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bob
bob.pad.base
Commits
28210018
Commit
28210018
authored
Feb 13, 2018
by
Pavel KORSHUNOV
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Moved LR algo from bob.pad.face
parent
9fcb42b2
Pipeline
#16597
failed with stage
in 7 minutes and 30 seconds
Changes
5
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1
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5 changed files
with
649 additions
and
227 deletions
+649
227
bob/pad/base/algorithm/LogRegr.py
bob/pad/base/algorithm/LogRegr.py
+437
0
bob/pad/base/algorithm/SVM.py
bob/pad/base/algorithm/SVM.py
+8
226
bob/pad/base/algorithm/__init__.py
bob/pad/base/algorithm/__init__.py
+2
0
bob/pad/base/utils/helper_functions.py
bob/pad/base/utils/helper_functions.py
+201
1
conda/meta.yaml
conda/meta.yaml
+1
0
No files found.
bob/pad/base/algorithm/LogRegr.py
0 → 100644
View file @
28210018
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bob/pad/base/algorithm/SVM.py
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28210018
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bob/pad/base/algorithm/__init__.py
View file @
28210018
from
.Algorithm
import
Algorithm
from
.SVM
import
SVM
from
OneClassGMM
import
OneClassGMM
from
LogRegr
import
LogRegr
def
__appropriate__
(
*
args
):
"""Says object was actually declared here, and not in the import module.
...
...
@@ 24,5 +25,6 @@ __appropriate__(
Algorithm
,
SVM
,
OneClassGMM
,
LogRegr
,
)
__all__
=
[
_
for
_
in
dir
()
if
not
_
.
startswith
(
'_'
)]
bob/pad/base/utils/helper_functions.py
View file @
28210018
...
...
@@ 278,4 +278,204 @@ def mean_std_normalize(features,
features_norm
=
np
.
vstack
(
row_norm_list
)
return
features_norm
,
features_mean
,
features_std
\ No newline at end of file
return
features_norm
,
features_mean
,
features_std
def
norm_train_data
(
real
,
attack
):
"""
Meanstd normalization of input data arrays. The mean and std normalizers
are computed using real class only.
**Parameters:**
``real`` : 2D :py:class:`numpy.ndarray`
Training features for the real class.
``attack`` : 2D :py:class:`numpy.ndarray`
Training features for the attack class.
**Returns:**
``real_norm`` : 2D :py:class:`numpy.ndarray`
Meanstd normalized training features for the real class.
``attack_norm`` : 2D :py:class:`numpy.ndarray`
Meanstd normalized training features for the attack class.
Or an empty list if ``one_class_flag = True``.
``features_mean`` : 1D :py:class:`numpy.ndarray`
Mean of the features.
``features_std`` : 1D :py:class:`numpy.ndarray`
Standart deviation of the features.
"""
real_norm
,
features_mean
,
features_std
=
mean_std_normalize
(
real
)
attack_norm
,
_
,
_
=
mean_std_normalize
(
attack
,
features_mean
,
features_std
)
return
real_norm
,
attack_norm
,
features_mean
,
features_std
def
split_data_to_train_cv
(
features
):
"""
This function is designed to split the input array of features into two
subset namely train and crossvalidation. These subsets can be used to tune the
hyperparameters of the SVM. The splitting is 50/50, the first half of the
samples in the input are selected to be train set, and the second half of
samples is crossvalidation.
**Parameters:**
``features`` : 2D :py:class:`numpy.ndarray`
Input array with feature vectors. The rows are samples, columns are features.
**Returns:**
``features_train`` : 2D :py:class:`numpy.ndarray`
Selected subset of train features.
``features_cv`` : 2D :py:class:`numpy.ndarray`
Selected subset of crossvalidation features.
"""
half_samples_num
=
np
.
int
(
features
.
shape
[
0
]
/
2
)
features_train
=
features
[
0
:
half_samples_num
,
:]
features_cv
=
features
[
half_samples_num
:
2
*
half_samples_num
+
1
,
:]
return
features_train
,
features_cv
def
norm_train_cv_data
(
real_train
,
real_cv
,
attack_train
,
attack_cv
,
one_class_flag
=
False
):
"""
Meanstd normalization of train and crossvalidation data arrays.
**Parameters:**
``real_train`` : 2D :py:class:`numpy.ndarray`
Subset of train features for the real class.
``real_cv`` : 2D :py:class:`numpy.ndarray`
Subset of crossvalidation features for the real class.
``attack_train`` : 2D :py:class:`numpy.ndarray`
Subset of train features for the attack class.
``attack_cv`` : 2D :py:class:`numpy.ndarray`
