From e3a9a96d7bc5aa1abe779eaa1660ab5946c7ec34 Mon Sep 17 00:00:00 2001
From: skbidiap <sushil.bhattacharjee@idiap.ch>
Date: Fri, 2 Dec 2016 15:43:55 +0100
Subject: [PATCH] doc. indentations fixed
---
bob/pad/base/evaluation/PadIsoMetrics.py | 58 +++++++++++++-----------
doc/conf.py | 3 +-
doc/implemented.rst | 6 +++
3 files changed, 40 insertions(+), 27 deletions(-)
diff --git a/bob/pad/base/evaluation/PadIsoMetrics.py b/bob/pad/base/evaluation/PadIsoMetrics.py
index 9dd2173..e4b0544 100644
--- a/bob/pad/base/evaluation/PadIsoMetrics.py
+++ b/bob/pad/base/evaluation/PadIsoMetrics.py
@@ -18,6 +18,8 @@ class PadIsoMetrics():
self.attack_name = 'attack' #attack_presentation_name #'attack'
def save_scores_hdf5(self, outfile, scores_dict):
+ """ saves input scores_dict dictionary in a hdf5 formatted file"""
+
h5out = bob.io.base.HDF5File(outfile, "w")
for p in scores_dict.keys():
@@ -33,6 +35,8 @@ class PadIsoMetrics():
del h5out
def load_scores_hdf5(self, infile):
+ """ loads a hdf5 file, and trys to construct a dictionary of scores. Returns the score-dictionary."""
+
h5in = bob.io.base.HDF5File(infile, "r")
scores_dict = {}
@@ -55,15 +59,16 @@ class PadIsoMetrics():
""" computes EER threshold using the scores in the supplied dictionary
Input:
scores_dict: dictionary where each key is the name of the presentation ('real' or one attack-type),
- and the corresponding value is a tuple: (scores, attack_potential).
- 'scores' should be a 1D numpy-array of floats containing scores
- 'attack_potential' should be one of the 3 letters 'A', 'B', or 'C')
- Scores for 'real' presentations will not have an associated 'attack_potential',
- so, if the value of a key is a tuple of length 1, the key-value pair is assumed
- to represent a 'real'-presentation set.
+ and the corresponding value is a tuple: (scores, attack_potential).
+ 'scores' should be a 1D numpy-array of floats containing scores
+ 'attack_potential' should be one of the 3 letters 'A', 'B', or 'C')
+ Scores for 'real' presentations will not have an associated 'attack_potential',
+ so, if the value of a key is a tuple of length 1, the key-value pair is assumed
+ to represent a 'real'-presentation set.
Return:
tuple of three floats: (eer_threshold, far, frr). These are computed using functions from bob.measure.
"""
+
real_scores = None
attack_scores = None
assert scores_dict is not None, 'no development score-set provided for computing EER'
@@ -93,16 +98,17 @@ class PadIsoMetrics():
""" computes HTER on test-set scores, using the supplied score-threshold.
Inputs:
scores_dict: dictionary where each key is the name of the presentation ('real' or one attack-type),
- and the corresponding value is a tuple: (scores, attack_potential).
- 'scores' should be a 1D numpy-array of floats containing scores
- 'attack_potential' should be one of the 3 letters 'A', 'B', or 'C')
- Scores for 'real' presentations will not have an associated 'attack_potential',
- so, if the value of a key is a tuple of length 1, the key-value pair is assumed
- to represent a 'real'-presentation set.
+ and the corresponding value is a tuple: (scores, attack_potential).
+ 'scores' should be a 1D numpy-array of floats containing scores
+ 'attack_potential' should be one of the 3 letters 'A', 'B', or 'C')
+ Scores for 'real' presentations will not have an associated 'attack_potential',
+ so, if the value of a key is a tuple of length 1, the key-value pair is assumed
+ to represent a 'real'-presentation set.
score_threshold: (float) value to be used for thresholding scores.
Return:
tuple of three floats: (hter, far, frr). These are computed using functions from bob.measure.
