# Source code for stumpy.stumped

```
# 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 inspect
import numpy as np
from . import config, core
from .aamped import aamped
from .stump import _stump
def _dask_stumped(
dask_client,
T_A,
T_B,
m,
M_T,
μ_Q,
Σ_T_inverse,
σ_Q_inverse,
M_T_m_1,
μ_Q_m_1,
T_A_subseq_isfinite,
T_B_subseq_isfinite,
T_A_subseq_isconstant,
T_B_subseq_isconstant,
diags,
ignore_trivial,
k,
):
"""
Compute the z-normalized (top-k) matrix profile with a distributed dask cluster
This is a highly distributed implementation around the Numba JIT-compiled
parallelized `_stump` function which computes the (top-k) matrix profile according
to STOMPopt with Pearson correlations.
Parameters
----------
dask_client : client
A Dask Distributed client. Setting up a distributed cluster is beyond
the scope of this library. Please refer to the Dask Distributed
documentation.
T_A : numpy.ndarray
The time series or sequence for which to compute the matrix profile
T_B : numpy.ndarray
The time series or sequence that will be used to annotate T_A. For every
subsequence in T_A, its nearest neighbor in T_B will be recorded. Default is
`None` which corresponds to a self-join.
m : int
Window size
M_T : numpy.ndarray
Sliding mean of time series, `T`
μ_Q : numpy.ndarray
Mean of the query sequence, `Q`, relative to the current sliding window
Σ_T_inverse : numpy.ndarray
Inverse sliding standard deviation of time series, `T`
σ_Q_inverse : numpy.ndarray
Inverse standard deviation of the query sequence, `Q`, relative to the current
M_T_m_1 : numpy.ndarray
Sliding mean of time series, `T`, using a window size of `m-1`
μ_Q_m_1 : numpy.ndarray
Mean of the query sequence, `Q`, relative to the current sliding window and
using a window size of `m-1`
T_A_subseq_isfinite : numpy.ndarray
A boolean array that indicates whether a subsequence in `T_A` contains a
`np.nan`/`np.inf` value (False)
T_B_subseq_isfinite : numpy.ndarray
A boolean array that indicates whether a subsequence in `T_B` contains a
`np.nan`/`np.inf` value (False)
T_A_subseq_isconstant : numpy.ndarray
A boolean array that indicates whether a subsequence in `T_A` is constant (True)
T_B_subseq_isconstant : numpy.ndarray
A boolean array that indicates whether a subsequence in `T_B` is constant (True)
diags : numpy.ndarray
The diagonal indices
ignore_trivial : bool, default True
Set to `True` if this is a self-join. Otherwise, for AB-join, set this
to `False`. Default is `True`.
k : int, default 1
The number of top `k` smallest distances used to construct the matrix profile.
Note that this will increase the total computational time and memory usage
when k > 1. If you have access to a GPU device, then you may be able to
leverage `gpu_stump` for better performance and scalability.
Returns
-------
out : numpy.ndarray
When k = 1 (default), the first column consists of the matrix profile,
the second column consists of the matrix profile indices, the third column
consists of the left matrix profile indices, and the fourth column consists
of the right matrix profile indices. However, when k > 1, the output array
will contain exactly 2 * k + 2 columns. The first k columns (i.e., out[:, :k])
consists of the top-k matrix profile, the next set of k columns
(i.e., out[:, k:2k]) consists of the corresponding top-k matrix profile
indices, and the last two columns (i.e., out[:, 2k] and out[:, 2k+1] or,
equivalently, out[:, -2] and out[:, -1]) correspond to the top-1 left
matrix profile indices and the top-1 right matrix profile indices, respectively.
