# Source code for stumpy.stimp

```
# 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, scrump
from .aamp_stimp import aamp_stimp, aamp_stimped
from .stump import stump
from .stumped import stumped
def _normalize_pan(pan, ms, bfs_indices, n_processed):
"""
Normalize the pan matrix profile nearest neighbor distances (inplace) relative
to the corresponding subsequence length from which they were computed
Parameters
----------
pan : numpy.ndarray
The pan matrix profile
ms : numpy.ndarray
The breadth-first-search sorted subsequence window sizes
bfs_indices : numpy.ndarray
The breadth-first-search indices
n_processed : numpy.ndarray
The number of subsequence window sizes and breadth-first-search indices to
normalize
Returns
-------
None
"""
idx = bfs_indices[:n_processed]
norm = 1.0 / (2.0 * np.sqrt(ms[:n_processed]))
pan[idx] = np.minimum(1.0, pan[idx] * np.expand_dims(norm, 1))
class _stimp:
"""
Compute the Pan Matrix Profile
This is based on the SKIMP algorithm.
Parameters
----------
T : numpy.ndarray
The time series or sequence for which to compute the pan matrix profile
min_m : int, default 3
The starting (or minimum) subsequence window size for which a matrix profile
may be computed
max_m : int, default None
The stopping (or maximum) subsequence window size for which a matrix profile
may be computed. When `max_m = Non`, this is set to the maximum allowable
subsequence window size
step : int, default 1
The step between subsequence window sizes
percentage : float, default 0.01
The percentage of the full matrix profile to compute for each subsequence
window size. When `percentage < 1.0`, then the `scrump` algorithm is used.
Otherwise, the `stump` algorithm is used when the exact matrix profile is
requested.
pre_scrump : bool, default True
A flag for whether or not to perform the PreSCRIMP calculation prior to
computing SCRIMP. If set to `True`, this is equivalent to computing
SCRIMP++. This parameter is ignored when `percentage = 1.0`.
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
The matrix profile function to use when `percentage = 1.0`
T_subseq_isconstant_func : function, default None
A custom, user-defined function that returns a boolean array that indicates
whether a subsequence in `T` 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.
Attributes
----------
PAN_ : numpy.ndarray
The transformed (i.e., normalized, contrasted, binarized, and repeated)
pan matrix profile
M_ : numpy.ndarray
The full list of (breadth first search (level) ordered) subsequence window
sizes
Methods
-------
update():
Compute the next matrix profile using the next available (breadth-first-search
(level) ordered) subsequence window size and update the pan matrix profile
Notes
-----
`DOI: 10.1109/ICBK.2019.00031 \
<https://www.cs.ucr.edu/~eamonn/PAN_SKIMP%20%28Matrix%20Profile%20XX%29.pdf>`__
See Table 2
"""
def __init__(
self,
T,
min_m=3,
max_m=None,
step=1,
percentage=0.01,
pre_scrump=True,
client=None,
device_id=None,
mp_func=stump,
T_subseq_isconstant_func=None,
):
"""
Initialize the `stimp` object and compute the Pan Matrix Profile
Parameters
----------
T : numpy.ndarray
The time series or sequence for which to compute the pan matrix profile
min_m : int, default 3
The minimum subsequence window size to consider computing a matrix profile
for
max_m : int, default None
The maximum subsequence window size to consider computing a matrix profile
for. When `max_m = None`, this is set to the maximum allowable subsequence
window size
step : int, default 1
The step between subsequence window sizes
percentage : float, default 0.01
The percentage of the full matrix profile to compute for each subsequence
window size. When `percentage < 1.0`, then the `scrump` algorithm is used.
Otherwise, the `stump` algorithm is used when the exact matrix profile is
requested.
pre_scrump : bool, default True
A flag for whether or not to perform the PreSCRIMP calculation prior to
computing SCRIMP. If set to `True`, this is equivalent to computing
SCRIMP++. This parameter is ignored when `percentage = 1.0`.
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
The matrix profile function to use when `percentage = 1.0`
T_subseq_isconstant_func : function, default None
A custom, user-defined function that returns a boolean array that indicates
whether a subsequence in `T` 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.
