def difference(self, reference, hypothesis, uem=None, uemified=False):
"""Get error analysis as `Annotation`
Labels are (status, reference_label, hypothesis_label) tuples.
`status` is either 'correct', 'confusion', 'missed detection' or
'false alarm'.
`reference_label` is None in case of 'false alarm'.
`hypothesis_label` is None in case of 'missed detection'.
Parameters
----------
uemified : bool, optional
Returns "uemified" version of reference and hypothesis.
Defaults to False.
Returns
-------
errors : `Annotation`
"""
R, H, common_timeline = self.uemify(reference,
hypothesis,
uem=uem,
collar=self.collar,
skip_overlap=self.skip_overlap,
returns_timeline=True)
errors = Annotation(uri=reference.uri, modality=reference.modality)
# loop on all segments
for segment in common_timeline:
# list of labels in reference segment
rlabels = R.get_labels(segment, unique=False)
# list of labels in hypothesis segment
hlabels = H.get_labels(segment, unique=False)
_, details = self.matcher(rlabels, hlabels)
for r, h in details[MATCH_CORRECT]:
track = errors.new_track(segment, prefix=MATCH_CORRECT)
errors[segment, track] = (MATCH_CORRECT, r, h)
for r, h in details[MATCH_CONFUSION]:
track = errors.new_track(segment, prefix=MATCH_CONFUSION)
errors[segment, track] = (MATCH_CONFUSION, r, h)
for r in details[MATCH_MISSED_DETECTION]:
track = errors.new_track(segment,
prefix=MATCH_MISSED_DETECTION)
errors[segment, track] = (MATCH_MISSED_DETECTION, r, None)
for h in details[MATCH_FALSE_ALARM]:
track = errors.new_track(segment, prefix=MATCH_FALSE_ALARM)
errors[segment, track] = (MATCH_FALSE_ALARM, None, h)
if uemified:
return reference, hypothesis, errors
else:
return errors
def regression(self, reference, before, after, uem=None, uemified=False):
_, before, errors_before = self.difference(
reference, before, uem=uem, uemified=True)
reference, after, errors_after = self.difference(
reference, after, uem=uem, uemified=True)
behaviors = Annotation(uri=reference.uri, modality=reference.modality)
# common (up-sampled) timeline
common_timeline = errors_after.get_timeline().union(
errors_before.get_timeline())
common_timeline = common_timeline.segmentation()
# align 'before' errors on common timeline
B = self._tagger(errors_before, common_timeline)
# align 'after' errors on common timeline
A = self._tagger(errors_after, common_timeline)
for segment in common_timeline:
old_errors = B.get_labels(segment, unique=False)
new_errors = A.get_labels(segment, unique=False)
n1 = len(old_errors)
n2 = len(new_errors)
n = max(n1, n2)
match = np.zeros((n, n), dtype=int)
for i1, e1 in enumerate(old_errors):
for i2, e2 in enumerate(new_errors):
match[i1, i2] = self._match_errors(e1, e2)
mapping = self.munkres.compute(2 - match)
for i1, i2 in mapping:
if i1 >= n1:
track = behaviors.new_track(segment,
candidate=REGRESSION,
prefix=REGRESSION)
behaviors[segment, track] = (
REGRESSION, None, new_errors[i2])
elif i2 >= n2:
track = behaviors.new_track(segment,
candidate=IMPROVEMENT,
prefix=IMPROVEMENT)
behaviors[segment, track] = (
IMPROVEMENT, old_errors[i1], None)
elif old_errors[i1][0] == MATCH_CORRECT:
if new_errors[i2][0] == MATCH_CORRECT:
track = behaviors.new_track(segment,
candidate=BOTH_CORRECT,
prefix=BOTH_CORRECT)
behaviors[segment, track] = (
BOTH_CORRECT, old_errors[i1], new_errors[i2])
else:
track = behaviors.new_track(segment,
candidate=REGRESSION,
prefix=REGRESSION)
behaviors[segment, track] = (
REGRESSION, old_errors[i1], new_errors[i2])
else:
if new_errors[i2][0] == MATCH_CORRECT:
track = behaviors.new_track(segment,
candidate=IMPROVEMENT,
prefix=IMPROVEMENT)
behaviors[segment, track] = (
IMPROVEMENT, old_errors[i1], new_errors[i2])
else:
track = behaviors.new_track(segment,
candidate=BOTH_INCORRECT,
prefix=BOTH_INCORRECT)
behaviors[segment, track] = (
BOTH_INCORRECT, old_errors[i1], new_errors[i2])
behaviors = behaviors.smooth()
if uemified:
return reference, before, after, behaviors
else:
return behaviors
def regression(self, reference, before, after, uem=None, uemified=False):
_, before, errors_before = self.difference(reference,
before,
uem=uem,
uemified=True)
reference, after, errors_after = self.difference(reference,
