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Remove deprecation from previous release #689

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Merged
charavelg merged 1 commit into from
Jul 22, 2026
+99 −1,906
Merged

Remove deprecation from previous release #689

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248 changes: 83 additions & 165 deletions tests/test_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,18 +30,10 @@ def test_dtw_accumulated_matrix():
s1 = cast([1, 2, 3], array_type)
s2 = cast([1.0, 2.0, 2.0, 3.0], array_type)
mask = tslearn.metrics.compute_mask(s1, s2, be=be)
matrix_1 = tslearn.metrics.dtw_accumulated_matrix(s1, s2, mask, be=be)
with pytest.deprecated_call():
matrix_2 = tslearn.metrics.dtw_variants.accumulated_matrix(
to_time_series(s1),
to_time_series(s2),
tslearn.metrics.compute_mask(s1, s2, be=be),
be=be
)
matrix = tslearn.metrics.dtw_accumulated_matrix(s1, s2, mask, be=be)
expected = backend.array([[0.0, 1.0, 2.0, 6.0], [1.0, 0.0, 0.0, 1.0], [5.0, 1.0, 1.0, 0.0]])
for matrix in [matrix_1, matrix_2]:
assert backend.belongs_to_backend(matrix)
np.testing.assert_array_equal(matrix, expected)
assert backend.belongs_to_backend(matrix)
np.testing.assert_array_equal(matrix, expected)


def test_dtw():
Expand Down Expand Up @@ -69,24 +61,10 @@ def test_dtw():
if not backend.is_numpy:
assert backend.belongs_to_backend(dist)

with pytest.deprecated_call():
deprecated_path, deprecated_dist = tslearn.metrics.dtw_variants.dtw_path(
cast([1, 2, 3], array_type),
cast([1.0, 2.0, 2.0, 3.0, 4.0], array_type),
be=be
)
assert deprecated_path == path
assert deprecated_dist == dist

with pytest.raises(ValueError):
tslearn.metrics.dtw_path([], [], be=be)
with pytest.raises(ValueError):
tslearn.metrics.dtw_path([1], [[1, 2]], be=be)
with warnings.catch_warnings():
with pytest.raises(ValueError):
tslearn.metrics.dtw_variants.dtw_path([], [], be=be)
with pytest.raises(ValueError):
tslearn.metrics.dtw_variants.dtw_path([1], [[1, 2]], be=be)

# dtw
n1, n2, d = 15, 10, 3
Expand All @@ -103,23 +81,10 @@ def test_dtw():
wrong_feature_size_y = cast(np.random.randn(n2, 2), array_type)
tslearn.metrics.dtw(x, wrong_feature_size_y, be=be)

with pytest.deprecated_call():
deprecated_dist = tslearn.metrics.dtw_variants.dtw(
cast([1, 2, 3], array_type),
cast([1.0, 2.0, 2.0, 3.0, 4.0], array_type),
be=be
)
assert deprecated_dist == dist

with pytest.raises(ValueError):
tslearn.metrics.dtw([], [], be=be)
with pytest.raises(ValueError):
tslearn.metrics.dtw([1], [[1, 2]], be=be)
with warnings.catch_warnings():
with pytest.raises(ValueError):
tslearn.metrics.dtw_variants.dtw([], [], be=be)
with pytest.raises(ValueError):
tslearn.metrics.dtw_variants.dtw([1], [[1, 2]], be=be)

# cdist_dtw
dists = tslearn.metrics.cdist_dtw(
Expand All @@ -146,15 +111,6 @@ def test_dtw():
np.testing.assert_array_equal(dists, parallel_dists)
assert backend.belongs_to_backend(parallel_dists)

with pytest.deprecated_call():
deprecated_dists = tslearn.metrics.dtw_variants.cdist_dtw(
cast([[1, 2, 2, 3], [1.0, 2.0, 3.0, 4.0]], array_type),
[[1, 2, 3], [2, 3, 4, 5]], # The second dataset can not be cast to array because of its shape
be=be
)
np.testing.assert_allclose(deprecated_dists, dists)
assert backend.belongs_to_backend(deprecated_dists)


def test_ctw():
for be in backends:
Expand Down Expand Up @@ -431,97 +387,90 @@ def test_masks():
for array_type in array_types:
backend = instantiate_backend(be)

