diff --git a/bob/bio/base/script/figure.py b/bob/bio/base/script/figure.py
index 5821dc459398d1cc47f21c47be866267961cf8d9..5dc0f046db4ccbd4594eb6c58762d5fb2087b745 100644
--- a/bob/bio/base/script/figure.py
+++ b/bob/bio/base/script/figure.py
@@ -1,118 +1,133 @@
-'''Plots and measures for bob.bio.base'''
+"""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 bob.measure import plot, utils
from tabulate import tabulate
import logging
LOGGER = logging.getLogger("bob.bio.base")
+
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
+ self._x_label = ctx.meta.get("x_label") or "FMR"
+ default_y_label = "1 - FNMR" if self._tpr else "FNMR"
+ self._y_label = ctx.meta.get("y_label") or default_y_label
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 (%)'
+ self._x_label = ctx.meta.get("x_label") or "FMR (%)"
+ self._y_label = ctx.meta.get("y_label") or "FNMR (%)"
class Cmc(measure_figure.PlotBase):
- ''' Handles the plotting of Cmc '''
+ """ 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._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
def compute(self, idx, input_scores, input_names):
- ''' Plot CMC for dev and eval data using
- :py:func:`bob.measure.plot.cmc`'''
+ """ 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]
+ 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)
+ 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 = '--'
+ 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)
+ 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)
+ 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)
class Dir(measure_figure.PlotBase):
- ''' Handles the plotting of DIR curve'''
+ """ 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'
+ 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"
def compute(self, idx, input_scores, input_names):
- ''' Plot DIR for dev and eval data using
- :py:func:`bob.measure.plot.detection_identification_curve`'''
+ """ 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]
+ 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)
+ input_scores[0],
+ rank=self._rank,
+ logx=self._semilogx,
+ color=self._colors[idx],
+ linestyle=linestyle,
+ label=self._label("dev", idx),
)
- linestyle = '--'
+ 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)
+ 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)
+ 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:
@@ -120,56 +135,66 @@ class Dir(measure_figure.PlotBase):
class Metrics(measure_figure.Metrics):
- ''' Compute metrics from score files'''
+ """ 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
- )
+ 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)
def init_process(self):
- if self._criterion == 'rr':
- self._thres = [None] * self.n_systems if self._thres is None else \
- self._thres
+ if self._criterion == "rr":
+ self._thres = (
+ [None] * self.n_systems if self._thres is None else self._thres
+ )
def compute(self, idx, input_scores, input_names):
- ''' Compute metrics for the given criteria'''
+ """ 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]]
+ 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])
+ 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]]
+ 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])
+ 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
+ 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]
)
- 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
+ file=self.log_file,
)
else:
click.echo(
"[minDCF] User defined Threshold: %e" % threshold,
- file=self.log_file
+ file=self.log_file,
)
# apply threshold to development set
far, frr = bob.measure.farfrr(
@@ -177,11 +202,12 @@ class Metrics(measure_figure.Metrics):
)
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]]
+ dev_mindcf_str = "%.1f%%" % ((cost * far + (1 - cost) * frr) * 100.0)
+ 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(
@@ -189,27 +215,23 @@ class Metrics(measure_figure.Metrics):
)
eval_far_str = "%.1f%%" % (100 * far)
eval_frr_str = "%.1f%%" % (100 * frr)
- eval_mindcf_str = "%.1f%%" % (
- (cost * far + (1 - cost) * frr) * 100.)
+ eval_mindcf_str = "%.1f%%" % ((cost * far + (1 - cost) * frr) * 100.0)
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])
+ 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]]
+ 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])
+ 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]
)
@@ -217,24 +239,24 @@ class Metrics(measure_figure.Metrics):
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
- )
+ 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]]]
+ 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')
+ headers.append("Evaluation")
rows[0].append(all_metrics[1][0])
rows[1].append(all_metrics[1][1])
rows[2].append(all_metrics[1][2])
@@ -246,25 +268,31 @@ class Metrics(measure_figure.Metrics):
class MultiMetrics(measure_figure.MultiMetrics):
- '''Compute metrics from score files'''
+ """Compute metrics from score files"""
def __init__(self, ctx, scores, evaluation, func_load):
super(MultiMetrics, self).__init__(
- ctx, scores, evaluation, func_load,
+ 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'))
+ "Failure to Acquire",
+ "False Match Rate",
+ "False Non Match Rate",
+ "False Accept Rate",
+ "False Reject Rate",
+ "Half Total Error Rate",
+ ),
+ )
class Hist(measure_figure.Hist):
- ''' Histograms for biometric scores '''
+ """ 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._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'
+ neg[0], n=1, label="Zero-effort impostors", alpha=0.8, color="C0"
)