Since I had to use the R package smacof, yet the rest of my code usually is in python, I thought the fastest thing to do would be wrap smacof in python. Using the rpy2┬álib, you can do this in quite a straightforward manner for any piece of R you’d like to use in python. The data exchange between numpy and R is also nicely taken care of.

Here is the standard US cities distances example in R/python/smackoff.

[code lang=”python”]

numpy imports

from numpy import *
import pylab as plb
from scipy.spatial.distance import pdist, squareform

These imports take care of setting up the numpy->R conversion later

import rpy2.robjects as ro
from rpy2.robjects.numpy2ri import numpy2ri
ro.conversion.py2ri = numpy2ri

import the wrapped smacof from R

from rpy2.robjects.packages import importr
rsmacof = importr(‘smacof’)

the standard biggest 10 US cities pairwise distances dataset

of length N*(N-1)/2

modified from

labels = [‘Atl’,’Chi’,’Den’,’Hou’,’LA’,’Mia’,’NYC’,’SF’,’Sea’,’WDC’]
dist = [587,1212,701,1936,604,748,2139,2182,543,920,940,1745,1188,713,1858,

if name == ‘main‘:

convert the pairwise distances to distance matrix

d = squareform(dist)

run smacof through rpy2 with the numpy.ndarray d, rpy2 takes care of

numpy->R conversion because we set the correct flag earlier

stress = rsmacof.smacofSym(d, 2)

turns out the embedding is returned in the index 3, a cast to array

is all you have to do for the R->numpy conversion

data = array(stress[3])
data = -1*data # flip the axis to reflect the observer outside of earth (lolz)

plot data + labels

for i,label in enumerate(labels):
plb.title(“US cities embedding by le Smackoff”)

Edit: Seems like metric and nonmetric MDS are in queue for the next release of the machine learning library scikit-learn.