def test_compute_P_ABBA(T_A, T_B):
m = 3
n_A = T_A.shape[0]
n_B = T_B.shape[0]
ref_P_ABBA = np.empty(n_A - m + 1 + n_B - m + 1, dtype=np.float64)
comp_P_ABBA = np.empty(n_A - m + 1 + n_B - m + 1, dtype=np.float64)
ref_P_ABBA[:n_A - m + 1] = naive.stump(T_A, m, T_B)[:, 0]
ref_P_ABBA[n_A - m + 1:] = naive.stump(T_B, m, T_A)[:, 0]
_compute_P_ABBA(T_A, T_B, m, comp_P_ABBA)
npt.assert_almost_equal(ref_P_ABBA, comp_P_ABBA)
def test_motifs_two_motifs():
# Fix seed, because in some case motifs can be off by an index resulting in test
# fails, which is caused since one of the motifs is not repeated perfectly in T.
np.random.seed(1234)
# The time series is random noise with two motifs for m=10:
# * (almost) identical step functions at indices 10, 110 and 210
# * identical linear slopes at indices 70 and 170
T = np.random.normal(size=300)
m = 20
T[10:30] = 1
T[12:28] = 2
T[110:130] = 3
T[112:128] = 6
T[120] = 6.6
T[210:230] = 1
T[212:228] = 2
T[220] = 1.9
# naive.distance(naive.z_norm(T[10:30]), naive.z_norm(T[110:130])) = 0.47
# naive.distance(naive.z_norm(T[10:30]), naive.z_norm(T[210:230])) = 0.24
# naive.distance(naive.z_norm(T[110:130]), naive.z_norm(T[210:230])) = 0.72
# Hence T[10:30] is the motif representative for this motif
T[70:90] = np.arange(m) * 0.1
T[170:190] = np.arange(m) * 0.1
# naive.distance(naive.z_norm(T[70:90]), naive.z_norm(T[170:190])) = 0.0
max_motifs = 2
mp = naive.stump(T, m)
# left_indices = [[70, 170, -1], [10, 210, 110]]
left_profile_values = [
[0.0, 0.0, np.nan],
[
0.0,
naive.distance(core.z_norm(T[10:30]), core.z_norm(T[210:230])),
naive.distance(core.z_norm(T[10:30]), core.z_norm(T[110:130])),
],
]
right_distance_values, right_indices = motifs(
T,
mp[:, 0],
max_motifs=max_motifs,
max_distance=0.5,
cutoff=np.inf,
)
# 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,
decimal=6)
# Reset seed
np.random.seed(None)
def naive_consensus_search(Ts, m):
"""
Brute force consensus motif from
<https://www.cs.ucr.edu/~eamonn/consensus_Motif_ICDM_Long_version.pdf>
See Table 1
Note that there is a bug in the pseudocode at line 8 where `i` should be `j`.
This implementation fixes it.
"""
k = len(Ts)
bsf_radius = np.inf
Ts_idx = 0
subseq_idx = 0
for j in range(k):
radii = np.zeros(len(Ts[j]) - m + 1)
for i in range(k):
if i != j:
mp = naive.stump(Ts[j], m, Ts[i])
radii = np.maximum(radii, mp[:, 0])
min_radius_idx = np.argmin(radii)
min_radius = radii[min_radius_idx]
if min_radius < bsf_radius:
bsf_radius = min_radius
Ts_idx = j
subseq_idx = min_radius_idx
return naive_get_central_motif(Ts, bsf_radius, Ts_idx, subseq_idx, m)
def test_motifs_max_matches():
