Add legend options, small changes utils function and doc

bob/pad/base/script/det.py

@@ -10,6 +10,8 @@ from . import figure
 @common_options.scores_argument(min_arg=2, force_eval=True, nargs=-1)
 @common_options.output_plot_file_option(default_out='det.pdf')
 @common_options.legends_option()
+@common_options.no_legend_option()
+@common_options.legend_loc_option(dflt='upper-right')
 @common_options.title_option()
 @common_options.const_layout_option()
 @common_options.style_option()
@@ -18,7 +20,7 @@ from . import figure
     'no-spoof', 'ns', '', False
 )
 @verbosity_option()
-@common_options.axes_val_option(dflt=[0.01, 95, 0.01, 95])
+@common_options.axes_val_option(dflt='0.01,95,0.01,95')
 @common_options.x_rotation_option(dflt=45)
 @common_options.x_label_option()
 @common_options.y_label_option()

bob/pad/base/script/epc.py

@@ -10,6 +10,8 @@ from . import figure
 @common_options.scores_argument(min_arg=2, force_eval=True, nargs=-1)
 @common_options.output_plot_file_option(default_out='epc.pdf')
 @common_options.legends_option()
+@common_options.no_legend_option()
+@common_options.legend_loc_option()
 @common_options.title_option()
 @common_options.const_layout_option()
 @common_options.x_label_option()
@@ -51,6 +53,8 @@ def epc(ctx, scores, **kwargs):
 @common_options.scores_argument(min_arg=2, force_eval=True, nargs=-1)
 @common_options.output_plot_file_option(default_out='epsc.pdf')
 @common_options.legends_option()
+@common_options.no_legend_option()
+@common_options.legend_loc_option()
 @common_options.const_layout_option()
 @common_options.x_label_option()
 @common_options.y_label_option()

bob/pad/base/script/error_utils.py

@@ -13,12 +13,20 @@ import argparse
 def calc_pass_rate(threshold, attacks):
     """Calculates the rate of successful spoofing attacks
 
-  Keyword parameters:
+    Parameters
+    ----------
+    threshold :
+      the threshold used for classification
+    scores :
+      numpy with the scores of the spoofing attacks
+
+    Returns
+    -------
+    float
+      rate of successful spoofing attacks
+    """
+    return (attacks >= threshold).mean()
 
-    - threshold - the threshold used for classification
-    - attack: numpy with the scores of the spoofing attacks
-  """
-    return sum(1 for i in attacks if i >= threshold) / float(attacks.size)
 
 def weighted_neg_error_rate_criteria(data,
                                      weight,
@@ -298,30 +306,32 @@ def epsc_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos, thresholds,
     (omega and beta) and thresholds (the thresholds need to be computed first 
     using the method: epsc_thresholds() before passing to this method)
 
-  Keyword arguments:
-
-    - licit_neg - numpy.array of scores for the negatives (licit scenario)
-    - licit_pos - numpy.array of scores for the positives (licit scenario)
-    - spoof_neg - numpy.array of scores for the negatives (spoof scenario)
-    - spoof_pos - numpy.array of scores for the positives (spoof scenario)
-    - thresholds - numpy.ndarray with threshold values
-    - omega - numpy.array of the omega parameter balancing between impostors 
-    and spoofing attacks
-    - beta - numpy.array of the beta parameter balancing between real accesses
-    and all negatives (impostors and spoofing attacks)
-  """
-
-    if not isinstance(omega, list) and not isinstance(
-            omega, tuple) and not isinstance(omega, numpy.ndarray):
-        omega = numpy.array([omega])
-    else:
-        omega = numpy.array(omega)
-
-    if not isinstance(beta, list) and not isinstance(
-            beta, tuple) and not isinstance(beta, numpy.ndarray):
-        beta = numpy.array([beta])
-    else:
-        beta = numpy.array(beta)
+    Parameters
+    ----------
+    licit_neg : array_like
+        array of scores for the negatives (licit scenario)
+    licit_pos : array_like
+        array of scores for the positives (licit scenario)
+    spoof_neg : array_like
+        array of scores for the negatives (spoof scenario)
+    spoof_pos : array_like
+        array of scores for the positives (spoof scenario)
+    thresholds : array_like
+        ndarray with threshold values
+    omega : array_like
+        array of the omega parameter balancing between impostors
+        and spoofing attacks
+    beta : array_like
+        array of the beta parameter balancing between real accesses
+        and all negatives (impostors and spoofing attacks)
+
+    Returns
+    -------
+    far_w_errors: array_like
+        FAR_w
+    wer_wb_errors: array_like
+        WER_wb
+    """
 
