I want to calculate Dynamic Time Warping (DTW) distances in a dataframe. The result must be a new dataframe (a distance matrix) which includes the pairwise dtw distances among each row.
For Euclidean Distance I use the following code:
from scipy.spatial.distance import pdist, squareform
euclidean_dist = squareform(pdist(sample_dataframe,'euclidean'))
I need a similar code for DTW.
Thanks in advance.
There are various ways one might do that. I'll leave two options bellow.
In case one wants to know the difference between the euclidean distance and DTW, this is a good resource.
Option 1
Using fastdtw.
Install it with
pip install fastdtw
Then use it as following
import numpy as np from scipy.spatial.distance import euclidean
from fastdtw import fastdtw
x = np.array([[1,1], [2,2], [3,3], [4,4], [5,5]])
y = np.array([[2,2],
[3,3], [4,4]])
distance, path = fastdtw(x, y, dist=euclidean)
print(distance)
Option 2 (Source)
def dtw(s, t):
n, m = len(s), len(t)
dtw_matrix = np.zeros((n+1, m+1))
for i in range(n+1):
for j in range(m+1):
dtw_matrix[i, j] = np.inf
dtw_matrix[0, 0] = 0
for i in range(1, n+1):
for j in range(1, m+1):
cost = abs(s[i-1] - t[j-1])
# take last min from a square box
last_min = np.min([dtw_matrix[i-1, j], dtw_matrix[i, j-1], dtw_matrix[i-1, j-1]])
dtw_matrix[i, j] = cost + last_min
return dtw_matrix
It works like the following
