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# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# coding: utf-8
# pylint: disable=wildcard-import, unused-wildcard-import, too-many-lines
"""Sparse NDArray API of MXNet."""
from __future__ import absolute_import
from __future__ import division
try:
from __builtin__ import slice as py_slice
from __builtin__ import sum as py_sum
except ImportError:
from builtins import slice as py_slice
from builtins import sum as py_sum
import ctypes
import warnings
import operator
from array import array as native_array
__all__ = ["_ndarray_cls", "csr_matrix", "row_sparse_array",
"BaseSparseNDArray", "CSRNDArray", "RowSparseNDArray",
"add", "subtract", "multiply", "divide"]
import numpy as np
from ..base import NotSupportedForSparseNDArray
from ..base import _LIB, numeric_types
from ..base import c_array_buf, mx_real_t, integer_types
from ..base import mx_uint, NDArrayHandle, check_call
from ..context import Context
from . import _internal
from . import op
try:
from .gen_sparse import * # pylint: disable=redefined-builtin
except ImportError:
pass
from ._internal import _set_ndarray_class
from .ndarray import NDArray, _storage_type, _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP
from .ndarray import _STORAGE_TYPE_STR_TO_ID, _STORAGE_TYPE_ROW_SPARSE, _STORAGE_TYPE_CSR
from .ndarray import _STORAGE_TYPE_UNDEFINED, _STORAGE_TYPE_DEFAULT
from .ndarray import zeros as _zeros_ndarray
from .ndarray import array as _array
from .ndarray import _ufunc_helper
try:
import scipy.sparse as spsp
except ImportError:
spsp = None
_STORAGE_AUX_TYPES = {
'row_sparse': [np.int64],
'csr': [np.int64, np.int64]
}
def _new_alloc_handle(stype, shape, ctx, delay_alloc, dtype, aux_types, aux_shapes=None):
"""Return a new handle with specified storage type, shape, dtype and context.
Empty handle is only used to hold results
Returns
-------
handle
A new empty ndarray handle
"""
hdl = NDArrayHandle()
for aux_t in aux_types:
if np.dtype(aux_t) != np.dtype("int64"):
raise NotImplementedError("only int64 is supported for aux types")
aux_type_ids = [int(_DTYPE_NP_TO_MX[np.dtype(aux_t).type]) for aux_t in aux_types]
aux_shapes = [(0,) for aux_t in aux_types] if aux_shapes is None else aux_shapes
aux_shape_lens = [len(aux_shape) for aux_shape in aux_shapes]
aux_shapes = py_sum(aux_shapes, ())
num_aux = mx_uint(len(aux_types))
check_call(_LIB.MXNDArrayCreateSparseEx(
ctypes.c_int(int(_STORAGE_TYPE_STR_TO_ID[stype])),
c_array_buf(mx_uint, native_array('I', shape)),
mx_uint(len(shape)),
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
ctypes.c_int(int(delay_alloc)),
ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
num_aux,
c_array_buf(ctypes.c_int, native_array('i', aux_type_ids)),
c_array_buf(mx_uint, native_array('I', aux_shape_lens)),
c_array_buf(mx_uint, native_array('I', aux_shapes)),
ctypes.byref(hdl)))
return hdl
class BaseSparseNDArray(NDArray):
"""The base class of an NDArray stored in a sparse storage format.
See CSRNDArray and RowSparseNDArray for more details.
"""
def __repr__(self):
"""Returns a string representation of the sparse array."""
shape_info = 'x'.join(['%d' % x for x in self.shape])
# The data content is not displayed since the array usually has big shape
return '\n<%s %s @%s>' % (self.__class__.__name__,
shape_info, self.context)
def __add__(self, other):
return add(self, other)
def __sub__(self, other):
return subtract(self, other)
def __mul__(self, other):
return multiply(self, other)
def __div__(self, other):
return divide(self, other)
def __iadd__(self, other):
raise NotImplementedError()
def __isub__(self, other):
raise NotImplementedError()
def __imul__(self, other):
raise NotImplementedError()
def __idiv__(self, other):
raise NotImplementedError()
def __itruediv__(self, other):
raise NotImplementedError()
def _sync_copyfrom(self, source_array):
raise NotImplementedError()
def _at(self, idx):
raise NotSupportedForSparseNDArray(self._at, '[idx]', idx)
def _slice(self, start, stop):
raise NotSupportedForSparseNDArray(self._slice, None, start, stop)
def reshape(self, *shape, **kwargs):
raise NotSupportedForSparseNDArray(self.reshape, None, shape)
@property
def size(self):
# the `size` for a sparse ndarray is ambiguous, hence disabled.
raise NotImplementedError()
def _aux_type(self, i):
"""Data-type of the array's ith aux data.
Returns
-------
numpy.dtype
This BaseSparseNDArray's aux data type.
"""
aux_type = ctypes.c_int()
check_call(_LIB.MXNDArrayGetAuxType(self.handle, i, ctypes.byref(aux_type)))
return _DTYPE_MX_TO_NP[aux_type.value]
@property
def _num_aux(self):
"""The number of aux data used to help store the sparse ndarray.
"""
return len(_STORAGE_AUX_TYPES[self.stype])
@property
def _aux_types(self):
"""The data types of the aux data for the BaseSparseNDArray.
"""
aux_types = []
num_aux = self._num_aux
for i in range(num_aux):
aux_types.append(self._aux_type(i))
return aux_types
def asnumpy(self):
"""Return a dense ``numpy.ndarray`` object with value copied from this array
"""
return self.tostype('default').asnumpy()
def astype(self, dtype, copy=True):
"""Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
allocated ndarray on the same context. If this is set to
`False`, and the dtype requested is the same as the ndarray's
dtype, the ndarray is returned instead of a copy.
