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Migrate tests/test_motifs.py to npt.assert_allclose #1202

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Merged
seanlaw merged 1 commit into from
Aug 26, 2026
Merged

Migrate tests/test_motifs.py to npt.assert_allclose #1202

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114 changes: 58 additions & 56 deletions tests/test_motifs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -166,20 +166,20 @@ def test_motifs_one_motif():
m = 3
max_motifs = 1

left_indices = [[0, 5, 9]]
left_profile_values = [[0.0, 0.0, 0.0]]
ref_indices = [[0, 5, 9]]
ref_profile_values = [[0.0, 0.0, 0.0]]

mp = naive.stump(T, m)
right_distance_values, right_indices = motifs(
cmp_distance_values, cmp_indices = motifs(
T,
mp[:, 0],
max_distance=lambda D: 0.001, # Also test lambda functionality
max_motifs=max_motifs,
cutoff=np.inf,
)

npt.assert_array_equal(left_indices, right_indices)
npt.assert_almost_equal(left_profile_values, right_distance_values)
npt.assert_array_equal(cmp_indices, ref_indices)
npt.assert_allclose(cmp_distance_values, ref_profile_values, atol=1.5e-07)


def test_motifs_two_motifs():
Expand Down Expand Up @@ -217,8 +217,8 @@ def test_motifs_two_motifs():

mp = naive.stump(T, m)

# left_indices = [[70, 170, -1], [10, 210, 110]]
left_profile_values = [
# ref_indices = [[70, 170, -1], [10, 210, 110]]
ref_profile_values = [
[0.0, 0.0, np.nan],
[
0.0,
Expand All @@ -227,7 +227,7 @@ def test_motifs_two_motifs():
],
]

right_distance_values, right_indices = motifs(
cmp_distance_values, cmp_indices = motifs(
T,
mp[:, 0],
max_motifs=max_motifs,
Expand All @@ -237,7 +237,7 @@ def test_motifs_two_motifs():

# We ignore indices because of sorting ambiguities for equal distances.
# As long as the distances are correct, the indices will be too.
npt.assert_almost_equal(left_profile_values, right_distance_values)
npt.assert_allclose(cmp_distance_values, ref_profile_values, atol=1.5e-07)


def test_motifs_max_matches():
Expand Down Expand Up @@ -277,20 +277,20 @@ def test_motifs_max_matches():
max_motifs = 2
max_matches = 3

left_indices = [[0, 7], [4, 11]]
left_profile_values = [
ref_indices = [[0, 7], [4, 11]]
ref_profile_values = [
[0.0, 0.0],
[
0.0,
naive.distance(
core.z_norm(T[left_indices[1][0] : left_indices[1][0] + m]),
core.z_norm(T[left_indices[1][1] : left_indices[1][1] + m]),
core.z_norm(T[ref_indices[1][0] : ref_indices[1][0] + m]),
core.z_norm(T[ref_indices[1][1] : ref_indices[1][1] + m]),
),
],
]

mp = naive.stump(T, m)
right_distance_values, right_indices = motifs(
cmp_distance_values, cmp_indices = motifs(
T,
mp[:, 0],
max_motifs=max_motifs,
Expand All @@ -301,7 +301,7 @@ def test_motifs_max_matches():

# We ignore indices because of sorting ambiguities for equal distances.
# As long as the distances are correct, the indices will be too.
npt.assert_almost_equal(left_profile_values, right_distance_values)
npt.assert_allclose(cmp_distance_values, ref_profile_values, atol=1.5e-07)


def test_motifs_max_matches_max_distances_inf():
Expand Down Expand Up @@ -342,21 +342,21 @@ def test_motifs_max_matches_max_distances_inf():
max_matches = 2
max_distance = np.inf

left_indices = [[0, 7], [4, 11]]
left_profile_values = [
ref_indices = [[0, 7], [4, 11]]
ref_profile_values = [
[0.0, 0.0],
[
0.0,
naive.distance(
core.z_norm(T[left_indices[1][0] : left_indices[1][0] + m]),
core.z_norm(T[left_indices[1][1] : left_indices[1][1] + m]),
core.z_norm(T[ref_indices[1][0] : ref_indices[1][0] + m]),
core.z_norm(T[ref_indices[1][1] : ref_indices[1][1] + m]),
),
],
]

