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Merge pull request #780 from bouchnic/sparse

Sparse
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theano/sparse/basic.py
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"""
Classes for handling sparse matrices.
"""Classes for handling sparse matrices.
To read about different sparse formats, see
U{http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps}.
@todo: Automatic methods for determining best sparse format?
http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps
"""
# TODO
# Automatic methods for determining best sparse format?
from itertools import izip
import sys
......@@ -28,7 +29,6 @@ def register_specialize(lopt, *tags, **kwargs):
lopt.__name__, lopt, 'fast_run',
*tags)
""" Types of sparse matrices to use for testing """
_mtypes = [scipy.sparse.csc_matrix, scipy.sparse.csr_matrix]
#_mtypes = [sparse.csc_matrix, sparse.csr_matrix, sparse.dok_matrix,
......@@ -104,15 +104,19 @@ def _kmap_hash(a):
# Wrapper type
def as_sparse_variable(x, name=None):
"""
Wrapper around SparseVariable constructor.
@param x: A sparse matrix. as_sparse_variable reads dtype and format
properties out of this sparse matrix.
@return: SparseVariable version of sp.
"""Wrapper around SparseVariable constructor to construct
a Variable with a sparse matrix with the same dtype and
format.
@todo Verify that sp is sufficiently sparse, and raise a warning if it is
not
:param x: A sparse matrix.
:return: SparseVariable version of `x`.
"""
# TODO
# Verify that sp is sufficiently sparse, and raise a
# warning if it is not
if isinstance(x, gof.Apply):
if len(x.outputs) != 1:
raise ValueError("It is ambiguous which output of a "
......@@ -132,9 +136,15 @@ as_sparse = as_sparse_variable
def as_sparse_or_tensor_variable(x, name=None):
"""Same as `as_sparse_variable` but If we can't make a
sparse variable, we try to make a tensor variable.
format.
:param x: A sparse matrix.
:return: SparseVariable or TensorVariable version of `x`.
"""
If we can't make a sparse variable, we try to make a tensor variable.
"""
try:
return as_sparse_variable(x, name)
except (ValueError, TypeError):
......@@ -142,10 +152,19 @@ def as_sparse_or_tensor_variable(x, name=None):
def verify_grad_sparse(op, pt, structured=False, *args, **kwargs):
"""Wrapper for theano.test.unittest_tools.py:verify_grad wich
converts sparse variables back and forth.
:param op: Op to check.
:param pt: List of inputs to realize the tests.
:param structured: True to tests with a structured grad,
False otherwise.
:param *args: Other `verify_grad` parameters if any.
:param **kwargs: Other `verify_grad` keywords if any.
:return: None
"""
Wrapper for theano.test.unittest_tools.py:verify_grad
Converts sparse variables back and forth.
"""
conv_none = lambda x: x
def conv_csr(ind, indptr, shp):
......@@ -354,6 +373,7 @@ class SparseConstant(gof.Constant, _sparse_py_operators):
def __repr__(self):
return str(self)
class SparseType(gof.Type):
"""
@type dtype: numpy dtype string such as 'int64' or 'float64' (among others)
......@@ -510,28 +530,44 @@ discrete_dtypes = int_dtypes + uint_dtypes
# CONSTRUCTION
class CSMProperties(gof.Op):
"""Extract all of .data .indices and .indptr
:note: We won't implement infer_shape for this op now. This will
ask that we implement an GetNNZ op, and this op will keep
the dependence on the input of this op. So this won't help
to remove computations in the graph. To remove computation,
we will need to make an infer_sparse_pattern feature to
remove computations. Doing this is trickier then the
infer_shape feature. For example, how do we handle the case
when some op create some 0 values? So there is dependence
on the values themselves. We could write an infer_shape for
the last output that is the shape, but I dough this will
get used.
"""Extract all of .data, .indices, .indptr and .shape.
For specific field, `csm_data`, `csm_indices`, `csm_indptr`
and `csm_shape` are provided. Also, `kmap` could be
set through to constructor to specified the parts
of the parameter `data` the op should return.Fancy indexing
with numpy.ndarray should be used for this purpose.
:param csm: Sparse matrix in CSR or CSC format.
:return: (data, indices, indptr, shape), the properties
of `csm`.
:note:
- The grad implemented is regular, i.e. not structured.
- `infer_shape` method is not available for this op.
"""
# NOTE
# We won't implement infer_shape for this op now. This will
# ask that we implement an GetNNZ op, and this op will keep
# the dependence on the input of this op. So this won't help
# to remove computations in the graph. To remove computation,
# we will need to make an infer_sparse_pattern feature to
# remove computations. Doing this is trickier then the
# infer_shape feature. For example, how do we handle the case
# when some op create some 0 values? So there is dependence
# on the values themselves. We could write an infer_shape for
# the last output that is the shape, but I dough this will
# get used.
# we don't return a view of the shape, we create a new ndarray from the
# shape tuple.
view_map = {0: [0], 1: [0], 2: [0]}
kmap = None
""" WRITEME """
"""Indexing to speficied what part of the data parameter
should be use to construct the sparse matrix."""
def __init__(self, kmap=None):
self.kmap = kmap
......@@ -592,16 +628,43 @@ def csm_shape(csm):
class CSM(gof.Op):
"""Construct a CSC or CSR matrix from the internal representation """
"""Construct a CSC or CSR matrix from the internal
representation.
The format for the sparse array can be specified
through the constructor. Also, `kmap` could be
set through to constructor to specified the parts
of the parameter `data` the op should use to construct
the sparse matrix. Fancy indexing with numpy.ndarray
should be used for this purpose.
:param data: One dimensionnal tensor representing
the data of the sparse to construct.
:param indices: One dimensionnal tensor of integers
representing the indices of the sparse
matrix to construct.
:param indptr: One dimensionnal tensor of integers
representing the indice pointer for
the sparse matrix to construct.
:param shape: One dimensionnal tensor of integers
representing the shape of the sparse
matrix to construct.
:return: A sparse matrix having the properties
speficied by the inputs.
:note:
- The grad method returns a dense vector, so it provide
a regular grad.
"""
# should view the other inputs too, but viewing multiple inputs is not
view_map = {0: [0]}
#currently supported by the destroyhandler
format = None
"""WRITEME"""
kmap = None
"""WRITEME"""
"""Indexing to speficied what part of the data parameter
should be use to construct the sparse matrix."""