"""
self._T = T.copy()
if max_m is None:
max_m = max(min_m + 1, core.get_max_window_size(self._T.shape[0]))
M = np.arange(min_m, max_m + 1, step).astype(np.int64)
else:
min_m, max_m = sorted([min_m, max_m])
M = np.arange(
max(3, min_m),
min(core.get_max_window_size(self._T.shape[0]), max_m) + 1,
step,
).astype(np.int64)
self._bfs_indices = core._bfs_indices(M.shape[0])
self._M = M[self._bfs_indices]
self._n_processed = 0
percentage = np.clip(percentage, 0.0, 1.0)
self._percentage = percentage
self._pre_scrump = pre_scrump
self._partial_mp_func = core._get_partial_mp_func(
mp_func, client=client, device_id=device_id
)
if T_subseq_isconstant_func is None:
T_subseq_isconstant_func = core._rolling_isconstant
if not callable(T_subseq_isconstant_func): # pragma: no cover
msg = (
"`T_subseq_isconstant_func` was expected to be a callable function "
+ f"but {type(T_subseq_isconstant_func)} was found."
)
raise ValueError(msg)
self._T_subseq_isconstant_func = T_subseq_isconstant_func
self._PAN = np.full(
(self._M.shape[0], self._T.shape[0]), fill_value=np.inf, dtype=np.float64
)
def update(self):
"""
Update the pan matrix profile by computing a single matrix profile using the
next available subsequence window size
Parameters
----------
None
Returns
-------
None
Notes
-----
`DOI: 10.1109/ICBK.2019.00031 \
<https://www.cs.ucr.edu/~eamonn/PAN_SKIMP%20%28Matrix%20Profile%20XX%29.pdf>`__
See Table 2
"""
if self._n_processed < self._M.shape[0]:
m = self._M[self._n_processed]
if self._percentage < 1.0:
approx = scrump(
self._T,
m,
ignore_trivial=True,
percentage=self._percentage,
pre_scrump=self._pre_scrump,
k=1,
T_A_subseq_isconstant=self._T_subseq_isconstant_func,
)
approx.update()
self._PAN[
self._bfs_indices[self._n_processed], : approx.P_.shape[0]
] = approx.P_
else:
out = self._partial_mp_func(
self._T,
m,
ignore_trivial=True,
T_A_subseq_isconstant=self._T_subseq_isconstant_func,
)
self._PAN[
self._bfs_indices[self._n_processed], : out[:, 0].shape[0]
] = out[:, 0]
self._n_processed += 1
def pan(self, threshold=0.2, normalize=True, contrast=True, binary=True, clip=True):
"""
Generate a transformed (i.e., normalized, contrasted, binarized, and repeated)
pan matrix profile
Parameters
----------
threshold : float, default 0.2
The distance `threshold` in which to center the pan matrix profile around
for best contrast and this value is also used for binarizing the pan matrix
profile
normalize : bool, default True
A flag for whether or not each individual matrix profile within the pan
matrix profile is normalized by its corresponding subsequence window size.
If set to `True`, normalization is performed.
contrast : bool, default True
A flag for whether or not the pan matrix profile is centered around the
desired `threshold` in order to provide higher contrast. If set to `True`,
centering is performed.
binary : bool, default True
A flag for whether or not the pan matrix profile is binarized. If set to
`True`, all values less than or equal to `threshold` are set to `0.0` while
all other values are set to `1.0`.
clip : bool, default True
A flag for whether or not the pan matrix profile is clipped. If set to
`True`, all values are ensured to be clipped between `0.0` and `1.0`.