Examples
--------
>>> x = mx.nd.sparse.zeros('row_sparse', (2,3), dtype='float32')
>>> y = x.astype('int32')
>>> y.dtype
"""
if not copy and np.dtype(dtype) == self.dtype:
return self
res = zeros(shape=self.shape, ctx=self.context,
dtype=dtype, stype=self.stype)
self.copyto(res)
return res
def copyto(self, other):
"""Copies the value of this array to another array.
Parameters
----------
other : NDArray or CSRNDArray or RowSparseNDArray or Context
The destination array or context.
Returns
-------
NDArray or CSRNDArray or RowSparseNDArray
The copied array.
"""
# pylint: disable= no-member, protected-access
if isinstance(other, NDArray):
if other.handle is self.handle:
warnings.warn('You are attempting to copy an array to itself', RuntimeWarning)
return False
return _internal._copyto(self, out=other)
elif isinstance(other, Context):
hret = _ndarray_cls(_new_alloc_handle(self.stype, self.shape, other,
True, self.dtype, self._aux_types))
return _internal._copyto(self, out=hret)
else:
raise TypeError('copyto does not support type ' + str(type(other)))
# pylint: enable= no-member, protected-access
def check_format(self, full_check=True):
"""Check whether the NDArray format is valid.
Parameters
----------
full_check : bool, optional
If `True`, rigorous check, O(N) operations. Otherwise
basic check, O(1) operations (default True).
"""
check_call(_LIB.MXNDArraySyncCheckFormat(self.handle, ctypes.c_bool(full_check)))
def _data(self):
"""A deep copy NDArray of the data array associated with the BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetDataNDArray(self.handle, ctypes.byref(hdl)))
return NDArray(hdl)
def _aux_data(self, i):
""" Get a deep copy NDArray of the i-th aux data array associated with the
BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetAuxNDArray(self.handle, i, ctypes.byref(hdl)))
return NDArray(hdl)
# pylint: disable=abstract-method
[docs]class CSRNDArray(BaseSparseNDArray):
"""A sparse representation of 2D NDArray in the Compressed Sparse Row format.
A CSRNDArray represents an NDArray as three separate arrays: `data`,
`indptr` and `indices`. It uses the CSR representation where the column indices for
row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding values are stored
in ``data[indptr[i]:indptr[i+1]]``.
The column indices for a given row are expected to be sorted in ascending order.
Duplicate column entries for the same row are not allowed.
Example
-------
>>> a = mx.nd.array([[0, 1, 0], [2, 0, 0], [0, 0, 0], [0, 0, 3]])
>>> a = a.tostype('csr')
>>> a.data.asnumpy()
array([ 1., 2., 3.], dtype=float32)
>>> a.indices.asnumpy()
array([1, 0, 2])
>>> a.indptr.asnumpy()
array([0, 1, 2, 2, 3])
See Also
--------
csr_matrix: Several ways to construct a CSRNDArray
"""
def __reduce__(self):
return CSRNDArray, (None,), super(CSRNDArray, self).__getstate__()
def __iadd__(self, other):
(self + other).copyto(self)
return self
def __isub__(self, other):
(self - other).copyto(self)
return self
def __imul__(self, other):
(self * other).copyto(self)
return self
def __idiv__(self, other):
(self / other).copyto(self)
return self
def __itruediv__(self, other):
(self / other).copyto(self)
return self
[docs] def __getitem__(self, key):
"""x.__getitem__(i) <=> x[i]
Returns a newly created NDArray based on the indexing key.
Parameters
----------
key : int or slice
Indexing key.
Examples
--------
>>> indptr = np.array([0, 2, 3, 6])
>>> indices = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> a = mx.nd.sparse.csr_matrix((data, indices, indptr), shape=(3, 3))
>>> a.asnumpy()
array([[ 1., 0., 2.],
[ 0., 0., 3.],
[ 4., 5., 6.]], dtype=float32)
>>> a[1:2].asnumpy()
array([[ 0., 0., 3.]], dtype=float32)
>>> a[1].asnumpy()
array([[ 0., 0., 3.]], dtype=float32)
>>> a[-1].asnumpy()
array([[ 4., 5., 6.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
if isinstance(key, int):
if key == -1:
begin = self.shape[0] - 1
else:
begin = key
return op.slice(self, begin=begin, end=begin+1)
if isinstance(key, py_slice):
if key.step is not None:
raise ValueError('CSRNDArray only supports continuous slicing on axis 0')
if key.start is not None or key.stop is not None:
begin = key.start if key.start else 0
end = key.stop if key.stop else self.shape[0]
return op.slice(self, begin=begin, end=end)
else:
return self
if isinstance(key, tuple):
raise ValueError('Multi-dimension indexing is not supported')
raise ValueError('Undefined behaviour for {}'.format(key))
# pylint: enable= no-member, protected-access
[docs] def __setitem__(self, key, value):
"""x.__setitem__(i, y) <=> x[i]=y
Set self[key] to value. Only slice key [:] is supported.
Parameters
----------
key : slice
The indexing key.
value : NDArray or CSRNDArray or numpy.ndarray
The value to set.
