sklearn matrix factorization example

Question

I am using a code currently given at http://www.quuxlabs.com/blog

It gives good result. And I can clearly see what changes in the matrix has happened .

Also I tried to use sklearn library at sklearn.decomposition.NMF But results I got with the same input are not good enough. Maybe i am missing something.

Here is my sample code -

from sklearn.decomposition import NMF , ProjectedGradientNMF
R = [
     [5,3,0,1],
     [4,0,0,1],
     [1,1,0,5],
     [1,0,0,4],
     [0,1,5,4],
    ]
R = numpy.array(R)
nmf = NMF(beta=0.001, eta=0.0001, init='random', max_iter=2000,nls_max_iter=20000, random_state=0, sparseness=None,tol=0.001)
nR = nmf.fit_transform(R)
print nR
print
print nmf.reconstruction_err_
print

It is not maintaining exiting/filled values in matrix as I can see using the code given in the blog.

Can someone help me understand !

Answer 1

Hmmm ... very dumb of me !!! I gone through nmf.py and found out that fit_tranform returns only W and nmf.component_ get value of H. Dot product of those gives new R.

from sklearn.decomposition import NMF , ProjectedGradientNMF
R = [
     [5,3,0,1],
     [4,0,0,1],
     [1,1,0,5],
     [1,0,0,4],
     [0,1,5,4],
    ]
R = numpy.array(R)
nmf = NMF()
W = nmf.fit_transform(R);
H = nmf.components_;
nR = numpy.dot(W,H)
print nR