Returns
-------
None
"""
PAN = self._PAN.copy()
# Retrieve the row indices where the matrix profile was actually computed
idx = self._bfs_indices[: self._n_processed]
sorted_idx = np.sort(idx)
PAN[PAN == np.inf] = np.nan
if normalize:
_normalize_pan(PAN, self._M, self._bfs_indices, self._n_processed)
if contrast:
core._contrast_pan(PAN, threshold, self._bfs_indices, self._n_processed)
if binary:
core._binarize_pan(PAN, threshold, self._bfs_indices, self._n_processed)
if clip:
PAN[idx] = np.clip(PAN[idx], 0.0, 1.0)
# Below, for each matrix profile that was computed, we take that matrix profile
# and copy/repeat it downwards to replace other rows in the `PAN` where the
# matrix profile has yet to be computed. Instead of only having lines/values in
# the rows where matrix profiles were computed, this gives us the "blocky" look
nrepeat = np.diff(np.append(-1, sorted_idx))
PAN[: np.sum(nrepeat)] = np.repeat(PAN[sorted_idx], nrepeat, axis=0)
PAN[np.isnan(PAN)] = np.nanmax(PAN)
return PAN
@property
def PAN_(self):
"""
Get the transformed (i.e., normalized, contrasted, binarized, and repeated) pan
matrix profile
Parameters
----------
None
Returns
-------
None
"""
return self.pan().astype(np.float64)
@property
def M_(self):
"""
Get all of the (breadth first searched (level) ordered) subsequence window sizes
Parameters
----------
None
Returns
-------
None
"""
return self._M.astype(np.int64)
# @property
# def bfs_indices_(self):
# """
# Get the breadth first search (level order) indices
# """
# return self._bfs_indices.astype(np.int64)
# @property
# def n_processed_(self): # pragma: no cover
# """
# Get the total number of windows that have been processed
# """
# return self._n_processed
[docs]
@core.non_normalized(
aamp_stimp,
exclude=["pre_scrump", "normalize", "p", "T_subseq_isconstant_func", "pre_scraamp"],
replace={"pre_scrump": "pre_scraamp"},
)
class stimp(_stimp):
"""
Compute the Pan Matrix Profile
This is based on the SKIMP algorithm.
Parameters
----------
T : numpy.ndarray
The time series or sequence for which to compute the pan matrix profile
min_m : int, default 3
The starting (or minimum) subsequence window size for which a matrix profile
may be computed
max_m : int, default None
The stopping (or maximum) subsequence window size for which a matrix profile
may be computed. When `max_m = Non`, this is set to the maximum allowable
subsequence window size
step : int, default 1
The step between subsequence window sizes
percentage : float, default 0.01
The percentage of the full matrix profile to compute for each subsequence
window size. When `percentage < 1.0`, then the `scrump` algorithm is used.
Otherwise, the `stump` algorithm is used when the exact matrix profile is
requested.
pre_scrump : bool, default True
A flag for whether or not to perform the PreSCRIMP calculation prior to
computing SCRIMP. If set to `True`, this is equivalent to computing
SCRIMP++. This parameter is ignored when `percentage = 1.0`.
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. This parameter is
ignored when `normalize == True`.
T_subseq_isconstant_func : function, default None
A custom, user-defined function that returns a boolean array that indicates
whether a subsequence in `T` 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.
Attributes
----------
PAN_ : numpy.ndarray
The transformed (i.e., normalized, contrasted, binarized, and repeated)
pan matrix profile
M_ : numpy.ndarray
The full list of (breadth first search (level) ordered) subsequence window
sizes
Methods
-------
update():
Compute the next matrix profile using the next available (breadth-first-search
(level) ordered) subsequence window size and update the pan matrix profile
See Also
--------
stumpy.stimped : Compute the Pan Matrix Profile with a distributed dask cluster
stumpy.gpu_stimp : Compute the Pan Matrix Profile with with one or more GPU devices
Notes
-----
`DOI: 10.1109/ICBK.2019.00031 \
<https://www.cs.ucr.edu/~eamonn/PAN_SKIMP%20%28Matrix%20Profile%20XX%29.pdf>`__
See Table 2
Examples
--------
>>> import stumpy
>>> import numpy as np
>>> pmp = stumpy.stimp(np.array([584., -11., 23., 79., 1001., 0., -19.]))
>>> pmp.update()
>>> pmp.PAN_
array([[0., 1., 1., 1., 1., 1., 1.],
[0., 1., 1., 1., 1., 1., 1.]])
"""
def __init__(
self,
T,
min_m=3,
max_m=None,
step=1,
percentage=0.01,
pre_scrump=True,
normalize=True,
p=2.0,
T_subseq_isconstant_func=None,
):
"""
Initialize the `stimp` object and compute the Pan Matrix Profile
Parameters
----------
T : numpy.ndarray
The time series or sequence for which to compute the pan matrix profile
min_m : int, default 3
The minimum subsequence window size to consider computing a matrix profile
for
max_m : int, default None
The maximum subsequence window size to consider computing a matrix profile
for. When `max_m = None`, this is set to the maximum allowable subsequence
window size
step : int, default 1
The step between subsequence window sizes
percentage : float, default 0.01
The percentage of the full matrix profile to compute for each subsequence
window size. When `percentage < 1.0`, then the `scrump` algorithm is used.
