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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/11/05 00:10:14 UTC
[GitHub] piiswrong closed pull request #8362: Add asscipy feature
piiswrong closed pull request #8362: Add asscipy feature
URL: https://github.com/apache/incubator-mxnet/pull/8362
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diff --git a/docs/api/python/ndarray/sparse.md b/docs/api/python/ndarray/sparse.md
index 0424405508..9b742f4fc5 100644
--- a/docs/api/python/ndarray/sparse.md
+++ b/docs/api/python/ndarray/sparse.md
@@ -124,6 +124,7 @@ We summarize the interface for each class in the following sections.
CSRNDArray.copyto
CSRNDArray.as_in_context
CSRNDArray.asnumpy
+ CSRNDArray.asscipy
CSRNDArray.asscalar
CSRNDArray.astype
CSRNDArray.tostype
diff --git a/python/mxnet/ndarray/sparse.py b/python/mxnet/ndarray/sparse.py
index a31ec01f4f..45a269a10d 100644
--- a/python/mxnet/ndarray/sparse.py
+++ b/python/mxnet/ndarray/sparse.py
@@ -492,6 +492,27 @@ def copyto(self, other):
else:
raise TypeError('copyto does not support type ' + str(type(other)))
+ def asscipy(self):
+ """Returns a ``scipy.sparse.csr.csr_matrix`` object with value copied from this array
+
+ Examples
+ --------
+ >>> x = mx.nd.sparse.zeros('csr', (2,3))
+ >>> y = x.asscipy()
+ >>> type(y)
+ <type 'scipy.sparse.csr.csr_matrix'>
+ >>> y
+ <2x3 sparse matrix of type '<type 'numpy.float32'>'
+ with 0 stored elements in Compressed Sparse Row format>
+ """
+ data = self.data.asnumpy()
+ indices = self.indices.asnumpy()
+ indptr = self.indptr.asnumpy()
+ if not spsp:
+ raise ImportError("scipy is not available. \
+ Please check if the scipy python bindings are installed.")
+ return spsp.csr_matrix((data, indices, indptr), shape=self.shape, dtype=self.dtype)
+
# pylint: disable=abstract-method
class RowSparseNDArray(BaseSparseNDArray):
"""A sparse representation of a set of NDArray row slices at given indices.
@@ -798,10 +819,30 @@ def csr_matrix(arg1, shape=None, ctx=None, dtype=None):
The default dtype is ``data.dtype`` if ``data`` is an NDArray or numpy.ndarray, \
float32 otherwise.
+ - csr_matrix((data, (row, col)))
+ to construct a CSRNDArray based on the COOrdinate format \
+ using three seperate arrays, \
+ where ``row[i]`` is the row index of the element, \
+ ``col[i]`` is the column index of the element \
+ and ``data[i]`` is the data corresponding to the element. All the missing \
+ elements in the input are taken to be zeroes.
+ - **data** (*array_like*) - An object exposing the array interface, which \
+ holds all the non-zero entries of the matrix in COO format.
+ - **row** (*array_like*) - An object exposing the array interface, which \
+ stores the row index for each non zero element in ``data``.
+ - **col** (*array_like*) - An object exposing the array interface, which \
+ stores the col index for each non zero element in ``data``.
+ - **shape** (*tuple of int, optional*) - The shape of the array. The default \
+ shape is inferred from the ``row`` and ``col`` arrays.
+ - **ctx** (*Context, optional*) - Device context \
+ (default is the current default context).
+ - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
+ The default dtype is float32.
+
Parameters
----------
- arg1: NDArray, CSRNDArray, numpy.ndarray, scipy.sparse.csr.csr_matrix, tuple of int or tuple \
- of array_like
+ arg1: tuple of int, tuple of array_like, array_like, CSRNDArray, scipy.sparse.csr_matrix, \
+ scipy.sparse.coo_matrix, tuple of int or tuple of array_like
The argument to help instantiate the csr matrix. See above for further details.
shape : tuple of int, optional
The shape of the csr matrix.
