From fd18322634501dedcd0ae76eaa823fcf37751ac8 Mon Sep 17 00:00:00 2001
From: Amir Mohammadi <amir.mohammadi@idiap.ch>
Date: Mon, 25 Jul 2016 14:10:34 +0200
Subject: [PATCH] New handling of scores
---
bob/fusion/base/algorithm/MLP.py | 6 +-
bob/fusion/base/script/bob_fuse.py | 80 ++++++---
.../script/plot_fusion_decision_boundary.py | 159 ++++++++++--------
bob/fusion/base/test/test_scripts.py | 16 +-
bob/fusion/base/tools/__init__.py | 1 +
bob/fusion/base/tools/command_line.py | 36 ++--
bob/fusion/base/tools/common.py | 81 +++++++++
bob/fusion/base/tools/plotting.py | 34 ++--
8 files changed, 273 insertions(+), 140 deletions(-)
create mode 100644 bob/fusion/base/tools/common.py
diff --git a/bob/fusion/base/algorithm/MLP.py b/bob/fusion/base/algorithm/MLP.py
index e3898e8..44bf878 100644
--- a/bob/fusion/base/algorithm/MLP.py
+++ b/bob/fusion/base/algorithm/MLP.py
@@ -21,7 +21,7 @@ class MLP(AlgorithmBob):
def __init__(self,
n_systems=2,
- hidden_layers=None,
+ hidden_layers=(3,),
seed=None,
machine=None,
trainer=None,
@@ -29,9 +29,7 @@ class MLP(AlgorithmBob):
super(MLP, self).__init__(
classifier=self,
*args, **kwargs)
- if hidden_layers is None:
- hidden_layers = [3]
- self.mlp_shape = [n_systems] + hidden_layers + [1]
+ self.mlp_shape = [n_systems] + list(hidden_layers) + [1]
self.seed = seed
self.machine = machine
self.trainer = trainer
diff --git a/bob/fusion/base/script/bob_fuse.py b/bob/fusion/base/script/bob_fuse.py
index 435ce68..8d50149 100755
--- a/bob/fusion/base/script/bob_fuse.py
+++ b/bob/fusion/base/script/bob_fuse.py
@@ -5,14 +5,13 @@
from __future__ import print_function, absolute_import, division
import os
-import numpy as np
from bob.io.base import create_directories_safe
-from bob.measure.load import load_score, get_all_scores,\
- get_negatives_positives_all, dump_score
+from bob.measure.load import load_score, dump_score
from bob.bio.base import utils
-from ..tools import parse_arguments, write_info
+from ..tools import parse_arguments, write_info, get_gza_from_lines_list, \
+ check_consistency, get_scores, remove_nan
import bob.core
logger = bob.core.log.setup("bob.fusion.base")
@@ -25,35 +24,68 @@ def fuse(args, command_line_parameters):
write_info(args, command_line_parameters)
# load the scores
- score_lines_list_dev = [load_score(path, ncolumns=args.score_type)
- for path in args.dev_files]
- scores_dev = get_all_scores(score_lines_list_dev)
- (neg, pos) = get_negatives_positives_all(score_lines_list_dev)
+ score_lines_list_train = [load_score(path, ncolumns=args.score_type)
+ for path in args.train_files]
+ if args.dev_files:
+ score_lines_list_dev = [load_score(path, ncolumns=args.score_type)
+ for path in args.dev_files]
if args.eval_files:
score_lines_list_eval = [load_score(path, ncolumns=args.score_type)
for path in args.eval_files]
- scores_eval = get_all_scores(score_lines_list_eval)
+
+ # genuine, zero effort impostor, and attack list of
+ # train, development and evaluation data.