for sakoe_chiba_mask in [tslearn.metrics.sakoe_chiba_mask,
tslearn.metrics.dtw_variants.sakoe_chiba_mask]:
sk_mask = tslearn.metrics.sakoe_chiba_mask(4, 4, 1, be=be)
reference_mask = np.array(
[
[True, True, False, False],
[True, True, True, False],
[False, True, True, True],
[False, False, True, True],
]
)
np.testing.assert_allclose(sk_mask, reference_mask)
assert backend.belongs_to_backend(sk_mask)

sk_mask = tslearn.metrics.sakoe_chiba_mask(7, 3, 1, be=be)
reference_mask = np.array(
[
[True, True, False],
[True, True, True],
[True, True, True],
[True, True, True],
[True, True, True],
[True, True, True],
[False, True, True],
]
)
np.testing.assert_allclose(sk_mask, reference_mask)
assert backend.belongs_to_backend(sk_mask)

i_mask = tslearn.metrics.itakura_mask(6, 6, be=be)
reference_mask = np.array(
[
[True, False, False, False, False, False],
[False, True, True, False, False, False],
[False, True, True, True, False, False],
[False, False, True, True, True, False],
[False, False, False, True, True, False],
[False, False, False, False, False, True],
]
)
np.testing.assert_allclose(i_mask, reference_mask)
assert backend.belongs_to_backend(i_mask)

sk_mask = sakoe_chiba_mask(4, 4, 1, be=be)
reference_mask = np.array(
[
[True, True, False, False],
[True, True, True, False],
[False, True, True, True],
[False, False, True, True],
]
)
np.testing.assert_allclose(sk_mask, reference_mask)
assert backend.belongs_to_backend(sk_mask)
backend = instantiate_backend(be, array_type)

sk_mask = sakoe_chiba_mask(7, 3, 1, be=be)
reference_mask = np.array(
[
[True, True, False],
[True, True, True],
[True, True, True],
[True, True, True],
[True, True, True],
[True, True, True],
[False, True, True],
]
)
np.testing.assert_allclose(sk_mask, reference_mask)
assert backend.belongs_to_backend(sk_mask)
# Test masks for different combinations of global_constraints /
# sakoe_chiba_radius / itakura_max_slope
sz = 10
mask_no_constraint = tslearn.metrics.compute_mask(sz, sz, be=be)
np.testing.assert_array_equal(mask_no_constraint, np.full((sz, sz), True))

for itakura_mask in [tslearn.metrics.itakura_mask,
tslearn.metrics.dtw_variants.itakura_mask]:
i_mask = itakura_mask(6, 6, be=be)
reference_mask = np.array(
[
[True, False, False, False, False, False],
[False, True, True, False, False, False],
[False, True, True, True, False, False],
[False, False, True, True, True, False],
[False, False, False, True, True, False],
[False, False, False, False, False, True],
]
)
np.testing.assert_allclose(i_mask, reference_mask)
assert backend.belongs_to_backend(i_mask)

for compute_mask in [tslearn.metrics.compute_mask,
tslearn.metrics.dtw_variants.compute_mask]:
backend = instantiate_backend(be, array_type)

# Test masks for different combinations of global_constraints /
# sakoe_chiba_radius / itakura_max_slope
sz = 10
mask_no_constraint = compute_mask(sz, sz, be=be)
np.testing.assert_array_equal(mask_no_constraint, np.full((sz, sz), True))

ts0 = cast(np.empty((sz, 1)), array_type)
ts1 = cast(np.empty((sz, 1)), array_type)
mask_no_constraint = compute_mask(
ts0, ts1, global_constraint=0, be=be
)
np.testing.assert_array_equal(mask_no_constraint, np.full((sz, sz), True))
assert backend.belongs_to_backend(mask_no_constraint)
ts0 = cast(np.empty((sz, 1)), array_type)
ts1 = cast(np.empty((sz, 1)), array_type)
mask_no_constraint = tslearn.metrics.compute_mask(
ts0, ts1, global_constraint=0, be=be
)
np.testing.assert_array_equal(mask_no_constraint, np.full((sz, sz), True))
assert backend.belongs_to_backend(mask_no_constraint)

