stumpy/stumpy/aampi.py at main · stumpy-dev/stumpy · GitHub

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# STUMPY

import numpy as np

from . import config, core

from .aamp import aamp

class aampi:

# needs to be enhanced to support top-k matrix profile

"""

Compute an incremental non-normalized (i.e., without z-normalization) matrix profile

for streaming data

Parameters

----------

T : numpy.ndarray

The time series or sequence for which the non-normalized matrix profile and

matrix profile indices will be returned

m : int

Window size

egress : bool, default True

If set to `True`, the oldest data point in the time series is removed and

the time series length remains constant rather than forever increasing

p : float, default 2.0

The p-norm to apply for computing the Minkowski distance.

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.

mp : numpy.ndarray, default None

A pre-computed matrix profile (and corresponding matrix profile indices).

This is a 2D array of shape `(len(T) - m + 1, 2 * k + 2)`, where the first `k`

columns are top-k matrix profile, and the next `k` columns are their

corresponding indices. The last two columns correspond to the top-1 left and

top-1 right matrix profile indices. When None (default), this array is computed

internally using `stumpy.aamp`.

Attributes

----------

P_ : numpy.ndarray

The updated matrix profile for `T`

I_ : numpy.ndarray

The updated matrix profile indices for `T`

left_P_ : numpy.ndarray

The updated left matrix profile for `T`

left_I_ : numpy.ndarray

The updated left matrix profile indices for `T`

T_ : numpy.ndarray

The updated time series or sequence for which the matrix profile and matrix

profile indices are computed

Methods

-------

update(t)

Append a single new data point, `t`, to the time series, `T`, and update the

matrix profile

Notes

-----

`arXiv:1901.05708 \

<https://arxiv.org/pdf/1901.05708.pdf>`__

See Algorithm 1

Note that we have extended this algorithm for AB-joins as well.

"""

def __init__(self, T, m, egress=True, p=2.0, k=1, mp=None):

"""

Initialize the `aampi` object

Parameters

----------

T : numpy.ndarray

The time series or sequence for which the unnormalized matrix profile and

matrix profile indices will be returned

m : int

Window size

egress : bool, default True

If set to `True`, the oldest data point in the time series is removed and

the time series length remains constant rather than forever increasing

p : float, default 2.0

The p-norm to apply for computing the Minkowski distance.

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.

mp : numpy.ndarray, default None

A pre-computed matrix profile (and corresponding matrix profile indices).

This is a 2D array of shape `(len(T) - m + 1, 2 * k + 2)`, where the first

`k` columns are top-k matrix profile, and the next `k` columns are their

corresponding indices. The last two columns correspond to the top-1 left

and top-1 right matrix profile indices. When None (default), this array is

computed internally using `stumpy.aamp`.

Returns

-------

None

"""

self._T = core._preprocess(T)

core.check_window_size(m, max_size=self._T.shape[0])

self._m = m

self._n = self._T.shape[0]

self._excl_zone = int(np.ceil(self._m / config.STUMPY_EXCL_ZONE_DENOM))

self._egress = egress

self._p = p

self._k = k

if mp is None:

mp = aamp(self._T, self._m, p=self._p, k=self._k)

else:

mp = mp.copy()

if mp.shape != (

len(self._T) - self._m + 1,

2 * self._k + 2,

): # pragma: no cover

msg = (

f"The shape of `mp` must match ({len(T) - m + 1}, {2 * k + 2}) but "

+ f"found {mp.shape} instead."

)

raise ValueError(msg)

self._P = mp[:, : self._k].astype(np.float64)

self._I = mp[:, self._k : 2 * self._k].astype(np.int64)

self._left_I = mp[:, 2 * self._k].astype(np.int64)

self._left_P = np.full_like(self._left_I, np.inf, dtype=np.float64)

self._left_P[:] = np.inf

self._T_isfinite = np.isfinite(self._T)

self._T, self._T_subseq_isfinite = core.preprocess_non_normalized(

self._T, self._m

)

# Retrieve the left matrix profile values

# Since each matrix profile value is the minimum between the left and right

# matrix profile values, we can save time by re-computing only the left matrix

# profile value when the matrix profile index is equal to the right matrix

# profile index.

mask = self._left_I == self._I[:, 0]

self._left_P[mask] = self._P[mask, 0]

# Only re-compute the `i`-th left matrix profile value, `self._left_P[i]`,

# when `self._I[i] != self._left_I[i]`

for i in np.flatnonzero(self._left_I >= 0 & ~mask):

j = self._left_I[i]

if j >= 0:

self._left_P[i] = np.linalg.norm(

self._T[i : i + self._m] - self._T[j : j + self._m], ord=self._p

)

Q = self._T[-self._m :]

self._p_norm = core.mass_absolute(Q, self._T, p=self._p) ** self._p

if self._egress:

self._p_norm_new = np.empty(self._p_norm.shape[0], dtype=np.float64)

self._n_appended = 0

def update(self, t):

"""

Append a single new data point, `t`, to the existing time series `T` and update

the non-normalized (i.e., without z-normalization) matrix profile and matrix

profile indices.

Parameters

----------

t : float

A single new data point to be appended to `T`

Returns

-------

None

Notes

-----

`arXiv:1901.05708 \

<https://arxiv.org/pdf/1901.05708.pdf>`__

See Algorithm 1

Note that we have extended this algorithm for AB-joins as well.