+ idx1, gen_lt, zei_lt, atk_lt = get_gza_from_lines_list(score_lines_list_train)
+ if args.dev_files:
+ _, gen_ld, zei_ld, atk_ld = get_gza_from_lines_list(score_lines_list_dev)
+ if args.eval_files:
+ _, gen_le, zei_le, atk_le = get_gza_from_lines_list(score_lines_list_eval)
# check if score lines are consistent
if not args.skip_check:
- score_lines0 = score_lines_list_dev[0]
- for score_lines in score_lines_list_dev[1:]:
- assert(np.all(score_lines['claimed_id'] == score_lines0['claimed_id']))
- assert(np.all(score_lines['real_id'] == score_lines0['real_id']))
+ check_consistency(gen_lt, zei_lt, atk_lt)
+ if args.dev_files:
+ check_consistency(gen_ld, zei_ld, atk_ld)
if args.eval_files:
- score_lines0 = score_lines_list_eval[0]
- for score_lines in score_lines_list_eval[1:]:
- assert(np.all(score_lines['claimed_id'] == score_lines0['claimed_id']))
- assert(np.all(score_lines['real_id'] == score_lines0['real_id']))
+ check_consistency(gen_le, zei_le, atk_le)
+
+ scores_train = get_scores(gen_lt, zei_lt, atk_lt)
+ train_neg = get_scores(zei_lt, atk_lt)
+ train_pos = get_scores(gen_lt)
+ if args.dev_files:
+ scores_dev = get_scores(gen_ld, zei_ld, atk_ld)
+ dev_neg = get_scores(zei_ld, atk_ld)
+ dev_pos = get_scores(gen_ld)
+ else:
+ dev_neg, dev_pos = None, None
+ if args.eval_files:
+ scores_eval = get_scores(gen_le, zei_le, atk_le)
+
+ # check for nan values
+ found_nan = False
+ found_nan, _, scores_train = remove_nan(scores_train, found_nan)
+ found_nan, _, train_neg = remove_nan(train_neg, found_nan)
+ found_nan, _, train_pos = remove_nan(train_pos, found_nan)
+ if args.dev_files:
+ found_nan, nan_dev, scores_dev = remove_nan(scores_dev, found_nan)
+ found_nan, _, dev_neg = remove_nan(dev_neg, found_nan)
+ found_nan, _, dev_pos = remove_nan(dev_pos, found_nan)
+ if args.eval_files:
+ found_nan, nan_eval, scores_eval = remove_nan(scores_eval, found_nan)
+
+ if found_nan:
+ logger.warn('Some nan values were removed.')
# train the preprocessors
- algorithm.train_preprocessors(scores_dev)
+ algorithm.train_preprocessors(scores_train)
# preprocess data
- scores_dev = algorithm.preprocess(scores_dev)
+ train_neg, train_pos = algorithm.preprocess(train_neg), algorithm.preprocess(train_pos)
+ if args.dev_files:
+ scores_dev = algorithm.preprocess(scores_dev)
+ dev_neg, dev_pos = algorithm.preprocess(dev_neg), algorithm.preprocess(dev_pos)
if args.eval_files:
scores_eval = algorithm.preprocess(scores_eval)
- neg, pos = algorithm.preprocess(neg), algorithm.preprocess(pos)
# train the model
if utils.check_file(args.model_file, args.force, 1000):
@@ -61,16 +93,16 @@ def fuse(args, command_line_parameters):
"- Fusion: model '%s' already exists.", args.model_file)
algorithm = algorithm.load(args.model_file)
else:
- algorithm.train(neg, pos)
+ algorithm.train(train_neg, train_pos, dev_neg, dev_pos)
algorithm.save(args.model_file)
# fuse the scores (dev)
if utils.check_file(args.fused_dev_file, args.force, 1000):
logger.info(
"- Fusion: scores '%s' already exists.", args.fused_dev_file)
- else:
+ elif args.dev_files:
fused_scores_dev = algorithm.fuse(scores_dev)
- score_lines = score_lines_list_dev[0]
+ score_lines = score_lines_list_dev[idx1][~nan_dev]
score_lines['score'] = fused_scores_dev
create_directories_safe(os.path.dirname(args.fused_dev_file))
dump_score(args.fused_dev_file, score_lines)
@@ -82,7 +114,7 @@ def fuse(args, command_line_parameters):
"- Fusion: scores '%s' already exists.", args.fused_eval_file)
else:
fused_scores_eval = algorithm.fuse(scores_eval)
- score_lines = score_lines_list_eval[0]
+ score_lines = score_lines_list_eval[idx1][~nan_eval]
score_lines['score'] = fused_scores_eval
create_directories_safe(os.path.dirname(args.fused_eval_file))
dump_score(args.fused_eval_file, score_lines)
diff --git a/bob/fusion/base/script/plot_fusion_decision_boundary.py b/bob/fusion/base/script/plot_fusion_decision_boundary.py
index 346d3c1..3e5c6ae 100644
--- a/bob/fusion/base/script/plot_fusion_decision_boundary.py
+++ b/bob/fusion/base/script/plot_fusion_decision_boundary.py
@@ -1,39 +1,16 @@
#!/usr/bin/env python
-"""Plot decision boundraries of the fusion algorithm.
