evaluate.py 20.1 KB
Newer Older
Manuel Günther's avatar
Manuel Günther committed
1
2
3
4
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :

"""This script evaluates the given score files and computes EER, HTER.
5
6
7
It also is able to plot CMC and ROC curves.
You can set the environment variable BOB_NO_STYLE_CHANGES to any value to avoid
this script from changing the matplotlib style values. """
Manuel Günther's avatar
Manuel Günther committed
8

9
from __future__ import print_function
Manuel Günther's avatar
Manuel Günther committed
10
11
12
import bob.measure

import argparse
13
14
import numpy
import math
Manuel Günther's avatar
Manuel Günther committed
15
16
17
import os

# matplotlib stuff
18
19
import matplotlib
matplotlib.use('pdf')  # avoids TkInter threaded start
Manuel Günther's avatar
Manuel Günther committed
20
21
22
from matplotlib import pyplot
from matplotlib.backends.backend_pdf import PdfPages

23
24
25
26
27
28
if not os.environ.get('BOB_NO_STYLE_CHANGES'):
  # increase the default line width and font size
  matplotlib.rc('lines', linewidth=4)
  matplotlib.rc('font', size=18)


Manuel Günther's avatar
Manuel Günther committed
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
import bob.core
logger = bob.core.log.setup("bob.bio.base")


def command_line_arguments(command_line_parameters):
  """Parse the program options"""

  # set up command line parser
  parser = argparse.ArgumentParser(description=__doc__,
      formatter_class=argparse.ArgumentDefaultsHelpFormatter)

  parser.add_argument('-d', '--dev-files', required=True, nargs='+', help = "A list of score files of the development set.")
  parser.add_argument('-e', '--eval-files', nargs='+', help = "A list of score files of the evaluation set; if given it must be the same number of files as the --dev-files.")

  parser.add_argument('-s', '--directory', default = '.', help = "A directory, where to find the --dev-files and the --eval-files")

Manuel Günther's avatar
Manuel Günther committed
45
  parser.add_argument('-c', '--criterion', choices = ('EER', 'HTER', 'FAR'), help = "If given, the threshold of the development set will be computed with this criterion.")
46
  parser.add_argument('-f', '--far-value', type=float, default=0.001, help = "The FAR value for which to evaluate (only for --criterion FAR)")
Manuel Günther's avatar
Manuel Günther committed
47
48
49
50
  parser.add_argument('-x', '--cllr', action = 'store_true', help = "If given, Cllr and minCllr will be computed.")
  parser.add_argument('-m', '--mindcf', action = 'store_true', help = "If given, minDCF will be computed.")
  parser.add_argument('--cost', default=0.99,  help='Cost for FAR in minDCF')
  parser.add_argument('-r', '--rr', action = 'store_true', help = "If given, the Recognition Rate will be computed.")
51
  parser.add_argument('-t', '--thresholds', type=float, nargs='+', help = "If given, the Recognition Rate will incorporate an Open Set handling, rejecting all scores that are below the given threshold; when multiple thresholds are given, they are applied in the same order as the --dev-files.")
Manuel Günther's avatar
Manuel Günther committed
52
53
54
  parser.add_argument('-l', '--legends', nargs='+', help = "A list of legend strings used for ROC, CMC and DET plots; if given, must be the same number than --dev-files.")
  parser.add_argument('-F', '--legend-font-size', type=int, default=18, help = "Set the font size of the legends.")
  parser.add_argument('-P', '--legend-position', type=int, help = "Set the font size of the legends.")
Manuel Günther's avatar
Manuel Günther committed
55
  parser.add_argument('-T', '--title', nargs = '+', help = "Overwrite the default title of the plot for development (and evaluation) set")
Manuel Günther's avatar
Manuel Günther committed
56
57
58
  parser.add_argument('-R', '--roc', help = "If given, ROC curves will be plotted into the given pdf file.")
  parser.add_argument('-D', '--det', help = "If given, DET curves will be plotted into the given pdf file.")
  parser.add_argument('-C', '--cmc', help = "If given, CMC curves will be plotted into the given pdf file.")
André Anjos's avatar
André Anjos committed
59
  parser.add_argument('-E', '--epc', help = "If given, EPC curves will be plotted into the given pdf file. For this plot --eval-files is mandatory.")
60
  parser.add_argument('-M', '--min-far-value', type=float, default=1e-4, help = "Select the minimum FAR value used in ROC plots; should be a power of 10.")
61
  parser.add_argument('-L', '--far-line-at', type=float, help = "If given, draw a veritcal line at this FAR value in the ROC plots.")
62
  parser.add_argument('--parser', default = '4column', choices = ('4column', '5column'), help="The style of the resulting score files. The default fits to the usual output of score files.")
Manuel Günther's avatar
Manuel Günther committed
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86

