Commit ca793710 authored by Theophile GENTILHOMME's avatar Theophile GENTILHOMME

[script][figure] Modify metrics according to measure changes

parent 628f83b0
......@@ -5,10 +5,9 @@ import click
import matplotlib.pyplot as mpl
import bob.measure.script.figure as measure_figure
import bob.measure
from bob.measure import plot
from bob.measure import (plot, utils)
from tabulate import tabulate
class Roc(measure_figure.Roc):
def __init__(self, ctx, scores, evaluation, func_load):
super(Roc, self).__init__(ctx, scores, evaluation, func_load)
......@@ -114,6 +113,14 @@ class Dir(measure_figure.PlotBase):
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
)
def init_process(self):
if self._criterion == 'rr':
self._thres = [None] * self.n_systems if self._thres is None else \
......@@ -122,7 +129,7 @@ class Metrics(measure_figure.Metrics):
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, 'Development %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':
......@@ -205,12 +212,28 @@ class Metrics(measure_figure.Metrics):
tabulate(raws, headers, self._tablefmt), file=self.log_file
)
else:
self.names = (
'Failure to Acquire', 'False Match Rate',
'False Non Match Rate', 'False Accept Rate',
'False Reject Rate', 'Half Total Error Rate'
)
super(Metrics, self).compute(idx, input_scores, input_names)
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)
class MultiMetrics(measure_figure.MultiMetrics):
......
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