# Source code for stumpy.ostinato

```
# STUMPY
# Copyright 2019 TD Ameritrade. Released under the terms of the 3-Clause BSD license.
# STUMPY is a trademark of TD Ameritrade IP Company, Inc. All rights reserved.
import numpy as np
from . import core
from .aamp_ostinato import aamp_ostinato, aamp_ostinatoed
from .stump import stump
from .stumped import stumped
def _across_series_nearest_neighbors(
Ts, Ts_idx, subseq_idx, m, M_Ts, Σ_Ts, Ts_subseq_isconstant
):
"""
For multiple time series find, per individual time series, the subsequences closest
to a given query.
Parameters
----------
Ts : list
A list of time series for which to find the nearest neighbor subsequences that
are closest to the query subsequence `Ts[Ts_idx][subseq_idx : subseq_idx + m]`
Ts_idx : int
The index of time series in `Ts` which contains the query subsequence
subseq_idx : int
The subsequence index in the time series `Ts[Ts_idx]` that contains the query
subsequence
m : int
Window size
M_Ts : list
A list of rolling window means for each time series in `Ts`
Σ_Ts : list
A list of rolling window standard deviations for each time series in `Ts`
Ts_subseq_isconstant : list
A list of rolling window isconstant for each time series in `Ts`
Returns
-------
nns_radii : numpy.ndarray
Radii to subsequences in each time series of `Ts` that are closest to the
query subsequence `Ts[Ts_idx][subseq_idx : subseq_idx + m]`
nns_subseq_idx : numpy.ndarray
Indices to subsequences in each time series of `Ts` that are closest to the
query subsequence `Ts[Ts_idx][subseq_idx : subseq_idx + m]`
"""
k = len(Ts)
Q = Ts[Ts_idx][subseq_idx : subseq_idx + m]
nns_radii = np.zeros(k, dtype=np.float64)
nns_subseq_idx = np.zeros(k, dtype=np.int64)
for i in range(k):
QT = core.sliding_dot_product(Ts[Ts_idx][subseq_idx : subseq_idx + m], Ts[i])
distance_profile = core._mass(
Q,
Ts[i],
QT,
M_Ts[Ts_idx][subseq_idx],
Σ_Ts[Ts_idx][subseq_idx],
M_Ts[i],
Σ_Ts[i],
Ts_subseq_isconstant[Ts_idx][subseq_idx],
Ts_subseq_isconstant[i],
)
nns_subseq_idx[i] = np.argmin(distance_profile)
nns_radii[i] = distance_profile[nns_subseq_idx[i]]
return nns_radii, nns_subseq_idx
def _get_central_motif(
Ts, bsf_radius, bsf_Ts_idx, bsf_subseq_idx, m, M_Ts, Σ_Ts, Ts_subseq_isconstant
):
"""
Compare subsequences with the same radius and return the most central motif (i.e.,
having the smallest average nearest neighbor radii)
Parameters
----------
Ts : list
A list of time series for which to find the most central motif
bsf_radius : float
Best-so-far sradius found by a consensus search algorithm
bsf_Ts_idx : int
The index of time series in `Ts` where the `bsf_radius` was first observed
bsf_subseq_idx : int
The subsequence index in `Ts[bsf_Ts_idx]` that has radius `bsf_radius`
m : int
Window size
M_Ts : list
A list of rolling window means for each time series in `Ts`
Σ_Ts : list
A list of rolling window standard deviations for each time series in `Ts`
Ts_subseq_isconstant : list
A list of rolling window isconstant for each time series in `Ts`
Returns
-------
bsf_radius : float
The updated best-so-far radius of the most central consensus motif
bsf_Ts_idx : int
The updated index of time series in `Ts` which contains the most central
consensus motif
bsf_subseq_idx : int
The updated subsequence index in the time series `Ts[bsf_Ts_idx]` that contains
the most central consensus motif
"""
bsf_nns_radii, bsf_nns_subseq_idx = _across_series_nearest_neighbors(
Ts, bsf_Ts_idx, bsf_subseq_idx, m, M_Ts, Σ_Ts, Ts_subseq_isconstant
)
bsf_nns_mean_radii = bsf_nns_radii.mean()
candidate_nns_Ts_idx = np.flatnonzero(np.isclose(bsf_nns_radii, bsf_radius))
candidate_nns_subseq_idx = bsf_nns_subseq_idx[candidate_nns_Ts_idx]
for Ts_idx, subseq_idx in zip(candidate_nns_Ts_idx, candidate_nns_subseq_idx):
candidate_nns_radii, _ = _across_series_nearest_neighbors(
Ts, Ts_idx, subseq_idx, m, M_Ts, Σ_Ts, Ts_subseq_isconstant
