def test_fingerprint_similarity_parallel(test_method, expected_score):
"""Test score matrix with known values for the provided methods."""
spectrum0 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={})
fingerprint1 = numpy.array(
[0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0])
spectrum1 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={"fingerprint": fingerprint1})
fingerprint2 = numpy.array(
[0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1])
spectrum2 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={"fingerprint": fingerprint2})
similarity_measure = FingerprintSimilarity(similarity_measure=test_method)
score_matrix = similarity_measure.matrix([spectrum0, spectrum1, spectrum2],
[spectrum0, spectrum1, spectrum2])
expected_matrix = numpy.array([[1., expected_score], [expected_score, 1.]])
assert score_matrix[1:, 1:] == pytest.approx(
expected_matrix, 0.001), "Expected different values."
assert numpy.all(numpy.isnan(score_matrix[:,
0])), "Expected 'nan' entries."
assert numpy.all(numpy.isnan(
score_matrix[0, :])), "Expected 'nan' entries."
def test_fingerprint_similarity_parallel_cosine_set_empty_to_0():
"""Test cosine score matrix with known values. Set non-exising values to 0."""
spectrum0 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={})
fingerprint1 = numpy.array(
[0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0])
spectrum1 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={"fingerprint": fingerprint1})
fingerprint2 = numpy.array(
[0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1])
spectrum2 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={"fingerprint": fingerprint2})
similarity_measure = FingerprintSimilarity(set_empty_scores=0,
similarity_measure="cosine")
score_matrix = similarity_measure.matrix([spectrum0, spectrum1, spectrum2],
[spectrum0, spectrum1, spectrum2])
assert score_matrix == pytest.approx(
numpy.array([[0, 0, 0], [0, 1., 0.84515425], [0, 0.84515425, 1.]]),
0.001), "Expected different values."
def test_fingerprint_similarity_with_scores_sorting():
"""Test if score works with Scores.scores_by_query and sorting."""
spectrum0 = Spectrum(mz=numpy.array([100.0, 101.0], dtype="float"),
intensities=numpy.array([0.4, 0.5], dtype="float"),
metadata={})
fingerprint1 = numpy.array(
[0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0])
spectrum1 = Spectrum(mz=numpy.array([100.0, 101.0], dtype="float"),
intensities=numpy.array([0.4, 0.5], dtype="float"),
metadata={"fingerprint": fingerprint1})
fingerprint2 = numpy.array(
[0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1])
spectrum2 = Spectrum(mz=numpy.array([100.0, 101.0], dtype="float"),
intensities=numpy.array([0.4, 0.5], dtype="float"),
metadata={"fingerprint": fingerprint2})
similarity_measure = FingerprintSimilarity(set_empty_scores=0,
similarity_measure="cosine")
scores = calculate_scores([spectrum0, spectrum1, spectrum2],
[spectrum0, spectrum1, spectrum2],
similarity_measure)
scores_by_ref_sorted = scores.scores_by_query(spectrum1, sort=True)
expected_scores = numpy.array([1.0, 0.84515425, 0.0])
assert numpy.allclose(numpy.array([x[1] for x in scores_by_ref_sorted]), expected_scores, atol=1e-6), \
"Expected different scores and/or order."
def create_dummy_scores_symmetric():
spectrums = create_dummy_spectrums()
# Create Scores object by calculating dice scores
similarity_measure = FingerprintSimilarity("dice")
scores = calculate_scores(spectrums, spectrums, similarity_measure)
return scores
def test_fingerprint_similarity_pair_calculations(test_method, expected_score):
"""Test cosine score pair with two fingerprint."""
fingerprint1 = numpy.array(
[1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0])
spectrum1 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={"fingerprint": fingerprint1})
fingerprint2 = numpy.array(
[0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1])
spectrum2 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={"fingerprint": fingerprint2})
similarity_measure = FingerprintSimilarity(similarity_measure=test_method)
score_pair = similarity_measure.pair(spectrum1, spectrum2)
assert score_pair == pytest.approx(expected_score,
1e-6), "Expected different score."
def create_dummy_scores():
spectrums = create_dummy_spectrums()
references = spectrums[:5]
queries = spectrums[5:]
# Create Scores object by calculating dice scores
similarity_measure = FingerprintSimilarity("dice")
scores = calculate_scores(references, queries, similarity_measure)
return scores
def test_fingerprint_similarity_parallel_empty_fingerprint(test_method):
"""Test score matrix with empty fingerprint using the provided methods."""
fingerprint1 = numpy.array(
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
spectrum1 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={"fingerprint": fingerprint1})
fingerprint2 = numpy.array(
[0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1])
spectrum2 = Spectrum(mz=numpy.array([], dtype="float"),
intensities=numpy.array([], dtype="float"),
metadata={"fingerprint": fingerprint2})
similarity_measure = FingerprintSimilarity(similarity_measure=test_method)
score_matrix = similarity_measure.matrix([spectrum1, spectrum2],
[spectrum1, spectrum2])
assert score_matrix == pytest.approx(numpy.array([[0, 0], [0, 1.]]),
0.001), "Expected different values."