-
-Usage:
- plot_fusion_decision_boundary.py SCORE_FILE SCORE_FILE MODEL_FILE
- [-v... | --verbose...] [options]
- plot_fusion_decision_boundary.py (-h | --help)
- plot_fusion_decision_boundary.py (-V | --version)
-
-Options:
- -o, --output PLOT_FILE The path to save the plot. [default: scatter.pdf]
- --score-type {4,5} The format the scores are provided. [default: 4]
- -v, --verbose Increase the verbosity level from 0 (only error
- messages) to 1 (warnings), 2 (log messages), 3 (debug
- information) by adding the --verbose option as often
- as desired (e.g. '-vvv' for debug). [default: 0]
- -g, --group N If given scores will be grouped into N samples.
- Give -1 for no grouping. [default: -1]
- --grouping {random, kmeans} The gouping algorithm used. [default: kmeans]
- -h --help Show this screen.
- -V, --version Show version.
-
-"""
-
-from docopt import docopt
+"""Plot decision boundraries of the fusion algorithm."""
+
+import argparse
import matplotlib.pyplot as plt
-import numpy
+import numpy as np
import bob.fusion.base
import bob.core
-from bob.measure.load import load_score, get_negatives_positives,\
- get_all_scores
-from bob.measure import eer_threshold
-from ..tools import grouping
-
+from bob.measure.load import load_score
+from ..tools import grouping, get_gza_from_lines_list, \
+ get_scores, remove_nan, check_consistency
logger = bob.core.log.setup("bob.fusion.base")
@@ -53,14 +30,14 @@ def plot_boundary_decision(algorithm, scores, score_labels, threshold,
'''
Plots the boundary decision of the Algorithm
- @param score_labels numpy.array A (scores.shape[0]) array containing
+ @param score_labels np.array A (scores.shape[0]) array containing
the true labels of scores.
@param threshold float threshold of the decision boundary
'''
if legends is None:
- legends = ['Impostor', 'Genuine']
- markers = ['x', 'o']
+ legends = ['Impostor', 'Attack', 'Genuine']
+ markers = ['x', 'o', 's']
if scores.shape[1] > 2:
raise NotImplementedError(
@@ -74,27 +51,25 @@ def plot_boundary_decision(algorithm, scores, score_labels, threshold,
Y = score_labels
x_min, x_max = X[:, i1].min() - x_pad, X[:, i1].max() + x_pad
y_min, y_max = X[:, i2].min() - y_pad, X[:, i2].max() + y_pad
- xx, yy = numpy.meshgrid(
- numpy.linspace(x_min, x_max, resolution),
- numpy.linspace(y_min, y_max, resolution))
- temp = numpy.c_[xx.ravel(), yy.ravel()]
+ xx, yy = np.meshgrid(
+ np.linspace(x_min, x_max, resolution),
+ np.linspace(y_min, y_max, resolution))
+ temp = np.c_[xx.ravel(), yy.ravel()]
temp = algorithm.preprocess(temp)
Z = (algorithm.fuse(temp) > threshold).reshape(xx.shape)
- contourf = plt.contour(xx, yy, Z, 1, alpha=1, cmap=plt.cm.viridis)
+ contourf = plt.contourf(xx, yy, Z, 1, alpha=1, cmap=plt.cm.viridis)
if do_grouping:
- negatives, positives = X[numpy.logical_not(Y)], X[Y]
- negatives, positives = grouping(negatives, positives, **kwargs)
- X = numpy.concatenate((negatives, positives), axis=0)
- Y = numpy.concatenate(
- (numpy.zeros(negatives.shape[0], dtype=numpy.bool8),
- numpy.ones(positives.shape[0], dtype=numpy.bool8)),
- axis=0)
-
- negatives, positives = X[numpy.logical_not(Y)], X[Y]
- colors = plt.cm.viridis(numpy.linspace(0, 1, 2))
- for i, X in enumerate((negatives, positives)):
+ gen = grouping(X[Y == 0, :], **kwargs)
+ zei = grouping(X[Y == 1, :], **kwargs)
+ atk = grouping(X[Y == 2, :], **kwargs)
+ else:
+ gen = X[Y == 0, :]
+ zei = X[Y == 1, :]
+ atk = X[Y == 2, :]
+ colors = plt.cm.viridis(np.linspace(0, 1, 3))
+ for i, X in enumerate((zei, atk, gen)):
plt.scatter(
X[:, 0], X[:, 1], marker=markers[i], alpha=alpha,
c=colors[i], label=legends[i])
@@ -112,38 +87,90 @@ def plot_boundary_decision(algorithm, scores, score_labels, threshold,
def main(command_line_parameters=None):
- args = docopt(__doc__, argv=command_line_parameters,
- version=bob.fusion.base.get_config())
- bob.core.log.set_verbosity_level(logger, args['--verbose'])
+
+ # setup command line parameters
+ parser = argparse.ArgumentParser(
+ description=__doc__,
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+ parser.add_argument('-e', '--eval-files', nargs='+', required=True,
+ help='A list of score files to be plotted usually the '
+ 'evaluation set.')