"""
+
assert ((score_threshold is not None) and isinstance(score_threshold, (int, long, float)) ), 'input score_threshold should be a number (float or integer).'
real_scores = None
@@ -132,6 +138,7 @@ class PadIsoMetrics():
def _check_attack_potential(self, attack_potential):
""" For now, we assume three levels of attack-potential: 'C'>'B'>'A' """
+
if attack_potential is None:
attack_potential = 'C'
if attack_potential not in ['A', 'B', 'C']:
@@ -144,13 +151,13 @@ class PadIsoMetrics():
""" computes BPCER on test-set scores, using either the supplied score-threshold,
or the threshold computed from the EER of the development set
Inputs:
- scores: a 1D numpy-array of scores corresponding to genuine (bona-fide) presentations.
-
- score_threshold: a floating point number specifying the score-threshold to be used for deciding accept/reject.
+ scores: a 1D numpy-array of scores corresponding to genuine (bona-fide) presentations.
+ score_threshold: a floating point number specifying the score-threshold to be used for deciding accept/reject.
Return:
- floating-point number representing the bpcer computed for the input score-set
+ floating-point number representing the bpcer computed for the input score-set
"""
+
bonafide_scores = None
if isinstance(scores, dict):
#extract 'real' scores from dictionary
@@ -175,19 +182,18 @@ class PadIsoMetrics():
"""computes APCER as defined in ISO standard. For now, we assume three levels of attack-potential: 'C'>'B'>'A'
Inputs:
- scores_dict: a dictionary where each key corresponds to a specific PAI (presentation-attack-instrument)
- Keys corresponding to PAIs will have as value a list of 2 elements:
- 1st element: a 1D numpy-array of scores
- 2nd element: a single letter 'A', 'B', or 'C', specifying the attack-potential of the PAI.
+ scores_dict: a dictionary where each key corresponds to a specific PAI (presentation-attack-instrument)
+ Keys corresponding to PAIs will have as value a list of 2 elements:
+ 1st element: a 1D numpy-array of scores
+ 2nd element: a single letter 'A', 'B', or 'C', specifying the attack-potential of the PAI.
- attack_potential: a letter 'A', 'B', or 'C', specifying the attack_potential at which the APCER is to be computed
-
- score_threshold: a floating point number specifying the score-threshold to be used for deciding accept/reject.
+ attack_potential: a letter 'A', 'B', or 'C', specifying the attack_potential at which the APCER is to be computed
+ score_threshold: a floating point number specifying the score-threshold to be used for deciding accept/reject.
Returns:
- tuple consisting of 2 elements:
- 1st element: apcer at specified attack-potential
- 2nd element: dictionary of hter of individual PAIs that have attack-potential at or below input-parameter attack_potential.
+ tuple consisting of 2 elements:
+ 1st element: apcer at specified attack-potential
+ 2nd element: dictionary of hter of individual PAIs that have attack-potential at or below input-parameter attack_potential.
"""
attack_potential = self._check_attack_potential( attack_potential)
diff --git a/doc/conf.py b/doc/conf.py
index 5e0205c..a00bb35 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -39,6 +39,7 @@ nitpicky = True
# Ignores stuff we can't easily resolve on other project's sphinx manuals
nitpick_ignore = []
+keep_warnings = True
# Allows the user to override warnings from a separate file
if os.path.exists('nitpick-exceptions.txt'):
@@ -261,4 +262,4 @@ def member_function_test(app, what, name, obj, skip, options):
return False
def setup(app):
- app.connect('autodoc-skip-member', member_function_test)
\ No newline at end of file
+ app.connect('autodoc-skip-member', member_function_test)
diff --git a/doc/implemented.rst b/doc/implemented.rst
index 7228f2b..9beafd9 100644
--- a/doc/implemented.rst
+++ b/doc/implemented.rst
@@ -44,6 +44,12 @@ Algorithms
.. automodule:: bob.pad.base.algorithm
+Evaluation
+~~~~~~~~~~
+
+.. automodule:: bob.pad.base.evaluation
+
+
Databases
---------
--
GitLab