)
if (
np.isclose(candidate_nns_radii.max(), bsf_radius)
and candidate_nns_radii.mean() < bsf_nns_mean_radii
):
bsf_Ts_idx = Ts_idx
bsf_subseq_idx = subseq_idx
bsf_nns_mean_radii = candidate_nns_radii.mean()
return bsf_radius, bsf_Ts_idx, bsf_subseq_idx
def _ostinato(
Ts,
m,
M_Ts,
Σ_Ts,
Ts_subseq_isconstant,
client=None,
device_id=None,
mp_func=stump,
):
"""
Find the consensus motif amongst a list of time series
Parameters
----------
Ts : list
A list of time series for which to find the consensus motif
m : int
Window size
M_Ts : list
A list of rolling window means for each time series in `Ts`
Σ_Ts : list
A list of rolling window standard deviations for each time series in `Ts`
Ts_subseq_isconstant : list
A list of rolling window isconstant for each time series in `Ts`
client : client, default None
A Dask or Ray Distributed client. Setting up a distributed cluster is beyond
the scope of this library. Please refer to the Dask or Ray Distributed
documentation.
device_id : int or list, default None
The (GPU) device number to use. The default value is `0`. A list of
valid device ids (int) may also be provided for parallel GPU-STUMP
computation. A list of all valid device ids can be obtained by
executing `[device.id for device in numba.cuda.list_devices()]`.
mp_func : function, default stump
Specify a custom matrix profile function to use for computing matrix profiles
Returns
-------
bsf_radius : float
The (best-so-far) Radius of the consensus motif
bsf_Ts_idx : int
The time series index in `Ts` which contains the consensus motif
bsf_subseq_idx : int
The subsequence index within time series `Ts[bsf_Ts_idx]` the contains the
consensus motif
Notes
-----
`DOI: 10.1109/ICDM.2019.00140 \
<https://www.cs.ucr.edu/~eamonn/consensus_Motif_ICDM_Long_version.pdf>`__
See Table 2
The ostinato algorithm proposed in the paper finds the best radius
in `Ts`. Intuitively, the radius is the minimum distance of a
subsequence to encompass at least one nearest neighbor subsequence
from all other time series. The best radius in `Ts` is the minimum
radius amongst all radii. Some data sets might contain multiple
subsequences which have the same optimal radius.
The greedy Ostinato algorithm only finds one of them, which might
not be the most central motif. The most central motif amongst the
subsequences with the best radius is the one with the smallest mean
distance to nearest neighbors in all other time series. To find this
central motif it is necessary to search the subsequences with the
best radius via `stumpy.ostinato._get_central_motif`
"""
bsf_radius = np.inf
bsf_Ts_idx = 0
bsf_subseq_idx = 0
partial_mp_func = core._get_partial_mp_func(
mp_func, client=client, device_id=device_id
)
k = len(Ts)
for j in range(k):
if j < (k - 1):
h = j + 1
else:
h = 0
mp = partial_mp_func(
Ts[j],
m,
Ts[h],
ignore_trivial=False,
T_A_subseq_isconstant=Ts_subseq_isconstant[j],
T_B_subseq_isconstant=Ts_subseq_isconstant[h],
)
si = np.argsort(mp[:, 0])
for q in si:
radius = mp[q, 0]
if radius >= bsf_radius:
break
for i in range(k):
if i != j and i != h:
QT = core.sliding_dot_product(Ts[j][q : q + m], Ts[i])
radius = np.max(
(
radius,
np.min(
core._mass(
Ts[j][q : q + m],
Ts[i],
QT,
M_Ts[j][q],
Σ_Ts[j][q],
M_Ts[i],
Σ_Ts[i],
Ts_subseq_isconstant[j][q],
Ts_subseq_isconstant[i],
)
),
)
)
if radius >= bsf_radius:
break
if radius < bsf_radius:
bsf_radius, bsf_Ts_idx, bsf_subseq_idx = radius, j, q
return bsf_radius, bsf_Ts_idx, bsf_subseq_idx
[docs]@core.non_normalized(
aamp_ostinato,
exclude=["normalize", "p", "Ts_subseq_isconstant"],
)
def ostinato(Ts, m, normalize=True, p=2.0, Ts_subseq_isconstant=None):
"""
Find the z-normalized consensus motif of multiple time series
This is a wrapper around the vanilla version of the ostinato algorithm
which finds the best radius and a helper function that finds the most
central conserved motif.