@@ -832,9 +873,24 @@ def csr_matrix(arg1, shape=None, ctx=None, dtype=None):
if isinstance(arg1, tuple):
arg_len = len(arg1)
if arg_len == 2:
- # empty matrix with shape
- _check_shape(arg1, shape)
- return empty('csr', arg1, ctx=ctx, dtype=dtype)
+ # construct a sparse csr matrix from
+ # scipy coo matrix if input format is coo
+ if isinstance(arg1[1], tuple) and len(arg1[1]) == 2:
+ data, (row, col) = arg1
+ if isinstance(data, NDArray):
+ data = data.asnumpy()
+ if isinstance(row, NDArray):
+ row = row.asnumpy()
+ if isinstance(col, NDArray):
+ col = col.asnumpy()
+ coo = spsp.coo_matrix((data, (row, col)), shape=shape)
+ _check_shape(coo.shape, shape)
+ csr = coo.tocsr()
+ return array(csr, ctx=ctx, dtype=dtype)
+ else:
+ # empty matrix with shape
+ _check_shape(arg1, shape)
+ return empty('csr', arg1, ctx=ctx, dtype=dtype)
elif arg_len == 3:
# data, indices, indptr
return _csr_matrix_from_definition(arg1[0], arg1[1], arg1[2], shape=shape,
diff --git a/tests/python/unittest/test_sparse_ndarray.py b/tests/python/unittest/test_sparse_ndarray.py
index 0bbb5ffaaa..03a4307757 100644
--- a/tests/python/unittest/test_sparse_ndarray.py
+++ b/tests/python/unittest/test_sparse_ndarray.py
@@ -476,17 +476,38 @@ def check_create_csr_from_nd(shape, density, dtype):
assert same(csr_created.indptr.asnumpy(), indptr.asnumpy())
assert same(csr_created.indices.asnumpy(), indices.asnumpy())
# verify csr matrix dtype and ctx is consistent from the ones provided
- assert csr_created.dtype == dtype
- assert csr_created.data.dtype == dtype
- assert csr_created.context == Context.default_ctx
+ assert csr_created.dtype == dtype, (csr_created, dtype)
+ assert csr_created.data.dtype == dtype, (csr_created.data.dtype, dtype)
+ assert csr_created.context == Context.default_ctx, (csr_created.context, Context.default_ctx)
csr_copy = mx.nd.array(csr_created)
assert(same(csr_copy.asnumpy(), csr_created.asnumpy()))
+ def check_create_csr_from_coo(shape, density, dtype):
+ matrix = rand_ndarray(shape, 'csr', density)
+ sp_csr = matrix.asscipy()
+ sp_coo = sp_csr.tocoo()
+ csr_created = mx.nd.sparse.csr_matrix((sp_coo.data, (sp_coo.row, sp_coo.col)), shape=shape, dtype=dtype)
+ assert csr_created.stype == 'csr'
+ assert same(csr_created.data.asnumpy(), sp_csr.data)
+ assert same(csr_created.indptr.asnumpy(), sp_csr.indptr)
+ assert same(csr_created.indices.asnumpy(), sp_csr.indices)
+ csr_copy = mx.nd.array(csr_created)
+ assert(same(csr_copy.asnumpy(), csr_created.asnumpy()))
+ # verify csr matrix dtype and ctx is consistent
+ assert csr_created.dtype == dtype, (csr_created.dtype, dtype)
+ assert csr_created.data.dtype == dtype, (csr_created.data.dtype, dtype)
+ assert csr_created.context == Context.default_ctx, (csr_created.context, Context.default_ctx)
+
def check_create_csr_from_scipy(shape, density, f):
def assert_csr_almost_equal(nd, sp):
assert_almost_equal(nd.data.asnumpy(), sp.data)
assert_almost_equal(nd.indptr.asnumpy(), sp.indptr)
assert_almost_equal(nd.indices.asnumpy(), sp.indices)
+ sp_csr = nd.asscipy()
+ assert_almost_equal(sp_csr.data, sp.data)
+ assert_almost_equal(sp_csr.indptr, sp.indptr)
+ assert_almost_equal(sp_csr.indices, sp.indices)
+ assert(sp.dtype == sp_csr.dtype), (sp.dtype, sp_csr.dtype)
try:
import scipy.sparse as spsp
@@ -498,8 +519,8 @@ def assert_csr_almost_equal(nd, sp):
indptr = np.array([0, 2, 3, 7])
indices = np.array([0, 2, 2, 0, 1, 2, 1])
data = np.array([1, 2, 3, 4, 5, 6, 1])
- non_canonical_csr = spsp.csr_matrix((data, indices, indptr), shape=(3, 3))
- canonical_csr_nd = f(non_canonical_csr)
+ non_canonical_csr = spsp.csr_matrix((data, indices, indptr), shape=(3, 3), dtype=csr_nd.dtype)
+ canonical_csr_nd = f(non_canonical_csr, dtype=csr_nd.dtype)
canonical_csr_sp = non_canonical_csr.copy()
canonical_csr_sp.sum_duplicates()
canonical_csr_sp.sort_indices()
@@ -514,10 +535,10 @@ def assert_csr_almost_equal(nd, sp):
for density in densities:
shape = rand_shape_2d(dim0, dim1)
check_create_csr_from_nd(shape, density, dtype)
+ check_create_csr_from_coo(shape, density, dtype)
check_create_csr_from_scipy(shape, density, mx.nd.sparse.array)
check_create_csr_from_scipy(shape, density, mx.nd.array)
-
def test_create_row_sparse():
dim0 = 50
dim1 = 50
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