"""

if self._egress:

self._update_egress(t)

else:

self._update(t)

def _update_egress(self, t):

"""

Ingress a new data point, egress the oldest data point, and update the matrix

profile and matrix profile indices

Parameters

----------

t : float

A single new data point to be appended to `T`

Returns

-------

None

"""

self._n = self._T.shape[0]

l = self._n - self._m + 1 - 1 # Subtract 1 due to egress

self._T[:-1] = self._T[1:]

self._T[-1] = t

self._n_appended += 1

self._p_norm[:-1] = self._p_norm[1:]

S = self._T[l:]

t_drop = self._T[l - 1]

self._T_isfinite[:-1] = self._T_isfinite[1:]

self._T_subseq_isfinite[:-1] = self._T_subseq_isfinite[1:]

self._I[:-1] = self._I[1:]

self._P[:-1] = self._P[1:]

self._left_I[:-1] = self._left_I[1:]

self._left_P[:-1] = self._left_P[1:]

if np.isfinite(t):

self._T_isfinite[-1] = True

else:

self._T_isfinite[-1] = False

t = 0

self._T[-1] = 0

S[-1] = 0

self._T_subseq_isfinite[-1] = np.all(self._T_isfinite[-self._m :])

self._p_norm_new[1:] = (

self._p_norm[:l]

- np.power(abs(self._T[:l] - t_drop), self._p)

+ np.power(abs(self._T[self._m :] - t), self._p)

)

self._p_norm_new[0] = (

np.linalg.norm(self._T[: self._m] - S[: self._m], ord=self._p) ** self._p

)

mask = self._p_norm_new < config.STUMPY_P_NORM_THRESHOLD

self._p_norm_new[mask] = 0

D = np.power(self._p_norm_new, 1.0 / self._p)

D[~self._T_subseq_isfinite] = np.inf

if np.any(~self._T_isfinite[-self._m :]):

D[:] = np.inf

core._update_incremental_PI(

D, self._P, self._I, self._excl_zone, n_appended=self._n_appended

)

# All neighbors of the last subsequence are on its left. So, its (top-1)

# matrix profile value/index and its left matrix profile value/index must

# be equal.

self._left_P[-1] = self._P[-1, 0]

self._left_I[-1] = self._I[-1, 0]

self._p_norm[:] = self._p_norm_new

def _update(self, t):

"""

Ingress a new data point and update the (top-k) matrix profile and matrix

profile indices without egressing the oldest data point

Parameters

----------

t : float

A single new data point to be appended to `T`

Returns

-------

None

"""

self._n = self._T.shape[0]

l = self._n - self._m + 1

T_new = np.append(self._T, t)

p_norm_new = np.empty(self._p_norm.shape[0] + 1, dtype=np.float64)

S = T_new[l:]

t_drop = T_new[l - 1]

if np.isfinite(t):

self._T_isfinite = np.append(self._T_isfinite, True)

else:

self._T_isfinite = np.append(self._T_isfinite, False)

t = 0

T_new[-1] = 0

S[-1] = 0

self._T_subseq_isfinite = np.append(

self._T_subseq_isfinite, np.all(self._T_isfinite[-self._m :])

)

p_norm_new[1:] = (

self._p_norm[:l]

- np.power(abs(T_new[:l] - t_drop), self._p)

+ np.power(abs(T_new[self._m :] - t), self._p)

)

p_norm_new[0] = (

np.linalg.norm(T_new[: self._m] - S[: self._m], ord=self._p) ** self._p

)

mask = p_norm_new < config.STUMPY_P_NORM_THRESHOLD

p_norm_new[mask] = 0

D = np.power(p_norm_new, 1.0 / self._p)

D[~self._T_subseq_isfinite] = np.inf

if np.any(~self._T_isfinite[-self._m :]):

D[:] = np.inf

P_new = np.full(self._k, np.inf, dtype=np.float64)

I_new = np.full(self._k, -1, dtype=np.int64)

self._P = np.append(self._P, P_new.reshape(1, -1), axis=0)

self._I = np.append(self._I, I_new.reshape(1, -1), axis=0)

core._update_incremental_PI(D, self._P, self._I, self._excl_zone, n_appended=0)

left_I_new = self._I[-1, 0]

left_P_new = self._P[-1, 0]

self._T = T_new

self._left_P = np.append(self._left_P, left_P_new)

self._left_I = np.append(self._left_I, left_I_new)

self._p_norm = p_norm_new

@property

def P_(self):

"""

Get the (top-k) matrix profile. When `k=1` (default), the output is

a 1D array consisting of the matrix profile. When `k > 1`, the

output is a 2D array that has exactly `k` columns and it consists of the

top-k matrix profile.

Parameters

----------

None

Returns

-------

out : numpy.ndarray

The (top-k) matrix profile

"""

if self._k == 1:

return self._P.flatten().astype(np.float64)

else:

return self._P.astype(np.float64)

@property

def I_(self):

"""

Get the (top-k) matrix profile indices. When `k=1` (default), the output is

a 1D array consisting of the matrix profile indices. When `k > 1`, the

output is a 2D array that has exactly `k` columns and it consists of the

top-k matrix profile indices.

Parameters

----------

None

Returns

-------

out : numpy.ndarray

The (top-k) matrix profile indices

"""

if self._k == 1:

return self._I.flatten().astype(np.int64)

else:

return self._I.astype(np.int64)

@property

def left_P_(self):

"""

Get the (top-1) left matrix profile

Parameters

----------

None

Returns

-------

out : numpy.ndarray

The (top-1) left matrix profile

"""

return self._left_P.astype(np.float64)

@property

def left_I_(self):

"""

Get the (top-1) left matrix profile indices

Parameters

----------

None

Returns

-------

out : numpy.ndarray

The (top-1) left matrix profile indices

"""

return self._left_I.astype(np.int64)

@property

def T_(self):

"""

Get the time series

Parameters

----------

None

Returns

-------

out : numpy.ndarray

The time series

"""

return self._T

stumpy/stumpy/aampi.py at main · stumpy-dev/stumpy · GitHub

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