Library for time series distances (e.g. Dynamic Time Warping) used in the DTAI Research Group. The library offers a pure Python implementation and a fast implementation in C. The C implementation has only Cython as a dependency. It is compatible with Numpy and Pandas and implemented to avoid unnecessary data copy operations.
Documentation: http://dtaidistance.readthedocs.io
Wannes Meert, Kilian Hendrickx, & Toon Van Craenendonck. wannesm/dtaidistance (Version v2.0.0). Zenodo. http://doi.org/10.5281/zenodo.3981067
New in v2:
ssize_t
instead of int
allows for larger data structures on 64 bit
machines and be more compatible with Numpy.max_dist
argument turned out to be similar to Silva and Batista's work
on PrunedDTW [7]. The toolbox now implements a version that is equal to PrunedDTW
since it prunes more partial distances. Additionally, a use_pruning
argument
is added to automatically set max_dist
to the Euclidean distance, as suggested
by Silva and Batista, to speed up the computation (a new method ub_euclidean
is available).dtaidistance.dtw_ndim
package.$ pip install dtaidistance
In case the C based version is not available, see the documentation for
alternative installation options. In case
OpenMP
is not available on your system add the --noopenmp
global option.
The library has no dependency on Numpy. But if Numpy is available, some additional functionality is provided. If you want to make sure this is also installed then use:
$ pip install dtaidistance[numpy]
The source code is available at github.com/wannesm/dtaidistance.
from dtaidistance import dtw
from dtaidistance import dtw_visualisation as dtwvis
import numpy as np
s1 = np.array([0., 0, 1, 2, 1, 0, 1, 0, 0, 2, 1, 0, 0])
s2 = np.array([0., 1, 2, 3, 1, 0, 0, 0, 2, 1, 0, 0, 0])
path = dtw.warping_path(s1, s2)
dtwvis.plot_warping(s1, s2, path, filename="warp.png")
Only the distance measure based on two sequences of numbers:
from dtaidistance import dtw
s1 = [0, 0, 1, 2, 1, 0, 1, 0, 0]
s2 = [0, 1, 2, 0, 0, 0, 0, 0, 0]
distance = dtw.distance(s1, s2)
print(distance)
The fastest version (30-300 times) uses c directly but requires an array as input (with the double type),
and (optionally) also prunes computations by setting max_dist
to the Euclidean upper bound:
from dtaidistance import dtw
import array
s1 = array.array('d',[0, 0, 1, 2, 1, 0, 1, 0, 0])
s2 = array.array('d',[0, 1, 2, 0, 0, 0, 0, 0, 0])
d = dtw.distance_fast(s1, s2, use_pruning=True)
Or you can use a numpy array (with dtype double or float):
from dtaidistance import dtw
import numpy as np
s1 = np.array([0, 0, 1, 2, 1, 0, 1, 0, 0], dtype=np.double)
s2 = np.array([0.0, 1, 2, 0, 0, 0, 0, 0, 0])
d = dtw.distance_fast(s1, s2, use_pruning=True)
Check the __doc__
for information about the available arguments:
print(dtw.distance.__doc__)
A number of options are foreseen to early stop some paths the dynamic programming algorithm is exploring or tune the distance measure computation:
window
: Only allow for shifts up to this amount away from the two diagonals.max_dist
: Stop if the returned distance measure will be larger than this value.max_step
: Do not allow steps larger than this value.max_length_diff
: Return infinity if difference in length of two series is larger.penalty
: Penalty to add if compression or expansion is applied (on top of the distance).psi
: Psi relaxation to ignore begin and/or end of sequences (for cylical sequences) [2].use_pruning
: Prune computations based on the Euclidean upper bound.If, next to the distance, you also want the full matrix to see all possible warping paths:
from dtaidistance import dtw
s1 = [0, 0, 1, 2, 1, 0, 1, 0, 0]
s2 = [0, 1, 2, 0, 0, 0, 0, 0, 0]
distance, paths = dtw.warping_paths(s1, s2)
print(distance)
print(paths)
The matrix with all warping paths can be visualised as follows:
from dtaidistance import dtw
from dtaidistance import dtw_visualisation as dtwvis
import random
import numpy as np
x = np.arange(0, 20, .5)
s1 = np.sin(x)
s2 = np.sin(x - 1)
random.seed(1)
for idx in range(len(s2)):
if random.random() < 0.05:
s2[idx] += (random.random() - 0.5) / 2
d, paths = dtw.warping_paths(s1, s2, window=25, psi=2)
best_path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(s1, s2, paths, best_path)
Notice the psi
parameter that relaxes the matching at the beginning and end.
In this example this results in a perfect match even though the sine waves are slightly shifted.
To compute the DTW distance measures between all sequences in a list of sequences, use the method dtw.distance_matrix
.
You can set variables to use more or less c code (use_c
and use_nogil
) and parallel or serial execution
(parallel
).
The distance_matrix
method expects a list of lists/arrays:
from dtaidistance import dtw
import numpy as np
series = [
np.array([0, 0, 1, 2, 1, 0, 1, 0, 0], dtype=np.double),
np.array([0.0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0]),
np.array([0.0, 0, 1, 2, 1, 0, 0, 0])]
ds = dtw.distance_matrix_fast(series)
or a matrix (in case all series have the same length):
from dtaidistance import dtw
import numpy as np
series = np.matrix([
[0.0, 0, 1, 2, 1, 0, 1, 0, 0],
[0.0, 1, 2, 0, 0, 0, 0, 0, 0],
[0.0, 0, 1, 2, 1, 0, 0, 0, 0]])
ds = dtw.distance_matrix_fast(series)
You can instruct the computation to only fill part of the distance measures matrix. For example to distribute the computations over multiple nodes, or to only compare source series to target series.
from dtaidistance import dtw
import numpy as np
series = np.matrix([
[0., 0, 1, 2, 1, 0, 1, 0, 0],
[0., 1, 2, 0, 0, 0, 0, 0, 0],
[1., 2, 0, 0, 0, 0, 0, 1, 1],
[0., 0, 1, 2, 1, 0, 1, 0, 0],
[0., 1, 2, 0, 0, 0, 0, 0, 0],
[1., 2, 0, 0, 0, 0, 0, 1, 1]])
ds = dtw.distance_matrix_fast(series, block=((1, 4), (3, 5)))
The output in this case will be:
# 0 1 2 3 4 5
[[ inf inf inf inf inf inf] # 0
[ inf inf inf 1.4142 0.0000 inf] # 1
[ inf inf inf 2.2360 1.7320 inf] # 2
[ inf inf inf inf 1.4142 inf] # 3
[ inf inf inf inf inf inf] # 4
[ inf inf inf inf inf inf]] # 5
A distance matrix can be used for time series clustering. You can use existing methods such as
scipy.cluster.hierarchy.linkage
or one of two included clustering methods (the latter is a
wrapper for the SciPy linkage method).
from dtaidistance import clustering
# Custom Hierarchical clustering
model1 = clustering.Hierarchical(dtw.distance_matrix_fast, {})
cluster_idx = model1.fit(series)
# Augment Hierarchical object to keep track of the full tree
model2 = clustering.HierarchicalTree(model1)
cluster_idx = model2.fit(series)
# SciPy linkage clustering
model3 = clustering.LinkageTree(dtw.distance_matrix_fast, {})
cluster_idx = model3.fit(series)
For models that keep track of the full clustering tree (HierarchicalTree
or LinkageTree
), the
tree can be visualised:
model.plot("myplot.png")
Optional:
Development:
DTAI distance code.
Copyright 2016-2021 KU Leuven, DTAI Research Group
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.