From 6ea7c229c717e110085ca6514688a98ac4b84323 Mon Sep 17 00:00:00 2001
From: Theophile GENTILHOMME <tgentilhomme@jurasix08.idiap.ch>
Date: Fri, 20 Apr 2018 14:31:16 +0200
Subject: [PATCH] Made changes according to the modifications of the
bob.measure base class (more generic implementation)
---
bob/bio/base/script/commands.py | 20 ++++-----
bob/bio/base/script/figure.py | 76 ++++++++++++++++-----------------
2 files changed, 45 insertions(+), 51 deletions(-)
diff --git a/bob/bio/base/script/commands.py b/bob/bio/base/script/commands.py
index 1c8e7554..c6e01585 100644
--- a/bob/bio/base/script/commands.py
+++ b/bob/bio/base/script/commands.py
@@ -8,8 +8,6 @@ from bob.measure.script import common_options
from bob.extension.scripts.click_helper import (verbosity_option,
open_file_mode_option)
-FUNC_SPLIT = lambda x: load.load_files(x, load.split)
-FUNC_CMC = lambda x: load.load_files(x, load.cmc)
def rank_option(**kwargs):
'''Get option for rank parameter'''
@@ -62,9 +60,9 @@ def metrics(ctx, scores, evaluation, **kargs):
$ bob bio metrics {dev,eval}-scores1 {dev,eval}-scores2
"""
if 'criter' in ctx.meta and ctx.meta['criter'] == 'rr':
- process = bio_figure.Metrics(ctx, scores, evaluation, FUNC_CMC)
+ process = bio_figure.Metrics(ctx, scores, evaluation, load.cmc)
else:
- process = bio_figure.Metrics(ctx, scores, evaluation, FUNC_SPLIT)
+ process = bio_figure.Metrics(ctx, scores, evaluation, load.split)
process.run()
@click.command()
@@ -106,7 +104,7 @@ def roc(ctx, scores, evaluation, **kargs):
$ bob bio roc -o my_roc.pdf dev-scores1 eval-scores1
"""
- process = bio_figure.Roc(ctx, scores, evaluation, FUNC_SPLIT)
+ process = bio_figure.Roc(ctx, scores, evaluation, load.split)
process.run()
@click.command()
@@ -146,11 +144,11 @@ def det(ctx, scores, evaluation, **kargs):
$ bob bio det -o my_det.pdf dev-scores1 eval-scores1
"""
- process = bio_figure.Det(ctx, scores, evaluation, FUNC_SPLIT)
+ process = bio_figure.Det(ctx, scores, evaluation, load.split)
process.run()
@click.command()
-@common_options.scores_argument(eval_mandatory=True, nargs=-1)
+@common_options.scores_argument(min_arg=2, nargs=-1)
@common_options.output_plot_file_option(default_out='epc.pdf')
@common_options.titles_option()
@common_options.points_curve_option()
@@ -175,7 +173,7 @@ def epc(ctx, scores, **kargs):
$ bob bio epc -o my_epc.pdf dev-scores1 eval-scores1
"""
- process = measure_figure.Epc(ctx, scores, True, FUNC_SPLIT)
+ process = measure_figure.Epc(ctx, scores, True, load.split)
process.run()
@click.command()
@@ -215,7 +213,7 @@ def cmc(ctx, scores, evaluation, **kargs):
$ bob bio cmc -o my_roc.pdf dev-scores1 eval-scores1
"""
- process = bio_figure.Cmc(ctx, scores, evaluation, FUNC_CMC)
+ process = bio_figure.Cmc(ctx, scores, evaluation, load.cmc)
process.run()
@click.command()
@@ -264,7 +262,7 @@ def dic(ctx, scores, evaluation, **kargs):
$ bob bio dic -o my_roc.pdf dev-scores1 eval-scores1
"""
- process = bio_figure.Dic(ctx, scores, evaluation, FUNC_CMC)
+ process = bio_figure.Dic(ctx, scores, evaluation, load.cmc)
process.run()
@click.command()
@@ -306,7 +304,7 @@ def hist(ctx, scores, evaluation, **kwargs):
$ bob bio hist --criter --show-dev hter dev-scores1 eval-scores1
"""
- process = bio_figure.Hist(ctx, scores, evaluation, FUNC_SPLIT)