+
+ parser.add_argument('-m', '--model-file', required=True,
+ help='Path to Model.pkl of a saved bob.fusion.algorithm.')
+
+ parser.add_argument('-t', '--threshold', type=float, default=0, required=True,
+ help='The threshold to classify scores after fusion.'
+ 'Usually calculated from fused development set.')
+
+ parser.add_argument('-o', '--output', default='scatter.pdf',
+ help='The path to save the plot.')
+
+ parser.add_argument('-g', '--group', default=0, type=int,
+ help='If given scores will be grouped into N samples.')
+
+ parser.add_argument('-G', '--grouping', choices=('random', 'kmeans'),
+ default='kmeans',
+ help='The gouping algorithm to be used.')
+
+ parser.add_argument('--skip-check', action='store_true',
+ help='If provided, score files are not checked '
+ 'for consistency')
+
+ parser.add_argument('--score-type', choices=(4, 5), default=None,
+ help='The format the scores are provided.')
+
+ bob.core.log.add_command_line_option(parser)
+
+ # parse command line options
+ args = parser.parse_args(command_line_parameters)
+ bob.core.log.set_verbosity_level(logger, args.verbose)
# load the algorithm
algorithm = bob.fusion.base.algorithm.Algorithm()
- algorithm = algorithm.load(args['MODEL_FILE'])
+ algorithm = algorithm.load(args.model_file)
# load the scores
- score_lines_list = [
- load_score(path, int(args['--score-type'])) for
- path in args['SCORE_FILE']]
- scores = get_all_scores(score_lines_list)
- score_lines = numpy.array(score_lines_list[0])
- score_lines['score'] = algorithm.fuse(algorithm.preprocess(scores))
- threshold = eer_threshold(*get_negatives_positives(score_lines))
- score_labels = score_lines['claimed_id'] == score_lines['real_id']
+ score_lines_list_eval = [load_score(path, ncolumns=args.score_type)
+ for path in args.eval_files]
+
+ # genuine, zero effort impostor, and attack list
+ idx1, gen_le, zei_le, atk_le = get_gza_from_lines_list(score_lines_list_eval)
+
+ # check if score lines are consistent
+ if not args.skip_check:
+ check_consistency(gen_le, zei_le, atk_le)
+
+ # concatenate the scores and create the labels
+ scores = get_scores(gen_le, zei_le, atk_le)
+ score_labels = np.zeros((scores.shape[0],))
+ gensize = gen_le[0].shape[0]
+ zeisize = zei_le[0].shape[0]
+ score_labels[:gensize] = 0
+ score_labels[gensize: gensize + zeisize] = 1
+ score_labels[gensize + zeisize:] = 2
+ found_nan, nan_idx, scores = remove_nan(scores, False)
+ score_labels = score_labels[~nan_idx]
+
+ if found_nan:
+ logger.warn('{} nan values were removed.'.format(np.sum(nan_idx)))
# plot the decision boundary
do_grouping = True
- if int(args['--group']) == -1:
+ if args.group < 1:
do_grouping = False
plot_boundary_decision(
- algorithm, scores, score_labels, threshold,
+ algorithm, scores, score_labels, args.threshold,
do_grouping=do_grouping,
- npoints=int(args['--group']),
+ npoints=args.group,
seed=0,
- gformat=args['--grouping']
+ gformat=args.grouping
)
- plt.savefig(args['--output'])
+ plt.savefig(args.output)
plt.close()
+
if __name__ == '__main__':
main()
diff --git a/bob/fusion/base/test/test_scripts.py b/bob/fusion/base/test/test_scripts.py
index 9bfe632..ef143b8 100644