# set `row_wise` to True so that we can compare the indices of motifs as well
mp = naive.stump(T, m, row_wise=True)
right_distance_values, right_indices = motifs(
cmp_distance_values, cmp_indices = motifs(
T,
mp[:, 0],
max_motifs=max_motifs,
Expand All @@ -365,8 +365,8 @@ def test_motifs_max_matches_max_distances_inf():
max_matches=max_matches,
)

npt.assert_almost_equal(left_indices, right_indices)
npt.assert_almost_equal(left_profile_values, right_distance_values)
npt.assert_allclose(cmp_indices, ref_indices, atol=1.5e-07)
npt.assert_allclose(cmp_distance_values, ref_profile_values, atol=1.5e-07)


def test_naive_match_exclusion_zone():
Expand All @@ -378,12 +378,12 @@ def test_naive_match_exclusion_zone():
m = Q.shape[0]
excl_zone = int(np.ceil(m / 4))

left = [
ref = [
[0, 1],
[naive.distance(core.z_norm(Q), core.z_norm(T[5 : 5 + m])), 5],
[naive.distance(core.z_norm(Q), core.z_norm(T[9 : 9 + m])), 9],
]
right = list(
cmp = list(
naive_match(
Q,
T,
Expand All @@ -392,9 +392,11 @@ def test_naive_match_exclusion_zone():
)
)
# To avoid sorting errors we first sort based on distance and then based on indices
right.sort(key=lambda x: (x[1], x[0]))
cmp.sort(key=lambda x: (x[1], x[0]))

npt.assert_almost_equal(left, right)
npt.assert_allclose(
np.array(cmp).astype(np.float64), np.array(ref).astype(np.float64), atol=1.5e-07
)


@pytest.mark.parametrize("Q, T", test_data)
Expand All @@ -403,21 +405,21 @@ def test_match(Q, T):
excl_zone = int(np.ceil(m / 4))
max_distance = 0.3

left = naive_match(
ref = naive_match(
Q,
T,
excl_zone,
max_distance=max_distance,
)

right = match(
cmp = match(
Q,
T,
max_matches=None,
max_distance=lambda D: max_distance, # also test lambda functionality
)

npt.assert_almost_equal(left, right)
npt.assert_allclose(cmp.astype(np.float64), ref.astype(np.float64), atol=1.5e-07)


@pytest.mark.parametrize("Q, T", test_data)
Expand All @@ -426,7 +428,7 @@ def test_match_mean_stddev(Q, T):
excl_zone = int(np.ceil(m / 4))
max_distance = 0.3

left = naive_match(
ref = naive_match(
Q,
T,
excl_zone,
Expand All @@ -435,7 +437,7 @@ def test_match_mean_stddev(Q, T):

M_T, Σ_T = naive.compute_mean_std(T, len(Q))

right = match(
cmp = match(
Q,
T,
M_T,
Expand All @@ -444,7 +446,7 @@ def test_match_mean_stddev(Q, T):
max_distance=lambda D: max_distance, # also test lambda functionality
)

npt.assert_almost_equal(left, right)
npt.assert_allclose(cmp.astype(np.float64), ref.astype(np.float64), atol=1.5e-07)


@pytest.mark.parametrize("Q, T", test_data)
Expand All @@ -457,28 +459,28 @@ def test_match_isconstant(Q, T):
naive.isconstant_func_stddev_threshold, quantile_threshold=0.05
)

left = naive_match(
ref = naive_match(
Q,
T,
excl_zone,
max_distance=max_distance,
T_subseq_isconstant=T_subseq_isconstant,
)

right = match(
cmp = match(
Q,
T,
max_matches=None,
max_distance=lambda D: max_distance, # also test lambda functionality
T_subseq_isconstant=T_subseq_isconstant,
)

npt.assert_almost_equal(left, right)
npt.assert_allclose(cmp.astype(np.float64), ref.astype(np.float64), atol=1.5e-07)

# Test for when Q is constant
Q_subseq_isconstant = np.array([True])

left = naive_match(
ref = naive_match(
Q,
T,
excl_zone,
Expand All @@ -487,7 +489,7 @@ def test_match_isconstant(Q, T):
Q_subseq_isconstant=Q_subseq_isconstant,
)

right = match(
cmp = match(
Q,
T,
max_matches=None,
Expand All @@ -496,7 +498,7 @@ def test_match_isconstant(Q, T):
Q_subseq_isconstant=Q_subseq_isconstant,
)

npt.assert_almost_equal(left, right)
npt.assert_allclose(cmp.astype(np.float64), ref.astype(np.float64), atol=1.5e-07)