mask_itakura = compute_mask(
ts0, ts1, global_constraint=1, be=be
)
mask_itakura_bis = compute_mask(
ts0, ts1, itakura_max_slope=2.0, be=be
)
np.testing.assert_allclose(mask_itakura, mask_itakura_bis)
assert backend.belongs_to_backend(mask_itakura)
mask_itakura = tslearn.metrics.compute_mask(
ts0, ts1, global_constraint=1, be=be
)
mask_itakura_bis = tslearn.metrics.compute_mask(
ts0, ts1, itakura_max_slope=2.0, be=be
)
np.testing.assert_allclose(mask_itakura, mask_itakura_bis)
assert backend.belongs_to_backend(mask_itakura)

mask_sakoe = compute_mask(
ts0, ts1, global_constraint=2, be=be
)
mask_sakoe_bis = compute_mask(
ts0, ts1, sakoe_chiba_radius=1, be=be
)
np.testing.assert_allclose(mask_sakoe, mask_sakoe_bis)
assert backend.belongs_to_backend(mask_sakoe)

np.testing.assert_raises(
RuntimeWarning,
compute_mask,
ts0,
ts1,
sakoe_chiba_radius=1,
itakura_max_slope=2.0,
be=be,
)
mask_sakoe = tslearn.metrics.compute_mask(
ts0, ts1, global_constraint=2, be=be
)
mask_sakoe_bis = tslearn.metrics.compute_mask(
ts0, ts1, sakoe_chiba_radius=1, be=be
)
np.testing.assert_allclose(mask_sakoe, mask_sakoe_bis)
assert backend.belongs_to_backend(mask_sakoe)

np.testing.assert_raises(
RuntimeWarning,
tslearn.metrics.compute_mask,
ts0,
ts1,
sakoe_chiba_radius=1,
itakura_max_slope=2.0,
be=be,
)

# Tests for estimators that can set masks through metric_params
n, sz, d = 15, 10, 3
Expand Down Expand Up @@ -580,34 +529,14 @@ def test_gak():
be=be
)
np.testing.assert_allclose(s, 2.0, atol=1e-5)
with pytest.deprecated_call():
backend.testing.assert_allclose(
s,
tslearn.metrics.softdtw_variants.sigma_gak(
dataset,
n_samples=200,
random_state=0,
be=be
)
)

# GAK
s1, s2 = cast([1, 2, 2, 3], array_type), cast([1.0, 2.0, 3.0, 4.0], array_type)
g = tslearn.metrics.gak(s1, s2, sigma=2, be=be)
np.testing.assert_allclose(g, 0.656297, atol=1e-5)
with pytest.deprecated_call():
backend.testing.assert_allclose(
g,
tslearn.metrics.softdtw_variants.gak(s1, s2, sigma=2, be=be)
)

g = tslearn.metrics.unnormalized_gak(s1, s2, be=be)
np.testing.assert_allclose(g, 3.745675, atol=1e-5)
with pytest.deprecated_call():
backend.testing.assert_allclose(
g,
tslearn.metrics.softdtw_variants.unnormalized_gak(s1, s2, be=be)
)

g = tslearn.metrics.cdist_gak(
dataset, sigma=2.0, be=be
Expand All @@ -616,11 +545,7 @@ def test_gak():
g, np.array([[1.0, 0.656297], [0.656297, 1.0]]), atol=1e-5
)
assert backend.belongs_to_backend(g)
with pytest.deprecated_call():
backend.testing.assert_allclose(
g,
tslearn.metrics.softdtw_variants.cdist_gak(dataset, sigma=2, be=be)
)

g = tslearn.metrics.cdist_gak(
[[1, 2, 2], [1.0, 2.0, 3.0, 4.0]], # Can not be cast to array because of its shape
cast([[1, 2, 2, 3], [1.0, 2.0, 3.0, 4.0]], array_type),
Expand Down Expand Up @@ -744,13 +669,6 @@ def test_dtw_path_from_metric():
# Use dtw_path as a reference
path_ref, dist_ref = tslearn.metrics.dtw_path(s1, s2, be=be)

with pytest.deprecated_call():
deprecated_path, deprecated_dist = tslearn.metrics.dtw_variants.dtw_path_from_metric(
s1, s2, metric="sqeuclidean", be=be
)
np.testing.assert_equal(path_ref, deprecated_path)
np.testing.assert_allclose(backend.sqrt(deprecated_dist), dist_ref)

# Test of using a scipy distance function
path, dist = tslearn.metrics.dtw_path_from_metric(
s1, s2, metric="sqeuclidean", be=be
Expand Down
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