_hashval = None
"""Pre-computed hash value, defined by __init__"""
......@@ -628,16 +691,6 @@ class CSM(gof.Op):
return self._hashval
def make_node(self, data, indices, indptr, shape):
"""Build a SparseVariable from the internal parametrization
:param data:
:param indices:
:param indptr:
:type data: 1-d tensor
:type indices: 1-d tensor of ints
:type indptr: 1-d tensor of ints
"""
data = tensor.as_tensor_variable(data)
if not isinstance(indices, tensor.TensorVariable):
......@@ -653,13 +706,13 @@ class CSM(gof.Op):
if data.type.ndim != 1:
raise TypeError('data argument must be a vector', data.type,
data.type.ndim)
if indices.type.ndim != 1 or indices.type.dtype != 'int32':
if indices.type.ndim != 1 or indices.type.dtype not in discrete_dtypes:
raise TypeError('indices must be vector of integers', indices,
indices.type)
if indptr.type.ndim != 1 or indptr.type.dtype != 'int32':
if indptr.type.ndim != 1 or indptr.type.dtype not in discrete_dtypes:
raise TypeError('indices must be vector of integers', indptr,
indptr.type)
if shape.type.ndim != 1 or shape.type.dtype != 'int32':
if shape.type.ndim != 1 or shape.type.dtype not in discrete_dtypes:
raise TypeError('n_rows must be integer type', shape, shape.type)
return gof.Apply(self,
......@@ -668,7 +721,6 @@ class CSM(gof.Op):
format=self.format).make_variable()])
def perform(self, node, (data, indices, indptr, shape), (out,)):
"""Build a csc_matrix"""
# for efficiency, if remap does nothing, then do not apply it
if self.kmap is not None:
data = data[self.kmap]
......@@ -694,7 +746,6 @@ class CSM(gof.Op):
copy=False)
def grad(self, (x_data, x_indices, x_indptr, x_shape), (g_out,)):
"""Return a gradient on the data vector"""
g_data, g_indices, g_indptr, g_shape = csm_properties(g_out)
#unpack the data vector and wrap it as a 1d TensorType
g_data = csm_grad(self.kmap)(x_data, x_indices, x_indptr, x_shape,
......@@ -714,20 +765,20 @@ CSR = CSM('csr')
class CSMGrad(gof.op.Op):
"""
This Op computes the gradient of the CSM Op. CSM creates a matrix from
data, indices, and ind_ptr vectors; it's gradient is the gradient of
the data vector only. There are two complexities to calculate this
gradient:
1. The gradient may be sparser than the input matrix defined by (data,
indices, ind_ptr). In this case, the data vector of the gradient will have
less elements than the data vector of the input because sparse formats
remove 0s. Since we are only returning the gradient of the data vector, the
relevant 0s need to be added back.
2. The elements in the sparse dimension are not guaranteed to be sorted.
Therefore, the input data vector may have a different order than the
gradient data vector.
"""
# Note
# This Op computes the gradient of the CSM Op. CSM creates a matrix from
# data, indices, and indptr vectors; it's gradient is the gradient of
# the data vector only. There are two complexities to calculate this
# gradient:
# 1. The gradient may be sparser than the input matrix defined by (data,
# indices, indptr). In this case, the data vector of the gradient will have
# less elements than the data vector of the input because sparse formats
# remove 0s. Since we are only returning the gradient of the data vector,
# the relevant 0s need to be added back.
# 2. The elements in the sparse dimension are not guaranteed to be sorted.
# Therefore, the input data vector may have a different order than the
# gradient data vector.
def __init__(self, kmap=None):
self.kmap = kmap
if self.kmap is None:
......@@ -969,9 +1020,20 @@ zcast = Cast('complex128')
class DenseFromSparse(gof.op.Op):
"""Convert a sparse matrix to a dense one.
:param x: A sparse matrix.
:return: A dense matrix, the same as `x`.
:note:
- The grad implementation can be controlled
through the constructor via the `structured`
parameter. `True` will provide a structured
grad while `False` will provide a regular
grad. By default, the grad is structured.
"""
Convert a sparse matrix to an `ndarray`.
"""
def __init__(self, structured=True):
self.sparse_grad = structured
......@@ -1018,9 +1080,23 @@ dense_from_sparse = DenseFromSparse()
class SparseFromDense(gof.op.Op):
"""Convert a dense matrix to a sparse matrix.
To convert in CSR format, use `csr_from_dense`
and to convert in CSC format, use `csc_from_dense`.
:param x: A dense matrix.
:return: The same as `x` in a sparse matrix
format.
:note:
- The grad implementation is regular, i.e.
not structured.
- The output sparse format can also be controlled
via the `format` parameter in the constructor.
"""
Convert an `ndarray` matrix to a sparse matrix.
"""
def __init__(self, format):
self.format = format
......@@ -1071,22 +1147,32 @@ csc_from_dense = SparseFromDense('csc')
# Indexing
class GetItem2d(gof.op.Op):
"""
Implement a subtensor of sparse variable and that return a sparse matrix.
"""Implement a subtensor of sparse variable and that return a
sparse matrix.
If you want to take only one element of a sparse matrix see
`GetItemScalar` that return a tensor scalar.
If you want to take only one element of a sparse matrix see the
class GetItemScalar that return a tensor scalar.
.. note::
:note: that subtensor selection always returns a matrix so
indexing with [a:b, c:d] is forced. If one index is a scalar,
e.g. x[a:b, c] and x[a, b:c], generate an error. Use instead
x[a:b, c:c+1] and x[a:a+1, b:c].
Subtensor selection always returns a matrix, so indexing
with [a:b, c:d] is forced. If one index is a scalar. For
instance, x[a:b, c] and x[a, b:c], generate an error. Use
instead x[a:b, c:c+1] and x[a:a+1, b:c].
The above indexing methods are not supported because the rval
The above indexing methods are not supported because the return value
would be a sparse matrix rather than a sparse vector, which is a
deviation from numpy indexing rule. This decision is made largely
for keeping the consistency between numpy and theano. Subjected
for keeping the consistency between numpy and theano. Subjected
to modification when sparse vector is supported.
:param x: Sparse matrix.
:param index: Tuple of slice object.
:return: The slice corresponding in `x`.
:note:
- The grad is not implemented for this op.
"""
def __eq__(self, other):
......@@ -1096,7 +1182,7 @@ class GetItem2d(gof.op.Op):
return hash(type(self))
# Fred:Too complicated for now. If you need it, look at
# the Subtensor.infer_shape.
# the Subtensor.infer_shape.
# def infer_shape(self, node, i0_shapes):
# return i0_shapes
......@@ -1172,12 +1258,21 @@ get_item_2d = GetItem2d()
class GetItemScalar(gof.op.Op):
"""
Implement a subtensor of a sparse variable that take two scalar as
index and return a scalar
"""Implement a subtensor of a sparse variable that take
two scalar as index and return a scalar.
If you want to take a slice of a sparse matrix see
`GetItem2d` that return a sparse matrix.