+ process = bio_figure.Hist(ctx, scores, evaluation, load.split)
process.run()
@click.command()
diff --git a/bob/bio/base/script/figure.py b/bob/bio/base/script/figure.py
index d8611d4f..3dcd984f 100644
--- a/bob/bio/base/script/figure.py
+++ b/bob/bio/base/script/figure.py
@@ -34,17 +34,16 @@ class Cmc(measure_figure.PlotBase):
self._y_label = self._y_label or 'Identification rate'
self._max_R = 0
- def compute(self, idx, dev_score, dev_file=None,
- eval_score=None, eval_file=None):
+ 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 measure_figure.LINESTYLES[idx % 14]
rank = plot.cmc(
- dev_score, logx=self._semilogx,
+ input_scores[0], logx=self._semilogx,
color=self._colors[idx], linestyle=linestyle,
- label=self._label('development', dev_file, idx)
+ label=self._label('development', input_names[0], idx)
)
self._max_R = max(rank, self._max_R)
linestyle = '--'
@@ -53,16 +52,16 @@ class Cmc(measure_figure.PlotBase):
linestyle = measure_figure.LINESTYLES[idx % 14]
rank = plot.cmc(
- eval_score, logx=self._semilogx,
+ input_scores[1], logx=self._semilogx,
color=self._colors[idx], linestyle=linestyle,
- label=self._label('eval', eval_file, idx)
+ label=self._label('eval', input_names[1], idx)
)
self._max_R = max(rank, self._max_R)
else:
rank = plot.cmc(
- dev_score, logx=self._semilogx,
+ input_scores[0], logx=self._semilogx,
color=self._colors[idx], linestyle=measure_figure.LINESTYLES[idx % 14],
- label=self._label('development', dev_file, idx)
+ label=self._label('development', input_names[0], idx)
)
self._max_R = max(rank, self._max_R)
@@ -77,17 +76,16 @@ class Dic(measure_figure.PlotBase):
self._x_label = self._title or 'FAR'
self._y_label = self._title or 'DIR'
- def compute(self, idx, dev_score, dev_file=None,
- eval_score=None, eval_file=None):
+ def compute(self, idx, input_scores, input_names):
''' Plot DIC 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 measure_figure.LINESTYLES[idx % 14]
plot.detection_identification_curve(
- dev_score, rank=self._rank, logx=self._semilogx,
+ input_scores[0], rank=self._rank, logx=self._semilogx,
color=self._colors[idx], linestyle=linestyle,
- label=self._label('development', dev_file, idx)
+ label=self._label('development', input_names[0], idx)
)
linestyle = '--'
if self._split:
@@ -95,37 +93,36 @@ class Dic(measure_figure.PlotBase):
linestyle = measure_figure.LINESTYLES[idx % 14]
plot.detection_identification_curve(
- eval_score, rank=self._rank, logx=self._semilogx,
+ input_scores[1], rank=self._rank, logx=self._semilogx,
color=self._colors[idx], linestyle=linestyle,
- label=self._label('eval', eval_file, idx)
+ label=self._label('eval', input_names[1], idx)
)
else:
- rank = plot.detection_identification_curve(
- dev_score, rank=self._rank, logx=self._semilogx,
+ plot.detection_identification_curve(
+ input_scores[0], rank=self._rank, logx=self._semilogx,
color=self._colors[idx], linestyle=measure_figure.LINESTYLES[idx % 14],
- label=self._label('development', dev_file, idx)
+ label=self._label('development', input_names[0], idx)
)
class Metrics(measure_figure.Metrics):
''' Compute metrics from score files'''
def init_process(self):
if self._criter == 'rr':
- self._thres = [None] * self.n_sytem if self._thres is None else \