@pytest.mark.parametrize("Q, T", test_data)
Expand All @@ -505,7 +507,7 @@ def test_match_mean_stddev_isconstant(Q, T):
excl_zone = int(np.ceil(m / 4))
max_distance = 0.3

left = naive_match(
ref = naive_match(
Q,
T,
excl_zone,
Expand All @@ -515,7 +517,7 @@ def test_match_mean_stddev_isconstant(Q, T):
T_subseq_isconstant = naive.rolling_isconstant(T, m)
M_T, Σ_T = naive.compute_mean_std(T, len(Q))

right = match(
cmp = match(
Q,
T,
M_T,
Expand All @@ -525,7 +527,7 @@ def test_match_mean_stddev_isconstant(Q, T):
T_subseq_isconstant=T_subseq_isconstant,
)

npt.assert_almost_equal(left, right)
npt.assert_allclose(cmp.astype(np.float64), ref.astype(np.float64), atol=1.5e-07)


def test_multi_match():
Expand All @@ -536,21 +538,21 @@ def test_multi_match():
excl_zone = int(np.ceil(m / 4))
max_distance = 0.3

left = naive_multi_match(
ref = naive_multi_match(
Q,
T,
excl_zone,
max_distance=max_distance,
)

right = match(
cmp = match(
Q,
T,
max_matches=None,
max_distance=lambda D: max_distance, # also test lambda functionality
)

npt.assert_almost_equal(left, right)
npt.assert_allclose(cmp.astype(np.float64), ref.astype(np.float64), atol=1.5e-07)


def test_multi_match_isconstant():
Expand All @@ -572,7 +574,7 @@ def test_multi_match_isconstant():
]
)

left = naive_multi_match(
ref = naive_multi_match(
Q,
T,
excl_zone,
Expand All @@ -581,7 +583,7 @@ def test_multi_match_isconstant():
Q_subseq_isconstant=Q_subseq_isconstant,
)

right = match(
cmp = match(
Q,
T,
max_matches=None,
Expand All @@ -590,7 +592,7 @@ def test_multi_match_isconstant():
Q_subseq_isconstant=Q_subseq_isconstant,
)

npt.assert_almost_equal(left, right)
npt.assert_allclose(cmp.astype(np.float64), ref.astype(np.float64), atol=1.5e-07)


def test_motifs():
Expand All @@ -608,7 +610,7 @@ def test_motifs():

# performant
mp = naive.stump(T, m, row_wise=True)
comp_distance, comp_indices = motifs(
cmp_distance, cmp_indices = motifs(
T,
mp[:, 0].astype(np.float64),
min_neighbors=1,
Expand All @@ -618,8 +620,8 @@ def test_motifs():
max_motifs=max_motifs,
)

npt.assert_almost_equal(ref_indices, comp_indices)
npt.assert_almost_equal(ref_distances, comp_distance)
npt.assert_allclose(cmp_indices, ref_indices, atol=1.5e-07)
npt.assert_allclose(cmp_distance, ref_distances, atol=1.5e-07)


def test_motifs_with_isconstant():
Expand All @@ -643,7 +645,7 @@ def test_motifs_with_isconstant():

# performant
mp = naive.stump(T, m, row_wise=True, T_A_subseq_isconstant=isconstant_custom_func)
comp_distance, comp_indices = motifs(
cmp_distance, cmp_indices = motifs(
T,
mp[:, 0].astype(np.float64),
min_neighbors=1,
Expand All @@ -654,8 +656,8 @@ def test_motifs_with_isconstant():
T_subseq_isconstant=isconstant_custom_func,
)

npt.assert_almost_equal(ref_distances, comp_distance)
npt.assert_almost_equal(ref_indices, comp_indices)
npt.assert_allclose(cmp_distance, ref_distances, atol=1.5e-07)
npt.assert_allclose(cmp_indices, ref_indices, atol=1.5e-07)


def test_motifs_with_max_matches_none():
Expand All @@ -669,7 +671,7 @@ def test_motifs_with_max_matches_none():

# performant
mp = naive.stump(T, m, row_wise=True)
comp_distance, comp_indices = motifs(
cmp_distance, cmp_indices = motifs(
T,
mp[:, 0].astype(np.float64),
min_neighbors=1,
Expand All @@ -681,5 +683,5 @@ def test_motifs_with_max_matches_none():

ref_len = len(T) - m + 1

npt.assert_(ref_len >= comp_distance.shape[1])
npt.assert_(ref_len >= comp_indices.shape[1])
npt.assert_(ref_len >= cmp_distance.shape[1])
npt.assert_(ref_len >= cmp_indices.shape[1])
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