:see: GetItem2d to return more than one element.
:param x: Sparse matrix.
:param index: Tuple of scalar..
:return: The item corresponding in `x`.
:note:
- The grad is not implemented for this op.
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1220,10 +1315,22 @@ class GetItemScalar(gof.op.Op):
get_item_scalar = GetItemScalar()
# Linear Algebra
class Transpose(gof.op.Op):
"""Return the transpose of the sparse matrix.
:param x: Sparse matrix.
:return: `x` transposed.
:note:
- The returned matrix will not be in the same format. `csc`
matrix will be changed in `csr` matrix and `csr` matrix in
`csc` matrix.
- The grad is regular, i.e. not structured.
"""
class Transpose(gof.op.Op):
format_map = {'csr': 'csc',
'csc': 'csr'}
......@@ -1242,7 +1349,7 @@ class Transpose(gof.op.Op):
[x],
[SparseType(dtype=x.type.dtype,
format=self.format_map[x.type.format]
).make_variable()])
).make_variable()])
def perform(self, node, (x, ), (out, )):
assert _is_sparse(x)
......@@ -1254,11 +1361,20 @@ class Transpose(gof.op.Op):
def infer_shape(self, node, shapes):
return [shapes[0][::-1]]
transpose = Transpose()
class Neg(gof.op.Op):
"""Return the negation of the sparse matrix.
:param x: Sparse matrix.
:return: -`x`.
:note:
- The grad is regular, i.e. not structured.
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1282,58 +1398,240 @@ class Neg(gof.op.Op):
def infer_shape(self, node, shapes):
return [shapes[0]]
neg = Neg()
class SpSum(gof.op.Op):
"""TODO: rewrite
Scale each columns of a sparse matrix by the
corresponding element of a dense vector
class ColScaleCSC(gof.op.Op):
# Scale each columns of a sparse matrix by the corresponding
# element of a dense vector
# :param x: A sparse matrix.
# :param s: A dense vector with length equal to the number
# of columns of `x`.
# :return: A sparse matrix in the same format as `x` which
# each column had been multiply by the corresponding
# element of `s`.
# :note:
# - The grad implemented is structured.
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, x, s):
if x.format != 'csc':
raise ValueError('x was not a csc matrix')
return gof.Apply(self, [x, s], [x.type()])
def perform(self, node, (x, s), (z,)):
M, N = x.shape
assert x.format == 'csc'
assert s.shape == (N, )
y = x.copy()
for j in xrange(0, N):
y.data[y.indptr[j]: y.indptr[j + 1]] *= s[j]
z[0] = y
def grad(self, (x, s), (gz,)):
return [col_scale(gz, s), sp_sum(x * gz, axis=0)]
def infer_shape(self, node, ins_shapes):
return [ins_shapes[0]]
def __str__(self):
return self.__class__.__name__
class RowScaleCSC(gof.op.Op):
# Scale each row of a sparse matrix by the corresponding element of
# a dense vector
# :param x: A sparse matrix.
# :param s: A dense vector with length equal to the number
# of rows of `x`.
# :return: A sparse matrix in the same format as `x` which
# each row had been multiply by the corresponding
# element of `s`.
# :note:
# - The grad implemented is structured.
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, x, s):
return gof.Apply(self, [x, s], [x.type()])
def perform(self, node, (x, s), (z,)):
M, N = x.shape
assert x.format == 'csc'
assert s.shape == (M, )
indices = x.indices
indptr = x.indptr
y_data = x.data.copy()
for j in xrange(0, N):
for i_idx in xrange(indptr[j], indptr[j + 1]):
y_data[i_idx] *= s[indices[i_idx]]
z[0] = scipy.sparse.csc_matrix((y_data, indices, indptr), (M, N))
def grad(self, (x, s), (gz,)):
return [row_scale(gz, s), sp_sum(x * gz, axis=1)]
def infer_shape(self, node, ins_shapes):
return [ins_shapes[0]]
def __str__(self):
return self.__class__.__name__
def col_scale(x, s):
"""Scale each columns of a sparse matrix by the corresponding
element of a dense vector
:param x: A sparse matrix.
:param s: A dense vector with length equal to the number
of columns of `x`.
:return: A sparse matrix in the same format as `x` which
each column had been multiply by the corresponding
element of `s`.
:note:
- The grad implemented is structured.
"""
axis = None
sparse_grad = False
if x.format == 'csc':
return ColScaleCSC()(x, s)
elif x.format == 'csr':
return RowScaleCSC()(x.T, s).T
else:
raise NotImplementedError()
def __init__(self, axis, sparse_grad=True):
"""
:param sparse_grad: if True, this instance ignores the
gradient on matrix elements which are implicitly 0.
"""
def row_scale(x, s):
"""Scale each row of a sparse matrix by the corresponding element of
a dense vector
:param x: A sparse matrix.
:param s: A dense vector with length equal to the number
of rows of `x`.
:return: A sparse matrix in the same format as `x` which
each row had been multiply by the corresponding
element of `s`.
:note:
- The grad implemented is structured.
"""
return col_scale(x.T, s).T
class SpSum(gof.op.Op):
"""Calculate the sum of a sparse matrix along a specify
axis.
It operates a reduction along the axis specified. When
`axis` is `None`, it is apply along all axis.
:param x: Sparse matrix.
:param axis: Axis along the sum is apply. Integers or `None`.
:param sparse_grad: `True` to have a structured grad. Boolean.
:return: The sum of `x` in a dense format.
:note:
- The grad implementation is controlled with the `sparse_grad`
parameter. `True` will provide a structured grad and `False`
will provide a regular grad. For both choice, the grad
return a sparse matrix having the same format as `x`.
- This op does not return a sparse matrix, but a dense tensor
matrix.
"""
def __init__(self, axis=None, sparse_grad=True):
super(SpSum, self).__init__()
self.axis = axis
self.sparse_grad = sparse_grad
self.structured = sparse_grad
if self.axis not in (None, 0, 1):
raise ValueError('illegal value for self.axis')
raise ValueError('Illegal value for self.axis.')
def __eq__(self, other):
#WARNING: judgement call...
#not using the sparse_grad in the comparison or hashing
#because it doesn't change the perform method therefore, we
#*do* want Sums with different sparse_grad values to be merged
#by the merge optimization.