     far_w_errors = numpy.ndarray((beta.size, omega.size), 'float64')
     wer_wb_errors = numpy.ndarray((beta.size, omega.size), 'float64')
@@ -340,34 +350,36 @@ def epsc_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos, thresholds,
 
 def all_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos, thresholds,
                     omega, beta):
-    """Calculates several error rates: FAR_w and HTER_w for the given weights
-    (omega and beta) and thresholds (the thresholds need to be computed first
+    """Calculates several error rates: FAR_w and WER_wb for the given weights 
+    (omega and beta) and thresholds (the thresholds need to be computed first 
     using the method: epsc_thresholds() before passing to this method)
 
-  Keyword arguments:
-
-    - licit_neg - numpy.array of scores for the negatives (licit scenario)
-    - licit_pos - numpy.array of scores for the positives (licit scenario)
-    - spoof_neg - numpy.array of scores for the negatives (spoof scenario)
-    - spoof_pos - numpy.array of scores for the positives (spoof scenario)
-    - thresholds - numpy.array with threshold values
-    - omega - numpy.array of the omega parameter balancing between impostors
-    and spoofing attacks
-    - beta - numpy.array of the beta parameter balancing between real accesses
-    and all negatives (impostors and spoofing attacks)
-  """
-
-    if not isinstance(omega, list) and not isinstance(
-            omega, tuple) and not isinstance(omega, numpy.ndarray):
-        omega = numpy.array([omega])
-    else:
-        omega = numpy.array(omega)
-
-    if not isinstance(beta, list) and not isinstance(
-            beta, tuple) and not isinstance(beta, numpy.ndarray):
-        beta = numpy.array([beta])
-    else:
-        beta = numpy.array(beta)
+    Parameters
+    ----------
+    licit_neg : array_like
+        array of scores for the negatives (licit scenario)
+    licit_pos : array_like
+        array of scores for the positives (licit scenario)
+    spoof_neg : array_like
+        array of scores for the negatives (spoof scenario)
+    spoof_pos : array_like
+        array of scores for the positives (spoof scenario)
+    thresholds : array_like
+        ndarray with threshold values
+    omega : array_like
+        array of the omega parameter balancing between impostors
+        and spoofing attacks
+    beta : array_like
+        array of the beta parameter balancing between real accesses
+        and all negatives (impostors and spoofing attacks)
+
+    Returns
+    -------
+    far_w_errors: array_like
+        FAR_w
+    wer_wb_errors: array_like
+        WER_wb
+    """
 
     frr_errors = numpy.ndarray((beta.size, omega.size), 'float64')
     far_errors = numpy.ndarray((beta.size, omega.size), 'float64')

bob/pad/base/script/figure.py

@@ -17,6 +17,7 @@ from . import error_utils
 
 ALL_CRITERIA = ('bpcer20', 'eer', 'min-hter')
 
+
 def calc_threshold(method, neg, pos):
     """Calculates the threshold based on the given method.
     The scores should be sorted!
@@ -52,6 +53,7 @@ def calc_threshold(method, neg, pos):
 
     return threshold
 
+
 class Metrics(measure_figure.Metrics):
     def __init__(self, ctx, scores, evaluation, func_load):
         super(Metrics, self).__init__(ctx, scores, evaluation, func_load)
@@ -90,6 +92,7 @@ class Metrics(measure_figure.Metrics):
                 file=self.log_file
             )
 