Examples
--------
>>> src = mx.nd.sparse.zeros('csr', (3,3))
>>> src.asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> # assign CSRNDArray with same storage type
>>> x = mx.nd.ones((3,3)).tostype('csr')
>>> x[:] = src
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> # assign NDArray to CSRNDArray
>>> x[:] = mx.nd.ones((3,3)) * 2
>>> x.asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
"""
if not self.writable:
raise ValueError('Failed to assign to a readonly CSRNDArray')
if isinstance(key, py_slice):
if key.step is not None or key.start is not None or key.stop is not None:
raise ValueError('Assignment with slice for CSRNDArray is not ' \
'implmented yet.')
if isinstance(value, NDArray):
# avoid copying to itself
if value.handle is not self.handle:
value.copyto(self)
elif isinstance(value, numeric_types):
raise ValueError("Assigning numeric types to CSRNDArray is " \
"not implemented yet.")
elif isinstance(value, (np.ndarray, np.generic)):
# TODO(haibin/anisub) check scipy.sparse and use _sync_copy_from to
# avoid the temporary copy
warnings.warn('Assigning non-NDArray object to CSRNDArray is not efficient',
RuntimeWarning)
tmp = _array(value)
tmp.copyto(self)
else:
raise TypeError('type %s not supported' % str(type(value)))
else:
assert(isinstance(key, (int, tuple)))
raise Exception('CSRNDArray only supports [:] for assignment')
@property
def indices(self):
"""A deep copy NDArray of the indices array of the CSRNDArray.
This generates a deep copy of the column indices of the current `csr` matrix.
Returns
-------
NDArray
This CSRNDArray's indices array.
"""
return self._aux_data(1)
@property
def indptr(self):
"""A deep copy NDArray of the indptr array of the CSRNDArray.
This generates a deep copy of the `indptr` of the current `csr` matrix.
Returns
-------
NDArray
This CSRNDArray's indptr array.
"""
return self._aux_data(0)
@property
def data(self):
"""A deep copy NDArray of the data array of the CSRNDArray.
This generates a deep copy of the `data` of the current `csr` matrix.
Returns
-------
NDArray
This CSRNDArray's data array.
"""
return self._data()
@indices.setter
def indices(self, indices):
raise NotImplementedError()
@indptr.setter
def indptr(self, indptr):
raise NotImplementedError()
@data.setter
def data(self, data):
raise NotImplementedError()
[docs] def tostype(self, stype):
"""Return a copy of the array with chosen storage type.
Returns
-------
NDArray or CSRNDArray
A copy of the array with the chosen storage stype
"""
# pylint: disable= no-member, protected-access
if stype == 'row_sparse':
raise ValueError("cast_storage from csr to row_sparse is not supported")
return op.cast_storage(self, stype=stype)
# pylint: enable= no-member, protected-access
[docs] def copyto(self, other):
"""Copies the value of this array to another array.
If ``other`` is a ``NDArray`` or ``CSRNDArray`` object, then ``other.shape`` and
``self.shape`` should be the same. This function copies the value from
``self`` to ``other``.
If ``other`` is a context, a new ``CSRNDArray`` will be first created on
the target context, and the value of ``self`` is copied.
Parameters
----------
other : NDArray or CSRNDArray or Context
The destination array or context.
Returns
-------
NDArray or CSRNDArray
The copied array. If ``other`` is an ``NDArray`` or ``CSRNDArray``, then the return
value and ``other`` will point to the same ``NDArray`` or ``CSRNDArray``.
"""
if isinstance(other, Context):
return super(CSRNDArray, self).copyto(other)
elif isinstance(other, NDArray):
stype = other.stype
if stype == 'default' or stype == 'csr':
return super(CSRNDArray, self).copyto(other)
else:
raise TypeError('copyto does not support destination NDArray stype ' + str(stype))
else:
raise TypeError('copyto does not support type ' + str(type(other)))
[docs] 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)
>>> y
<2x3 sparse matrix of type ''
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
[docs]class RowSparseNDArray(BaseSparseNDArray):
"""A sparse representation of a set of NDArray row slices at given indices.
A RowSparseNDArray represents a multidimensional NDArray using two separate arrays: `data` and
`indices`. The number of dimensions has to be at least 2.
- data: an NDArray of any dtype with shape [D0, D1, ..., Dn].
- indices: a 1-D int64 NDArray with shape [D0] with values sorted in ascending order.
The `indices` stores the indices of the row slices with non-zeros,
while the values are stored in `data`. The corresponding NDArray ``dense``
represented by RowSparseNDArray ``rsp`` has
``dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...]``
>>> dense.asnumpy()
array([[ 1., 2., 3.],
[ 0., 0., 0.],
[ 4., 0., 5.],
[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> rsp = dense.tostype('row_sparse')
>>> rsp.indices.asnumpy()
array([0, 2], dtype=int64)
>>> rsp.data.asnumpy()
array([[ 1., 2., 3.],
[ 4., 0., 5.]], dtype=float32)
A RowSparseNDArray is typically used to represent non-zero row slices of a large NDArray
of shape [LARGE0, D1, .. , Dn] where LARGE0 >> D0 and most row slices are zeros.
RowSparseNDArray is used principally in the definition of gradients for operations
that have sparse gradients (e.g. sparse dot and sparse embedding).
See Also
--------
row_sparse_array: Several ways to construct a RowSparseNDArray
"""
def __reduce__(self):
return RowSparseNDArray, (None,), super(RowSparseNDArray, self).__getstate__()
def __iadd__(self, other):
(self + other).copyto(self)
return self
def __isub__(self, other):
(self - other).copyto(self)
return self
def __imul__(self, other):
(self * other).copyto(self)
return self
def __idiv__(self, other):
(self / other).copyto(self)
return self
def __itruediv__(self, other):
(self / other).copyto(self)
return self
[docs] def __getitem__(self, key):
"""x.__getitem__(i) <=> x[i]
Returns a sliced view of this array.
Parameters
----------
key : slice
Indexing key.
