Commit 4e89bcb3 authored by Anjith GEORGE's avatar Anjith GEORGE
Browse files

Merge branch 'correct-apcer-calculation' into 'master'

Correct apcer calculation

Closes #31

See merge request !60
parents 47b3f2d0 8f66c86e
Pipeline #30336 passed with stages
in 12 minutes and 22 seconds
......@@ -7,11 +7,8 @@ from bob.pad.base.algorithm import Algorithm
import bob.learn.mlp
import bob.io.base
from bob.bio.video.utils import FrameContainer
from bob.pad.base.utils import convert_frame_cont_to_array
from bob.core.log import setup
logger = setup("bob.pad.base")
import logging
logger = logging.getLogger(__name__)
class MLP(Algorithm):
......@@ -42,11 +39,11 @@ class MLP(Algorithm):
criterion to stop the training: if the difference
between current and last loss is smaller than
this number, then stop training.
"""
Algorithm.__init__(self,
Algorithm.__init__(self,
performs_projection=True,
requires_projector_training=True,
requires_projector_training=True,
**kwargs)
self.hidden_units = hidden_units
......@@ -54,32 +51,31 @@ class MLP(Algorithm):
self.precision = precision
self.mlp = None
def train_projector(self, training_features, projector_file):
"""Trains the MLP
Parameters
----------
training_features : :any:`list` of :py:class:`numpy.ndarray`
training_features : :any:`list` of :py:class:`numpy.ndarray`
Data used to train the MLP. The real attempts are in training_features[0] and the attacks are in training_features[1]
projector_file : str
Filename where to save the trained model.
"""
# training is done in batch (i.e. using all training data)
batch_size = len(training_features[0]) + len(training_features[1])
# The labels
label_real = numpy.zeros((len(training_features[0]), 2), dtype='float64')
label_real[:, 0] = 1
label_attack = numpy.zeros((len(training_features[1]), 2), dtype='float64')
label_attack[:, 1] = 0
real = numpy.array(training_features[0])
attack = numpy.array(training_features[1])
X = numpy.vstack([real, attack])
Y = numpy.vstack([label_real, label_attack])
# Building MLP architecture
input_dim = real.shape[1]
shape = []
......@@ -89,16 +85,16 @@ class MLP(Algorithm):
# last layer contains two units: one for each class (i.e. real and attack)
shape.append(2)
shape = tuple(shape)
self.mlp = bob.learn.mlp.Machine(shape)
self.mlp.output_activation = bob.learn.activation.Logistic()
self.mlp.randomize()
trainer = bob.learn.mlp.BackProp(batch_size, bob.learn.mlp.CrossEntropyLoss(self.mlp.output_activation), self.mlp, train_biases=True)
n_iter = 0
previous_cost = 0
current_cost = 1
while (n_iter < self.max_iter) and (abs(previous_cost - current_cost) > self.precision):
while (n_iter < self.max_iter) and (abs(previous_cost - current_cost) > self.precision):
previous_cost = current_cost
trainer.train(self.mlp, X, Y)
current_cost = trainer.cost(self.mlp, X, Y)
......@@ -107,14 +103,13 @@ class MLP(Algorithm):
f = bob.io.base.HDF5File(projector_file, 'w')
self.mlp.save(f)
def project(self, feature):
"""Project the given feature
Parameters
----------
feature : :py:class:`numpy.ndarray`
feature : :py:class:`numpy.ndarray`
The feature to classify
Returns
......@@ -126,18 +121,17 @@ class MLP(Algorithm):
# feature = convert_frame_cont_to_array(feature)
return self.mlp(feature)
def score(self, toscore):
"""Returns the probability of the real class.
Parameters
----------
toscore : :py:class:`numpy.ndarray`
Returns
-------
float
probability of the authentication attempt to be real.
probability of the authentication attempt to be real.
"""
if toscore.ndim == 1:
return [toscore[0]]
......