# This requires them to compare equal.
# WARNING: judgement call...
# We are not using the structured in the comparison or hashing
# because it doesn't change the perform method therefore, we
# *do* want Sums with different structured values to be merged
# by the merge optimization and this requires them to compare equal.
return type(self) == type(other) and self.axis == other.axis
def __hash__(self):
# WARNING: judgement call...
# We are not using the structured in the comparison or hashing
# because it doesn't change the perform method therefore, we
# *do* want Sums with different structured values to be merged
# by the merge optimization and this requires them to compare equal.
return 76324 ^ hash(type(self)) ^ hash(self.axis)
def __str__(self):
return self.__class__.__name__ + "{axis=%s}" % str(self.axis)
def make_node(self, x):
###
# At least for small matrices (5x5), the .sum() method of a
# csc matrix returns a dense matrix as the result whether axis
# is 0 or 1... weird!
###
assert isinstance(x.type, theano.sparse.SparseType)
x = as_sparse_variable(x)
b = ()
if self.axis is not None:
b = (False,)
z = tensor.tensor(broadcastable=b, dtype=x.dtype)
z = tensor.TensorType(broadcastable=b, dtype=x.dtype)()
return gof.Apply(self, [x], [z])
def perform(self, node, (x,), (z,)):
if self.axis == None:
z[0] = numpy.asarray(x.sum())
else:
z[0] = numpy.asarray(x.sum(self.axis)).ravel()
def grad(self, (x,), (gz,)):
if x.dtype not in continuous_dtypes:
return [None]
if self.structured:
if self.axis is None:
r = gz * theano.sparse.sp_ones_like(x)
elif self.axis == 0:
r = col_scale(theano.sparse.sp_ones_like(x), gz)
elif self.axis == 1:
r = row_scale(theano.sparse.sp_ones_like(x), gz)
else:
raise ValueError('Illegal value for self.axis.')
else:
o_format = x.format
x = dense_from_sparse(x)
if _is_sparse_variable(gz):
gz = dense_from_sparse(gz)
if self.axis is None:
r = tensor.second(x, gz)
else:
ones = tensor.ones_like(x)
if self.axis == 0:
r = tensor.addbroadcast(gz.dimshuffle('x', 0), 0) * ones
elif self.axis == 1:
r = tensor.addbroadcast(gz.dimshuffle(0, 'x'), 1) * ones
else:
raise ValueError('Illegal value for self.axis.')
r = SparseFromDense(o_format)(r)
return [r]
def infer_shape(self, node, shapes):
r = None
if self.axis is None:
......@@ -1344,47 +1642,184 @@ class SpSum(gof.op.Op):
r = [(shapes[0][0],)]
return r
def __str__(self):
return self.__class__.__name__ + "{axis=%s}" % str(self.axis)
def sp_sum(x, axis=None, sparse_grad=False):
return SpSum(axis, sparse_grad)(x)
class Diag(gof.op.Op):
"""Extract the diagonal of a square sparse matrix as a dense
vector.
:param x: A square sparse matrix in csc format.
:return: A dense vector representing the diagonal elements.
:note:
- The grad implemented is regular, i.e. not structured, since
the output is a dense vector.
"""
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, x):
return gof.Apply(self, [x], [tensor.tensor(broadcastable=(False,),
dtype=x.dtype)])
def perform(self, node, (x,), (z,)):
if self.axis is None:
z[0] = numpy.asarray(x.sum())
else:
if self.axis == 0:
if x.format == 'csc':
z[0] = numpy.asarray(x.sum(axis=self.axis)).reshape(
(x.shape[1], ))
else:
z[0] = numpy.asarray(x.asformat(x.format).sum(
axis=self.axis)).reshape((x.shape[1],))
elif self.axis == 1:
if x.format == 'csr':
z[0] = numpy.asarray(x.sum(axis=self.axis)).reshape(
(x.shape[0],))
else:
z[0] = numpy.asarray(x.asformat(x.format).sum(
axis=self.axis)).reshape((x.shape[0],))
N, M = x.shape
if N != M:
raise ValueError('Diag only apply on square matrix')
z[0] = x.diagonal()
def grad(self, (x,), (gz,)):
if self.axis is None:
r = gz * theano.sparse.sp_ones_like(x)
elif self.axis == 0:
r = col_scale(theano.sparse.sp_ones_like(x), gz)
elif self.axis == 1:
r = row_scale(theano.sparse.sp_ones_like(x), gz)
return [square_diagonal(gz)]
def infer_shape(self, nodes, shapes):
return [(tensor.minimum(*shapes[0]), )]
def __str__(self):
return self.__class__.__name__
diag = Diag()
class SquareDiagonal(gof.op.Op):
"""Return a square sparse (csc) matrix whose diagonal
is given by the dense vector argument.
:param x: Dense vector for the diagonal.
:return: A sparse matrix having `x` as diagonal.
:note:
- The grad implemented is regular, i.e. not structured.
"""
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, diag):
diag = tensor.as_tensor_variable(diag)
if diag.type.ndim != 1:
raise TypeError('data argument must be a vector', diag.type)
return gof.Apply(self, [diag],
[SparseType(dtype=diag.dtype, format='csc')()])
def perform(self, node, inputs, (z,)):
diag, o_shape = inputs[0], inputs[0].shape * 2
N = len(diag)
data = diag[:N]
indices = range(N)
indptr = range(N + 1)
tup = (data, indices, indptr)
z[0] = scipy.sparse.csc_matrix(tup, copy=True)
def grad(self, inputs, (gz,)):
return [diag(gz)]
def infer_shape(self, nodes, shapes):
return [(shapes[0][0], shapes[0][0])]
def __str__(self):
return self.__class__.__name__
square_diagonal = SquareDiagonal()
class EnsureSortedIndices(gof.op.Op):
"""Resort indices of a sparse matrix.
CSR column indices are not necessarily sorted. Likewise
for CSC row indices. Use `ensure_sorted_indices` when sorted
indices are required (e.g. when passing data to other
libraries).
:param x: A sparse matrix.
:return: The same as `x` with indices sorted.
:note:
- The grad implemented is regular, i.e. not structured.
"""
def __init__(self, inplace):
self.inplace = inplace
if self.inplace:
self.view_map = {0: [0]}
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, x):
return gof.Apply(self, [x], [x.type()])
def perform(self, node, (x, ), (z, )):
if self.inplace:
z[0] = x.sort_indices()
else:
assert False
z[0] = x.sorted_indices()
if not self.sparse_grad:
r = theano.sparse.dense_from_sparse(r)
def grad(self, inputs, output_grad):
return [output_grad[0]]
return [r]
def infer_shape(self, node, i0_shapes):
return i0_shapes
def __str__(self):
if self.inplace:
return self.__class__.__name__ + "{inplace}"
else:
return self.__class__.__name__ + "{no_inplace}"
ensure_sorted_indices = EnsureSortedIndices(inplace=False)
def sp_sum(x, axis=None, sparse_grad=False):
return SpSum(axis, sparse_grad)(x)
def clean(x):
"""Remove explicit zeros from a sparse matrix, and
resort indices.
