How to print result of clustering in sklearn

Question

I have a sparse matrix

from scipy.sparse import *
M = csr_matrix((data_np, (rows_np, columns_np)));

then I'm doing clustering that way

from sklearn.cluster import KMeans
km = KMeans(n_clusters=n, init='random', max_iter=100, n_init=1, verbose=1)
km.fit(M)

and my question is extremely noob: how to print the clustering result without any extra information. I don't care about plotting or distances. I just need clustered rows looking that way

Cluster 1
row 1
row 2
row 3

Cluster 2
row 4
row 20
row 1000
...

How can I get it? Excuse me for this question.

Answer 1

Time to help myself. After

km.fit(M)

we run

labels = km.predict(M)

which returns labels, numpy.ndarray. Number of elements in this array equals number of rows. And each element means that a row belongs to the cluster. For example: if first element is 5 it means that row 1 belongs to cluster 5. Lets put our rows in a dictionary of lists looking this way {cluster_number:[row1, row2, row3], ...}

# in row_dict we store actual meanings of rows, in my case it's russian words
clusters = {}
    n = 0
    for item in labels:
        if item in clusters:
            clusters[item].append(row_dict[n])
        else:
            clusters[item] = [row_dict[n]]
        n +=1

and print the result

for item in clusters:
    print "Cluster ", item
    for i in clusters[item]:
        print i