  # add verbose option
  bob.core.log.add_command_line_option(parser)

  # parse arguments
  args = parser.parse_args(command_line_parameters)

  # set verbosity level
  bob.core.log.set_verbosity_level(logger, args.verbose)


  # some sanity checks:
  if args.eval_files is not None and len(args.dev_files) != len(args.eval_files):
    logger.error("The number of --dev-files (%d) and --eval-files (%d) are not identical", len(args.dev_files), len(args.eval_files))

  # update legends when they are not specified on command line
  if args.legends is None:
    args.legends = [f.replace('_', '-') for f in args.dev_files]
    logger.warn("Legends are not specified; using legends estimated from --dev-files: %s", args.legends)

  # check that the legends have the same length as the dev-files
  if len(args.dev_files) != len(args.legends):
    logger.error("The number of --dev-files (%d) and --legends (%d) are not identical", len(args.dev_files), len(args.legends))

87
88
89
90
91
92
93
94
  if args.thresholds is not None:
    if len(args.thresholds) == 1:
      args.thresholds = args.thresholds * len(args.dev_files)
    elif len(args.thresholds) != len(args.dev_files):
      logger.error("If given, the number of --thresholds imust be either 1, or the same as --dev-files (%d), but it is %d", len(args.dev_files), len(args.thresholds))
  else:
    args.thresholds = [None] * len(args.dev_files)

Manuel Günther's avatar
Manuel Günther committed
95
96
97
98
99
100
  if args.title is not None:
    if args.eval_files is None and len(args.title) != 1:
      logger.warning("Ignoring the title for the evaluation set, as no evaluation set is given")
    if args.eval_files is not None and len(args.title) < 2:
      logger.error("The title for the evaluation set is not specified")

Manuel Günther's avatar
Manuel Günther committed
101
102
103
  return args


104
def _plot_roc(frrs, colors, labels, title, fontsize=18, position=None, farfrrs=None):
Amir MOHAMMADI's avatar
Amir MOHAMMADI committed
105
  if position is None: position = 'lower right'
Manuel Günther's avatar
Manuel Günther committed
106
  figure = pyplot.figure()
107

Manuel Günther's avatar
Manuel Günther committed
108
109
  # plot FAR and CAR for each algorithm
  for i in range(len(frrs)):
110
    pyplot.semilogx([f for f in frrs[i][0]], [1. - f for f in frrs[i][1]], color=colors[i], lw=2, ms=10, mew=1.5, label=labels[i])
111
112
    if isinstance(farfrrs, list):
      pyplot.plot(farfrrs[i][0], (1.-farfrrs[i][1]), 'o', color=colors[i], markeredgecolor='black')
113

114
  # plot vertical bar, if desired
115
  if farfrrs is not None:
116
117
118
119
    if isinstance(farfrrs, float):
      pyplot.plot([farfrrs,farfrrs],[0.,1.], "--", color='black')
    else:
      pyplot.plot([x[0] for x in farfrrs], [(1.-x[1]) for x in farfrrs], '--', color='black', linewidth=1.5)
Manuel Günther's avatar
Manuel Günther committed
120

121
  # compute and apply tick marks
122
123
  min_far = frrs[0][0][0]
  ticks = [min_far]
124
125
  while ticks[-1] < 1.: ticks.append(ticks[-1] * 10.)
  pyplot.axis([min_far,1.,0.,1.])
126
  pyplot.xticks(ticks)
127
128
129
130