CSR column indices are not necessarily sorted. Likewise
for CSC row indices. Use `clean` when sorted
indices are required (e.g. when passing data to other
libraries) and to ensure there is no zeros in the data.
:param x: A sparse matrix.
:return: The same as `x` with indices sorted and zeros
removed.
:note:
- The grad implemented is regular, i.e. not structured.
"""
return ensure_sorted_indices(remove0(x))
class AddSS(gof.op.Op):
'''Add two sparse matrices '''
"""Add tw sparse matrix.
:param x: A sparse matrix.
:param y: A sparse matrix
:return: `x`+`y`
:note:
- The grad implemented is regular, i.e. not structured.
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1475,7 +1910,17 @@ add_s_s_data = AddSSData()
class AddSD(gof.op.Op):
''' Add a sparse and a dense matrix '''
"""Add a sparse and a dense matrix.
:param x: A sparse matrix.
:param y: A dense matrix
:return: `x`+`y`
:note:
- The grad implemented is structured on `x`.
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1568,24 +2013,23 @@ structured_add_s_v = StructuredAddSV()
class StructuredAddSVCSR(gof.Op):
"""Structured addition of a sparse matrix and a dense vector.
The elements of the vector are are only added to the corresponding
non-zero elements. Therefore, this operation outputs another sparse
matrix.
# Structured addition of a sparse matrix and a dense vector.
# The elements of the vector are are only added to the corresponding
# non-zero elements. Therefore, this operation outputs another sparse
# matrix.
:param a_data: Sparse matrix data.
:param a_indices: Sparse matrix indices.
:param a_indptr: Sparse matrix indptr.
:param b: Tensor type vector.
# :param a_data: Sparse matrix data.
# :param a_indices: Sparse matrix indices.
# :param a_indptr: Sparse matrix indptr.
# :param b: Tensor type vector.
:return: A sparse matrix containing the addition of the vector to
the data of the sparse matrix.
# :return: A sparse matrix containing the addition of the vector to
# the data of the sparse matrix.
:note:
- The a_* are the properties of a sparse matrix in csr
format.
- This op is used as an optimization for StructuredAddSV.
"""
# :note:
# - The a_* are the properties of a sparse matrix in csr
# format.
# - This op is used as an optimization for StructuredAddSV.
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1691,9 +2135,22 @@ structured_add_s_v_csr = StructuredAddSVCSR()
def add(x, y):
"""Add two matrices, at least one of which is sparse.
This method will provide the right op according
to the inputs.
:param x: A matrix variable.
:param y: A matrix variable.
:return: `x` + `y`
:note:
- At least one of `x` and `y` must be a sparse matrix.
- The grad will be structured only when one of the variable
will be a dense matrix.
"""
Add two matrices, at least one of which is sparse.
"""
if hasattr(x, 'getnnz'):
x = as_sparse_variable(x)
if hasattr(y, 'getnnz'):
......@@ -1714,11 +2171,37 @@ def add(x, y):
def sub(x, y):
"""Substact two matrices, at least one of which is sparse.
This method will provide the right op according
to the inputs.
:param x: A matrix variable.
:param y: A matrix variable.
:return: `x` - `y`
:note:
- At least one of `x` and `y` must be a sparse matrix.
- The grad will be structured only when one of the variable
will be a dense matrix.
"""
return x + (-y)
class MulSS(gof.op.Op):
''' Elementwise multiply a sparse and a sparse '''
"""Elementwise multiply a sparse and a sparse.
:param x: A sparse matrix.
:param y: A sparse matrix.
:return: `x` * `y`
:note:
- At least one of `x` and `y` must be a sparse matrix.
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1752,7 +2235,17 @@ mul_s_s = MulSS()
class MulSD(gof.op.Op):
''' Elementwise multiply a sparse and a ndarray '''
"""Elementwise multiply a sparse and a dense matrix.
:param x: A sparse matrix.
:param y: A dense matrix.
:return: `x` * `y`
:note:
- The grad is regular, i.e. not structured..
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1829,28 +2322,26 @@ class MulSD(gof.op.Op):
def infer_shape(self, node, shapes):
return [shapes[0]]
mul_s_d = MulSD()
class MulSDCSC(gof.Op):
"""Multiplication of sparse matrix by a broadcasted dense vector
element wise.
# Multiplication of sparse matrix by a broadcasted dense vector
# element wise.
:param a_data: Sparse matrix data.
:param a_indices: Sparse matrix indices.
:param a_indptr: Sparse matrix indptr.
:param b: Tensor type matrix.
# :param a_data: Sparse matrix data.
# :param a_indices: Sparse matrix indices.
# :param a_indptr: Sparse matrix indptr.
# :param b: Tensor type matrix.
:return: The multiplication of the two matrix element wise.
# :return: The multiplication of the two matrix element wise.
:note:
- `a_data`, `a_indices` and `a_indptr` must be the properties
of a sparse matrix in csc format.
- The dtype of `a_data`, i.e. the dtype of the sparse matrix,
cannot be a complex type.
- This op is used as an optimization of mul_s_d.
"""
# :note:
# - `a_data`, `a_indices` and `a_indptr` must be the properties
# of a sparse matrix in csc format.
# - The dtype of `a_data`, i.e. the dtype of the sparse matrix,
# cannot be a complex type.
# - This op is used as an optimization of mul_s_d.
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1949,23 +2440,22 @@ mul_s_d_csc = MulSDCSC()
class MulSDCSR(gof.Op):
"""Multiplication of sparse matrix by a broadcasted dense vector
element wise.
# Multiplication of sparse matrix by a broadcasted dense vector
# element wise.
:param a_data: Sparse matrix data.
:param a_indices: Sparse matrix indices.
:param a_indptr: Sparse matrix indptr.
:param b: Tensor type matrix.
# :param a_data: Sparse matrix data.
# :param a_indices: Sparse matrix indices.
# :param a_indptr: Sparse matrix indptr.
# :param b: Tensor type matrix.
:return: The multiplication of the two matrix element wise.
# :return: The multiplication of the two matrix element wise.
:note:
- `a_data`, `a_indices` and `a_indptr` must be the properties
of a sparse matrix in csr format.
- The dtype of `a_data`, i.e. the dtype of the sparse matrix,
cannot be a complex type.
- This op is used as an optimization of mul_s_d.
"""
# :note:
# - `a_data`, `a_indices` and `a_indptr` must be the properties
# of a sparse matrix in csr format.
# - The dtype of `a_data`, i.e. the dtype of the sparse matrix,
# cannot be a complex type.
# - This op is used as an optimization of mul_s_d.
def __eq__(self, other):
return (type(self) == type(other))
......@@ -2114,23 +2604,22 @@ mul_s_v = MulSV()
class MulSVCSR(gof.Op):
"""Multiplication of sparse matrix by a broadcasted dense vector
element wise.