--- a/bob/fusion/base/test/test_scripts.py
+++ b/bob/fusion/base/test/test_scripts.py
@@ -8,8 +8,8 @@ from bob.fusion.base.script.bob_fuse import main as bob_fuse
from bob.fusion.base.script.plot_fusion_decision_boundary import main as plot_fusion_decision_boundary
from bob.io.base.test_utils import datafile
-dev_files = [datafile("scores-dev-1", 'bob.fusion.base'),
- datafile("scores-dev-2", 'bob.fusion.base')]
+train_files = [datafile("scores-dev-1", 'bob.fusion.base'),
+ datafile("scores-dev-2", 'bob.fusion.base')]
eval_files = [datafile("scores-eval-1", 'bob.fusion.base'),
datafile("scores-eval-2", 'bob.fusion.base')]
@@ -22,22 +22,22 @@ def test_scripts():
fused_eval_file = os.path.join(tmpdir, 'scores-eval')
# test normally
- cmd = ['-i'] + dev_files + ['-o', fused_dev_file, '-a', 'llr']
+ cmd = ['-s', tmpdir, '-t'] + train_files + ['-o', fused_dev_file, '-a', 'llr']
bob_fuse(cmd)
- cmd = ['-i'] + dev_files + ['-I'] + eval_files + ['-o', fused_dev_file, '-O', fused_eval_file, '-a', 'llr']
+ cmd = ['-s', tmpdir, '-t'] + train_files + ['-e'] + eval_files + ['-o', fused_dev_file, '-O', fused_eval_file, '-a', 'llr']
bob_fuse(cmd)
# make inconsistency
wrong_dev2 = os.path.join(tmpdir, 'scores-dev-2')
- with open(wrong_dev2, 'w') as f1, open(dev_files[1]) as f2:
+ with open(wrong_dev2, 'w') as f1, open(train_files[1]) as f2:
lines = f2.readlines()
temp = lines[0].split()
temp = (temp[0], 'temp1_id', temp[2], temp[3])
lines[0] = ' '.join(temp) + '\n'
f1.writelines(lines)
- cmd = ['-i'] + dev_files[0:1] + [wrong_dev2] + ['-o', fused_dev_file, '-a', 'llr']
+ cmd = ['-s', tmpdir, '-t'] + train_files[0:1] + [wrong_dev2] + ['-o', fused_dev_file, '-a', 'llr']
try:
bob_fuse(cmd)
except AssertionError:
@@ -46,13 +46,13 @@ def test_scripts():
raise Exception('An AssertionError should have been raised.')
# this should not raise an error
- cmd = ['-i'] + dev_files[0:1] + [wrong_dev2] + ['-o', fused_dev_file, '-a', 'llr', '--skip-check']
+ cmd = ['-s', tmpdir, '-t'] + train_files[0:1] + [wrong_dev2] + ['-o', fused_dev_file, '-a', 'llr', '--skip-check']
bob_fuse(cmd)
# test plot
model_file = os.path.join(tmpdir, 'Model.pkl')
output = os.path.join(tmpdir, 'scatter.pdf')
- cmd = dev_files + [model_file, '-o', output]
+ cmd = train_files + [model_file, '-o', output]
plot_fusion_decision_boundary(cmd)
finally:
diff --git a/bob/fusion/base/tools/__init__.py b/bob/fusion/base/tools/__init__.py
index 133f547..3edd02f 100644
--- a/bob/fusion/base/tools/__init__.py
+++ b/bob/fusion/base/tools/__init__.py
@@ -1,3 +1,4 @@
+from common import *
from command_line import *
from plotting import *
diff --git a/bob/fusion/base/tools/command_line.py b/bob/fusion/base/tools/command_line.py
index f9662df..edbdd5e 100644
--- a/bob/fusion/base/tools/command_line.py
+++ b/bob/fusion/base/tools/command_line.py
@@ -36,25 +36,31 @@ def command_line_parser(description=__doc__, exclude_resources_from=[]):
description=description,
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- parser.add_argument('-i', '--dev-files', required=True,
+ parser.add_argument('-t', '--train-files', required=True,
nargs='+', help="A list of score files of "
- "the development set.")
