def test_unitary_alignment():
categories = ['Carol', 'Bob', 'Alice', 'Jeremy']
cat = np.array([[0, 0.5, 0.3, 0.7], [0.5, 0., 0.6, 0.4],
[0.3, 0.6, 0., 0.7], [0.7, 0.4, 0.7, 0.]])
combi_dis = CombinedCategoricalDissimilarity(categories=categories,
delta_empty=0.5,
cat_dissimilarity_matrix=cat,
alpha=1)
n_tuple = (('liza', Unit(Segment(12, 18), "Carol")),
('pierrot', Unit(Segment(12, 18), "Alice")), ('hadrien', None))
unitary_alignment = UnitaryAlignment(n_tuple)
assert (unitary_alignment.compute_disorder(combi_dis) == pytest.approx(
0.383, 0.001))
def test_positional_dissimilarity_figure10():
pos_dis = PositionalDissimilarity(delta_empty=1.0)
segments = {
(Segment(4, 14), Segment(40, 44)): 22.2,
(Segment(4, 14), Segment(4, 14)): 0.,
(Segment(4, 14), Segment(20, 25)): 3.2,
(Segment(4, 14), Segment(14, 24)): 1.,
(Segment(20, 30), Segment(20, 25)): 0.11,
(Segment(20, 25), Segment(14, 24)): 0.22,
}
for (seg_a, seg_b), value in segments.items():
unit_alignment = UnitaryAlignment(
(("liza", Unit(seg_a)), ("pierrot", Unit(seg_b))))
assert unit_alignment.compute_disorder(pos_dis) == pytest.approx(
value, 0.1)
def test_alignment_checking():
continuum = Continuum()
annotation = Annotation()
annotation[Segment(1, 5)] = 'Carol'
annotation[Segment(6, 8)] = 'Bob'
continuum.add_annotation('liza', annotation)
annotation = Annotation()
annotation[Segment(2, 6)] = 'Carol'
continuum.add_annotation('pierrot', annotation)
# checking valid alignment
alignment = Alignment([], continuum=continuum)
n_tuple = (('liza', Unit(Segment(1, 5), 'Carol')),
('pierrot', Unit(Segment(2, 6), 'Carol')))
unitary_alignment = UnitaryAlignment(n_tuple)
alignment.unitary_alignments.append(unitary_alignment)
# checking missing
with pytest.raises(SetPartitionError):
alignment.check(continuum)
n_tuple = (('liza', Unit(Segment(6, 8), 'Bob')), ('pierrot', None))
unitary_alignment = UnitaryAlignment(n_tuple)
alignment.unitary_alignments.append(unitary_alignment)
# checking valid alignment
alignment.check(continuum)
# checking with extra tuple
n_tuple = (('liza', Unit(Segment(6, 8), 'Bob')), ('pierrot', None))
unitary_alignment = UnitaryAlignment(n_tuple)
alignment.unitary_alignments.append(unitary_alignment)
# checking missing
with pytest.raises(SetPartitionError):
alignment.check(continuum)
def test_positional_dissimilarity_figure20_scale_effect():
pos_dis = PositionalDissimilarity(delta_empty=1.0)
unit_align_a = UnitaryAlignment(
(("pierrot", Unit(Segment(0, 7))), ("liza", Unit(Segment(0, 10)))))
unit_align_b = UnitaryAlignment(
(("pierrot", Unit(Segment(0, 21))), ("liza", Unit(Segment(0, 30)))))
assert (unit_align_a.compute_disorder(pos_dis) ==
unit_align_b.compute_disorder(pos_dis))
def test_alignment():
continuum = Continuum()
annotation = Annotation()
annotation[Segment(1, 5)] = 'Carol'
annotation[Segment(6, 8)] = 'Bob'
annotation[Segment(12, 18)] = 'Carol'
annotation[Segment(7, 20)] = 'Alice'
continuum.add_annotation('liza', annotation)
annotation = Annotation()
annotation[Segment(2, 6)] = 'Carol'
annotation[Segment(7, 8)] = 'Bob'
annotation[Segment(12, 18)] = 'Alice'
annotation[Segment(8, 10)] = 'Alice'
annotation[Segment(7, 19)] = 'Jeremy'
continuum.add_annotation('pierrot', annotation)
annotation = Annotation()
annotation[Segment(1, 6)] = 'Carol'
annotation[Segment(8, 10)] = 'Alice'
annotation[Segment(7, 19)] = 'Jeremy'
annotation[Segment(19, 20)] = 'Alice'
continuum.add_annotation('hadrien', annotation)
categories = ['Carol', 'Bob', 'Alice', 'Jeremy']
cat = np.array([[0, 0.5, 0.3, 0.7], [0.5, 0., 0.6, 0.4],
[0.3, 0.6, 0., 0.7], [0.7, 0.4, 0.7, 0.]])
