From 5e7070690dc726e47126a4a42eb63c4772362dda Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Wed, 9 Mar 2016 15:58:25 +0100
Subject: [PATCH] Solved issue #6
---
bob/measure/__init__.py | 17 ++---------
bob/measure/load.py | 63 ++++++++++++---------------------------
bob/measure/test_error.py | 32 +++++++++++++-------
3 files changed, 43 insertions(+), 69 deletions(-)
diff --git a/bob/measure/__init__.py b/bob/measure/__init__.py
index ff55169..b668707 100644
--- a/bob/measure/__init__.py
+++ b/bob/measure/__init__.py
@@ -130,22 +130,11 @@ def recognition_rate(cmc_scores, threshold=None):
correct += 1.
else:
-
#If threshold is NOT None, we have an openset identification
- if(len(pos)>0):
- # if we have positive scores the comparison is considered correct
- # if the positive score is higher than the threshold AND all negative scores
- max_pos = numpy.max(pos)
-
- if((threshold < max_pos) and (neg < max_pos).all()):
- correct += 1.
-
- else:
- #If we don't have a positive score we only will consider
- #a correct classification if ALL the negative scores are smaller than the threshold
- if (neg < threshold).all():
- correct += 1.
+ max_pos = numpy.max(pos)
+ if((threshold < max_pos) and (neg < max_pos).all()):
+ correct += 1.
# return relative number of correctly matched scores
return correct / float(len(cmc_scores))
diff --git a/bob/measure/load.py b/bob/measure/load.py
index 9039db9..f08e2e8 100644
--- a/bob/measure/load.py
+++ b/bob/measure/load.py
@@ -128,7 +128,7 @@ def split_four_column(filename):
return (numpy.array(neg, numpy.float64), numpy.array(pos, numpy.float64))
-def cmc_four_column(filename, load_only_negatives=False):
+def cmc_four_column(filename):
"""
cmc_four_column(filename) -> cmc_scores
@@ -148,9 +148,6 @@ def cmc_four_column(filename, load_only_negatives=False):
``filename`` : str or file-like
The file that will be opened with :py:func:`open_file` containing the scores.
- ``load_only_negatives`` : boolean
- Set this argument to **True** if you want also to load the probes that has negative scores **only** (used for open-set recognition).
-
**Returns:**
@@ -182,25 +179,17 @@ def cmc_four_column(filename, load_only_negatives=False):
retval = []
import logging
logger = logging.getLogger('bob')
- if(not load_only_negatives):
- for probe_name in sorted(pos_dict.keys()):
- if probe_name in neg_dict:
- retval.append((numpy.array(neg_dict[probe_name], numpy.float64), numpy.array(pos_dict[probe_name], numpy.float64)))
- else:
- logger.warn('For probe name "%s" there are only positive scores. This probe name is ignored.' % probe_name)
-
- #test if there are probes for which only negatives exist
- for probe_name in sorted(neg_dict.keys()):
- if not probe_name in pos_dict.keys():
- logger.warn('For probe name "%s" there are only negative scores. This probe name is ignored.' % probe_name)
-
- else:
- for probe_name in sorted(pos_dict.keys()):
+ for probe_name in sorted(pos_dict.keys()):
+ if probe_name in neg_dict:
retval.append((numpy.array(neg_dict[probe_name], numpy.float64), numpy.array(pos_dict[probe_name], numpy.float64)))
+ else:
+ logger.warn('For probe name "%s" there are only positive scores. This probe name is ignored.' % probe_name)
+
+ #test if there are probes for which only negatives exist
+ for probe_name in sorted(neg_dict.keys()):
+ if not probe_name in pos_dict.keys():
+ logger.warn('For probe name "%s" there are only negative scores. This probe name is ignored.' % probe_name)
- for probe_name in sorted(neg_dict.keys()):
- if not probe_name in pos_dict.keys():
- retval.append((numpy.array(neg_dict[probe_name], numpy.float64), numpy.array([], numpy.float64)))
return retval
@@ -288,7 +277,7 @@ def split_five_column(filename):
return (numpy.array(neg, numpy.float64), numpy.array(pos, numpy.float64))
-def cmc_five_column(filename, load_only_negatives=False):
+def cmc_five_column(filename):
"""
cmc_four_column(filename) -> cmc_scores
@@ -306,10 +295,6 @@ def cmc_five_column(filename, load_only_negatives=False):
``filename`` : str or file-like
The file that will be opened with :py:func:`open_file` containing the scores.
- ``load_only_negatives`` : boolean
- Set this argument to **True** if you want also to load the probes that has negative scores **only** (used for open-set recognition).