after,
uem=uem,
uemified=True)
behaviors = Annotation(uri=reference.uri, modality=reference.modality)
# common (up-sampled) timeline
common_timeline = errors_after.get_timeline().union(
errors_before.get_timeline())
common_timeline = common_timeline.segmentation()
# align 'before' errors on common timeline
B = self._tagger(errors_before, common_timeline)
# align 'after' errors on common timeline
A = self._tagger(errors_after, common_timeline)
for segment in common_timeline:
old_errors = B.get_labels(segment, unique=False)
new_errors = A.get_labels(segment, unique=False)
n1 = len(old_errors)
n2 = len(new_errors)
n = max(n1, n2)
match = np.zeros((n, n), dtype=int)
for i1, e1 in enumerate(old_errors):
for i2, e2 in enumerate(new_errors):
match[i1, i2] = self._match_errors(e1, e2)
mapping = self.munkres.compute(2 - match)
for i1, i2 in mapping:
if i1 >= n1:
track = behaviors.new_track(segment,
candidate=REGRESSION,
prefix=REGRESSION)
behaviors[segment,
track] = (REGRESSION, None, new_errors[i2])
elif i2 >= n2:
track = behaviors.new_track(segment,
candidate=IMPROVEMENT,
prefix=IMPROVEMENT)
behaviors[segment,
track] = (IMPROVEMENT, old_errors[i1], None)
elif old_errors[i1][0] == MATCH_CORRECT:
if new_errors[i2][0] == MATCH_CORRECT:
track = behaviors.new_track(segment,
candidate=BOTH_CORRECT,
prefix=BOTH_CORRECT)
behaviors[segment,
track] = (BOTH_CORRECT, old_errors[i1],
new_errors[i2])
else:
track = behaviors.new_track(segment,
candidate=REGRESSION,
prefix=REGRESSION)
behaviors[segment,
track] = (REGRESSION, old_errors[i1],
new_errors[i2])
else:
if new_errors[i2][0] == MATCH_CORRECT:
track = behaviors.new_track(segment,
candidate=IMPROVEMENT,
prefix=IMPROVEMENT)
behaviors[segment,
track] = (IMPROVEMENT, old_errors[i1],
new_errors[i2])
else:
track = behaviors.new_track(segment,
candidate=BOTH_INCORRECT,
prefix=BOTH_INCORRECT)
behaviors[segment,
track] = (BOTH_INCORRECT, old_errors[i1],
new_errors[i2])
behaviors = behaviors.support()
if uemified:
return reference, before, after, behaviors
else:
return behaviors
def difference(self, reference, hypothesis, uem=None, uemified=False):
"""Get error analysis as `Annotation`
Labels are (status, reference_label, hypothesis_label) tuples.
`status` is either 'correct', 'confusion', 'missed detection' or
'false alarm'.
`reference_label` is None in case of 'false alarm'.
`hypothesis_label` is None in case of 'missed detection'.
Parameters
----------
uemified : bool, optional
Returns "uemified" version of reference and hypothesis.
Defaults to False.
Returns
-------
errors : `Annotation`
"""
reference, hypothesis = self.uemify(
reference, hypothesis, uem=uem, collar=self.collar)
reference, hypothesis = self._handle_unknowns(reference, hypothesis)
# common (up-sampled) timeline
common_timeline = reference.get_timeline().union(
hypothesis.get_timeline())
common_timeline = common_timeline.segmentation()
# align reference on common timeline
R = self._tagger(reference, common_timeline)
# translate and align hypothesis on common timeline
H = self._tagger(hypothesis, common_timeline)
errors = Annotation(uri=reference.uri, modality=reference.modality)
# loop on all segments
for segment in common_timeline:
# list of labels in reference segment
rlabels = R.get_labels(segment, unknown=self.unknown, unique=False)
# list of labels in hypothesis segment
hlabels = H.get_labels(segment, unknown=self.unknown, unique=False)
_, details = self.matcher(rlabels, hlabels)
for r, h in details[MATCH_CORRECT]:
track = errors.new_track(segment, prefix=MATCH_CORRECT)
errors[segment, track] = (MATCH_CORRECT, r, h)
for r, h in details[MATCH_CONFUSION]:
track = errors.new_track(segment, prefix=MATCH_CONFUSION)
errors[segment, track] = (MATCH_CONFUSION, r, h)
for r in details[MATCH_MISSED_DETECTION]:
track = errors.new_track(segment,
prefix=MATCH_MISSED_DETECTION)
errors[segment, track] = (MATCH_MISSED_DETECTION, r, None)
for h in details[MATCH_FALSE_ALARM]:
track = errors.new_track(segment, prefix=MATCH_FALSE_ALARM)
errors[segment, track] = (MATCH_FALSE_ALARM, None, h)
if uemified:
return reference, hypothesis, errors
else:
return errors