Examples
--------
>>> x = mx.nd.sparse.zeros('row_sparse', (2, 3))
>>> x[:].asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
"""
if isinstance(key, int):
raise Exception("__getitem__ with int key is not implemented for RowSparseNDArray yet")
if isinstance(key, py_slice):
if key.step is not None or key.start is not None or key.stop is not None:
raise Exception('RowSparseNDArray only supports [:] for __getitem__')
else:
return self
if isinstance(key, tuple):
raise ValueError('Multi-dimension indexing is not supported')
raise ValueError('Undefined behaviour for {}'.format(key))
[docs] def __setitem__(self, key, value):
"""x.__setitem__(i, y) <=> x[i]=y
Set self[key] to value. Only slice key [:] is supported.
Parameters
----------
key : slice
The indexing key.
value : NDArray or numpy.ndarray
The value to set.
Examples
--------
>>> src = mx.nd.row_sparse([[1, 0, 2], [4, 5, 6]], [0, 2], (3,3))
>>> src.asnumpy()
array([[ 1., 0., 2.],
[ 0., 0., 0.],
[ 4., 5., 6.]], dtype=float32)
>>> # assign RowSparseNDArray with same storage type
>>> x = mx.nd.sparse.zeros('row_sparse', (3,3))
>>> x[:] = src
>>> x.asnumpy()
array([[ 1., 0., 2.],
[ 0., 0., 0.],
[ 4., 5., 6.]], dtype=float32)
>>> # assign NDArray to RowSparseNDArray
>>> x[:] = mx.nd.ones((3,3))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
if not self.writable:
raise ValueError('Failed to assign to a readonly RowSparseNDArray')
if isinstance(key, py_slice):
if key.step is not None or key.start is not None or key.stop is not None:
raise ValueError('Assignment with slice for RowSparseNDArray ' \
'is not implmented yet.')
if isinstance(value, NDArray):
# avoid copying to itself
if value.handle is not self.handle:
value.copyto(self)
elif isinstance(value, numeric_types):
_internal._set_value(float(value), out=self)
elif isinstance(value, (np.ndarray, np.generic)):
warnings.warn('Assigning non-NDArray object to RowSparseNDArray is not efficient',
RuntimeWarning)
tmp = _array(value)
tmp.copyto(self)
else:
raise TypeError('type %s not supported' % str(type(value)))
else:
assert(isinstance(key, (int, tuple)))
raise TypeError('RowSparseNDArray only supports [:] for assignment')
# pylint: enable= no-member, protected-access
@property
def indices(self):
"""A deep copy NDArray of the indices array of the RowSparseNDArray.
This generates a deep copy of the row indices of the current `row_sparse` matrix.
Returns
-------
NDArray
This RowSparseNDArray's indices array.
"""
return self._aux_data(0)
@property
def data(self):
"""A deep copy NDArray of the data array of the RowSparseNDArray.
This generates a deep copy of the `data` of the current `row_sparse` matrix.
Returns
-------
NDArray
This RowSparseNDArray's data array.
"""
return self._data()
@indices.setter
def indices(self, indices):
raise NotImplementedError()
@data.setter
def data(self, data):
raise NotImplementedError()
[docs] def tostype(self, stype):
"""Return a copy of the array with chosen storage type.
Returns
-------
NDArray or RowSparseNDArray
A copy of the array with the chosen storage stype
"""
# pylint: disable= no-member, protected-access
if stype == 'csr':
raise ValueError("cast_storage from row_sparse to csr is not supported")
return op.cast_storage(self, stype=stype)
# pylint: enable= no-member, protected-access
[docs] def copyto(self, other):
"""Copies the value of this array to another array.
If ``other`` is a ``NDArray`` or ``RowSparseNDArray`` object, then ``other.shape``
and ``self.shape`` should be the same. This function copies the value from
``self`` to ``other``.
If ``other`` is a context, a new ``RowSparseNDArray`` will be first created on
the target context, and the value of ``self`` is copied.
Parameters
----------
other : NDArray or RowSparseNDArray or Context
The destination array or context.
Returns
-------
NDArray or RowSparseNDArray
The copied array. If ``other`` is an ``NDArray`` or ``RowSparseNDArray``, then the
return value and ``other`` will point to the same ``NDArray`` or ``RowSparseNDArray``.
"""
if isinstance(other, Context):
return super(RowSparseNDArray, self).copyto(other)
elif isinstance(other, NDArray):
stype = other.stype
if stype == 'default' or stype == 'row_sparse':
return super(RowSparseNDArray, self).copyto(other)
else:
raise TypeError('copyto does not support destination NDArray stype ' + str(stype))
else:
raise TypeError('copyto does not support type ' + str(type(other)))
[docs] def retain(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`retain`.
The arguments are the same as for :py:func:`retain`, with
this array as data.
"""
return retain(self, *args, **kwargs)
def _prepare_src_array(source_array, dtype):
"""Prepare `source_array` so that it can be used to construct NDArray.
`source_array` is converted to a `np.ndarray` if it's neither an `NDArray` \
nor an `np.ndarray`.
"""
if not isinstance(source_array, NDArray) and not isinstance(source_array, np.ndarray):
try:
source_array = np.array(source_array, dtype=dtype)
except:
raise TypeError('values must be array like object')
return source_array
def _prepare_default_dtype(src_array, dtype):
"""Prepare the value of dtype if `dtype` is None. If `src_array` is an NDArray, numpy.ndarray
or scipy.sparse.csr.csr_matrix, return src_array.dtype. float32 is returned otherwise."""
if dtype is None:
if isinstance(src_array, (NDArray, np.ndarray)):
dtype = src_array.dtype
elif spsp and isinstance(src_array, spsp.csr.csr_matrix):
dtype = src_array.dtype
else:
dtype = mx_real_t
return dtype
def _check_shape(s1, s2):
"""check s1 == s2 if both are not None"""
if s1 and s2 and s1 != s2:
raise ValueError("Shape mismatch detected. " + str(s1) + " v.s. " + str(s2))
[docs]def csr_matrix(arg1, shape=None, ctx=None, dtype=None):
"""Creates a `CSRNDArray`, an 2D array with compressed sparse row (CSR) format.