- parser.add_argument('-I', '--eval-files', nargs='+',
+ "the train set.")
+ parser.add_argument('-d', '--dev-files', nargs='+',
+ help="A list of score files of the development set; "
+ "if given it must be the same number of files "
+ "as the --train-files.")
+ 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('-o', '--fused-dev-file',
- required=True, help='The fused development score file.')
- parser.add_argument(
- '-O', '--fused-eval-file', help='The fused evaluation score file.')
- parser.add_argument('--score-type', choices=[4, 5], default=4,
- help='The format the scores are provided.')
+ "as the --train-files.")
+ parser.add_argument('-o', '--fused-dev-file', default=None,
+ help='The fused development score file. '
+ 'Default is "scores-dev" in the --save-directory')
+ parser.add_argument('-O', '--fused-eval-file', default=None,
+ help='The fused evaluation score file. '
+ 'Default is "scores-eval" in the --save-directory')
+ parser.add_argument('--score-type', choices=[4, 5], default=None,
+ help='The format the scores are provided. If not '
+ 'provided, the number of columns will be guessed.')
parser.add_argument('--skip-check', action='store_true',
help='If provided, score files are not checked '
'for consistency')
parser.add_argument('-s', '--save-directory', help='The directory to save '
- 'the experiment artifacts. If not given, the directory'
- ' of fused-dev-file will be used.')
+ 'the experiment artifacts.', default='fusion_result')
config_group = parser.add_argument_group(
'Parameters defining the experiment', ' Most of these parameters can be a'
@@ -95,8 +101,10 @@ def initialize(parsers, command_line_parameters=None, skips=[]):
args.algorithm, 'algorithm', imports=args.imports)
# set base directories
- if args.save_directory is None:
- args.save_directory = os.path.dirname(args.fused_dev_file)
+ if args.fused_dev_file is None:
+ args.fused_dev_file = os.path.join(args.save_directory, 'scores-dev')
+ if args.fused_eval_file is None:
+ args.fused_eval_file = os.path.join(args.save_directory, 'scores-eval')
# result files
args.info_file = os.path.join(args.save_directory, 'Experiment.info')
diff --git a/bob/fusion/base/tools/common.py b/bob/fusion/base/tools/common.py
new file mode 100644
index 0000000..09235eb
--- /dev/null
+++ b/bob/fusion/base/tools/common.py
@@ -0,0 +1,81 @@
+import numpy as np
+
+import bob.core
+logger = bob.core.log.setup("bob.fusion.base")
+
+
+def get_2negatives_1positive(score_lines):
+ gen_mask = score_lines['claimed_id'] == score_lines['real_id']
+ atk_mask = np.logical_or(np.char.count(score_lines['real_id'], 'spoof/') > 0,
+ np.char.count(score_lines['real_id'], 'attack') > 0)
+ zei_mask = np.logical_and(np.logical_not(gen_mask), np.logical_not(atk_mask))
+ gen = score_lines[gen_mask]
+ zei = score_lines[zei_mask]
+ atk = score_lines[atk_mask]
+ return (gen, zei, atk, gen_mask, zei_mask, atk_mask)
+
+
+def check_consistency(gen_l, zei_l, atk_l):
+ if len(gen_l) < 2:
+ logger.error('Check failed since less than two system is available.')
