Clustering with hclust from Python

As long as I have RPy working, I did a first version of a clustering script. First we do our imports and grab the definitions of R objects:

import sys, random import matplotlib.pyplot as plt import numpy as np from rpy2 import robjects import hm_utils as hm random.seed(153) matrix = robjects.r['matrix'] hclust = robjects.r['hclust'] dist = robjects.r['dist']

Next we generate a numpy array with random values in the set {0.5 .. 1.0}. Then we pick a few rows and mark a few of the same columns in each by changing each column's value to a random one between 0.0 .. 0.2. These show up as the light rectangles in the plot. Then we just:

# convert to R 'matrix' and cluster A = list(hm.flatten(A)) M = matrix(A,nrow=R) d = dist(M, method='euclidean') result = hclust(d,method='average') o = result[2] # order

The only hard part is going back and forth between a Python array and R matrix. There's probably a better way to do it, but what I used was flatten from here before feeding to R, and this going the other way:

def to_array(M,R,C): A = np.array(list(M)) A.shape = R,C return A

We plot a heat map using code from here. As you can see in the graphic at the top of the post, hclust does a reasonable job of finding the structure we created. Rather than list the code, I put a zipped folder with the files on Dropbox here.