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......@@ -7,93 +7,235 @@ import bob.bio.base.script.gen as bio_gen
import bob.measure.script.figure as measure_figure
from bob.bio.base.score import load
from . import pad_figure as figure
from .error_utils import negatives_per_pai_and_positives
from functools import partial
SCORE_FORMAT = (
"Files must be 4-col format, see "
":py:func:`bob.bio.base.score.load.four_column`.")
CRITERIA = ('eer', 'min-hter', 'bpcer20')
"Files must be 4-col format, see " ":py:func:`bob.bio.base.score.load.four_column`."
)
CRITERIA = (
"eer",
"min-hter",
"far",
"bpcer5000",
"bpcer2000",
"bpcer1000",
"bpcer500",
"bpcer200",
"bpcer100",
"bpcer50",
"bpcer20",
"bpcer10",
"bpcer5",
"bpcer2",
"bpcer1",
)
def metrics_option(
sname="-m",
lname="--metrics",
name="metrics",
help="List of metrics to print. Provide a string with comma separated metric "
"names. For possible values see the default value.",
default="apcer_pais,apcer,bpcer,acer,fta,fpr,fnr,hter,far,frr,precision,recall,f1_score",
**kwargs
):
"""The metrics option"""
def custom_metrics_option(func):
def callback(ctx, param, value):
if value is not None:
value = value.split(",")
ctx.meta[name] = value
return value
return click.option(
sname,
lname,
default=default,
help=help,
show_default=True,
callback=callback,
**kwargs
)(func)
return custom_metrics_option
def regexps_option(
help="A list of regular expressions (by repeating this option) to be used to "
"categorize PAIs. Each regexp must match one type of PAI.",
**kwargs
):
def custom_regexps_option(func):
def callback(ctx, param, value):
ctx.meta["regexps"] = value
return value
return click.option(
"-r",
"--regexps",
default=None,
multiple=True,
help=help,
callback=callback,
**kwargs
)(func)
return custom_regexps_option
def regexp_column_option(
help="The column in the score files to match the regular expressions against.",
**kwargs
):
def custom_regexp_column_option(func):
def callback(ctx, param, value):
ctx.meta["regexp_column"] = value
return value
return click.option(
"-rc",
"--regexp-column",
default="real_id",
type=click.Choice(("claimed_id", "real_id", "test_label")),
help=help,
show_default=True,
callback=callback,
**kwargs
)(func)
return custom_regexp_column_option
@click.command()
@click.argument('outdir')
@click.option('-mm', '--mean-match', default=10, type=click.FLOAT,
show_default=True)
@click.option('-mnm', '--mean-non-match', default=-10,
type=click.FLOAT, show_default=True)
@click.option('-n', '--n-sys', default=1, type=click.INT, show_default=True)
@click.argument("outdir")
@click.option("-mm", "--mean-match", default=10, type=click.FLOAT, show_default=True)
@click.option(
"-mnm", "--mean-non-match", default=-10, type=click.FLOAT, show_default=True
)
@click.option("-n", "--n-sys", default=1, type=click.INT, show_default=True)
@verbosity_option()
@click.pass_context
def gen(ctx, outdir, mean_match, mean_non_match, n_sys, **kwargs):
"""Generate random scores.
Generates random scores in 4col or 5col format. The scores are generated
using Gaussian distribution whose mean is an input
parameter. The generated scores can be used as hypothetical datasets.
Invokes :py:func:`bob.bio.base.script.commands.gen`.
"""
ctx.meta['five_col'] = False
ctx.forward(bio_gen.gen)
@common_options.metrics_command(common_options.METRICS_HELP.format(
names='FtA, APCER, BPCER, FAR, FRR, ACER',
criteria=CRITERIA, score_format=SCORE_FORMAT,
hter_note='Note that FAR = APCER * (1 - FtA), '
'FRR = FtA + BPCER * (1 - FtA) and ACER = (APCER + BPCER) / 2.',
command='bob pad metrics'), criteria=CRITERIA)
def metrics(ctx, scores, evaluation, **kwargs):
process = figure.Metrics(ctx, scores, evaluation, load.split)
process.run()
"""Generate random scores.
