Source code for bob.bio.base.script.figure

"""Plots and measures for bob.bio.base"""

import logging
import math

import click
import matplotlib.pyplot as mpl

from tabulate import tabulate

import bob.measure
import bob.measure.script.figure as measure_figure

from bob.measure import plot

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._tpr else "FNMR" 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 Positive Identification Rate" self._y_label = self._y_label or "True Positive Identification Rate"
[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) * 100.0 ) 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( 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) * 100.0 ) 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] ) 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" )