combi_dis = CombinedCategoricalDissimilarity(categories=categories,
delta_empty=0.5,
cat_dissimilarity_matrix=cat)
set_unitary_alignments = []
n_tuple = (('liza', Unit(Segment(1, 5), 'Carol')),
('pierrot', Unit(Segment(2, 6),
'Carol')), ('hadrien',
Unit(Segment(1, 6), 'Carol')))
unitary_alignment = UnitaryAlignment(n_tuple)
set_unitary_alignments.append(unitary_alignment)
n_tuple = (('liza', Unit(Segment(6, 8),
'Bob')), ('pierrot', Unit(Segment(7, 8), 'Bob')),
('hadrien', Unit(Segment(8, 10), 'Alice')))
unitary_alignment = UnitaryAlignment(n_tuple)
set_unitary_alignments.append(unitary_alignment)
n_tuple = (('liza', Unit(Segment(7, 20), 'Alice')),
('pierrot', Unit(Segment(7, 19),
'Jeremy')), ('hadrien',
Unit(Segment(7, 19), 'Jeremy')))
unitary_alignment = UnitaryAlignment(n_tuple)
set_unitary_alignments.append(unitary_alignment)
n_tuple = (('liza', Unit(Segment(12, 18), 'Carol')),
('pierrot', Unit(Segment(12, 18), 'Alice')), ('hadrien', None))
unitary_alignment = UnitaryAlignment(n_tuple)
set_unitary_alignments.append(unitary_alignment)
n_tuple = (('liza', None), ('pierrot', Unit(Segment(8, 10), 'Alice')),
('hadrien', Unit(Segment(19, 20), 'Alice')))
unitary_alignment = UnitaryAlignment(n_tuple)
set_unitary_alignments.append(unitary_alignment)
alignment = Alignment(set_unitary_alignments,
continuum=continuum,
check_validity=True)
assert (alignment.compute_disorder(combi_dis) == pytest.approx(
5.35015024691358, 0.001))
def test_positional_dissimilarity():
continuum = Continuum()
annotation = Annotation()
annotation[Segment(1, 5)] = 'Carol'
annotation[Segment(6, 8)] = 'Bob'
annotation[Segment(12, 18)] = 'Carol'
annotation[Segment(7, 20)] = 'Alice'
continuum.add_annotation('liza', annotation)
annotation = Annotation()
annotation[Segment(2, 6)] = 'Carol'
annotation[Segment(7, 8)] = 'Bob'
annotation[Segment(12, 18)] = 'Alice'
annotation[Segment(8, 10)] = 'Alice'
annotation[Segment(7, 19)] = 'Jeremy'
continuum.add_annotation('pierrot', annotation)
pos_dis = PositionalDissimilarity(delta_empty=0.5)
list_dis = []
for liza_unit in continuum['liza']:
for pierrot_unit in continuum['pierrot']:
unit_alignment = UnitaryAlignment(
(("liza", liza_unit), ("pierrot", pierrot_unit)))
list_dis.append(unit_alignment.compute_disorder(pos_dis))
assert list_dis == pytest.approx([
0.03125, 1.62, 0.78125, 2.0, 2.88, 0.5, 0.05555555555555555,
0.36734693877551017, 0.5, 2.0, 0.6245674740484429, 0.36734693877551017,
0.0008, 0.26888888888888884, 0.06786703601108032, 2.4200000000000004,
2.2959183673469385, 0.05555555555555555, 1.125, 0.0
], 0.001)
liza_unit = Unit(Segment(45, 77), "")
pierrot_unit = Unit(Segment(16, 64), "")
unit_alignment_a = UnitaryAlignment(
(("liza", liza_unit), ("pierrot", pierrot_unit)))
unit_alignment_b = UnitaryAlignment(
(("pierrot", pierrot_unit), ("liza", liza_unit)))
assert (unit_alignment_a.compute_disorder(pos_dis) ==
unit_alignment_b.compute_disorder(pos_dis))
unit_alignment = UnitaryAlignment(
(("liza", liza_unit), ("pierrot", liza_unit)))
assert unit_alignment.compute_disorder(pos_dis) == np.float32(0.0)
def test_categorical_dissimilarity():
categories = ['A', 'B', 'C', 'D']
cat = np.array([[0, 0.5, 0.3, 0.7], [0.5, 0., 0.6, 0.4],
[0.3, 0.6, 0., 0.7], [0.7, 0.4, 0.7, 0.]])
