self.log_file)
[docs]class MultiMetrics(measure_figure.MultiMetrics):
'''Compute metrics from score files'''
def __init__(self, ctx, scores, evaluation, func_load):
super(MultiMetrics, self).__init__(
ctx, scores, evaluation, func_load,
names=(
'Failure to Acquire', 'False Match Rate',
'False Non Match Rate', 'False Accept Rate',
'False Reject Rate', 'Half Total Error Rate'))
[docs]class Hist(measure_figure.Hist):
''' Histograms for biometric scores '''
def _setup_hist(self, neg, pos):
self._title_base = 'Biometric scores'
self._density_hist(
pos[0], n=0, label='Genuines', alpha=0.9, color='C2'
)
self._density_hist(
neg[0], n=1, label='Zero-effort impostors', alpha=0.8, color='C0'
)