  # set label, legend and title
  pyplot.xlabel('FMR')
  pyplot.ylabel('1 - FNMR')
Manuel Günther's avatar
Manuel Günther committed
131
132
133
134
135
136
137
138
  pyplot.grid(True, color=(0.6,0.6,0.6))
  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)

  return figure


def _plot_det(dets, colors, labels, title, fontsize=18, position=None):
139
  if position is None: position = 'upper right'
Manuel Günther's avatar
Manuel Günther committed
140
141
142
143
144
145
146
147
148
149
  # open new page for current plot
  figure = pyplot.figure(figsize=(8.2,8))

  # plot the DET curves
  for i in range(len(dets)):
    pyplot.plot(dets[i][0], dets[i][1], color=colors[i], lw=2, ms=10, mew=1.5, label=labels[i])

  # change axes accordingly
  det_list = [0.0002, 0.001, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.7, 0.9, 0.95]
  ticks = [bob.measure.ppndf(d) for d in det_list]
150
  labels = [("%.5f" % d).rstrip('0').rstrip('.') for d in det_list]
151
  pyplot.xticks(ticks, [l if i % 2 else "" for i,l in enumerate(labels)])
Manuel Günther's avatar
Manuel Günther committed
152
153
154
  pyplot.yticks(ticks, labels)
  pyplot.axis((ticks[0], ticks[-1], ticks[0], ticks[-1]))

155
156
  pyplot.xlabel('FMR')
  pyplot.ylabel('FNMR')
Manuel Günther's avatar
Manuel Günther committed
157
158
159
160
161
  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)

  return figure

162

Manuel Günther's avatar
Manuel Günther committed
163
def _plot_cmc(cmcs, colors, labels, title, fontsize=18, position=None):
164
  if position is None: position = 'lower right'
Manuel Günther's avatar
Manuel Günther committed
165
166
167
  # open new page for current plot
  figure = pyplot.figure()

168
169
  max_R = 0
  # plot the CMC curves
Manuel Günther's avatar
Manuel Günther committed
170
  for i in range(len(cmcs)):
171
172
173
174
    probs = bob.measure.cmc(cmcs[i])
    R = len(probs)
    pyplot.semilogx(range(1, R+1), probs, figure=figure, color=colors[i], lw=2, ms=10, mew=1.5, label=labels[i])
    max_R = max(R, max_R)
Manuel Günther's avatar
Manuel Günther committed
175
176
177
178

  # change axes accordingly
  ticks = [int(t) for t in pyplot.xticks()[0]]
  pyplot.xlabel('Rank')
179
  pyplot.ylabel('Probability')
Manuel Günther's avatar
Manuel Günther committed
180
  pyplot.xticks(ticks, [str(t) for t in ticks])
181
  pyplot.axis([0, max_R, 0., 1.])
Manuel Günther's avatar
Manuel Günther committed
182
183
184
185
  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)

  return figure
André Anjos's avatar
André Anjos committed
186
187


188
def _plot_epc(scores_dev, scores_eval, colors, labels, title, fontsize=18, position=None):
189
  if position is None: position = 'upper center'
190
191
192
193
194
  # open new page for current plot
  figure = pyplot.figure()

  # plot the DET curves
  for i in range(len(scores_dev)):
195
196
    x,y = bob.measure.epc(scores_dev[i][0], scores_dev[i][1], scores_eval[i][0], scores_eval[i][1], 100)
    pyplot.plot(x, y, color=colors[i], label=labels[i], lw=2)
197
198
199

  # change axes accordingly
  pyplot.xlabel('alpha')
200
  pyplot.ylabel('HTER')
201
202
203
204
  pyplot.title(title)
  pyplot.grid(True)
  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)
205
  pyplot.xlim([-0.01, 1.01])
206
  pyplot.ylim([0., 0.51])
207

André Anjos's avatar
André Anjos committed
208
  return figure
209

Manuel Günther's avatar
Manuel Günther committed
210

211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
def remove_nan(scores):
    """removes the NaNs from the scores"""
    nans = numpy.isnan(scores)
    sum_nans = sum(nans)
    total = len(scores)
    return scores[numpy.where(~nans)], sum_nans, total


def get_fta(scores):
    """calculates the Failure To Acquire (FtA) rate"""
    fta_sum, fta_total = 0, 0
    neg, sum_nans, total = remove_nan(scores[0])
    fta_sum += sum_nans
    fta_total += total
    pos, sum_nans, total = remove_nan(scores[1])
    fta_sum += sum_nans
    fta_total += total
    return (neg, pos, fta_sum * 100 / float(fta_total))