# Multiplication of sparse matrix by a broadcasted dense vector
# element wise.
:param a_data: Sparse matrix data.
:param a_indices: Sparse matrix indices.
:param a_indptr: Sparse matrix indptr.
:param b: Tensor type matrix.
# :param a_data: Sparse matrix data.
# :param a_indices: Sparse matrix indices.
# :param a_indptr: Sparse matrix indptr.
# :param b: Tensor type matrix.
:return: The multiplication of the two matrix element wise.
# :return: The multiplication of the two matrix element wise.
:note:
- `a_data`, `a_indices` and `a_indptr` must be the properties
of a sparse matrix in csr format.
- The dtype of `a_data`, i.e. the dtype of the sparse matrix,
cannot be a complex type.
- This op is used as an optimization of MulSV.
"""
# :note:
# - `a_data`, `a_indices` and `a_indptr` must be the properties
# of a sparse matrix in csr format.
# - The dtype of `a_data`, i.e. the dtype of the sparse matrix,
# cannot be a complex type.
# - This op is used as an optimization of MulSV.
def __eq__(self, other):
return (type(self) == type(other))
......@@ -2224,9 +2713,22 @@ mul_s_v_csr = MulSVCSR()
def mul(x, y):
"""Multiply elementwise two matrices, at least one
of which is sparse.
This method will provide the right op according
to the inputs.
:param x: A matrix variable.
:param y: A matrix variable.
:return: `x` + `y`
:note:
- At least one of `x` and `y` must be a sparse matrix.
- The grad is regular, i.e. not structured.
"""
Multiply (elementwise) two matrices, at least one of which is sparse.
"""
x = as_sparse_or_tensor_variable(x)
y = as_sparse_or_tensor_variable(y)
......@@ -2293,7 +2795,7 @@ class HStack(gof.op.Op):
def grad(self, inputs, (gz, )):
is_continuous = [(inputs[i].dtype in tensor.continuous_dtypes)
for i in range(len(inputs))]
for i in range(len(inputs))]
if _is_sparse_variable(gz):
gz = DenseFromSparse()(gz)
......@@ -2502,6 +3004,58 @@ class Poisson(gof.op.Op):
poisson = Poisson()
class Binomial(gof.op.Op):
"""Return a sparse matrix having random values from a binomial
density having number of experiment `n` and probability of succes
`p`.
:param n: Tensor scalar representing the number of experiment.
:param p: Tensor scalar representing the probability of success.
:param shape: Tensor vector for the output shape.
:return: A sparse matrix of integers representing the number
of success.
"""
def __init__(self, format, dtype):
self.format = format
self.dtype = dtype
def __eq__(self, other):
return ((type(self) == type(other)) and
self.format == other.format and
self.dtype == other.dtype)
def __hash__(self):
return hash(type(self)) ^ hash(self.format) ^ hash(self.dtype)
def make_node(self, n, p, shape):
n = tensor.as_tensor_variable(n)
p = tensor.as_tensor_variable(p)
shape = tensor.as_tensor_variable(shape)
return gof.Apply(self, [n, p, shape], [SparseType(dtype=self.dtype,
format=self.format).make_variable()])
def perform(self, node, (n, p, shape, ), (out, )):
binomial = numpy.random.binomial(n, p, size=shape)
csx_matrix = getattr(scipy.sparse, self.format + '_matrix')
out[0] = csx_matrix(binomial, dtype=self.dtype)
def grad(self, (n, p, shape, ), (gz,)):
return None, None, None
def infer_shape(self, node, ins_shapes):
return [(node.inputs[2][0], node.inputs[2][1])]
def __str__(self):
return self.__class__.__name__
csr_fbinomial = Binomial('csr', 'float32')
csc_fbinomial = Binomial('csc', 'float32')
csr_dbinomial = Binomial('csr', 'float64')
csc_dbinomial = Binomial('csc', 'float64')
class Multinomial(gof.op.Op):
"""Return a sparse matrix having random values from a multinomial
density having number of experiment `n` and probability of succes
......@@ -2565,12 +3119,12 @@ multinomial = Multinomial()
# Structured monoid
def structured_monoid(tensor_op):
"""Generic operation to perform many kinds of monoid element-wise
operations on the non-zeros of a sparse matrix.
# Generic operation to perform many kinds of monoid element-wise
# operations on the non-zeros of a sparse matrix.
# The first parameter must always be a sparse matrix. The other parameters
# must be scalars which will be passed as argument to the tensor_op.
The first parameter must always be a sparse matrix. The other parameters
must be scalars which will be passed as argument to the tensor_op.
"""
def decorator(f):
def wrapper(*args):
x = as_sparse_variable(args[0])
......@@ -2641,12 +3195,22 @@ def structured_add(x):
# Dot
class StructuredDot(gof.Op):
"""Structured Dot is like dot, except that only the gradient wrt non-zero
elements of the sparse matrix A are calculated and propagated.
"""Structured Dot is like dot, except that only the
gradient wrt non-zero elements of the sparse matrix
`a` are calculated and propagated.
The output is presumed to be a dense matrix, and is represented by a
TensorType instance.
:param a: A sparse matrix.
:param b: A sparse or dense matrix.
:return: The dot product of `a` and `b`.
:note:
- The grad implemented is structured.
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -2727,12 +3291,27 @@ _structured_dot = StructuredDot()
def structured_dot(x, y):
"""Structured Dot is like dot, except that only the
gradient wrt non-zero elements of the sparse matrix
`a` are calculated and propagated.
The output is presumed to be a dense matrix, and is represented by a
TensorType instance.
:param a: A sparse matrix.
:param b: A sparse or dense matrix.
:return: The dot product of `a` and `b`.
:note:
- The grad implemented is structured.
"""
@todo: Maybe the triple-transposition formulation (when x is dense)
is slow. See if there is a direct way to do this.
(JB 20090528: Transposing tensors and sparse matrices is constant-time,
inplace, and fast.)