+ self._thres = [None] * self.n_systems if self._thres is None else \
self._thres
- def compute(self, idx, dev_score, dev_file=None,
- eval_score=None, eval_file=None):
+ def compute(self, idx, input_scores, input_names):
''' Compute metrics for the given criteria'''
title = self._titles[idx] if self._titles is not None else None
- headers = ['' or title, 'Development %s' % dev_file]
- if self._eval and eval_score is not None:
- headers.append('eval % s' % eval_file)
+ headers = ['' or title, 'Development %s' % input_names[0]]
+ if self._eval and input_scores[1] is not None:
+ headers.append('eval % s' % input_names[1])
if self._criter == 'rr':
- rr = bob.measure.recognition_rate(dev_score, self._thres[idx])
+ rr = bob.measure.recognition_rate(input_scores[0], self._thres[idx])
dev_rr = "%.1f%%" % (100 * rr)
raws = [['RR', dev_rr]]
- if self._eval and eval_score is not None:
- rr = bob.measure.recognition_rate(eval_score, self._thres[idx])
+ 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(
@@ -136,12 +133,12 @@ class Metrics(measure_figure.Metrics):
cost = 0.99 if 'cost' not in self._ctx.meta else\
self._ctx.meta['cost']
threshold = bob.measure.min_weighted_error_rate_threshold(
- dev_score[0], dev_score[1], cost
+ 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, dev_file, threshold),
+ % (cost, input_names[0], threshold),
file=self.log_file
)
else:
@@ -151,7 +148,7 @@ class Metrics(measure_figure.Metrics):
)
# apply threshold to development set
far, frr = bob.measure.farfrr(
- dev_score[0], dev_score[1], threshold
+ input_scores[0][0], input_scores[0][1], threshold
)
dev_far_str = "%.1f%%" % (100 * far)
dev_frr_str = "%.1f%%" % (100 * frr)
@@ -159,10 +156,10 @@ class Metrics(measure_figure.Metrics):
raws = [['FAR', dev_far_str],
['FRR', dev_frr_str],
['minDCF', dev_mindcf_str]]
- if self._eval and eval_score is not None:
+ if self._eval and input_scores[1] is not None:
# apply threshold to development set
far, frr = bob.measure.farfrr(
- eval_score[0], eval_score[1], threshold
+ input_scores[1][0], input_scores[1][1], threshold
)
eval_far_str = "%.1f%%" % (100 * far)
eval_frr_str = "%.1f%%" % (100 * frr)
@@ -174,19 +171,20 @@ class Metrics(measure_figure.Metrics):
tabulate(raws, headers, self._tablefmt), file=self.log_file
)
elif self._criter == 'cllr':
- cllr = bob.measure.calibration.cllr(dev_score[0], dev_score[1])
+ cllr = bob.measure.calibration.cllr(input_scores[0][0],
+ input_scores[0][1])
min_cllr = bob.measure.calibration.min_cllr(
- dev_score[0], dev_score[1]
+ 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 eval_score is not None:
- cllr = bob.measure.calibration.cllr(eval_score[0],
- eval_score[1])
+ 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(
- eval_score[0], eval_score[1]
+ input_scores[1][0], input_scores[1][1]
)
eval_cllr_str = "%.1f%%" % cllr
eval_min_cllr_str = "%.1f%%" % min_cllr
@@ -196,9 +194,7 @@ class Metrics(measure_figure.Metrics):
tabulate(raws, headers, self._tablefmt), file=self.log_file
)
else:
- super(Metrics, self).compute(
- idx, dev_score, dev_file, eval_score, eval_file
- )
+ super(Metrics, self).compute(idx, input_scores, input_names)
class Hist(measure_figure.Hist):
''' Histograms for biometric scores '''
--
GitLab