"""
n_A = T_A.shape[0]
n_B = T_B.shape[0]
l = n_A - m + 1
hosts = list(dask_client.ncores().keys())
nworkers = len(hosts)
ndist_counts = core._count_diagonal_ndist(diags, m, n_A, n_B)
diags_ranges = core._get_array_ranges(ndist_counts, nworkers, False)
diags_ranges += diags[0]
# Scatter data to Dask cluster
T_A_future = dask_client.scatter(T_A, broadcast=True, hash=False)
T_B_future = dask_client.scatter(T_B, broadcast=True, hash=False)
M_T_future = dask_client.scatter(M_T, broadcast=True, hash=False)
μ_Q_future = dask_client.scatter(μ_Q, broadcast=True, hash=False)
Σ_T_inverse_future = dask_client.scatter(Σ_T_inverse, broadcast=True, hash=False)
σ_Q_inverse_future = dask_client.scatter(σ_Q_inverse, broadcast=True, hash=False)
M_T_m_1_future = dask_client.scatter(M_T_m_1, broadcast=True, hash=False)
μ_Q_m_1_future = dask_client.scatter(μ_Q_m_1, broadcast=True, hash=False)
T_A_subseq_isfinite_future = dask_client.scatter(
T_A_subseq_isfinite, broadcast=True, hash=False
)
T_B_subseq_isfinite_future = dask_client.scatter(
T_B_subseq_isfinite, broadcast=True, hash=False
)
T_A_subseq_isconstant_future = dask_client.scatter(
T_A_subseq_isconstant, broadcast=True, hash=False
)
T_B_subseq_isconstant_future = dask_client.scatter(
T_B_subseq_isconstant, broadcast=True, hash=False
)
diags_futures = []
for i, host in enumerate(hosts):
diags_future = dask_client.scatter(
np.arange(diags_ranges[i, 0], diags_ranges[i, 1], dtype=np.int64),
workers=[host],
hash=False,
)
diags_futures.append(diags_future)
futures = []
for i in range(len(hosts)):
futures.append(
dask_client.submit(
_stump,
T_A_future,
T_B_future,
m,
M_T_future,
μ_Q_future,
Σ_T_inverse_future,
σ_Q_inverse_future,
M_T_m_1_future,
μ_Q_m_1_future,
T_A_subseq_isfinite_future,
T_B_subseq_isfinite_future,
T_A_subseq_isconstant_future,
T_B_subseq_isconstant_future,
diags_futures[i],
ignore_trivial,
k,
)
)
results = dask_client.gather(futures)
profile, profile_L, profile_R, indices, indices_L, indices_R = results[0]
for i in range(1, len(hosts)):
P, PL, PR, I, IL, IR = results[i]
# Update top-k matrix profile and matrix profile indices
core._merge_topk_PI(profile, P, indices, I)
# Update top-1 left matrix profile and matrix profile index
mask = PL < profile_L
profile_L[mask] = PL[mask]
indices_L[mask] = IL[mask]
# Update top-1 right matrix profile and matrix profile index
mask = PR < profile_R
profile_R[mask] = PR[mask]
indices_R[mask] = IR[mask]
out = np.empty((l, 2 * k + 2), dtype=object)
out[:, :k] = profile
out[:, k:] = np.column_stack((indices, indices_L, indices_R))
return out
[docs]@core.non_normalized(aamped)
def stumped(
client,
T_A,
m,
T_B=None,
ignore_trivial=True,
normalize=True,
p=2.0,
k=1,
T_A_subseq_isconstant=None,
T_B_subseq_isconstant=None,
):
"""
Compute the z-normalized matrix profile with a distributed dask/ray cluster
This is a highly distributed implementation around the Numba JIT-compiled
parallelized `_stump` function which computes the (top-k) matrix profile according
to STOMPopt with Pearson correlations.
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.
T_A : numpy.ndarray
The time series or sequence for which to compute the matrix profile
m : int
Window size
T_B : numpy.ndarray, default None
The time series or sequence that will be used to annotate T_A. For every
subsequence in T_A, its nearest neighbor in T_B will be recorded. Default is
`None` which corresponds to a self-join.
ignore_trivial : bool, default True
Set to `True` if this is a self-join. Otherwise, for AB-join, set this
to `False`. Default is `True`.
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`.
k : int, default 1
The number of top `k` smallest distances used to construct the matrix profile.
Note that this will increase the total computational time and memory usage
when k > 1. If you have access to a GPU device, then you may be able to
leverage `gpu_stump` for better performance and scalability.
T_A_subseq_isconstant : numpy.ndarray or function, default None
A boolean array that indicates whether a subsequence in `T_A` is constant
(True). Alternatively, a custom, user-defined function that returns a
boolean array that indicates whether a subsequence in `T_A` is constant
(True). The function must only take two arguments, `a`, a 1-D array,
and `w`, the window size, while additional arguments may be specified
by currying the user-defined function using `functools.partial`. Any
subsequence with at least one np.nan/np.inf will automatically have its
corresponding value set to False in this boolean array.
T_B_subseq_isconstant : numpy.ndarray or function, default None
A boolean array that indicates whether a subsequence in `T_B` is constant
(True). Alternatively, a custom, user-defined function that returns a
boolean array that indicates whether a subsequence in `T_B` is constant
(True). The function must only take two arguments, `a`, a 1-D array,
and `w`, the window size, while additional arguments may be specified
by currying the user-defined function using `functools.partial`. Any
subsequence with at least one np.nan/np.inf will automatically have its
corresponding value set to False in this boolean array.
Returns
-------
out : numpy.ndarray
When k = 1 (default), the first column consists of the matrix profile,
the second column consists of the matrix profile indices, the third column
consists of the left matrix profile indices, and the fourth column consists
of the right matrix profile indices. However, when k > 1, the output array
will contain exactly 2 * k + 2 columns. The first k columns (i.e., out[:, :k])
consists of the top-k matrix profile, the next set of k columns
(i.e., out[:, k:2k]) consists of the corresponding top-k matrix profile
indices, and the last two columns (i.e., out[:, 2k] and out[:, 2k+1] or,
equivalently, out[:, -2] and out[:, -1]) correspond to the top-1 left
matrix profile indices and the top-1 right matrix profile indices, respectively.