# This test covers the following:
# A time series contains motif A at four locations and motif B at two.
# If `max_motifs=2` the result should contain only the top two matches of motif A
# and the top two matches of motif B as two separate motifs.
T = np.array([
0.0, # motif A
1.0,
0.0,
2.3,
-1.0, # motif B
-1.0,
-2.0,
0.0, # motif A
1.0,
0.0,
-2.0,
-1.0, # motif B
-1.03,
-2.0,
-0.5,
2.0, # motif A
3.0,
2.04,
2.3,
2.0, # motif A
3.0,
2.02,
])
m = 3
max_motifs = 3
left_indices = [[0, 7], [4, 11]]
left_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]),
),
],
]
mp = naive.stump(T, m)
right_distance_values, right_indices = motifs(
T,
mp[:, 0],
max_motifs=max_motifs,
max_distance=0.1,
cutoff=np.inf,
max_matches=2,
)
# 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,
decimal=4)
def test_stumped_A_B_join(T_A, T_B, dask_cluster):
with Client(dask_cluster) as dask_client:
m = 3
ref_mp = naive.stump(T_A, m, T_B=T_B)
comp_mp = stumped(dask_client, T_A, m, T_B, ignore_trivial=False)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
def test_stumped_A_B_join(T_A, T_B, dask_cluster):
with Client(dask_cluster) as dask_client:
m = 3
left = naive.stump(T_A, m, T_B=T_B)
right = stumped(dask_client, T_A, m, T_B, ignore_trivial=False)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left, right)
def test_stumped_self_join(T_A, T_B, dask_cluster):
with Client(dask_cluster) as dask_client:
m = 3
zone = int(np.ceil(m / 4))
left = naive.stump(T_B, m, exclusion_zone=zone)
right = stumped(dask_client, T_B, m, ignore_trivial=True)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left, right)
def test_stumped_self_join_df(T_A, T_B, dask_cluster):
with Client(dask_cluster) as dask_client:
m = 3
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T_B, m, exclusion_zone=zone)
comp_mp = stumped(dask_client, pd.Series(T_B), m, ignore_trivial=True)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
def test_mpdist_vect_k(T_A, T_B, k):
m = 3
n_A = T_A.shape[0]
n_B = T_B.shape[0]
j = n_A - m + 1 # `k` is reserved for `P_ABBA` selection
P_ABBA = np.empty(2 * j, dtype=np.float64)
ref_mpdist_vect = np.empty(n_B - n_A + 1)
k = min(int(k), P_ABBA.shape[0] - 1)
for i in range(n_B - n_A + 1):
P_ABBA[:j] = naive.stump(T_A, m, T_B[i:i + n_A])[:, 0]
P_ABBA[j:] = naive.stump(T_B[i:i + n_A], m, T_A)[:, 0]
P_ABBA.sort()
ref_mpdist_vect[i] = P_ABBA[min(k, P_ABBA.shape[0] - 1)]
comp_mpdist_vect = _mpdist_vect(T_A, T_B, m, k=k)
npt.assert_almost_equal(ref_mpdist_vect, comp_mpdist_vect)
def test_stump_self_join_larger_window(T_A, T_B, m, dask_cluster):
with Client(dask_cluster) as dask_client:
if len(T_B) > m:
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T_B, m, exclusion_zone=zone)
comp_mp = stumped(dask_client, T_B, m, ignore_trivial=True)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
def test_stump_A_B_join(T_A, T_B):
m = 3
ref_mp = naive.stump(T_A, m, T_B=T_B)
comp_mp = stump(T_A, m, T_B, ignore_trivial=False)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
comp_mp = stump(pd.Series(T_A), m, pd.Series(T_B), ignore_trivial=False)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
def test_stump_A_B_join(T_A, T_B):
m = 3
left = naive.stump(T_A, m, T_B=T_B)
right = stump(T_A, m, T_B, ignore_trivial=False)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left, right)
right = stump(pd.Series(T_A), m, pd.Series(T_B), ignore_trivial=False)
naive.replace_inf(right)
npt.assert_almost_equal(left, right)
def test_stumped_one_constant_subsequence_A_B_join_swap(dask_cluster):
with Client(dask_cluster) as dask_client:
T_A = np.random.rand(20)
T_B = np.concatenate(
(np.zeros(20, dtype=np.float64), np.ones(5, dtype=np.float64)))
m = 3
ref_mp = naive.stump(T_A, m, T_B=T_B)
comp_mp = stumped(dask_client, T_A, m, T_B, ignore_trivial=False)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp[:, 0], comp_mp[:, 0]) # ignore indices
def test_stumped_one_constant_subsequence_self_join(dask_cluster):
with Client(dask_cluster) as dask_client:
T_A = np.concatenate(