+
 class HistPad(measure_figure.Hist):
     ''' Histograms for PAD '''
 
@@ -103,8 +106,6 @@ class HistPad(measure_figure.Hist):
             hatch='\\\\'
         )
 
-def _calc_pass_rate(threshold, scores):
-    return (scores >= threshold).mean()
 
 def _iapmr_dot(threshold, iapmr, real_data, **kwargs):
     # plot a dot on threshold versus IAPMR line and show IAPMR as a number
@@ -127,20 +128,23 @@ def _iapmr_dot(threshold, iapmr, real_data, **kwargs):
         mpl.text(threshold + (threshold - axlim[0]) / 12, 100. * iapmr,
                  '%.1f%%' % (100. * iapmr,), color='C3')
 
+
 def _iapmr_line_plot(scores, n_points=100, **kwargs):
     axlim = mpl.axis()
     step = (axlim[1] - axlim[0]) / float(n_points)
     thres = [(k * step) + axlim[0] for k in range(2, n_points - 1)]
     mix_prob_y = []
     for k in thres:
-        mix_prob_y.append(100. * _calc_pass_rate(k, scores))
+        mix_prob_y.append(100. * error_utils.calc_pass_rate(k, scores))
 
     mpl.plot(thres, mix_prob_y, label='IAPMR', color='C3', **kwargs)
 
+
 def _iapmr_plot(scores, threshold, iapmr, real_data, **kwargs):
     _iapmr_dot(threshold, iapmr, real_data, **kwargs)
     _iapmr_line_plot(scores, n_points=100, **kwargs)
 
+
 class HistVuln(measure_figure.Hist):
     ''' Histograms for vulnerability '''
 
@@ -169,15 +173,7 @@ class HistVuln(measure_figure.Hist):
             ax2.grid(False)
             real_data = True if 'real_data' not in self._ctx.meta else \
                     self._ctx.meta['real_data']
-            far, frr = farfrr(neg[0], pos[0], threshold)
             _iapmr_plot(neg[1], threshold, iapmr, real_data=real_data)
-            click.echo(
-                '%s (t=%.2g) = %.2f%%; IAPMR = %.2f%%' % (
-                    self._criterion.upper(),
-                    threshold,
-                    50*(far+frr), 100*iapmr
-                )
-            )
             n = idx % self._step_print
             col = n % self._ncols
             rest_print = self.n_systems - int(idx / self._step_print) * self._step_print
@@ -187,6 +183,7 @@ class HistVuln(measure_figure.Hist):
             ax2.yaxis.label.set_color('red')
             ax2.spines['right'].set_color('red')
 
+
 class PadPlot(measure_figure.PlotBase):
     '''Base class for PAD plots'''
     def __init__(self, ctx, scores, evaluation, func_load):
@@ -208,7 +205,10 @@ class PadPlot(measure_figure.PlotBase):
             li, la = ax.get_legend_handles_labels()
             lines += li
             labels += la
-        mpl.gca().legend(lines, labels, loc=0, fancybox=True, framealpha=0.5)
+        if self._disp_legend:
+            mpl.gca().legend(lines, labels, loc=self._legend_loc,
+                             fancybox=True, framealpha=0.5)
+
 
 class Epc(PadPlot):
     ''' Handles the plotting of EPC '''
@@ -277,12 +277,14 @@ class Epc(PadPlot):
             prob_ax.yaxis.set_ticklabels(["%.0f" % val for val in ylabels])
             prob_ax.set_axisbelow(True)
         title = self._legends[idx] if self._legends is not None else self._title
-        mpl.title(title)
+        if title.replace(' ', ''):
+            mpl.title(title)
         #legends for all axes
         self._plot_legends()
         mpl.xticks(rotation=self._x_rotation)
         self._pdf_page.savefig(mpl.gcf())
 