See Also
--------
stumpy.stump : Compute the z-normalized matrix profile
cluster
stumpy.gpu_stump : Compute the z-normalized matrix profile with one or more GPU
devices
stumpy.scrump : Compute an approximate z-normalized matrix profile
Notes
-----
`DOI: 10.1007/s10115-017-1138-x \
<https://www.cs.ucr.edu/~eamonn/ten_quadrillion.pdf>`__
See Section 4.5
The above reference outlines a general approach for traversing the distance
matrix in a diagonal fashion rather than in a row-wise fashion.
`DOI: 10.1145/3357223.3362721 \
<https://www.cs.ucr.edu/~eamonn/public/GPU_Matrix_profile_VLDB_30DraftOnly.pdf>`__
See Section 3.1 and Section 3.3
The above reference outlines the use of the Pearson correlation via Welford's
centered sum-of-products along each diagonal of the distance matrix in place of the
sliding window dot product found in the original STOMP method.
`DOI: 10.1109/ICDM.2016.0085 \
<https://www.cs.ucr.edu/~eamonn/STOMP_GPU_final_submission_camera_ready.pdf>`__
See Table II
This is a Dask distributed implementation of stump that scales
across multiple servers and is a convenience wrapper around the
parallelized `stump._stump` function
Timeseries, T_A, will be annotated with the distance location
(or index) of all its subsequences in another times series, T_B.
Return: For every subsequence, Q, in T_A, you will get a distance
and index for the closest subsequence in T_B. Thus, the array
returned will have length T_A.shape[0]-m+1. Additionally, the
left and right matrix profiles are also returned.
Note: Unlike in the Table II where T_A.shape is expected to be equal
to T_B.shape, this implementation is generalized so that the shapes of
T_A and T_B can be different. In the case where T_A.shape == T_B.shape,
then our algorithm reduces down to the same algorithm found in Table II.
Additionally, unlike STAMP where the exclusion zone is m/2, the default
exclusion zone for STOMP is m/4 (See Definition 3 and Figure 3).
For self-joins, set `ignore_trivial = True` in order to avoid the
trivial match.
Note that left and right matrix profiles are only available for self-joins.
Examples
--------
>>> import stumpy
>>> import numpy as np
>>> from dask.distributed import Client
>>> if __name__ == "__main__":
... with Client() as dask_client:
... stumpy.stumped(
... dask_client,
... np.array([584., -11., 23., 79., 1001., 0., -19.]),
... m=3)
array([[0.11633857113691416, 4, -1, 4],
[2.694073918063438, 3, -1, 3],
[3.0000926340485923, 0, 0, 4],
[2.694073918063438, 1, 1, -1],
[0.11633857113691416, 0, 0, -1]], dtype=object)
"""
if T_B is None:
T_B = T_A
ignore_trivial = True
T_B_subseq_isconstant = T_A_subseq_isconstant
(
T_A,
μ_Q,
σ_Q_inverse,
μ_Q_m_1,
T_A_subseq_isfinite,
T_A_subseq_isconstant,
) = core.preprocess_diagonal(T_A, m, T_subseq_isconstant=T_A_subseq_isconstant)
(
T_B,
M_T,
Σ_T_inverse,
M_T_m_1,
T_B_subseq_isfinite,
T_B_subseq_isconstant,
) = core.preprocess_diagonal(T_B, m, T_subseq_isconstant=T_B_subseq_isconstant)
if T_A.ndim != 1: # pragma: no cover
raise ValueError(
f"T_A is {T_A.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
if T_B.ndim != 1: # pragma: no cover
raise ValueError(
f"T_B is {T_B.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
core.check_window_size(m, max_size=min(T_A.shape[0], T_B.shape[0]))
ignore_trivial = core.check_ignore_trivial(T_A, T_B, ignore_trivial)
n_A = T_A.shape[0]
n_B = T_B.shape[0]
excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))
if ignore_trivial:
diags = np.arange(excl_zone + 1, n_A - m + 1, dtype=np.int64)
else:
diags = np.arange(-(n_A - m + 1) + 1, n_B - m + 1, dtype=np.int64)
_stumped = core._client_to_func(client)
out = _stumped(
client,
T_A,
T_B,
m,
M_T,
μ_Q,
Σ_T_inverse,
σ_Q_inverse,
M_T_m_1,
μ_Q_m_1,
T_A_subseq_isfinite,
T_B_subseq_isfinite,
T_A_subseq_isconstant,
T_B_subseq_isconstant,
diags,
ignore_trivial,
k,
)
core._check_P(out[:, 0])
return out
```