+ for score_lines_list in (gen_l, zei_l, atk_l):
+ if not score_lines_list:
+ continue
+ score_lines0 = score_lines_list[0]
+ for score_lines in score_lines_list[1:]:
+ assert(np.all(score_lines['claimed_id'] == score_lines0['claimed_id']))
+ assert(np.all(score_lines['real_id'] == score_lines0['real_id']))
+
+
+def get_scores(*args):
+ scores = []
+ for temp in zip(*args):
+ scores.append(np.concatenate([a['score'] for a in temp], axis=0))
+ return np.vstack(scores).T
+
+
+def remove_nan(samples, found_nan):
+ ncls = samples.shape[1]
+ nans = np.isnan(samples[:, 0])
+ for i in range(1, ncls):
+ nans = np.logical_or(nans, np.isnan(samples[:, i]))
+ return np.any(nans) or found_nan, nans, samples[~nans, :]
+
+
+def get_gza_from_lines_list(score_lines_list):
+ gen_l, zei_l, atk_l = [], [], []
+ for score_lines in score_lines_list:
+ gen, zei, atk, _, _, _ = get_2negatives_1positive(score_lines)
+ gen_l.append(gen)
+ zei_l.append(zei)
+ atk_l.append(atk)
+ zei_lengths = []
+ for zei in zei_l:
+ zei_lengths.append(zei.size)
+ zei_lengths = np.array(zei_lengths)
+ idx1 = 0 # used later if it does not enter the if.
+ if not (np.all(zei_lengths == 0) or np.all(zei_lengths > 0)):
+ # generate the missing ones
+ # find one that has zei
+ idx1 = zei_lengths.nonzero()[0][0]
+ zei_full = zei_l[idx1]
+ for idx2 in np.where(zei_lengths == 0)[0]:
+ if zei_l[idx2] is None:
+ continue
+ temp = np.array(zei_full)
+ # make sure we replace all scores.
+ temp['score'] = np.nan
+ # get the list of ids
+ real_ids = np.unique(temp['real_id'])
+ # find pad score of that id and replace the score
+ for real_id in real_ids:
+ # get the list of test_labels
+ test_labels = np.unique(temp['test_label'][temp['real_id'] == real_id])
+ for test_label in test_labels:
+ idx3 = np.logical_and(temp['real_id'] == real_id,
+ temp['test_label'] == test_label)
+ idx4 = np.logical_and(gen_l[idx2]['real_id'] == real_id,
+ gen_l[idx2]['test_label'] == test_label)
+ temp['score'][idx3] = gen_l[idx2]['score'][idx4]
+ zei_l[idx2] = temp
+ return idx1, gen_l, zei_l, atk_l
diff --git a/bob/fusion/base/tools/plotting.py b/bob/fusion/base/tools/plotting.py
index 4459912..06ef61b 100644
--- a/bob/fusion/base/tools/plotting.py
+++ b/bob/fusion/base/tools/plotting.py
@@ -4,38 +4,24 @@ import numpy
import bob.learn.em
-def grouping(negatives, positives,
- gformat='random', npoints=500, seed=None, **kwargs):
+def grouping(scores, gformat='random', npoints=500, seed=None, **kwargs):
- negatives = numpy.asarray(negatives)
- positives = numpy.asarray(positives)
+ scores = numpy.asarray(scores)
if(gformat == "kmeans"):
-
- kmeans_negatives = bob.learn.em.KMeansMachine(npoints, 2)
- kmeans_positives = bob.learn.em.KMeansMachine(npoints, 2)
-
- kmeansTrainer = bob.learn.em.KMeansTrainer()
-
+ kmeans_machine = bob.learn.em.KMeansMachine(npoints, 2)
+ kmeans_trainer = bob.learn.em.KMeansTrainer()
bob.learn.em.train(
- kmeansTrainer, kmeans_negatives, negatives, max_iterations=500,
+ kmeans_trainer, kmeans_machine, scores, max_iterations=500,
convergence_threshold=0.1)
- bob.learn.em.train(
- kmeansTrainer, kmeans_positives, positives, max_iterations=500,
- convergence_threshold=0.1)
-
- negatives = kmeans_negatives.means
- positives = kmeans_positives.means
+ scores = kmeans_machine.means
elif(gformat == "random"):
if seed is not None:
numpy.random.seed(seed)
- negatives_indexes = numpy.array(
- numpy.random.rand(npoints) * negatives.shape[0], dtype=int)
- positives_indexes = numpy.array(
- numpy.random.rand(npoints) * positives.shape[0], dtype=int)
+ scores_indexes = numpy.array(
+ numpy.random.rand(npoints) * scores.shape[0], dtype=int)
- negatives = negatives[negatives_indexes]
- positives = positives[positives_indexes]
+ scores = scores[scores_indexes]
- return negatives, positives
+ return scores
--
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