Otherwise, the `stump` algorithm is used when the exact matrix profile is
requested.
pre_scrump : bool, default True
A flag for whether or not to perform the PreSCRIMP calculation prior to
computing SCRIMP. If set to `True`, this is equivalent to computing
SCRIMP++. This parameter is ignored when `percentage = 1.0`.
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. This parameter is
ignored when `normalize == True`.
T_subseq_isconstant_func : function, default None
A custom, user-defined function that returns a boolean array that indicates
whether a subsequence in `T` 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.
"""
super().__init__(
T,
min_m=min_m,
max_m=max_m,
step=step,
percentage=percentage,
pre_scrump=pre_scrump,
mp_func=stump,
T_subseq_isconstant_func=T_subseq_isconstant_func,
)
[docs]
@core.non_normalized(
aamp_stimped,
exclude=["pre_scrump", "normalize", "p", "T_subseq_isconstant_func", "pre_scraamp"],
replace={"pre_scrump": "pre_scraamp"},
)
class stimped(_stimp):
"""
Compute the Pan Matrix Profile with a distributed dask/ray cluster
This is based on the SKIMP algorithm.
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 : numpy.ndarray
The time series or sequence for which to compute the pan matrix profile
min_m : int, default 3
The starting (or minimum) subsequence window size for which a matrix profile
may be computed
max_m : int, default None
The stopping (or maximum) subsequence window size for which a matrix profile
may be computed. When `max_m = Non`, this is set to the maximum allowable
subsequence window size
step : int, default 1
The step between subsequence window sizes
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. This parameter is
ignored when `normalize == True`.
T_subseq_isconstant_func : function, default None
A custom, user-defined function that returns a boolean array that indicates
whether a subsequence in `T` 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.
Attributes
----------
PAN_ : numpy.ndarray
The transformed (i.e., normalized, contrasted, binarized, and repeated)
pan matrix profile
M_ : numpy.ndarray
The full list of (breadth first search (level) ordered) subsequence window
sizes
Methods
-------
update():
Compute the next matrix profile using the next available (breadth-first-search
(level) ordered) subsequence window size and update the pan matrix profile
See Also
--------
stumpy.stimp : Compute the Pan Matrix Profile
stumpy.gpu_stimp : Compute the Pan Matrix Profile with with one or more GPU devices
Notes
-----
`DOI: 10.1109/ICBK.2019.00031 \
<https://www.cs.ucr.edu/~eamonn/PAN_SKIMP%20%28Matrix%20Profile%20XX%29.pdf>`__
See Table 2
Examples
--------
>>> import stumpy
>>> import numpy as np
>>> from dask.distributed import Client
>>> if __name__ == "__main__":
... with Client() as dask_client:
... pmp = stumpy.stimped(
... dask_client,
... np.array([584., -11., 23., 79., 1001., 0., -19.]))
... pmp.update()
... pmp.PAN_
array([[0., 1., 1., 1., 1., 1., 1.],
[0., 1., 1., 1., 1., 1., 1.]])
"""
def __init__(
self,
client,
T,
min_m=3,
max_m=None,
step=1,
normalize=True,
p=2.0,
T_subseq_isconstant_func=None,
):
"""
Initialize the `stimp` object and compute the Pan Matrix Profile
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 : numpy.ndarray
The time series or sequence for which to compute the pan matrix profile
min_m : int, default 3
The minimum subsequence window size to consider computing a matrix profile
for
max_m : int, default None
The maximum subsequence window size to consider computing a matrix profile
for. When `max_m = None`, this is set to the maximum allowable subsequence
window size
step : int, default 1
The step between subsequence window sizes
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. This parameter is
ignored when `normalize == True`.
T_subseq_isconstant_func : function, default None
A custom, user-defined function that returns a boolean array that indicates
whether a subsequence in `T` 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.
"""
super().__init__(
T,
min_m=min_m,
max_m=max_m,
step=step,
percentage=1.0,
pre_scrump=False,
client=client,
mp_func=stumped,
T_subseq_isconstant_func=T_subseq_isconstant_func,
)
```