"""
# @todo: Maybe the triple-transposition formulation (when x is dense)
# is slow. See if there is a direct way to do this.
# (JB 20090528: Transposing tensors and sparse matrices is constant-time,
# inplace, and fast.)
if hasattr(x, 'getnnz'):
x = as_sparse_variable(x)
if hasattr(y, 'getnnz'):
......@@ -2751,6 +3330,22 @@ def structured_dot(x, y):
class StructuredDotCSC(gof.Op):
# Structured Dot CSC is like dot, except that only the
# gradient wrt non-zero elements of the sparse matrix
# `a` are calculated and propagated.
# The output is presumed to be a dense matrix, and is represented by a
# TensorType instance.
# :param a: A sparse matrix in csc format.
# :param b: A sparse or dense matrix.
# :return: The dot product of `a` and `b`.
# :note:
# - The grad implemented is structured.
# - This op is used as an optimization for StructuredDot.
def __eq__(self, other):
return (type(self) == type(other))
......@@ -2775,19 +3370,18 @@ class StructuredDotCSC(gof.Op):
assert _is_dense(out[0]) # scipy 0.7 automatically converts to dense
def c_code(self, node, name, (a_val, a_ind, a_ptr, a_nrows, b), (z,), sub):
"""
C-implementation of the dot product of the sparse matrix A and matrix
B.
@param a_val: non-zero values of the sparse matrix
@param a_ind: column indices of the non-null values (.indices of a
scipy.csc_matrix)
@param a_ptr: a_ptr indicates col indices for col. i are in the range
a_ptr[i]:a_ptr[i+1]
@param n_rows: number of rows of sparse matrix
@param b: dense matrix to perform dot product with, as in dot(a, b)
@param z: return value
@param sub: TODO, not too sure, something to do with weave probably
"""
# C-implementation of the dot product of the sparse matrix A and matrix
# B.
# @param a_val: non-zero values of the sparse matrix
# @param a_ind: column indices of the non-null values (.indices of a
# scipy.csc_matrix)
# @param a_ptr: a_ptr indicates col indices for col. i are in the range
# a_ptr[i]:a_ptr[i+1]
# @param n_rows: number of rows of sparse matrix
# @param b: dense matrix to perform dot product with, as in dot(a, b)
# @param z: return value
# @param sub: TODO, not too sure, something to do with weave probably
if node.inputs[0].type.dtype in ('complex64', 'complex128'):
raise NotImplementedError('Complex types are not supported for a_val')
......@@ -2926,6 +3520,22 @@ sd_csc = StructuredDotCSC()
class StructuredDotCSR(gof.Op):
# Structured Dot CSR is like dot, except that only the
# gradient wrt non-zero elements of the sparse matrix
# `a` are calculated and propagated.
# The output is presumed to be a dense matrix, and is represented by a
# TensorType instance.
# :param a: A sparse matrix in csr format.
# :param b: A sparse or dense matrix.
# :return: The dot product of `a` and `b`.
# :note:
# - The grad implemented is structured.
# - This op is used as an optimization for StructuredDot.
def __eq__(self, other):
return (type(self) == type(other))
......@@ -3147,39 +3757,38 @@ sampling_dot = SamplingDot()
class SamplingDotCSR(gof.Op):
"""Operand optimized for calculating the dot product dot(`x`, `y`.T) = `z`
when you only want to calculate a subset of `z`.
It is equivalent to `p` o (`x` . `y`.T) where o is the element-wise
product, `x` and `y` operands of the dot product and `p` is a matrix
that contains 1 when the corresponding element of `z` should be calculated
and 0 when it shouldn't. Note that SamplingDot has a different interface
than `dot` because SamplingDot requires `x` to be a `m`x`k` matrix while
`y` is a `n`x`k` matrix instead of the usual `k`x`n` matrix.
.. note::
It will work if the pattern is not binary value, but if the
pattern doesn't have a high sparsity proportion it will be slower
then a more optimized dot followed by a normal elemwise
multiplication.
:param x: Tensor matrix.
:param y: Tensor matrix.
:param p_data: Sparse matrix data.
:param p_ind: Sparse matrix indices.
:param p_ptr: Sparse matric indptr.
:param p_ncols: Sparse matrix number of columns.
:return: A dense matrix containing the dot product of `x` by `y`.T only
where `p` is 1.
:note:
- If we have the input of mixed dtype, we insert cast elemwise
in the graph to be able to call blas function as they don't
allow mixed dtype.
- This op is used as an optimization for SamplingDot.
"""
# Operand optimized for calculating the dot product dot(`x`, `y`.T) = `z`
# when you only want to calculate a subset of `z`.
# It is equivalent to `p` o (`x` . `y`.T) where o is the element-wise
# product, `x` and `y` operands of the dot product and `p` is a matrix
# that contains 1 when the corresponding element of `z` should be
# calculated and 0 when it shouldn't. Note that SamplingDot has a different
# interface than `dot` because SamplingDot requires `x` to be a `m`x`k`
# matrix while `y` is a `n`x`k` matrix instead of the usual `k`x`n` matrix.
# .. note::
# It will work if the pattern is not binary value, but if the
# pattern doesn't have a high sparsity proportion it will be slower
# then a more optimized dot followed by a normal elemwise
# multiplication.
# :param x: Tensor matrix.
# :param y: Tensor matrix.
# :param p_data: Sparse matrix data.
# :param p_ind: Sparse matrix indices.
# :param p_ptr: Sparse matric indptr.
# :param p_ncols: Sparse matrix number of columns.
# :return: A dense matrix containing the dot product of `x` by `y`.T only
# where `p` is 1.
# :note:
# - If we have the input of mixed dtype, we insert cast elemwise
# in the graph to be able to call blas function as they don't
# allow mixed dtype.
# - This op is used as an optimization for SamplingDot.
def __eq__(self, other):
return type(self) == type(other)
......@@ -3409,6 +4018,21 @@ def structured_dot_grad(sparse_A, dense_B, ga):
class StructuredDotGradCSC(gof.Op):
# Op that produces the grad of StructuredDot.
# :param a_indices: Matrix indicies
# :param a_indptr: Matrix indptr
# :param b: Right operand
# :param g_ab: Accumulated gradient.
# :return: The grad of `a`.`b` for `a` accumulated
# with g_ab.
# :note:
# - The grad implemented is structured.
# - a_* are the corresponding properties of a sparse
# matrix in csc format.
def __eq__(self, other):
return (type(self) == type(other))
......@@ -3530,6 +4154,21 @@ sdg_csc = StructuredDotGradCSC()
class StructuredDotGradCSR(gof.Op):
# Op that produces the grad of StructuredDot.
# :param a_indices: Matrix indicies
# :param a_indptr: Matrix indptr
# :param b: Right operand
# :param g_ab: Accumulated gradient.
# :return: The grad of `a`.`b` for `a` accumulated
# with g_ab.
# :note:
# - The grad implemented is structured.
# - a_* are the corresponding properties of a sparse
# matrix in csr format.
def __eq__(self, other):
return (type(self) == type(other))
......@@ -3654,11 +4293,20 @@ sdg_csr = StructuredDotGradCSR()
class Dot(gof.op.Op):
"""
Operation for efficiently calculating the dot product when
"""Operation for efficiently calculating the dot product when
one or all operands is sparse. Supported format are CSC and CSR.