+
 class Epsc(PadPlot):
     ''' Handles the plotting of EPSC '''
     def __init__(self, ctx, scores, evaluation, func_load,
@@ -397,11 +399,13 @@ class Epsc(PadPlot):
                 axis.spines['right'].set_color('red')
 
         if self._var_param == 'omega':
-            mpl.title(title or (r"EPSC with $\beta$ = %.2f" %\
-                                self._fixed_param))
+            if title.replace(' ', ''):
+                mpl.title(title or (r"EPSC with $\beta$ = %.2f" %\
+                                    self._fixed_param))
         else:
-            mpl.title(title or (r"EPSC with $\omega$ = %.2f" %\
-                                self._fixed_param))
+            if title.replace(' ', ''):
+                mpl.title(title or (r"EPSC with $\omega$ = %.2f" %\
+                                    self._fixed_param))
 
         mpl.grid()
         self._plot_legends()
@@ -410,6 +414,7 @@ class Epsc(PadPlot):
         mpl.xticks(rotation=self._x_rotation)
         self._pdf_page.savefig()
 
+
 class Epsc3D(Epsc):
     ''' 3D EPSC plots for PAD'''
     def compute(self, idx, input_scores, input_names):
@@ -469,7 +474,8 @@ class Epsc3D(Epsc):
             r"WER$_{\omega,\beta}$ (%)" if self._wer else "IAPMR (%)"
         )
 
-        mpl.title(title or "3D EPSC")
+        if title.replace(' ', ''):
+            mpl.title(title or "3D EPSC")
 
         ax1.set_xticklabels(ax1.get_xticks())
         ax1.set_yticklabels(ax1.get_yticks())
@@ -477,6 +483,7 @@ class Epsc3D(Epsc):
 
         self._pdf_page.savefig()
 
+
 class Det(PadPlot):
     '''DET for PAD'''
     def __init__(self, ctx, scores, evaluation, func_load, criteria, real_data):
@@ -617,11 +624,13 @@ class Det(PadPlot):
             add = " and overlaid SPOOF scenario"
         title = self._title if self._title is not None else \
                 ('DET: LICIT' + add)
-        mpl.title(title)
+        if title.replace(' ', ''):
+            mpl.title(title)
         mpl.xlabel(self._x_label or "False Acceptance Rate (%)")
         mpl.ylabel(self._y_label or "False Rejection Rate (%)")
         mpl.grid(True, color=self._grid_color)
-        mpl.legend(loc='best')
+        if self._disp_legend:
+            mpl.legend(loc=self._legend_loc)
         self._set_axis()
         fig = mpl.gcf()
         mpl.xticks(rotation=self._x_rotation)
@@ -644,6 +653,7 @@ class Det(PadPlot):
         else:
             det_axis([0.01, 99, 0.01, 99])
 
+
 class FmrIapmr(PadPlot):
     '''FMR vs IAPMR'''
     def __init__(self, ctx, scores, evaluation, func_load):
@@ -682,11 +692,13 @@ class FmrIapmr(PadPlot):
         close pdf is needed '''
         #only for plots
         title = self._title if self._title is not None else "FMR vs IAPMR"
-        mpl.title(title)
+        if title.replace(' ', ''):
+            mpl.title(title)
         mpl.xlabel(self._x_label or "False Match Rate (%)")
         mpl.ylabel(self._y_label or "IAPMR (%)")
         mpl.grid(True, color=self._grid_color)
-        mpl.legend(loc='best')
+        if self._disp_legend:
+            mpl.legend(loc=self._legend_loc)
         self._set_axis()
         fig = mpl.gcf()
         mpl.xticks(rotation=self._x_rotation)

bob/pad/base/script/fmr_iapmr.py

@@ -10,6 +10,8 @@ from . import figure
 @common_options.scores_argument(min_arg=2, force_eval=True, nargs=-1)
 @common_options.output_plot_file_option(default_out='fmr_iapmr.pdf')
 @common_options.legends_option()
+@common_options.no_legend_option()
+@common_options.legend_loc_option()
 @common_options.title_option()
 @common_options.const_layout_option()
 @common_options.style_option()