Manuel Günther's avatar
Manuel Günther committed
230
231
232
233
234
235
236

def main(command_line_parameters=None):
  """Reads score files, computes error measures and plots curves."""

  args = command_line_arguments(command_line_parameters)

  # get some colors for plotting
237
238
239
240
241
242
  if len(args.dev_files) > 10:
    cmap = pyplot.cm.get_cmap(name='magma')
    colors = [cmap(i) for i in numpy.linspace(0, 1.0, len(args.dev_files) + 1)]
  else:
    # matplotlib 2.0 default color cycler list: Vega category10 palette
    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
243
244
              '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',
              '#bcbd22', '#17becf']
Manuel Günther's avatar
Manuel Günther committed
245

Manuel Günther's avatar
Manuel Günther committed
246
  if args.criterion or args.roc or args.det or args.epc or args.cllr or args.mindcf:
Manuel Günther's avatar
Manuel Günther committed
247
248
249
250
251
    score_parser = {'4column' : bob.measure.load.split_four_column, '5column' : bob.measure.load.split_five_column}[args.parser]

    # First, read the score files
    logger.info("Loading %d score files of the development set", len(args.dev_files))
    scores_dev = [score_parser(os.path.join(args.directory, f)) for f in args.dev_files]
252
253
    # remove nans
    scores_dev = [get_fta(s) for s in scores_dev]
Manuel Günther's avatar
Manuel Günther committed
254
255
256
257

    if args.eval_files:
      logger.info("Loading %d score files of the evaluation set", len(args.eval_files))
      scores_eval = [score_parser(os.path.join(args.directory, f)) for f in args.eval_files]
258
259
      # remove nans
      scores_eval = [get_fta(s) for s in scores_eval]
Manuel Günther's avatar
Manuel Günther committed
260
261
262
263
264
265


    if args.criterion:
      logger.info("Computing %s on the development " % args.criterion + ("and HTER on the evaluation set" if args.eval_files else "set"))
      for i in range(len(scores_dev)):
        # compute threshold on development set
Manuel Günther's avatar
Manuel Günther committed
266
267
268
269
        if args.criterion == 'FAR':
          threshold = bob.measure.far_threshold(scores_dev[i][0], scores_dev[i][1], args.far_value/100.)
        else:
          threshold = {'EER': bob.measure.eer_threshold, 'HTER' : bob.measure.min_hter_threshold} [args.criterion](scores_dev[i][0], scores_dev[i][1])
Manuel Günther's avatar
Manuel Günther committed
270
271
        # apply threshold to development set
        far, frr = bob.measure.farfrr(scores_dev[i][0], scores_dev[i][1], threshold)
Manuel Günther's avatar
Manuel Günther committed
272
        if args.criterion == 'FAR':
André Anjos's avatar
André Anjos committed
273
274
          print("The FRR at FAR=%2.3f%% of the development set of '%s' is %2.3f%% (CAR: %2.3f%%)" % (args.far_value, args.legends[i], frr * 100., 100.*(1-frr)))
        else:
Manuel Günther's avatar
Manuel Günther committed
275
          print("The %s of the development set of '%s' is %2.3f%%" % (args.criterion, args.legends[i], (far + frr) * 50.)) # / 2 * 100%
Manuel Günther's avatar
Manuel Günther committed
276
277
278
        if args.eval_files:
          # apply threshold to evaluation set
          far, frr = bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold)
Manuel Günther's avatar
Manuel Günther committed
279
280
          if args.criterion == 'FAR':
            print("The FRR of the evaluation set of '%s' is %2.3f%% (CAR: %2.3f%%)" % (args.legends[i], frr * 100., 100.*(1-frr))) # / 2 * 100%
André Anjos's avatar
André Anjos committed
281
          else:
Manuel Günther's avatar
Manuel Günther committed
282
            print("The HTER of the evaluation set of '%s' is %2.3f%%" % (args.legends[i], (far + frr) * 50.)) # / 2 * 100%
Manuel Günther's avatar
Manuel Günther committed
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314