Generates random scores in 4col or 5col format. The scores are generated
using Gaussian distribution whose mean is an input
parameter. The generated scores can be used as hypothetical datasets.
Invokes :py:func:`bob.bio.base.script.commands.gen`.
"""
ctx.meta["five_col"] = False
ctx.forward(bio_gen.gen)
@common_options.metrics_command(
common_options.METRICS_HELP.format(
names="FtA, APCER, BPCER, FPR, FNR, FAR, FRR, ACER, HTER, precision, recall, f1_score",
criteria=CRITERIA,
score_format=SCORE_FORMAT,
hter_note="Note that APCER = max(APCER_pais), BPCER=FNR, "
"FAR = FPR * (1 - FtA), "
"FRR = FtA + FNR * (1 - FtA), "
"ACER = (APCER + BPCER) / 2, "
"and HTER = (FPR + FNR) / 2. "
"You can control which metrics are printed using the --metrics option. "
"You can use --regexps and --regexp_column options to change the behavior "
"of finding Presentation Attack Instrument (PAI) types",
command="bob pad metrics",
),
criteria=CRITERIA,
epilog="""\b
More Examples:
\b
bob pad metrics -vvv -e -lg IQM,LBP -r print -r video -m fta,apcer_pais,apcer,bpcer,acer,hter \
/scores/oulunpu/{qm-svm,lbp-svm}/Protocol_1/scores/scores-{dev,eval}
See also ``bob pad multi-metrics``.
""",
)
@regexps_option()
@regexp_column_option()
@metrics_option()
def metrics(ctx, scores, evaluation, regexps, regexp_column, metrics, **kwargs):
load_fn = partial(
negatives_per_pai_and_positives, regexps=regexps, regexp_column=regexp_column
)
process = figure.Metrics(ctx, scores, evaluation, load_fn, metrics)
process.run()
@common_options.roc_command(
common_options.ROC_HELP.format(
score_format=SCORE_FORMAT, command='bob pad roc'))
common_options.ROC_HELP.format(score_format=SCORE_FORMAT, command="bob pad roc")
)
def roc(ctx, scores, evaluation, **kwargs):
process = figure.Roc(ctx, scores, evaluation, load.split)
process.run()
process = figure.Roc(ctx, scores, evaluation, load.split)
process.run()
@common_options.det_command(
common_options.DET_HELP.format(
score_format=SCORE_FORMAT, command='bob pad det'))
common_options.DET_HELP.format(score_format=SCORE_FORMAT, command="bob pad det")
)
def det(ctx, scores, evaluation, **kwargs):
process = figure.Det(ctx, scores, evaluation, load.split)
process.run()
process = figure.Det(ctx, scores, evaluation, load.split)
process.run()
@common_options.epc_command(
common_options.EPC_HELP.format(
score_format=SCORE_FORMAT, command='bob pad epc'))
common_options.EPC_HELP.format(score_format=SCORE_FORMAT, command="bob pad epc")
)
def epc(ctx, scores, **kwargs):
process = measure_figure.Epc(ctx, scores, True, load.split, hter='ACER')
process.run()
process = measure_figure.Epc(ctx, scores, True, load.split, hter="ACER")
process.run()
@common_options.hist_command(
common_options.HIST_HELP.format(
score_format=SCORE_FORMAT, command='bob pad hist'))
common_options.HIST_HELP.format(score_format=SCORE_FORMAT, command="bob pad hist")
)
def hist(ctx, scores, evaluation, **kwargs):
process = figure.Hist(ctx, scores, evaluation, load.split)
process.run()
process = figure.Hist(ctx, scores, evaluation, load.split)
process.run()
@common_options.evaluate_command(
common_options.EVALUATE_HELP.format(
score_format=SCORE_FORMAT, command='bob pad evaluate'),
criteria=CRITERIA)
score_format=SCORE_FORMAT, command="bob pad evaluate"
