String Distance Matrix in Python using pdist

Question

How to calculate Jaro Winkler distance matrix of strings in Python?

I have a large array of hand-entered strings (names and record numbers) and I\'m trying to find d

Answer 1

Here's a concise solution that requires neither numpy nor scipy:

from Levenshtein import jaro_winkler
data = ['Bob','Carl','Kristen','Calr', 'Doug']
dm = [[ jaro_winkler(a, b) for b in data] for a in data]
print('\n'.join([''.join([f'{item:6.2f}' for item in row]) for row in dm]))

  1.00  0.00  0.00  0.00  0.53
  0.00  1.00  0.46  0.93  0.00
  0.00  0.46  1.00  0.46  0.00
  0.00  0.93  0.46  1.00  0.00
  0.53  0.00  0.00  0.00  1.00

Answer 2

For anyone with a similar problem - One solution I just found is to extract the relevant code from the pdist function and add a [0] to the jaro_winkler function input to call the string out of the numpy array.

Example:

X = np.asarray(fname, order='c')
s = X.shape
m, n = s
dm = np.zeros((m * (m - 1)) // 2, dtype=np.double)

k = 0
for i in xrange(0, m - 1):
    for j in xrange(i + 1, m):
        dm[k] = jaro_winkler(X[i][0], X[j][0])
        k = k + 1

dms = squareform(dm)

Even though this algorithm works I'd still like to learn if there's a "right" computer-sciency-way to do this with the pdist function. Thanks, and hope this helps someone!

Answer 3

You need to wrap the distance function, like I demonstrated in the following example with the Levensthein distance

import numpy as np    
from Levenshtein import distance
from scipy.spatial.distance import pdist, squareform

# my list of strings
strings = ["hello","hallo","choco"]

# prepare 2 dimensional array M x N (M entries (3) with N dimensions (1)) 
transformed_strings = np.array(strings).reshape(-1,1)

# calculate condensed distance matrix by wrapping the Levenshtein distance function
distance_matrix = pdist(transformed_strings,lambda x,y: distance(x[0],y[0]))

# get square matrix
print(squareform(distance_matrix))

Output:
array([[ 0.,  1.,  4.],
       [ 1.,  0.,  4.],
       [ 4.,  4.,  0.]])