    if args.mindcf:
      logger.info("Computing minDCF on the development " + ("and on the evaluation set" if args.eval_files else "set"))
      for i in range(len(scores_dev)):
        # compute threshold on development set
        threshold = bob.measure.min_weighted_error_rate_threshold(scores_dev[i][0], scores_dev[i][1], args.cost)
        # apply threshold to development set
        far, frr = bob.measure.farfrr(scores_dev[i][0], scores_dev[i][1], threshold)
        print("The minDCF of the development set of '%s' is %2.3f%%" % (args.legends[i], (args.cost * far + (1-args.cost) * frr) * 100. ))
        if args.eval_files:
          # compute threshold on evaluation set
          threshold = bob.measure.min_weighted_error_rate_threshold(scores_eval[i][0], scores_eval[i][1], args.cost)
          # apply threshold to evaluation set
          far, frr = bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold)
          print("The minDCF of the evaluation set of '%s' is %2.3f%%" % (args.legends[i], (args.cost * far + (1-args.cost) * frr) * 100. ))


    if args.cllr:
      logger.info("Computing Cllr and minCllr on the development " + ("and on the evaluation set" if args.eval_files else "set"))
      for i in range(len(scores_dev)):
        cllr = bob.measure.calibration.cllr(scores_dev[i][0], scores_dev[i][1])
        min_cllr = bob.measure.calibration.min_cllr(scores_dev[i][0], scores_dev[i][1])
        print("Calibration performance on development set of '%s' is Cllr %1.5f and minCllr %1.5f " % (args.legends[i], cllr, min_cllr))
        if args.eval_files:
          cllr = bob.measure.calibration.cllr(scores_eval[i][0], scores_eval[i][1])
          min_cllr = bob.measure.calibration.min_cllr(scores_eval[i][0], scores_eval[i][1])
          print("Calibration performance on evaluation set of '%s' is Cllr %1.5f and minCllr %1.5f" % (args.legends[i], cllr, min_cllr))


    if args.roc:
      logger.info("Computing CAR curves on the development " + ("and on the evaluation set" if args.eval_files else "set"))
315
      min_far = int(math.floor(math.log(args.min_far_value, 10)))
316
      fars = [math.pow(10., i * 0.25) for i in range(min_far * 4, 0)] + [1.]
Manuel Günther's avatar
Manuel Günther committed
317
318
319
320
321
322
323
324
325
      frrs_dev = [bob.measure.roc_for_far(scores[0], scores[1], fars) for scores in scores_dev]
      if args.eval_files:
        frrs_eval = [bob.measure.roc_for_far(scores[0], scores[1], fars) for scores in scores_eval]

      logger.info("Plotting ROC curves to file '%s'", args.roc)
      try:
        # create a multi-page PDF for the ROC curve
        pdf = PdfPages(args.roc)
        # create a separate figure for dev and eval
326
        pdf.savefig(_plot_roc(frrs_dev, colors, args.legends, args.title[0] if args.title is not None else "ROC for development set", args.legend_font_size, args.legend_position, args.far_line_at), bbox_inches='tight')
Manuel Günther's avatar
Manuel Günther committed
327
328
        del frrs_dev
        if args.eval_files:
329
330
331
332
333
334
335
          if args.far_line_at is not None:
            farfrrs = []
            for i in range(len(scores_dev)):
              threshold = bob.measure.far_threshold(scores_dev[i][0], scores_dev[i][1], args.far_line_at)
              farfrrs.append(bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold))
          else:
            farfrrs = None
336
          pdf.savefig(_plot_roc(frrs_eval, colors, args.legends, args.title[1] if args.title is not None else "ROC for evaluation set", args.legend_font_size, args.legend_position, farfrrs), bbox_inches='tight')
Manuel Günther's avatar
Manuel Günther committed
337
338
339
          del frrs_eval
        pdf.close()
      except RuntimeError as e:
340
        raise RuntimeError("During plotting of ROC curves, the following exception occured:\n%s" % e)
Manuel Günther's avatar
Manuel Günther committed
341
342
343
344
345
346
347
348
349

    if args.det:
      logger.info("Computing DET curves on the development " + ("and on the evaluation set" if args.eval_files else "set"))
      dets_dev = [bob.measure.det(scores[0], scores[1], 1000) for scores in scores_dev]
      if args.eval_files:
        dets_eval = [bob.measure.det(scores[0], scores[1], 1000) for scores in scores_eval]