(np.zeros(20, dtype=np.float64), np.ones(5, dtype=np.float64)))
m = 3
zone = int(np.ceil(m / 4))
left = naive.stump(T_A, m, exclusion_zone=zone)
right = stumped(dask_client, T_A, m, ignore_trivial=True)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left[:, 0], right[:, 0]) # ignore indices
def test_stump_self_join_larger_window_df(T_A, T_B, dask_cluster):
with Client(dask_cluster) as dask_client:
for m in [8, 16, 32]:
if len(T_B) > m:
zone = int(np.ceil(m / 4))
left = naive.stump(T_B, m, exclusion_zone=zone)
right = stumped(dask_client, pd.Series(T_B), m, ignore_trivial=True)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left, right)
def test_stumped_one_constant_subsequence_self_join_df(dask_cluster):
with Client(dask_cluster) as dask_client:
T_A = np.concatenate(
(np.zeros(20, dtype=np.float64), np.ones(5, dtype=np.float64)))
m = 3
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T_A, m, exclusion_zone=zone)
comp_mp = stumped(dask_client, pd.Series(T_A), m, ignore_trivial=True)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp[:, 0], comp_mp[:, 0]) # ignore indices
def test_mpdist_vect(T_A, T_B):
m = 3
n_A = T_A.shape[0]
n_B = T_B.shape[0]
j = n_A - m + 1 # `k` is reserved for `P_ABBA` selection
P_ABBA = np.empty(2 * j, dtype=np.float64)
ref_mpdist_vect = np.empty(n_B - n_A + 1)
percentage = 0.05
k = min(math.ceil(percentage * (2 * n_A)), 2 * j - 1)
k = min(int(k), P_ABBA.shape[0] - 1)
for i in range(n_B - n_A + 1):
P_ABBA[:j] = naive.stump(T_A, m, T_B[i:i + n_A])[:, 0]
P_ABBA[j:] = naive.stump(T_B[i:i + n_A], m, T_A)[:, 0]
P_ABBA.sort()
ref_mpdist_vect[i] = P_ABBA[k]
comp_mpdist_vect = _mpdist_vect(T_A, T_B, m)
npt.assert_almost_equal(ref_mpdist_vect, comp_mpdist_vect)
def test_stump_self_join(T_A, T_B):
m = 3
zone = int(np.ceil(m / 4))
left = naive.stump(T_B, m, exclusion_zone=zone)
right = stump(T_B, m, ignore_trivial=True)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left, right)
right = stump(pd.Series(T_B), m, ignore_trivial=True)
naive.replace_inf(right)
npt.assert_almost_equal(left, right)
def test_stump_self_join(T_A, T_B):
m = 3
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T_B, m, exclusion_zone=zone)
comp_mp = stump(T_B, m, ignore_trivial=True)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
comp_mp = stump(pd.Series(T_B), m, ignore_trivial=True)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
def test_stumped_nan_zero_mean_self_join(dask_cluster):
with Client(dask_cluster) as dask_client:
T = np.array([-1, 0, 1, np.inf, 1, 0, -1])
m = 3
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T, m, exclusion_zone=zone)
comp_mp = stumped(dask_client, T, m, ignore_trivial=True)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
def test_stumped_one_subsequence_inf_A_B_join(T_A, T_B,
substitution_location_B,
dask_cluster):
with Client(dask_cluster) as dask_client:
m = 3
T_B_sub = T_B.copy()
T_B_sub[substitution_location_B] = np.inf
left = naive.stump(T_A, m, T_B=T_B_sub)
right = stumped(dask_client, T_A, m, T_B_sub, ignore_trivial=False)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left, right)
def test_stumped_one_subsequence_nan_A_B_join(T_A, T_B,
substitution_location_B,
dask_cluster):
with Client(dask_cluster) as dask_client:
m = 3
T_B_sub = T_B.copy()
T_B_sub[substitution_location_B] = np.nan
ref_mp = naive.stump(T_A, m, T_B=T_B_sub)
comp_mp = stumped(dask_client, T_A, m, T_B_sub, ignore_trivial=False)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
def test_stump_constant_subsequence_self_join():
T_A = np.concatenate(
(np.zeros(20, dtype=np.float64), np.ones(5, dtype=np.float64)))
m = 3
zone = int(np.ceil(m / 4))
left = naive.stump(T_A, m, exclusion_zone=zone)
right = stump(T_A, m, ignore_trivial=True)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left[:, 0], right[:, 0]) # ignore indices
right = stump(pd.Series(T_A), m, ignore_trivial=True)
naive.replace_inf(right)
npt.assert_almost_equal(left[:, 0], right[:, 0]) # ignore indices
def test_stumped_one_subsequence_inf_self_join(T_A, T_B,
substitution_location_B,
dask_cluster):