Subset of crossvalidation features for the attack class.
``one_class_flag`` : :py:class:`bool`
If set to ``True``, only positive/real samples will be used to
compute the mean and std normalization vectors. Set to ``True`` if
using oneclass SVM. Default: False.
**Returns:**
``real_train_norm`` : 2D :py:class:`numpy.ndarray`
Normalized subset of train features for the real class.
``real_cv_norm`` : 2D :py:class:`numpy.ndarray`
Normalized subset of crossvalidation features for the real class.
``attack_train_norm`` : 2D :py:class:`numpy.ndarray`
Normalized subset of train features for the attack class.
``attack_cv_norm`` : 2D :py:class:`numpy.ndarray`
Normalized subset of crossvalidation features for the attack class.
"""
if
not
(
one_class_flag
):
features_train
=
np
.
vstack
([
real_train
,
attack_train
])
features_train_norm
,
features_mean
,
features_std
=
mean_std_normalize
(
features_train
)
real_train_norm
=
features_train_norm
[
0
:
real_train
.
shape
[
0
],
:]
attack_train_norm
=
features_train_norm
[
real_train
.
shape
[
0
]:,
:]
real_cv_norm
,
_
,
_
=
mean_std_normalize
(
real_cv
,
features_mean
,
features_std
)
attack_cv_norm
,
_
,
_
=
mean_std_normalize
(
attack_cv
,
features_mean
,
features_std
)
else
:
# oneclass Classifier case
# only real class used for training in one class Classifier:
real_train_norm
,
features_mean
,
features_std
=
mean_std_normalize
(
real_train
)
attack_train_norm
,
_
,
_
=
mean_std_normalize
(
attack_train
,
features_mean
,
features_std
)
real_cv_norm
,
_
,
_
=
mean_std_normalize
(
real_cv
,
features_mean
,
features_std
)
attack_cv_norm
,
_
,
_
=
mean_std_normalize
(
attack_cv
,
features_mean
,
features_std
)
return
real_train_norm
,
real_cv_norm
,
attack_train_norm
,
attack_cv_norm
def
prepare_data_for_hyper_param_grid_search
(
training_features
,
n_samples
):
"""
This function converts a list of all training features returned by ``read_features``
method of the extractor to the subsampled train and crossvalidation arrays for both
real and attack classes.
**Parameters:**
``training_features`` : [[FrameContainer], [FrameContainer]]
A list containing two elements: [0]  a list of Frame Containers with
feature vectors for the real class; [1]  a list of Frame Containers with
feature vectors for the attack class.
``n_samples`` : :py:class:`int`
Number of uniformly selected feature vectors per class.
**Returns:**
``real_train`` : 2D :py:class:`numpy.ndarray`
Selected subset of train features for the real class.
The number of samples in this set is n_samples/2, which is defined
by split_data_to_train_cv method of this class.
``real_cv`` : 2D :py:class:`numpy.ndarray`
Selected subset of crossvalidation features for the real class.
The number of samples in this set is n_samples/2, which is defined
by split_data_to_train_cv method of this class.
``attack_train`` : 2D :py:class:`numpy.ndarray`
Selected subset of train features for the attack class.
The number of samples in this set is n_samples/2, which is defined
by split_data_to_train_cv method of this class.
``attack_cv`` : 2D :py:class:`numpy.ndarray`
Selected subset of crossvalidation features for the attack class.
The number of samples in this set is n_samples/2, which is defined
by split_data_to_train_cv method of this class.
"""
# training_features[0]  training features for the REAL class.
real
=
convert_and_prepare_features
(
training_features
[
0
])
# output is array
# training_features[1]  training features for the ATTACK class.
attack
=
convert_and_prepare_features
(
training_features
[
1
])
# output is array
# uniformly select subsets of features:
real_subset
=
select_uniform_data_subset
(
real
,
n_samples
)
attack_subset
=
select_uniform_data_subset
(
attack
,
n_samples
)
# split the data into train and crossvalidation:
real_train
,
real_cv
=
split_data_to_train_cv
(
real_subset
)
attack_train
,
attack_cv
=
split_data_to_train_cv
(
attack_subset
)
return
real_train
,
real_cv
,
attack_train
,
attack_cv
\ No newline at end of file
conda/meta.yaml
View file @
28210018
...
...
@@ 52,6 +52,7 @@ test:

coverage

sphinx

sphinx_rtd_theme

sklearn
about
:
home
:
https://www.idiap.ch/software/bob/
...
...
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