),
criteria=CRITERIA,
)
def evaluate(ctx, scores, evaluation, **kwargs):
common_options.evaluate_flow(
ctx, scores, evaluation, metrics, roc, det, epc, hist, **kwargs)
common_options.evaluate_flow(
ctx, scores, evaluation, metrics, roc, det, epc, hist, **kwargs
)
@common_options.multi_metrics_command(
common_options.MULTI_METRICS_HELP.format(
names='FtA, APCER, BPCER, FAR, FRR, ACER',
criteria=CRITERIA, score_format=SCORE_FORMAT,
command='bob pad multi-metrics'),
criteria=CRITERIA)
def multi_metrics(ctx, scores, evaluation, protocols_number, **kwargs):
ctx.meta['min_arg'] = protocols_number * (2 if evaluation else 1)
process = figure.MultiMetrics(
ctx, scores, evaluation, load.split)
process.run()
names="FtA, APCER, BPCER, FAR, FRR, ACER, HTER, precision, recall, f1_score",
criteria=CRITERIA,
score_format=SCORE_FORMAT,
command="bob pad multi-metrics",
),
criteria=CRITERIA,
epilog="""\b
More examples:
\b
bob pad multi-metrics -vvv -e -pn 6 -lg IQM,LBP -r print -r video \
/scores/oulunpu/{qm-svm,lbp-svm}/Protocol_3_{1,2,3,4,5,6}/scores/scores-{dev,eval}
See also ``bob pad metrics``.
""",
)
@regexps_option()
@regexp_column_option()
@metrics_option(default="fta,apcer_pais,apcer,bpcer,acer,hter")
def multi_metrics(
ctx, scores, evaluation, protocols_number, regexps, regexp_column, metrics, **kwargs
):
ctx.meta["min_arg"] = protocols_number * (2 if evaluation else 1)
load_fn = partial(
negatives_per_pai_and_positives, regexps=regexps, regexp_column=regexp_column
)
process = figure.MultiMetrics(ctx, scores, evaluation, load_fn, metrics)
process.run()
'''Runs error analysis on score sets, outputs metrics and plots'''
"""Runs error analysis on score sets, outputs metrics and plots"""
import bob.measure.script.figure as measure_figure
from bob.measure.utils import get_fta_list
from bob.measure import farfrr, precision_recall, f_score
import bob.bio.base.script.figure as bio_figure
from .error_utils import calc_threshold
from .error_utils import calc_threshold, apcer_bpcer
import click
from tabulate import tabulate
import numpy as np
ALL_CRITERIA = ('bpcer20', 'eer', 'min-hter')
def _normalize_input_scores(input_score, input_name):
pos, negs = input_score
# convert scores to sorted numpy arrays and keep a copy of all negatives
pos = np.ascontiguousarray(pos)
pos.sort()
all_negs = np.ascontiguousarray([s for neg in negs.values() for s in neg])
all_negs.sort()
# FTA is calculated on pos and all_negs so we remove nans from negs
for k, v in negs.items():
v = np.ascontiguousarray(v)
v.sort()
negs[k] = v[~np.isnan(v)]
neg_list, pos_list, fta_list = get_fta_list([(all_negs, pos)])
all_negs, pos, fta = neg_list[0], pos_list[0], fta_list[0]
return input_name, pos, negs, all_negs, fta
class Metrics(bio_figure.Metrics):
'''Compute metrics from score files'''
"""Compute metrics from score files"""
def __init__(self, ctx, scores, evaluation, func_load, names):
if isinstance(names, str):
names = names.split(",")
super(Metrics, self).__init__(ctx, scores, evaluation, func_load, names)
def get_thres(self, criterion, pos, negs, all_negs, far_value):
return calc_threshold(
criterion,
pos=pos,
negs=negs.values(),
all_negs=all_negs,
far_value=far_value,
is_sorted=True,
)
def _numbers(self, threshold, pos, negs, all_negs, fta):