      logger.info("Plotting DET curves to file '%s'", args.det)
      try:
350
        # create a multi-page PDF for the DET curve
Manuel Günther's avatar
Manuel Günther committed
351
352
        pdf = PdfPages(args.det)
        # create a separate figure for dev and eval
353
        pdf.savefig(_plot_det(dets_dev, colors, args.legends, args.title[0] if args.title is not None else "DET for development set", args.legend_font_size, args.legend_position), bbox_inches='tight')
Manuel Günther's avatar
Manuel Günther committed
354
355
        del dets_dev
        if args.eval_files:
356
          pdf.savefig(_plot_det(dets_eval, colors, args.legends, args.title[1] if args.title is not None else "DET for evaluation set", args.legend_font_size, args.legend_position), bbox_inches='tight')
Manuel Günther's avatar
Manuel Günther committed
357
358
359
          del dets_eval
        pdf.close()
      except RuntimeError as e:
360
        raise RuntimeError("During plotting of DET curves, the following exception occured:\n%s" % e)
Manuel Günther's avatar
Manuel Günther committed
361
362


363
    if args.epc:
Manuel Günther's avatar
Manuel Günther committed
364
      logger.info("Plotting EPC curves to file '%s'", args.epc)
André Anjos's avatar
André Anjos committed
365

366
367
      if not args.eval_files:
        raise ValueError("To plot the EPC curve the evaluation scores are necessary. Please, set it with the --eval-files option.")
André Anjos's avatar
André Anjos committed
368

369
      try:
370
        # create a multi-page PDF for the EPC curve
371
        pdf = PdfPages(args.epc)
372
        pdf.savefig(_plot_epc(scores_dev, scores_eval, colors, args.legends, args.title if args.title is not None else "" , args.legend_font_size, args.legend_position), bbox_inches='tight')
373
374
        pdf.close()
      except RuntimeError as e:
375
        raise RuntimeError("During plotting of EPC curves, the following exception occured:\n%s" % e)
376
377
378
379




Manuel Günther's avatar
Manuel Günther committed
380
381
382
383
384
385
386
  if args.cmc or args.rr:
    logger.info("Loading CMC data on the development " + ("and on the evaluation set" if args.eval_files else "set"))
    cmc_parser = {'4column' : bob.measure.load.cmc_four_column, '5column' : bob.measure.load.cmc_five_column}[args.parser]
    cmcs_dev = [cmc_parser(os.path.join(args.directory, f)) for f in args.dev_files]
    if args.eval_files:
      cmcs_eval = [cmc_parser(os.path.join(args.directory, f)) for f in args.eval_files]

387
388
389
390
391
392
    if args.cmc:
      logger.info("Plotting CMC curves to file '%s'", args.cmc)
      try:
        # create a multi-page PDF for the ROC curve
        pdf = PdfPages(args.cmc)
        # create a separate figure for dev and eval
393
        pdf.savefig(_plot_cmc(cmcs_dev, colors, args.legends, args.title[0] if args.title is not None else "CMC curve for development set", args.legend_font_size, args.legend_position), bbox_inches='tight')
394
        if args.eval_files:
395
          pdf.savefig(_plot_cmc(cmcs_eval, colors, args.legends, args.title[1] if args.title is not None else "CMC curve for evaluation set", args.legend_font_size, args.legend_position), bbox_inches='tight')
396
397
398
399
400
401
402
403
        pdf.close()
      except RuntimeError as e:
        raise RuntimeError("During plotting of ROC curves, the following exception occured:\n%s\nUsually this happens when the label contains characters that LaTeX cannot parse." % e)

    if args.rr:
      logger.info("Computing recognition rate on the development " + ("and on the evaluation set" if args.eval_files else "set"))
      for i in range(len(cmcs_dev)):
        rr = bob.measure.recognition_rate(cmcs_dev[i], args.thresholds[i])
Manuel Günther's avatar
Manuel Günther committed
404
        print("The Recognition Rate of the development set of '%s' is %2.3f%%" % (args.legends[i], rr * 100.))
405
406
407
        if args.eval_files:
          rr = bob.measure.recognition_rate(cmcs_eval[i], args.thresholds[i])
          print("The Recognition Rate of the development set of '%s' is %2.3f%%" % (args.legends[i], rr * 100.))