def test_scores_calculate_parallel():
dummy_similarity_function = DummySimilarityFunctionParallel()
scores = Scores(references=["r0", "r1", "r2"],
queries=["q0", "q1"],
similarity_function=dummy_similarity_function)
scores.calculate()
actual = list(scores)
expected = [("r0", "q0",
numpy.array([("r0q0", 4)],
dtype=dummy_similarity_function.score_datatype)),
("r0", "q1",
numpy.array([("r0q1", 4)],
dtype=dummy_similarity_function.score_datatype)),
("r1", "q0",
numpy.array([("r1q0", 4)],
dtype=dummy_similarity_function.score_datatype)),
("r1", "q1",
numpy.array([("r1q1", 4)],
dtype=dummy_similarity_function.score_datatype)),
("r2", "q0",
numpy.array([("r2q0", 4)],
dtype=dummy_similarity_function.score_datatype)),
("r2", "q1",
numpy.array([("r2q1", 4)],
dtype=dummy_similarity_function.score_datatype))]
assert actual == expected, "Expected different scores."
def test_scores_single_pair():
"""Test single pair input."""
dummy_similarity_function = DummySimilarityFunction()
scores = Scores(references=["A"],
queries=["B"],
similarity_function=dummy_similarity_function)
scores.calculate()
actual = scores.scores[0][0]
expected = ('AB', 2)
assert actual == expected, "Expected different scores."
def test_scores_init_with_numpy_array():
dummy_similarity_function = DummySimilarityFunction()
scores = Scores(references=numpy.asarray(["r0", "r1", "r2"]),
queries=numpy.asarray(["q0", "q1"]),
similarity_function=dummy_similarity_function)
assert scores.scores.shape == (3, 2)
def test_scores_init_with_tuple():
dummy_similarity_function = DummySimilarityFunction()
scores = Scores(references=("r0", "r1", "r2"),
queries=("q0", "q1"),
similarity_function=dummy_similarity_function)
assert scores.scores.shape == (3, 2)
def test_scores_next():
dummy_similarity_function = DummySimilarityFunction()
scores = Scores(references=["r", "rr", "rrr"],
queries=["q", "qq"],
similarity_function=dummy_similarity_function).calculate()
actual = list(scores)
expected = [("r", "q",
numpy.array([("rq", 2)],
dtype=dummy_similarity_function.score_datatype)),
("r", "qq",
numpy.array([("rqq", 3)],
dtype=dummy_similarity_function.score_datatype)),
("rr", "q",
numpy.array([("rrq", 3)],
dtype=dummy_similarity_function.score_datatype)),
("rr", "qq",
numpy.array([("rrqq", 4)],
dtype=dummy_similarity_function.score_datatype)),
("rrr", "q",
numpy.array([("rrrq", 4)],
dtype=dummy_similarity_function.score_datatype)),
("rrr", "qq",
numpy.array([("rrrqq", 5)],
dtype=dummy_similarity_function.score_datatype))]
assert actual == expected, "Expected different scores."
def test_scores_init_with_list():
dummy_similarity_function = DummySimilarityFunction()
scores = Scores(references=["r0", "r1", "r2"],
queries=["q0", "q1"],
similarity_function=dummy_similarity_function)
assert scores.scores.shape == (3, 2), "Expected different scores shape."
def test_scores_calculate():
dummy_similarity_function = DummySimilarityFunction()
scores = Scores(references=["r0", "r1", "r2"],
queries=["q0", "q1"],
similarity_function=dummy_similarity_function)
scores.calculate()
actual = list(scores)
expected = [
("r0", "q0", "r0q0", 4),
("r0", "q1", "r0q1", 4),
("r1", "q0", "r1q0", 4),
("r1", "q1", "r1q1", 4),
("r2", "q0", "r2q0", 4),
("r2", "q1", "r2q1", 4)
]
assert actual == expected, "Expected different scores."
def test_scores_init_with_references_dict():
dummy_similarity_function = DummySimilarityFunction()
with pytest.raises(AssertionError) as msg:
_ = Scores(references=dict(k0="r0", k1="r1", k2="r2"),
queries=["q0", "q1"],
similarity_function=dummy_similarity_function)
assert str(msg.value) == "Expected input argument 'references' to be list or tuple or numpy.ndarray."
def test_scores_next():
dummy_similarity_function = DummySimilarityFunction()
scores = Scores(references=["r", "rr", "rrr"],
queries=["q", "qq"],
similarity_function=dummy_similarity_function).calculate()
actual = list(scores)
expected = [("r", "q", "rq", 2), ("r", "qq", "rqq", 3),
("rr", "q", "rrq", 3), ("rr", "qq", "rrqq", 4),
("rrr", "q", "rrrq", 4), ("rrr", "qq", "rrrqq", 5)]
assert actual == expected
def test_scores_by_query_non_tuple_score():
"Test scores_by_query method."
spectrum_1 = Spectrum(mz=numpy.array([100, 150, 200.]),
intensities=numpy.array([0.7, 0.2, 0.1]),
metadata={'id': 'spectrum1'})
spectrum_2 = Spectrum(mz=numpy.array([100, 140, 190.]),
intensities=numpy.array([0.4, 0.2, 0.1]),
metadata={'id': 'spectrum2'})
spectrum_3 = Spectrum(mz=numpy.array([110, 140, 195.]),
intensities=numpy.array([0.6, 0.2, 0.1]),
metadata={'id': 'spectrum3'})
spectrum_4 = Spectrum(mz=numpy.array([100, 150, 200.]),
intensities=numpy.array([0.6, 0.1, 0.6]),
metadata={'id': 'spectrum4'})
references = [spectrum_1, spectrum_2, spectrum_3]
queries = [spectrum_2, spectrum_3, spectrum_4]
scores = Scores(references, queries, IntersectMz()).calculate()
selected_scores = scores.scores_by_query(spectrum_4)
expected_result = [(scores.references[i], scores.scores[i, 2]) for i in range(3)]
assert selected_scores == expected_result, "Expected different scores."
def test_scores_by_referencey():
"Test scores_by_reference method."
spectrum_1 = Spectrum(mz=numpy.array([100, 150, 200.]),
intensities=numpy.array([0.7, 0.2, 0.1]),
metadata={'id': 'spectrum1'})
spectrum_2 = Spectrum(mz=numpy.array([100, 140, 190.]),
intensities=numpy.array([0.4, 0.2, 0.1]),
metadata={'id': 'spectrum2'})
spectrum_3 = Spectrum(mz=numpy.array([110, 140, 195.]),
intensities=numpy.array([0.6, 0.2, 0.1]),
metadata={'id': 'spectrum3'})
spectrum_4 = Spectrum(mz=numpy.array([100, 150, 200.]),
intensities=numpy.array([0.6, 0.1, 0.6]),
metadata={'id': 'spectrum4'})
references = [spectrum_1, spectrum_2, spectrum_3]
queries = [spectrum_3, spectrum_4]
scores = Scores(references, queries, CosineGreedy()).calculate()
selected_scores = scores.scores_by_reference(spectrum_2)
expected_result = [(scores.queries[i], *scores.scores[1, i]) for i in range(2)]
assert selected_scores == expected_result, "Expected different scores."