Parameters
----------
Ts : list
A list of time series for which to find the most central consensus motif
m : int
Window size
normalize : bool, default True
When set to `True`, this z-normalizes subsequences prior to computing distances.
Otherwise, this function gets re-routed to its complementary non-normalized
equivalent set in the `@core.non_normalized` function decorator.
p : float, default 2.0
The p-norm to apply for computing the Minkowski distance. Minkowski distance is
typically used with `p` being 1 or 2, which correspond to the Manhattan distance
and the Euclidean distance, respectively. This parameter is ignored when
`normalize == True`.
Ts_subseq_isconstant : list, default None
A list of rolling window isconstant for each time series in `Ts`.
Returns
-------
central_radius : float
Radius of the most central consensus motif
central_Ts_idx : int
The time series index in `Ts` which contains the most central consensus motif
central_subseq_idx : int
The subsequence index within time series `Ts[central_motif_Ts_idx]` the contains
most central consensus motif
See Also
--------
stumpy.ostinatoed : Find the z-normalized consensus motif of multiple time series
with a distributed cluster
stumpy.gpu_ostinato : Find the z-normalized consensus motif of multiple time series
with one or more GPU devices
Notes
-----
`DOI: 10.1109/ICDM.2019.00140 \
<https://www.cs.ucr.edu/~eamonn/consensus_Motif_ICDM_Long_version.pdf>`__
See Table 2
The ostinato algorithm proposed in the paper finds the best radius
in `Ts`. Intuitively, the radius is the minimum distance of a
subsequence to encompass at least one nearest neighbor subsequence
from all other time series. The best radius in `Ts` is the minimum
radius amongst all radii. Some data sets might contain multiple
subsequences which have the same optimal radius.
The greedy Ostinato algorithm only finds one of them, which might
not be the most central motif. The most central motif amongst the
subsequences with the best radius is the one with the smallest mean
distance to nearest neighbors in all other time series. To find this
central motif it is necessary to search the subsequences with the
best radius via `stumpy.ostinato._get_central_motif`
Examples
--------
>>> import stumpy
>>> import numpy as np
>>> stumpy.ostinato(
... [np.array([584., -11., 23., 79., 1001., 0., 19.]),
... np.array([600., -10., 23., 17.]),
... np.array([ 1., 9., 6., 0.])],
... m=3)
(1.2370237678153826, 0, 4)
"""
if not isinstance(Ts, list): # pragma: no cover
raise ValueError(f"`Ts` is of type `{type(Ts)}` but a `list` is expected")
M_Ts = [None] * len(Ts)
Σ_Ts = [None] * len(Ts)
if Ts_subseq_isconstant is None:
Ts_subseq_isconstant = [None] * len(Ts)
for i, T in enumerate(Ts):
Ts[i], M_Ts[i], Σ_Ts[i], Ts_subseq_isconstant[i] = core.preprocess(
T, m, T_subseq_isconstant=Ts_subseq_isconstant[i]
)
bsf_radius, bsf_Ts_idx, bsf_subseq_idx = _ostinato(
Ts, m, M_Ts, Σ_Ts, Ts_subseq_isconstant
)
(
central_radius,
central_Ts_idx,
central_subseq_idx,
) = _get_central_motif(
Ts, bsf_radius, bsf_Ts_idx, bsf_subseq_idx, m, M_Ts, Σ_Ts, Ts_subseq_isconstant
)
return central_radius, central_Ts_idx, central_subseq_idx
[docs]@core.non_normalized(
aamp_ostinatoed,
exclude=["normalize", "p", "Ts_subseq_isconstant"],
)
def ostinatoed(client, Ts, m, normalize=True, p=2.0, Ts_subseq_isconstant=None):
"""
Find the z-normalized consensus motif of multiple time series with a distributed
cluster
This is a wrapper around the vanilla version of the ostinato algorithm
which finds the best radius and a helper function that finds the most
central conserved motif.
