sparse 0.2.0

This Supports

• NumPy ufuncs (where zeros are preserved)

• Binary operations with other COO objects, where zeros are preserved.

• Binary operations with Scipy sparse matrices, where zeros are preserved.

• Binary operations with scalars, where zeros are preserved.

• Broadcasting binary operations and broadcast_to.

• Reductions (sum, max, min, prod, …)

• Reshape

• Transpose

• Tensordot

• triu, tril

• Slicing with integers, lists, and slices (with no step value)

• Concatenation and stacking

This may yet support

A “does not support” list is hard to build because it is infinitely long. However the following things are in scope, relatively doable, and not yet built (help welcome).

• Incremental buliding of arrays and inplace updates

• More operations supported by Numpy ndarray`s, such as :code:`argmin and argmax.

• Array building functions such as eye, spdiags. See building sparse matrices.

• Linear algebra operations such as inv, norm and solve. See scipy.sparse.linalg.

There are no plans to support

• Parallel computing (though Dask.array may use this in the future)

Example

pip install sparse

import numpy as np
n = 1000
ndims = 4
nnz = 1000000
coords = np.random.randint(0, n - 1, size=(ndims, nnz))
data = np.random.random(nnz)

import sparse
x = sparse.COO(coords, data, shape=((n,) * ndims))
x
# <COO: shape=(1000, 1000, 1000, 1000), dtype=float64, nnz=1000000>

x.nbytes
# 16000000

y = sparse.tensordot(x, x, axes=((3, 0), (1, 2)))

y
# <COO: shape=(1000, 1000, 1000, 1000), dtype=float64, nnz=1001588>

z = y.sum(axis=(0, 1, 2))
z
# <COO: shape=(1000,), dtype=float64, nnz=999>

z.todense()
# array([ 244.0671803 ,  246.38455787,  243.43383158,  256.46068737,
#         261.18598416,  256.36439011,  271.74177584,  238.56059193,
#         ...

How does this work?

Scipy.sparse implements decent 2-d sparse matrix objects for the standard layouts, notably for our purposes CSR, CSC, and COO. However it doesn’t include support for sparse arrays of greater than 2 dimensions.

This library extends the COO layout, which stores the row index, column index, and value of every element:

It is straightforward to extend the COO layout to an arbitrary number of dimensions:

This makes it easy to store a multidimensional sparse array, but we still need to reimplement all of the array operations like transpose, reshape, slicing, tensordot, reductions, etc., which can be quite challenging in general.

Fortunately in many cases we can leverage the existing SciPy.sparse algorithms if we can intelligently transpose and reshape our multi-dimensional array into an appropriate 2-d sparse matrix, perform a modified sparse matrix operation, and then reshape and transpose back. These reshape and transpose operations can all be done at numpy speeds by modifying the arrays of coordinates. After scipy.sparse runs its operations (coded in C) then we can convert back to using the same path of reshapings and transpositions in reverse.

This approach is not novel; it has been around in the multidimensional array community for a while. It is also how some operations in numpy work. For example the numpy.tensordot function performs transposes and reshapes so that it can use the numpy.dot function for matrix multiplication which is backed by fast BLAS implementations. The sparse.tensordot code is very slight modification of numpy.tensordot, replacing numpy.dot with scipy.sprarse.csr_matrix.dot.

LICENSE

This is licensed under New BSD-3