def test_neighboring_finds_neighbor_alignments(self):
# arrange
a_info = AlignmentInfo((0, 3, 2), (None, 'des', 'œufs', 'verts'),
('UNUSED', 'green', 'eggs'), [[], [], [2], [1]])
ibm_model = IBMModel([])
# act
neighbors = ibm_model.neighboring(a_info)
# assert
neighbor_alignments = set()
for neighbor in neighbors:
neighbor_alignments.add(neighbor.alignment)
expected_alignments = set([
# moves
(0, 0, 2),
(0, 1, 2),
(0, 2, 2),
(0, 3, 0),
(0, 3, 1),
(0, 3, 3),
# swaps
(0, 2, 3),
# original alignment
(0, 3, 2)
])
self.assertEqual(neighbor_alignments, expected_alignments)
def test_neighboring_returns_neighbors_with_pegged_alignment(self):
# arrange
a_info = AlignmentInfo(
(0, 3, 2),
(None, 'des', 'œufs', 'verts'),
('UNUSED', 'green', 'eggs'),
[[], [], [2], [1]]
)
ibm_model = IBMModel([])
# act: peg 'eggs' to align with 'œufs'
neighbors = ibm_model.neighboring(a_info, 2)
# assert
neighbor_alignments = set()
for neighbor in neighbors:
neighbor_alignments.add(neighbor.alignment)
expected_alignments = set([
# moves
(0, 0, 2), (0, 1, 2), (0, 2, 2),
# no swaps
# original alignment
(0, 3, 2)
])
self.assertEqual(neighbor_alignments, expected_alignments)
def test_neighboring_finds_neighbor_alignments(self):
# arrange
a_info = AlignmentInfo(
(0, 3, 2),
(None, 'des', 'œufs', 'verts'),
('UNUSED', 'green', 'eggs'),
[[], [], [2], [1]]
)
ibm_model = IBMModel([])
# act
neighbors = ibm_model.neighboring(a_info)
# assert
neighbor_alignments = set()
for neighbor in neighbors:
neighbor_alignments.add(neighbor.alignment)
expected_alignments = set([
# moves
(0, 0, 2), (0, 1, 2), (0, 2, 2),
(0, 3, 0), (0, 3, 1), (0, 3, 3),
# swaps
(0, 2, 3),
# original alignment
(0, 3, 2)
])
self.assertEqual(neighbor_alignments, expected_alignments)
def test_neighboring_sets_neighbor_alignment_info(self):
# arrange
a_info = AlignmentInfo((0, 3, 2), (None, 'des', 'œufs', 'verts'),
('UNUSED', 'green', 'eggs'), [[], [], [2], [1]])
ibm_model = IBMModel([])
# act
neighbors = ibm_model.neighboring(a_info)
# assert: select a few particular alignments
for neighbor in neighbors:
if neighbor.alignment == (0, 2, 2):
moved_alignment = neighbor
elif neighbor.alignment == (0, 3, 2):
swapped_alignment = neighbor
self.assertEqual(moved_alignment.cepts, [[], [], [1, 2], []])
self.assertEqual(swapped_alignment.cepts, [[], [], [2], [1]])
def test_neighboring_sets_neighbor_alignment_info(self):
# arrange
a_info = AlignmentInfo(
(0, 3, 2),
(None, 'des', 'œufs', 'verts'),
('UNUSED', 'green', 'eggs'),
[[], [], [2], [1]]
)
ibm_model = IBMModel([])
# act
neighbors = ibm_model.neighboring(a_info)
# assert: select a few particular alignments
for neighbor in neighbors:
if neighbor.alignment == (0, 2, 2):
moved_alignment = neighbor
elif neighbor.alignment == (0, 3, 2):
swapped_alignment = neighbor
self.assertEqual(moved_alignment.cepts, [[], [], [1, 2], []])
self.assertEqual(swapped_alignment.cepts, [[], [], [2], [1]])
def test_neighboring_returns_neighbors_with_pegged_alignment(self):
# arrange
a_info = AlignmentInfo((0, 3, 2), (None, 'des', 'œufs', 'verts'),
('UNUSED', 'green', 'eggs'), [[], [], [2], [1]])
ibm_model = IBMModel([])
# act: peg 'eggs' to align with 'œufs'
neighbors = ibm_model.neighboring(a_info, 2)
# assert
neighbor_alignments = set()
for neighbor in neighbors:
neighbor_alignments.add(neighbor.alignment)
expected_alignments = set([
# moves
(0, 0, 2),
(0, 1, 2),
(0, 2, 2),
# no swaps
# original alignment
(0, 3, 2)
])
self.assertEqual(neighbor_alignments, expected_alignments)
def test_hillclimb(self):
# arrange
initial_alignment = AlignmentInfo((0, 3, 2), None, None, None)
def neighboring_mock(a, j):
if a.alignment == (0, 3, 2):
return set([
AlignmentInfo((0, 2, 2), None, None, None),
AlignmentInfo((0, 1, 1), None, None, None)
])
elif a.alignment == (0, 2, 2):
return set([
AlignmentInfo((0, 3, 3), None, None, None),
AlignmentInfo((0, 4, 4), None, None, None)
])
return set()
def prob_t_a_given_s_mock(a):
prob_values = {
(0, 3, 2): 0.5,
(0, 2, 2): 0.6,
(0, 1, 1): 0.4,
(0, 3, 3): 0.6,
(0, 4, 4): 0.7
}
return prob_values.get(a.alignment, 0.01)
ibm_model = IBMModel([])
ibm_model.neighboring = neighboring_mock
ibm_model.prob_t_a_given_s = prob_t_a_given_s_mock
# act
best_alignment = ibm_model.hillclimb(initial_alignment)
# assert: hill climbing goes from (0, 3, 2) -> (0, 2, 2) -> (0, 4, 4)
self.assertEqual(best_alignment.alignment, (0, 4, 4))
def test_hillclimb(self):
# arrange
initial_alignment = AlignmentInfo((0, 3, 2), None, None, None)
def neighboring_mock(a, j):
if a.alignment == (0, 3, 2):
return set([
AlignmentInfo((0, 2, 2), None, None, None),
AlignmentInfo((0, 1, 1), None, None, None)
])
elif a.alignment == (0, 2, 2):
return set([
AlignmentInfo((0, 3, 3), None, None, None),
AlignmentInfo((0, 4, 4), None, None, None)
])
return set()
def prob_t_a_given_s_mock(a):
prob_values = {
(0, 3, 2): 0.5,
(0, 2, 2): 0.6,
(0, 1, 1): 0.4,
(0, 3, 3): 0.6,
(0, 4, 4): 0.7
}
return prob_values.get(a.alignment, 0.01)
ibm_model = IBMModel([])
ibm_model.neighboring = neighboring_mock
ibm_model.prob_t_a_given_s = prob_t_a_given_s_mock
# act
best_alignment = ibm_model.hillclimb(initial_alignment)
# assert: hill climbing goes from (0, 3, 2) -> (0, 2, 2) -> (0, 4, 4)
self.assertEqual(best_alignment.alignment, (0, 4, 4))