pais = list(negs.keys())
apcer_pais, apcer, bpcer = apcer_bpcer(threshold, pos, *[negs[k] for k in pais])
apcer_pais = {k: apcer_pais[i] for i, k in enumerate(pais)}
acer = (apcer + bpcer) / 2.0
fpr, fnr = farfrr(all_negs, pos, threshold)
hter = (fpr + fnr) / 2.0
far = fpr * (1 - fta)
frr = fta + fnr * (1 - fta)
nn = all_negs.shape[0] # number of attack
fp = int(round(fpr * nn)) # number of false positives
np = pos.shape[0] # number of bonafide
fn = int(round(fnr * np)) # number of false negatives
# precision and recall
precision, recall = precision_recall(all_negs, pos, threshold)
# f_score
f1_score = f_score(all_negs, pos, threshold, 1)
metrics = dict(
apcer_pais=apcer_pais,
apcer=apcer,
bpcer=bpcer,
acer=acer,
fta=fta,
fpr=fpr,
fnr=fnr,
hter=hter,
far=far,
frr=frr,
fp=fp,
nn=nn,
fn=fn,
np=np,
precision=precision,
recall=recall,
f1_score=f1_score,
)
return metrics
def __init__(self, ctx, scores, evaluation, func_load,
names=('FtA', 'APCER', 'BPCER', 'FAR', 'FRR', 'HTER')):
super(Metrics, self).__init__(
ctx, scores, evaluation, func_load, names
def _strings(self, metrics):
n_dec = ".%df" % self._decimal
for k, v in metrics.items():
if k in ("precision", "recall", "f1_score"):
metrics[k] = "%s" % format(v, n_dec)
elif k in ("np", "nn", "fp", "fn"):
continue
elif k in ("fpr", "fnr"):
if "fp" in metrics:
metrics[k] = "%s%% (%d/%d)" % (
format(100 * v, n_dec),
metrics["fp" if k == "fpr" else "fn"],
metrics["np" if k == "fpr" else "nn"],
)
else:
metrics[k] = "%s%%" % format(100 * v, n_dec)
elif k == "apcer_pais":
metrics[k] = {
k1: "%s%%" % format(100 * v1, n_dec) for k1, v1 in v.items()
}
else:
metrics[k] = "%s%%" % format(100 * v, n_dec)
return metrics
def _get_all_metrics(self, idx, input_scores, input_names):
""" Compute all metrics for dev and eval scores"""
for i, (score, name) in enumerate(zip(input_scores, input_names)):
input_scores[i] = _normalize_input_scores(score, name)
dev_file, dev_pos, dev_negs, dev_all_negs, dev_fta = input_scores[0]
if self._eval:
eval_file, eval_pos, eval_negs, eval_all_negs, eval_fta = input_scores[1]
threshold = (
self.get_thres(self._criterion, dev_pos, dev_negs, dev_all_negs, self._far)
if self._thres is None
else self._thres[idx]
)
title = self._legends[idx] if self._legends is not None else None
if self._thres is None:
far_str = ""
if self._criterion == "far" and self._far is not None:
far_str = str(self._far)
click.echo(
"[Min. criterion: %s %s] Threshold on Development set `%s`: %e"
% (self._criterion.upper(), far_str, title or dev_file, threshold),
file=self.log_file,
)
else:
click.echo(
"[Min. criterion: user provided] Threshold on "
"Development set `%s`: %e" % (dev_file or title, threshold),
file=self.log_file,
)
res = []
res.append(
self._strings(
self._numbers(threshold, dev_pos, dev_negs, dev_all_negs, dev_fta)
)
)
def get_thres(self, criterion, dev_neg, dev_pos, far):
if self._criterion == 'bpcer20':
return calc_threshold('bpcer20', dev_neg, dev_pos)
if self._eval:
# computes statistics for the eval set based on the threshold a priori
res.append(
self._strings(
self._numbers(
threshold, eval_pos, eval_negs, eval_all_negs, eval_fta
)
)
)
else:
return super(Metrics, self).get_thres(
criterion, dev_neg, dev_pos, far)
res.append(None)
return res