def test_triples_empty_edges():
graph = Graph()
pronunciations = [list("wat"), list("what")]
graph.attach(pronunciations)
canonical = ["wa", "wh", "wha", "wat", "hat", "at"]
strings = [
"".join([node.value for node in triple if node]) for triple in graph.triples()
]
assert sorted(canonical) == sorted(strings)
def test_triples():
graph = Graph()
pronunciations = [list("hello"), list("halo")]
graph.attach(pronunciations)
canonical = ["he", "ha", "hel", "hal", "ell", "llo", "alo", "lo"]
strings = [
"".join([node.value for node in triple if node]) for triple in graph.triples()
]
assert sorted(canonical) == sorted(strings)
def create_graph_and_check(canonical, *changed):
graph = Graph()
canonical = deep_phn(canonical)
changed = [deep_phn(it) for it in changed]
for word in canonical:
graph.attach([word])
apply(graph)
canonical = [flatten(canonical), *[flatten(it) for it in changed]]
assert sorted(canonical) == sorted(graph.to_list())
def check_closest_tensor(tensor,
tensor_dict,
pronunciations,
expected_meta={},
**kwargs):
graph = Graph()
graph.attach(pronunciations)
meta = closest(tensor, graph, tensor_dict=tensor_dict, **kwargs)
assert_closest(expected_meta, meta)
def test_attach_multi_different_beginning_and_ending():
graph = Graph()
pronunciations = [list("am"), list("ofi")]
graph.attach(pronunciations)
assert sorted(graph.to_list()) == sorted(pronunciations)
def test_attach_multi_omission():
graph = Graph()
pronunciations = [list("what"), list("wat")]
graph.attach(pronunciations)
assert sorted(graph.to_list()) == sorted(pronunciations)
def test_attach_multi_different_beginning_and_length():
graph = Graph()
pronunciations = [list("wat"), list("hwat")]
graph.attach(pronunciations)
assert sorted(graph.to_list()) == sorted(pronunciations)
def test_attach_multi_three():
graph = Graph()
pronunciations = [list("hi"), list("ho"), list("hu")]
graph.attach(pronunciations)
assert sorted(graph.to_list()) == sorted(pronunciations)
def test_attach_multi_different_ending():
graph = Graph()
pronunciations = [list("hi"), list("ho")]
graph.attach(pronunciations)
assert sorted(graph.to_list()) == sorted(pronunciations)
def test_attach_multi():
graph = Graph()
pronunciations = [list("hello"), list("halo")]
graph.attach(pronunciations)
assert sorted(graph.to_list()) == sorted(pronunciations)
def test_attach_single():
graph = Graph()
pronunciations = [list("hey")]
graph.attach(pronunciations)
assert graph.to_list() == pronunciations
def test_initial_transitions():
graph = Graph()
pronunciations = [list("wat"), list("what")]
graph.attach(pronunciations)
np.testing.assert_equal(graph.initial_transitions, np.array([1, 0.5, 0.5, 0.25]))
def test_transition_matrix():
graph = Graph()
pronunciations = [list("wat"), list("what")]
graph.attach(pronunciations)
canonical = np.array([[1, 1, 1, 0.5], [0, 1, 1, 0.5], [0, 0, 1, 1], [0, 0, 0, 1]])
np.testing.assert_equal(canonical, graph.transition_matrix)
def test_distance_matrix():
graph = Graph()
pronunciations = [list("wat"), list("what")]
graph.attach(pronunciations)
canonical = [[0, 1, 1, 2], [0, 0, 1, 2], [0, 0, 0, 1], [0, 0, 0, 0]]
np.testing.assert_equal(canonical, graph.distance_matrix)
def test_iter():
graph = Graph()
pronunciations = [list("hello"), list("halo")]
graph.attach(pronunciations)
assert sorted([node.value for node in graph.nodes]) == sorted(list("helloa"))
def check_closest_phns(phns, pronunciations, expected_meta):
graph = Graph()
graph.attach(pronunciations)
meta = closest(phns, graph)
assert_closest(expected_meta, meta)