with Client(dask_cluster) as dask_client:
m = 3
T_B_sub = T_B.copy()
T_B_sub[substitution_location_B] = np.inf
zone = int(np.ceil(m / 4))
left = naive.stump(T_B_sub, m, exclusion_zone=zone)
right = stumped(dask_client, T_B_sub, m, ignore_trivial=True)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left, right)
def test_stumped_one_constant_subsequence_A_B_join_df(dask_cluster):
with Client(dask_cluster) as dask_client:
T_A = np.random.rand(20)
T_B = np.concatenate(
(np.zeros(20, dtype=np.float64), np.ones(5, dtype=np.float64)))
m = 3
left = naive.stump(T_A, m, T_B=T_B)
right = stumped(dask_client,
pd.Series(T_A),
m,
pd.Series(T_B),
ignore_trivial=False)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_almost_equal(left[:, 0], right[:, 0]) # ignore indices
def test_stumped_one_subsequence_nan_self_join(T_A, T_B,
substitution_location_B,
dask_cluster):
with Client(dask_cluster) as dask_client:
m = 3
T_B_sub = T_B.copy()
T_B_sub[substitution_location_B] = np.nan
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T_B_sub, m, exclusion_zone=zone)
comp_mp = stumped(dask_client, T_B_sub, m, ignore_trivial=True)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(ref_mp, comp_mp)
def test_stumped_identical_subsequence_A_B_join(dask_cluster):
with Client(dask_cluster) as dask_client:
identical = np.random.rand(8)
T_A = np.random.rand(20)
T_B = np.random.rand(20)
T_A[1:1 + identical.shape[0]] = identical
T_B[11:11 + identical.shape[0]] = identical
m = 3
ref_mp = naive.stump(T_A, m, T_B=T_B)
comp_mp = stumped(dask_client, T_A, m, T_B, ignore_trivial=False)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(
ref_mp[:, 0], comp_mp[:, 0],
decimal=config.STUMPY_TEST_PRECISION) # ignore indices
def test_stumped_identical_subsequence_self_join(dask_cluster):
with Client(dask_cluster) as dask_client:
identical = np.random.rand(8)
T_A = np.random.rand(20)
T_A[1:1 + identical.shape[0]] = identical
T_A[11:11 + identical.shape[0]] = identical
m = 3
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T_A, m, exclusion_zone=zone)
comp_mp = stumped(dask_client, T_A, m, ignore_trivial=True)
naive.replace_inf(ref_mp)
naive.replace_inf(comp_mp)
npt.assert_almost_equal(
ref_mp[:, 0], comp_mp[:, 0],
decimal=config.STUMPY_TEST_PRECISION) # ignore indices
def test_stimp_100_percent(T):
threshold = 0.2
percentage = 1.0
min_m = 3
n = T.shape[0] - min_m + 1
pan = stimp(
T,
min_m=min_m,
max_m=None,
step=1,
percentage=percentage,
pre_scrump=True,
# normalize=True,
)
for i in range(n):
pan.update()
ref_PAN = np.full((pan.M_.shape[0], T.shape[0]), fill_value=np.inf)
for idx, m in enumerate(pan.M_[:n]):
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T, m, T_B=None, exclusion_zone=zone)
ref_PAN[pan._bfs_indices[idx], :ref_mp.shape[0]] = ref_mp[:, 0]
# Compare raw pan
cmp_PAN = pan._PAN
naive.replace_inf(ref_PAN)
naive.replace_inf(cmp_PAN)
npt.assert_almost_equal(ref_PAN, cmp_PAN)
# Compare transformed pan
cmp_pan = pan.PAN_
ref_pan = naive.transform_pan(pan._PAN, pan._M, threshold,
pan._bfs_indices, pan._n_processed)
naive.replace_inf(ref_pan)
naive.replace_inf(cmp_pan)
npt.assert_almost_equal(ref_pan, cmp_pan)
def test_stimp_100_percent(T):
percentage = 1.0
min_m = 3
n = T.shape[0] - min_m + 1
seed = np.random.randint(100000)
np.random.seed(seed)
pmp = stimp(
T,
min_m=min_m,
max_m=None,
step=1,
percentage=percentage,
pre_scrump=True,
normalize=True,
)
for i in range(n):
pmp.update()
ref_P = np.full((pmp.M_.shape[0], T.shape[0]), fill_value=np.inf)
ref_I = np.ones((pmp.M_.shape[0], T.shape[0]), dtype=np.int64) * -1
np.random.seed(seed)
for idx, m in enumerate(pmp.M_[:n]):
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T, m, T_B=None, exclusion_zone=zone)
ref_P[pmp.bfs_indices_[idx], :ref_mp.shape[0]] = ref_mp[:, 0]
ref_I[pmp.bfs_indices_[idx], :ref_mp.shape[0]] = ref_mp[:, 1]
comp_P = pmp.P_
comp_I = pmp.I_
naive.replace_inf(ref_P)
naive.replace_inf(ref_I)
naive.replace_inf(comp_P)
naive.replace_inf(comp_I)
npt.assert_almost_equal(ref_P, comp_P)
npt.assert_almost_equal(ref_I, comp_I)