-
-
**Returns:**
``cmc_scores`` : [(array_like(1D, float), array_like(1D, float))]
@@ -336,25 +321,15 @@ def cmc_five_column(filename, load_only_negatives=False):
retval = []
import logging
logger = logging.getLogger('bob')
- if(not load_only_negatives):
- for probe_name in sorted(pos_dict.keys()):
- if probe_name in neg_dict:
- retval.append((numpy.array(neg_dict[probe_name], numpy.float64), numpy.array(pos_dict[probe_name], numpy.float64)))
- else:
- logger.warn('For probe name "%s" there are only positive scores. This probe name is ignored.' % probe_name)
- # test if there are probes for which only negatives exist
- for probe_name in sorted(neg_dict.keys()):
- if not probe_name in pos_dict.keys():
- logger.warn('For probe name "%s" there are only negative scores. This probe name is ignored.' % probe_name)
- else:
-
- for probe_name in sorted(pos_dict.keys()):
+ for probe_name in sorted(pos_dict.keys()):
+ if probe_name in neg_dict:
retval.append((numpy.array(neg_dict[probe_name], numpy.float64), numpy.array(pos_dict[probe_name], numpy.float64)))
-
- for probe_name in sorted(neg_dict.keys()):
- if not probe_name in pos_dict.keys():
- retval.append((numpy.array(neg_dict[probe_name], numpy.float64), numpy.array([], numpy.float64)))
-
+ else:
+ logger.warn('For probe name "%s" there are only positive scores. This probe name is ignored.' % probe_name)
+ # test if there are probes for which only negatives exist
+ for probe_name in sorted(neg_dict.keys()):
+ if not probe_name in pos_dict.keys():
+ logger.warn('For probe name "%s" there are only negative scores. This probe name is ignored.' % probe_name)
return retval
diff --git a/bob/measure/test_error.py b/bob/measure/test_error.py
index 45d026c..5c43b4d 100644
--- a/bob/measure/test_error.py
+++ b/bob/measure/test_error.py
@@ -326,20 +326,30 @@ def test_calibration():
def test_open_set_recognition_rate():
+ far_value = 0.01
+
#No error files
- scores = bob.measure.load.cmc_four_column(F("scores-cmc-4col-open-set.txt"), load_only_negatives=True)
- assert bob.measure.recognition_rate(scores, threshold=0.5), 1.0
- assert bob.measure.recognition_rate(scores, threshold=10.), 0.222222222222
+ cmc_scores = bob.measure.load.cmc_four_column(F("scores-cmc-4col-open-set.txt"))
+ normal_scores = bob.measure.load.split_four_column(F("scores-cmc-4col-open-set.txt"))
+ assert bob.measure.recognition_rate(cmc_scores), 1.0
+ assert bob.measure.recognition_rate(cmc_scores, threshold=0.5), 1.0
+ t = bob.measure.far_threshold(normal_scores[0], normal_scores[1],far_value)
+ assert bob.measure.recognition_rate(cmc_scores, threshold=t), 1.0
#One error
- scores = bob.measure.load.cmc_four_column(F("scores-cmc-4col-open-set-one-error.txt"),
- load_only_negatives=True)
- assert bob.measure.recognition_rate(scores, threshold=0.5), 0.888888888889
- assert bob.measure.recognition_rate(scores, threshold=10.), 0.222222222222
+ cmc_scores = bob.measure.load.cmc_four_column(F("scores-cmc-4col-open-set.txt"))
+ normal_scores = bob.measure.load.split_four_column(F("scores-cmc-4col-open-set.txt"))
+ assert bob.measure.recognition_rate(cmc_scores), 0.857142857143
+ assert bob.measure.recognition_rate(cmc_scores, threshold=0.5), 0.857142857143
+ t = bob.measure.far_threshold(normal_scores[0], normal_scores[1],far_value)
+ assert bob.measure.recognition_rate(cmc_scores, threshold=t), 0.857142857143
#Two errors
- scores = bob.measure.load.cmc_four_column(F("scores-cmc-4col-open-set-two-errors.txt"),
- load_only_negatives=True)
- assert bob.measure.recognition_rate(scores, threshold=0.5), 0.777777777778
- assert bob.measure.recognition_rate(scores, threshold=10.), 0.111111111111
+ cmc_scores = bob.measure.load.cmc_four_column(F("scores-cmc-4col-open-set.txt"))
+ normal_scores = bob.measure.load.split_four_column(F("scores-cmc-4col-open-set.txt"))
+ assert bob.measure.recognition_rate(cmc_scores), 0.857142857143
+ assert bob.measure.recognition_rate(cmc_scores, threshold=0.5), 0.857142857143
+ t = bob.measure.far_threshold(normal_scores[0], normal_scores[1],far_value)
+ assert bob.measure.recognition_rate(cmc_scores, threshold=t), 0.0
+
--
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