Parameters
----------
client : client
A Dask or Ray Distributed client. Setting up a distributed cluster is beyond
the scope of this library. Please refer to the Dask or Ray Distributed
documentation.
Ts : list
A list of time series for which to find the most central consensus motif
m : int
Window size
normalize : bool, default True
When set to `True`, this z-normalizes subsequences prior to computing distances.
Otherwise, this function gets re-routed to its complementary non-normalized
equivalent set in the `@core.non_normalized` function decorator.
p : float, default 2.0
The p-norm to apply for computing the Minkowski distance. Minkowski distance is
typically used with `p` being 1 or 2, which correspond to the Manhattan distance
and the Euclidean distance, respectively. This parameter is ignored when
`normalize == True`.
Ts_subseq_isconstant : list, default None
A list of rolling window isconstant for each time series in `Ts`.
Returns
-------
central_radius : float
Radius of the most central consensus motif
central_Ts_idx : int
The time series index in `Ts` which contains the most central consensus motif
central_subseq_idx : int
The subsequence index within time series `Ts[central_motif_Ts_idx]` the contains
most central consensus motif
See Also
--------
stumpy.ostinato : Find the z-normalized consensus motif of multiple time series
stumpy.gpu_ostinato : Find the z-normalized consensus motif of multiple time series
with one or more GPU devices
Notes
-----
`DOI: 10.1109/ICDM.2019.00140 \
<https://www.cs.ucr.edu/~eamonn/consensus_Motif_ICDM_Long_version.pdf>`__
See Table 2
The ostinato algorithm proposed in the paper finds the best radius
in `Ts`. Intuitively, the radius is the minimum distance of a
subsequence to encompass at least one nearest neighbor subsequence
from all other time series. The best radius in `Ts` is the minimum
radius amongst all radii. Some data sets might contain multiple
subsequences which have the same optimal radius.
The greedy Ostinato algorithm only finds one of them, which might
not be the most central motif. The most central motif amongst the
subsequences with the best radius is the one with the smallest mean
distance to nearest neighbors in all other time series. To find this
central motif it is necessary to search the subsequences with the
best radius via `stumpy.ostinato._get_central_motif`
Examples
--------
>>> import stumpy
>>> import numpy as np
>>> from dask.distributed import Client
>>> if __name__ == "__main__":
... with Client() as dask_client:
... stumpy.ostinatoed(
... dask_client,
... [np.array([584., -11., 23., 79., 1001., 0., 19.]),
... np.array([600., -10., 23., 17.]),
... np.array([ 1., 9., 6., 0.])],
... m=3)
(1.2370237678153826, 0, 4)
"""
if not isinstance(Ts, list): # pragma: no cover
raise ValueError(f"`Ts` is of type `{type(Ts)}` but a `list` is expected")
M_Ts = [None] * len(Ts)
Σ_Ts = [None] * len(Ts)
if Ts_subseq_isconstant is None:
Ts_subseq_isconstant = [None] * len(Ts)
for i, T in enumerate(Ts):
Ts[i], M_Ts[i], Σ_Ts[i], Ts_subseq_isconstant[i] = core.preprocess(
T, m, T_subseq_isconstant=Ts_subseq_isconstant[i]
)
bsf_radius, bsf_Ts_idx, bsf_subseq_idx = _ostinato(
Ts,
m,
M_Ts,
Σ_Ts,
Ts_subseq_isconstant,
client=client,
mp_func=stumped,
)
(
central_radius,
central_Ts_idx,
central_subseq_idx,
) = _get_central_motif(
Ts, bsf_radius, bsf_Ts_idx, bsf_subseq_idx, m, M_Ts, Σ_Ts, Ts_subseq_isconstant
)
return central_radius, central_Ts_idx, central_subseq_idx
```