The CSRNDArray can be instantiated in several ways:
- csr_matrix(D):
to construct a CSRNDArray with a dense 2D array ``D``
- **D** (*array_like*) - An object exposing the array interface, an object whose \
`__array__` method returns an array, or any (nested) sequence.
- **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 ``D.dtype`` if ``D`` is an NDArray or numpy.ndarray, \
float32 otherwise.
- csr_matrix(S)
to construct a CSRNDArray with a sparse 2D array ``S``
- **S** (*CSRNDArray or scipy.sparse.csr.csr_matrix*) - A sparse matrix.
- **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 ``S.dtype``.
- csr_matrix((M, N))
to construct an empty CSRNDArray with shape ``(M, N)``
- **M** (*int*) - Number of rows in the matrix
- **N** (*int*) - Number of columns in the matrix
- **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.
- csr_matrix((data, indices, indptr))
to construct a CSRNDArray based on the definition of compressed sparse row format \
using three separate arrays, \
where the column indices for row i are stored in ``indices[indptr[i]:indptr[i+1]]`` \
and their corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``. \
The column indices for a given row are expected to be **sorted in ascending order.** \
Duplicate column entries for the same row are not allowed.
- **data** (*array_like*) - An object exposing the array interface, which \
holds all the non-zero entries of the matrix in row-major order.
- **indices** (*array_like*) - An object exposing the array interface, which \
stores the column index for each non-zero element in ``data``.
- **indptr** (*array_like*) - An object exposing the array interface, which \
stores the offset into ``data`` of the first non-zero element number of each \
row of the matrix.
- **shape** (*tuple of int, optional*) - The shape of the array. The default \
shape is inferred from the indices and indptr 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 ``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: 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.
ctx: Context, optional
Device context (default is the current default context).
dtype: str or numpy.dtype, optional
The data type of the output array.
Returns
-------
CSRNDArray
A `CSRNDArray` with the `csr` storage representation.
Example
-------
>>> a = mx.nd.sparse.csr_matrix(([1, 2, 3], [1, 0, 2], [0, 1, 2, 2, 3]), shape=(4, 3))
>>> a.asnumpy()
array([[ 0., 1., 0.],
[ 2., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 3.]], dtype=float32)
See Also
--------
CSRNDArray : MXNet NDArray in compressed sparse row format.
"""
# construct a csr matrix from (M, N) or (data, indices, indptr)
if isinstance(arg1, tuple):
arg_len = len(arg1)
if arg_len == 2:
# 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,
ctx=ctx, dtype=dtype)
else:
raise ValueError("Unexpected length of input tuple: " + str(arg_len))
else:
# construct a csr matrix from a sparse / dense one
if isinstance(arg1, CSRNDArray) or (spsp and isinstance(arg1, spsp.csr.csr_matrix)):
# construct a csr matrix from scipy or CSRNDArray
_check_shape(arg1.shape, shape)
return array(arg1, ctx=ctx, dtype=dtype)
elif isinstance(arg1, RowSparseNDArray):
raise ValueError("Unexpected input type: RowSparseNDArray")
else:
# construct a csr matrix from a dense one
# prepare default ctx and dtype since mx.nd.array doesn't use default values
# based on source_array
dtype = _prepare_default_dtype(arg1, dtype)
# create dns array with provided dtype. ctx is not passed since copy across
# ctx requires dtype to be the same
dns = _array(arg1, dtype=dtype)
if ctx is not None and dns.context != ctx:
dns = dns.as_in_context(ctx)
_check_shape(dns.shape, shape)
return dns.tostype('csr')
def _csr_matrix_from_definition(data, indices, indptr, shape=None, ctx=None,
dtype=None, indices_type=None, indptr_type=None):
"""Create a `CSRNDArray` based on data, indices and indptr"""
# pylint: disable= no-member, protected-access
storage_type = 'csr'
# context
ctx = Context.default_ctx if ctx is None else ctx
# types
dtype = _prepare_default_dtype(data, dtype)
indptr_type = _STORAGE_AUX_TYPES[storage_type][0] if indptr_type is None else indptr_type
indices_type = _STORAGE_AUX_TYPES[storage_type][1] if indices_type is None else indices_type
# prepare src array and types
data = _prepare_src_array(data, dtype)
indptr = _prepare_src_array(indptr, indptr_type)
indices = _prepare_src_array(indices, indices_type)
# TODO(junwu): Convert data, indptr, and indices to mxnet NDArrays
# if they are not for now. In the future, we should provide a c-api
# to accept np.ndarray types to copy from to result.data and aux_data
if not isinstance(data, NDArray):
data = _array(data, ctx, dtype)
if not isinstance(indptr, NDArray):
indptr = _array(indptr, ctx, indptr_type)
if not isinstance(indices, NDArray):
indices = _array(indices, ctx, indices_type)
if shape is None:
if indices.shape[0] == 0:
raise ValueError('invalid shape')
shape = (len(indptr) - 1, op.max(indices).asscalar() + 1)
# verify shapes
aux_shapes = [indptr.shape, indices.shape]
if data.ndim != 1 or indptr.ndim != 1 or indices.ndim != 1 or \
indptr.shape[0] == 0 or len(shape) != 2:
raise ValueError('invalid shape')
result = CSRNDArray(_new_alloc_handle(storage_type, shape, ctx, False, dtype,
[indptr_type, indices_type], aux_shapes))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, data.handle, ctypes.c_int(-1)))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, indptr.handle, ctypes.c_int(0)))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, indices.handle, ctypes.c_int(1)))
return result
# pylint: enable= no-member, protected-access
[docs]def row_sparse_array(arg1, shape=None, ctx=None, dtype=None):
"""Creates a `RowSparseNDArray`, a multidimensional row sparse array with a set of \
tensor slices at given indices.