cat_dis = CombinedCategoricalDissimilarity(categories=categories,
cat_dissimilarity_matrix=cat,
delta_empty=0.5,
alpha=0.0,
beta=1.0)
fake_seg = Segment(0, 1)
unit_alignment = UnitaryAlignment(
(("Carol", Unit(fake_seg, "A")), ("John", Unit(fake_seg, "D"))))
assert unit_alignment.compute_disorder(cat_dis) == np.float32(0.35)
unit_alignment = UnitaryAlignment(
(("Carol", Unit(fake_seg, "A")), ("John", Unit(fake_seg, "A"))))
assert unit_alignment.compute_disorder(cat_dis) == np.float32(0.0)
unit_alignment_a = UnitaryAlignment(
(("Carol", Unit(fake_seg, "A")), ("John", Unit(fake_seg, "B"))))
unit_alignment_b = UnitaryAlignment(
(("Carol", Unit(fake_seg, "B")), ("John", Unit(fake_seg, "A"))))
assert (unit_alignment_a.compute_disorder(cat_dis) ==
unit_alignment_b.compute_disorder(cat_dis))
def test_combi_categorical_dissimilarity():
continuum = Continuum()
annotation = Annotation()
annotation[Segment(1, 5)] = 'Carol'
annotation[Segment(6, 8)] = 'Bob'
annotation[Segment(12, 18)] = 'Carol'
annotation[Segment(7, 20)] = 'Alice'
continuum.add_annotation('liza', annotation)
annotation = Annotation()
annotation[Segment(2, 6)] = 'Carol'
annotation[Segment(7, 8)] = 'Bob'
annotation[Segment(12, 18)] = 'Alice'
annotation[Segment(8, 10)] = 'Alice'
annotation[Segment(7, 19)] = 'Jeremy'
continuum.add_annotation('pierrot', annotation)
categories = ['Carol', 'Bob', 'Alice', 'Jeremy']
cat = np.array([[0, 0.5, 0.3, 0.7], [0.5, 0., 0.6, 0.4],
[0.3, 0.6, 0., 0.7], [0.7, 0.4, 0.7, 0.]])
combi_dis = CombinedCategoricalDissimilarity(categories=categories,
delta_empty=0.5,
cat_dissimilarity_matrix=cat,
alpha=3,
beta=1)
list_dis = []
for liza_unit in continuum['liza']:
for pierrot_unit in continuum['pierrot']:
unit_alignment = UnitaryAlignment(
(("liza", liza_unit), ("pierrot", pierrot_unit)))
list_dis.append(unit_alignment.compute_disorder(combi_dis))
print(len(list_dis))
assert list_dis == pytest.approx([
0.09375, 5.11, 2.69375, 6.15, 8.790000000000001, 1.75,
0.16666666666666666, 1.3020408163265305, 1.8, 6.3, 2.0237024221453286,
1.4020408163265305, 0.3524, 0.8066666666666665, 0.20360110803324097,
7.260000000000002, 7.137755102040815, 0.5166666666666666, 3.525, 0.15
], 0.001)
unit_align_a = UnitaryAlignment(
(("liza", Unit(Segment(1, 5),
"Carol")), ("pierrot", Unit(Segment(7, 19), "Jeremy"))))
unit_align_b = UnitaryAlignment((
("pierrot", Unit(Segment(7, 19), "Jeremy")),
("liza", Unit(Segment(1, 5), "Carol")),
))
assert (unit_align_a.compute_disorder(combi_dis) ==
unit_align_b.compute_disorder(combi_dis))
same_align = UnitaryAlignment(
(("liza", Unit(Segment(1, 5),
"Carol")), ("pierrot", Unit(Segment(1, 5), "Carol"))))
assert same_align.compute_disorder(combi_dis) == np.float32(0.0)