def test_align_unbalanced(self):
ll_ref = [
annotations.Label('Postauto', 24.195341, 24.698972),
annotations.Label('Postauto', 197.02371, 197.57205),
annotations.Label('Postauto', 212.287814, 212.807224),
annotations.Label('Postauto', 219.599014, 220.278193),
annotations.Label('Postauto', 220.582608, 221.290058),
annotations.Label('Postauto', 225.81222, 226.306647),
annotations.Label('Postauto', 237.598244, 238.276765),
annotations.Label('Postauto', 271.85043, 272.443479),
annotations.Label('Postauto', 312.722146, 313.320455),
annotations.Label('Postauto', 317.542805, 318.274582),
annotations.Label('Postauto', 325.259014, 325.895457),
annotations.Label('Postauto', 339.426438, 339.965573),
annotations.Label('Postauto', 343.373961, 343.893371),
annotations.Label('Postauto', 355.208638, 355.720159),
annotations.Label('Postauto', 367.627159, 368.142625)
]
ll_hyp = [
annotations.Label('Postauto', 197.0, 197.5),
annotations.Label('Billag', 1070.5, 1071.0)
]
aligner = alignment.BipartiteMatchingAligner()
matches = aligner.align(ll_ref, ll_hyp)
assert len(matches) == 16
def test_align(self, kws_ref_and_hyp_label_list):
ll_ref, ll_hyp = kws_ref_and_hyp_label_list
aligner = alignment.BipartiteMatchingAligner(
substitution_penalty=2,
non_overlap_penalty_weight=1
)
matches = aligner.align(ll_ref.labels, ll_hyp.labels)
expected_matches = [
alignment.LabelPair(annotations.Label('up', start=5.28, end=5.99),
annotations.Label('up', start=5.20, end=5.88)),
alignment.LabelPair(annotations.Label('down', start=10.35, end=11.12),
annotations.Label('right', start=10.30, end=11.08)),
alignment.LabelPair(annotations.Label('right', start=20.87, end=22.01), None),
alignment.LabelPair(annotations.Label('up', start=33.00, end=33.4), None),
alignment.LabelPair(annotations.Label('up', start=33.4, end=33.8), None),
alignment.LabelPair(annotations.Label('down', start=39.28, end=40.0),
annotations.Label('down', start=39.27, end=40.01)),
alignment.LabelPair(None, annotations.Label('up', start=32.00, end=32.5)),
alignment.LabelPair(None, annotations.Label('up', start=34.2, end=34.8)),
alignment.LabelPair(None, annotations.Label('left', start=39.3, end=39.9))
]
assert sorted(expected_matches) == sorted(matches)
def test_evaluate_computes_confusion(self, ll_ref, ll_hyp):
aligner = alignment.BipartiteMatchingAligner()
result = evaluator.EventEvaluator(aligner).evaluate(ll_ref, ll_hyp)
assert result.confusion.total == 6
assert result.confusion.correct == 2
assert result.confusion.substitutions == 1
assert result.confusion.deletions == 3
assert result.confusion.insertions == 3
def test_align_empty_hyp_and_ref_returns_empty_list(self):
ll_ref = []
ll_hyp = []
aligner = alignment.BipartiteMatchingAligner(
substitution_penalty=2,
non_overlap_penalty_weight=1
)
matches = aligner.align(ll_ref, ll_hyp)
assert matches == []
def test_align_empty_ref_returns_insertions(self):
ll_ref = []
ll_hyp = [annotations.Label('greasy', 1.4, 1.9)]
aligner = alignment.BipartiteMatchingAligner(
substitution_penalty=2,
non_overlap_penalty_weight=1
)
matches = aligner.align(ll_ref, ll_hyp)
assert matches == [
alignment.LabelPair(None, annotations.Label('greasy', 1.4, 1.9))
]
def test_evaluate_computes_correct_alignment(self, ll_ref, ll_hyp):
aligner = alignment.BipartiteMatchingAligner()
result = evaluator.EventEvaluator(aligner).evaluate(ll_ref, ll_hyp)
expected_matches = [
alignment.LabelPair(annotations.Label('up', start=5.28, end=5.99),
annotations.Label('up', start=5.20, end=5.88)),
alignment.LabelPair(annotations.Label('down', start=10.35, end=11.12),
annotations.Label('right', start=10.30, end=11.08)),
alignment.LabelPair(annotations.Label('right', start=20.87, end=22.01), None),
alignment.LabelPair(annotations.Label('up', start=33.00, end=33.4), None),
alignment.LabelPair(annotations.Label('up', start=33.4, end=33.8), None),
alignment.LabelPair(annotations.Label('down', start=39.28, end=40.0),
annotations.Label('down', start=39.27, end=40.01)),
alignment.LabelPair(None, annotations.Label('up', start=32.00, end=32.5)),
alignment.LabelPair(None, annotations.Label('up', start=34.2, end=34.8)),
alignment.LabelPair(None, annotations.Label('left', start=39.3, end=39.9))
]
assert isinstance(result, evaluator.EventEvaluation)
assert sorted(expected_matches) == sorted(result.label_pairs)
def __init__(self, aligner=None):
if aligner is None:
aligner = alignment.BipartiteMatchingAligner()
super(KWSEvaluator, self).__init__(aligner)