The RowSparseNDArray can be instantiated in several ways:
- row_sparse_array(D):
to construct a RowSparseNDArray with a dense ndarray ``D``
- **D** (*array_like*) - An object exposing the array interface, an object whose \
`__array__` method returns an array, or any (nested) sequence.
- **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 ``D.dtype`` if ``D`` is an NDArray or numpy.ndarray, \
float32 otherwise.
- row_sparse_array(S)
to construct a RowSparseNDArray with a sparse ndarray ``S``
- **S** (*RowSparseNDArray*) - A sparse ndarray.
- **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 ``S.dtype``.
- row_sparse_array((D0, D1 .. Dn))
to construct an empty RowSparseNDArray with shape ``(D0, D1, ... Dn)``
- **D0, D1 .. Dn** (*int*) - The shape of the ndarray
- **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.
- row_sparse_array((data, indices))
to construct a RowSparseNDArray based on the definition of row sparse format \
using two separate arrays, \
where the `indices` stores the indices of the row slices with non-zeros,
while the values are stored in `data`. The corresponding NDArray ``dense``
represented by RowSparseNDArray ``rsp`` has \
``dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...]``
The row indices for are expected to be **sorted in ascending order.** \
- **data** (*array_like*) - An object exposing the array interface, which \
holds all the non-zero row slices of the array.
- **indices** (*array_like*) - An object exposing the array interface, which \
stores the row index for each row slice with non-zero elements.
- **shape** (*tuple of int, optional*) - The shape of the array. The default \
shape is inferred from the indices and indptr 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, numpy.ndarray, RowSparseNDArray, tuple of int or tuple of array_like
The argument to help instantiate the row sparse ndarray. See above for further details.
shape : tuple of int, optional
The shape of the row sparse ndarray.
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array.
Returns
-------
RowSparseNDArray
An `RowSparseNDArray` with the `row_sparse` storage representation.
Example
-------
>>> a = mx.nd.sparse.row_sparse_array(([[1, 2], [3, 4]], [1, 4]), shape=(6, 2))
>>> a.asnumpy()
array([[ 0., 0.],
[ 1., 2.],
[ 0., 0.],
[ 0., 0.],
[ 3., 4.],
[ 0., 0.]], dtype=float32)
See Also
--------
RowSparseNDArray : MXNet NDArray in row sparse format.
"""
# construct a row sparse array from (D0, D1 ..) or (data, indices)
if isinstance(arg1, tuple):
arg_len = len(arg1)
if arg_len < 2:
raise ValueError("Unexpected length of input tuple: " + str(arg_len))
elif arg_len > 2:
# empty ndarray with shape
_check_shape(arg1, shape)
return empty('row_sparse', arg1, ctx=ctx, dtype=dtype)
else:
# len(arg1) = 2, is either shape or (data, indices)
if isinstance(arg1[0], integer_types) and isinstance(arg1[1], integer_types):
# empty ndarray with shape
_check_shape(arg1, shape)
return empty('row_sparse', arg1, ctx=ctx, dtype=dtype)
else:
# data, indices, indptr
return _row_sparse_ndarray_from_definition(arg1[0], arg1[1], shape=shape,
ctx=ctx, dtype=dtype)
else:
# construct a row sparse ndarray from a dense / sparse array
if isinstance(arg1, RowSparseNDArray):
# construct a row sparse ndarray from RowSparseNDArray
_check_shape(arg1.shape, shape)
return array(arg1, ctx=ctx, dtype=dtype)
elif isinstance(arg1, CSRNDArray):
raise ValueError("Unexpected input type: CSRNDArray")
else:
# construct a csr matrix from a dense one
# prepare default dtype since mx.nd.array doesn't use default values
# based on source_array
dtype = _prepare_default_dtype(arg1, dtype)
# create dns array with provided dtype. ctx is not passed since copy across
# ctx requires dtype to be the same
dns = _array(arg1, dtype=dtype)
if ctx is not None and dns.context != ctx:
dns = dns.as_in_context(ctx)
_check_shape(dns.shape, shape)
return dns.tostype('row_sparse')
def _row_sparse_ndarray_from_definition(data, indices, shape=None, ctx=None,
dtype=None, indices_type=None):
"""Create a `RowSparseNDArray` based on data and indices"""
storage_type = 'row_sparse'
# context
ctx = Context.default_ctx if ctx is None else ctx
# types
dtype = _prepare_default_dtype(data, dtype)
indices_type = _STORAGE_AUX_TYPES[storage_type][0] if indices_type is None else indices_type
# prepare src array and types
data = _prepare_src_array(data, dtype)
indices = _prepare_src_array(indices, indices_type)
# TODO(junwu): Convert data, indptr, and indices to mxnet NDArrays
# if they are not for now. In the future, we should provide a c-api
# to accept np.ndarray types to copy from to result.data and aux_data
if not isinstance(data, NDArray):
data = _array(data, ctx, dtype)
if not isinstance(indices, NDArray):
indices = _array(indices, ctx, indices_type)
if shape is None:
num_indices = indices.shape[0]
if num_indices == 0:
raise ValueError('invalid shape')
dim0 = indices[num_indices - 1].asscalar() + 1
shape = (dim0, ) + data.shape[1:]
# verify shapes
if data.ndim != len(shape) or indices.ndim != 1 or np.prod(shape[1:]) == 0:
raise ValueError("invalid shape")
result = RowSparseNDArray(_new_alloc_handle(storage_type, shape, ctx, False, dtype,
[indices_type], [indices.shape]))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, data.handle, ctypes.c_int(-1)))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, indices.handle, ctypes.c_int(0)))
return result
def _ndarray_cls(handle, writable=True, stype=_STORAGE_TYPE_UNDEFINED):
if stype == _STORAGE_TYPE_UNDEFINED:
stype = _storage_type(handle)
if stype == _STORAGE_TYPE_DEFAULT:
return NDArray(handle, writable=writable)
elif stype == _STORAGE_TYPE_CSR:
return CSRNDArray(handle, writable=writable)
elif stype == _STORAGE_TYPE_ROW_SPARSE:
return RowSparseNDArray(handle, writable=writable)
else:
raise Exception("unknown storage type: %s"%stype)
_set_ndarray_class(_ndarray_cls)
[docs]def add(lhs, rhs):
"""Returns element-wise sum of the input arrays with broadcasting.
Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
``mx.nd.broadcast_plus(lhs, rhs)`` when shapes of lhs and rhs do not
match. If lhs.shape == rhs.shape, this is equivalent to
``mx.nd.elemwise_add(lhs, rhs)``
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.abs
Parameters
----------
lhs : scalar or array
First array to be added.
rhs : scalar or array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise sum of the input arrays.
Examples
--------
>>> a = mx.nd.ones((2,3)).tostype('csr')
>>> b = mx.nd.ones((2,3)).tostype('csr')
>>> a.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> b.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (a+b).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> c = mx.nd.ones((2,3)).tostype('row_sparse')
>>> d = mx.nd.ones((2,3)).tostype('row_sparse')
>>> c.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> d.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (c+d).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
if isinstance(lhs, NDArray) and isinstance(rhs, NDArray) and lhs.shape == rhs.shape:
return _ufunc_helper(
lhs,
rhs,
op.elemwise_add,
operator.add,
_internal._plus_scalar,
None)
return _ufunc_helper(
lhs,
rhs,
op.broadcast_add,
operator.add,
_internal._plus_scalar,
None)
# pylint: enable= no-member, protected-access
[docs]def subtract(lhs, rhs):
"""Returns element-wise difference of the input arrays with broadcasting.
Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
``mx.nd.broadcast_minus(lhs, rhs)`` when shapes of lhs and rhs do not
match. If lhs.shape == rhs.shape, this is equivalent to
``mx.nd.elemwise_sub(lhs, rhs)``
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or array
First array to be subtracted.
rhs : scalar or array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.__spec__
Returns
-------
NDArray
The element-wise difference of the input arrays.
Examples
--------
>>> a = mx.nd.ones((2,3)).tostype('csr')
>>> b = mx.nd.ones((2,3)).tostype('csr')
>>> a.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> b.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (a-b).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> c = mx.nd.ones((2,3)).tostype('row_sparse')
>>> d = mx.nd.ones((2,3)).tostype('row_sparse')
>>> c.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> d.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (c-d).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
if isinstance(lhs, NDArray) and isinstance(rhs, NDArray) and lhs.shape == rhs.shape:
return _ufunc_helper(
lhs,
rhs,
op.elemwise_sub,
operator.sub,
_internal._minus_scalar,
None)
return _ufunc_helper(
lhs,
rhs,
op.broadcast_sub,
operator.sub,
_internal._minus_scalar,
None)
# pylint: enable= no-member, protected-access
[docs]def multiply(lhs, rhs):
"""Returns element-wise product of the input arrays with broadcasting.
Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``
when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape,
this is equivalent to ``mx.nd.elemwise_mul(lhs, rhs)``
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or array
First array to be multiplied.
rhs : scalar or array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise multiplication of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3)).tostype('csr')
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(3)
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([ 0., 1., 2.], dtype=float32)
>>> (x*2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x*y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.sparse.multiply(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (x*z).asnumpy()
array([[ 0., 1., 2.],
[ 0., 1., 2.]], dtype=float32)
>>> mx.nd.sparse.multiply(x, z).asnumpy()
array([[ 0., 1., 2.],
[ 0., 1., 2.]], dtype=float32)
>>> z = z.reshape((1, 3))
>>> z.asnumpy()
array([[ 0., 1., 2.]], dtype=float32)
>>> (x*z).asnumpy()
array([[ 0., 1., 2.],
[ 0., 1., 2.]], dtype=float32)
>>> mx.nd.sparse.multiply(x, z).asnumpy()
array([[ 0., 1., 2.],
[ 0., 1., 2.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
if isinstance(lhs, NDArray) and isinstance(rhs, NDArray) and lhs.shape == rhs.shape:
return _ufunc_helper(
lhs,
rhs,
op.elemwise_mul,
operator.mul,
_internal._mul_scalar,
None)
return _ufunc_helper(
lhs,
rhs,
op.broadcast_mul,
operator.mul,
_internal._mul_scalar,
None)
# pylint: enable= no-member, protected-access
[docs]def divide(lhs, rhs):
"""Returns element-wise division of the input arrays with broadcasting.
Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``
when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape,
this is equivalent to ``mx.nd.elemwise_div(lhs, rhs)``
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or array
First array in division.
rhs : scalar or array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise division of the input arrays.
