sparse matrix svd in python

Does 开发者_开发技巧anyone know how to perform svd operation on a sparse matrix in python? It seems that there is no such functionality provided in scipy.sparse.linalg.

Sounds like sparsesvd is what you're looking for! SVDLIBC efficiently wrapped in Python (no extra data copies made in RAM).

Simply run "easy_install sparsesvd" to install.

You can use the Divisi library to accomplish this; from the home page:

• It is a library written in Python, using a C library (SVDLIBC) to perform the sparse SVD operation using the Lanczos algorithm. Other mathematical computations are performed by NumPy.

You can try scipy.sparse.linalg.svd, although the documentation is still a work-in-progress and thus rather laconic.

A simple example using python-recsys library:

from recsys.algorithm.factorize import SVD

svd = SVD()
svd.load_data(dataset)
svd.compute(k=100, mean_center=True)

ITEMID1 = 1  # Toy Story
svd.similar(ITEMID1)
# Returns:
# [(1,    1.0),                 # Toy Story
#  (3114, 0.87060391051018071), # Toy Story 2
#  (2355, 0.67706936677315799), # A bug's life
#  (588,  0.5807351496754426),  # Aladdin
#  (595,  0.46031829709743477), # Beauty and the Beast
#  (1907, 0.44589398718134365), # Mulan
#  (364,  0.42908159895574161), # The Lion King
#  (2081, 0.42566581277820803), # The Little Mermaid
#  (3396, 0.42474056361935913), # The Muppet Movie
#  (2761, 0.40439361857585354)] # The Iron Giant

ITEMID2 = 2355 # A bug's life
svd.similarity(ITEMID1, ITEMID2)
# 0.67706936677315799