Examples
--------
>>> x = (mx.nd.ones((2,3))*6).tostype('csr')
>>> y = mx.nd.arange(2).reshape((2,1)) + 1
>>> z = mx.nd.arange(3) + 1
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> z.asnumpy()
array([ 1., 2., 3.], dtype=float32)
>>> x/2
>>> (x/3).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x/y).asnumpy()
array([[ 6., 6., 6.],
[ 3., 3., 3.]], dtype=float32)
>>> mx.nd.sparse.divide(x,y).asnumpy()
array([[ 6., 6., 6.],
[ 3., 3., 3.]], dtype=float32)
>>> (x/z).asnumpy()
array([[ 6., 3., 2.],
[ 6., 3., 2.]], dtype=float32)
>>> mx.nd.sprase.divide(x,z).asnumpy()
array([[ 6., 3., 2.],
[ 6., 3., 2.]], dtype=float32)
>>> z = z.reshape((1,3))
>>> z.asnumpy()
array([[ 1., 2., 3.]], dtype=float32)
>>> (x/z).asnumpy()
array([[ 6., 3., 2.],
[ 6., 3., 2.]], dtype=float32)
>>> mx.nd.sparse.divide(x,z).asnumpy()
array([[ 6., 3., 2.],
[ 6., 3., 2.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
if isinstance(lhs, NDArray) and isinstance(rhs, NDArray) and lhs.shape == rhs.shape:
return _ufunc_helper(
lhs,
rhs,
op.elemwise_div,
operator.truediv,
_internal._div_scalar,
None)
return _ufunc_helper(
lhs,
rhs,
op.broadcast_div,
operator.truediv,
_internal._div_scalar,
None)
# pylint: enable= no-member, protected-access
[docs]def zeros(stype, shape, ctx=None, dtype=None, **kwargs):
"""Return a new array of given shape and type, filled with zeros.
Parameters
----------
stype: string
The storage type of the empty array, such as 'row_sparse', 'csr', etc
shape : int or tuple of int
The shape of the empty array
ctx : Context, optional
An optional device context (default is the current default context)
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`)
Returns
-------
RowSparseNDArray or CSRNDArray
A created array
Examples
--------
>>> mx.nd.sparse.zeros('csr', (1,2))
>>> mx.nd.sparse.zeros('row_sparse', (1,2), ctx=mx.cpu(), dtype='float16').asnumpy()
array([[ 0., 0.]], dtype=float16)
"""
# pylint: disable= no-member, protected-access
if stype == 'default':
return _zeros_ndarray(shape, ctx=ctx, dtype=dtype, **kwargs)
if ctx is None:
ctx = Context.default_ctx
dtype = mx_real_t if dtype is None else dtype
if stype == 'row_sparse' or stype == 'csr':
aux_types = _STORAGE_AUX_TYPES[stype]
else:
raise ValueError("unknown storage type" + stype)
out = _ndarray_cls(_new_alloc_handle(stype, shape, ctx, True, dtype, aux_types))
return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, out=out, **kwargs)
# pylint: enable= no-member, protected-access
[docs]def empty(stype, shape, ctx=None, dtype=None):
"""Returns a new array of given shape and type, without initializing entries.
Parameters
----------
stype: string
The storage type of the empty array, such as 'row_sparse', 'csr', etc
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
Returns
-------
CSRNDArray or RowSparseNDArray
A created array.
"""
if isinstance(shape, int):
shape = (shape, )
if ctx is None:
ctx = Context.default_ctx
if dtype is None:
dtype = mx_real_t
assert(stype is not None)
if stype == 'csr' or stype == 'row_sparse':
return zeros(stype, shape, ctx=ctx, dtype=dtype)
else:
raise Exception("unknown stype : " + str(stype))
[docs]def array(source_array, ctx=None, dtype=None):
"""Creates a sparse array from any object exposing the array interface.
Parameters
----------
source_array : RowSparseNDArray, CSRNDArray or scipy.sparse.csr.csr_matrix
The source sparse array
ctx : Context, optional
The default context is ``source_array.context`` if ``source_array`` is an NDArray. \
The current default context otherwise.
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `numpy.ndarray` or `scipy.sparse.csr.csr_matrix`, \
`float32` otherwise.
Returns
-------
RowSparseNDArray or CSRNDArray
An array with the same contents as the `source_array`.
Examples
--------
>>> import scipy.sparse as spsp
>>> csr = spsp.csr_matrix((2, 100))
>>> mx.nd.sparse.array(csr)
>>> mx.nd.sparse.array(mx.nd.sparse.zeros('csr', (3, 2)))
>>> mx.nd.sparse.array(mx.nd.sparse.zeros('row_sparse', (3, 2)))
"""
ctx = Context.default_ctx if ctx is None else ctx
if isinstance(source_array, NDArray):
assert(source_array.stype != 'default'), \
"Please use `tostype` to create RowSparseNDArray or CSRNDArray from an NDArray"
# prepare dtype and ctx based on source_array, if not provided
dtype = _prepare_default_dtype(source_array, dtype)
# if both dtype and ctx are different from source_array, we cannot copy directly
if source_array.dtype != dtype and source_array.context != ctx:
arr = empty(source_array.stype, source_array.shape, dtype=dtype)
arr[:] = source_array
arr = arr.as_in_context(ctx)
else:
arr = empty(source_array.stype, source_array.shape, dtype=dtype, ctx=ctx)
arr[:] = source_array
return arr
elif spsp and isinstance(source_array, spsp.csr.csr_matrix):
# TODO(haibin) implement `_sync_copy_from` with scipy csr object to reduce a copy
# preprocess scipy csr to canonical form
csr = source_array.sorted_indices()
csr.sum_duplicates()
dtype = _prepare_default_dtype(source_array, dtype)
return csr_matrix((csr.data, csr.indices, csr.indptr), shape=csr.shape, \
dtype=dtype, ctx=ctx)
elif isinstance(source_array, (np.ndarray, np.generic)):
raise ValueError("Please use mx.nd.array to create an NDArray with source_array of type ",
type(source_array))
else:
raise ValueError("Unexpected source_array type: ", type(source_array))