The output of the operation is dense.
:param x: Matrix variable.
:param y: Matrix variable.
:return: The dot product `x`.`y` in a dense format.
:note:
- The grad implemented is regular, i.e. not structured.
- At least one of `x` or `y` must be a sparse matrix.
"""
def __eq__(self, other):
return type(self) == type(other)
......@@ -3724,11 +4372,20 @@ _dot = Dot()
def dot(x, y):
"""
Operation for efficiently calculating the dot product when
"""Operation for efficiently calculating the dot product when
one or all operands is sparse. Supported format are CSC and CSR.
The output of the operation is dense.
:param x: Matrix variable.
:param y: Matrix variable.
:return: The dot product `x`.`y` in a dense format.
:note:
- The grad implemented is regular, i.e. not structured.
- At least one of `x` or `y` must be a sparse matrix.
"""
if hasattr(x, 'getnnz'):
x = as_sparse_variable(x)
if hasattr(y, 'getnnz'):
......@@ -3744,16 +4401,23 @@ def dot(x, y):
class Usmm(gof.op.Op):
"""
Performs the expression is alpha * x y + z
"""Performs the expression is `alpha` * `x` `y` + `z`.
:param x: Matrix variable.
:param y: Matrix variable.
:param z: Dense matrix.
:param alpha: A tensor scalar.
x or y are sparse matrix(the other can be sparse or dense)
z is a dense matrix
alpha is a scalar
:return: The dense matrix resulting from `alpha` * `x` `y` + `z`.
:note: We don't implement the infer_shape as it is inserted by optimization
only
:note:
- The grad is not implemented for this op.
- At least one of `x` or `y` must be a sparse matrix.
"""
# We don't implement the infer_shape as it is
# inserted by optimization only.
def __eq__(self, other):
return type(self) == type(other)
......@@ -3811,17 +4475,20 @@ usmm = Usmm()
class UsmmCscDense(gof.Op):
"""
Performs the expression is alpha * x y + z
This is an optimized operation for the case when x is in CSC format.
# Performs the expression is `alpha` * `x` `y` + `z`.
x are sparse matrix
y, z is a dense matrix
alpha is a scalar
# :param x: Matrix variable.
# :param y: Matrix variable.
# :param z: Dense matrix.
# :param alpha: A tensor scalar.
# :return: The dense matrix resulting from `alpha` * `x` `y` + `z`.
# :note:
# - The grad is not implemented for this op.
# - Optimized version os Usmm when `x` is in csc format and
# `y` is dense.
:note: We don't implement the infer_shape as it is inserted by optimization
only
"""
def __init__(self, inplace):
self.inplace = inplace
if inplace:
......
theano/sparse/sandbox/sp.py
浏览文件 @ 685f5fd8
......@@ -9,6 +9,7 @@ U{http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps}.
#### COPIED FROM hpu/icml09/sp.py
import numpy
import scipy
from scipy import sparse as scipy_sparse
import theano
......@@ -18,7 +19,11 @@ from theano.gof.python25 import all, any
from theano.sparse.basic import Remove0, remove0
# To maintain compatibility
from theano.sparse import SpSum, sp_sum
from theano.sparse import (
SpSum, sp_sum,
ColScaleCSC, RowScaleCSC, col_scale, row_scale,
Diag, diag, SquareDiagonal, square_diagonal,
EnsureSortedIndices, ensure_sorted_indices, clean)
def register_specialize(lopt, *tags, **kwargs):
......@@ -27,206 +32,6 @@ def register_specialize(lopt, *tags, **kwargs):
*tags)
class Diag(Op):
"""
Extract the diagonal of a square sparse matrix as a dense vector.
"""
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def __str__(self):
return "Diag"
def make_node(self, x):
return gof.Apply(self, [x], [tensor.tensor(broadcastable=(False,),
dtype=x.dtype)])
def perform(self, node, (x,), (z,)):
M, N = x.shape
if M != N:
raise ValueError("DenseDiag argument not square. Shape:", x.shape)
assert x.format == 'csc'
data = x.data
indices = x.indices
indptr = x.indptr
diag = numpy.zeros(N, x.dtype)
#TODO: try using ndarrays and then prune() on the result
# it could be optimized in the case the sparse structure
# does not allow index duplication
for j in xrange(0, N):
for i_idx in xrange(indptr[j], indptr[j + 1]):
if indices[i_idx] == j:
diag[j] += data[i_idx]
z[0] = diag
def grad(self, (diag,), (gz,)):
return [square_diagonal(gz)]
def infer_shape(self, nodes, shapes):
matrix_shape = shapes[0]
diag_length = matrix_shape[0]
return [(diag_length,)]
diag = Diag()
class SquareDiagonal(Op):
"""
Return a square sparse (csc) matrix whose diagonal
is given by the dense vector argument.
"""
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def __str__(self):
return "SquareDiagonal"
def make_node(self, diag):
diag = tensor.as_tensor_variable(diag)
if diag.type.ndim != 1:
raise TypeError('data argument must be a vector', diag.type)
return gof.Apply(self, [diag],
[sparse.SparseType(dtype=diag.dtype, format='csc')()])
def perform(self, node, (diag,), (z,)):
N, = diag.shape
indptr = range(N + 1)
indices = indptr[0:N]
z[0] = scipy_sparse.csc_matrix((diag, indices, indptr),
(N, N), copy=True)
def grad(self, input, (gz,)):
return [diag(gz)]
def infer_shape(self, nodes, shapes):
diag_length = shapes[0][0]
return [(diag_length, diag_length)]
square_diagonal = SquareDiagonal()
class ColScaleCSC(Op):
"""
Scale each columns of a sparse matrix by the corresponding element
of a dense vector
"""
def make_node(self, x, s):
if x.format != 'csc':
raise ValueError('x was not a csc matrix')
return gof.Apply(self, [x, s], [x.type()])
def perform(self, node, (x, s), (z,)):
M, N = x.shape
assert x.format == 'csc'
assert s.shape == (N,)
y = x.copy()
for j in xrange(0, N):
y.data[y.indptr[j]: y.indptr[j + 1]] *= s[j]
z[0] = y
def grad(self, (x, s), (gz,)):
return [col_scale(gz, s), sp_sum(x * gz, axis=0)]
class RowScaleCSC(Op):
"""
Scale each row of a sparse matrix by the corresponding element of
a dense vector
"""
def make_node(self, x, s):
return gof.Apply(self, [x, s], [x.type()])
def perform(self, node, (x, s), (z,)):
M, N = x.shape
assert x.format == 'csc'
assert s.shape == (M,)
indices = x.indices
indptr = x.indptr
y_data = x.data.copy()
for j in xrange(0, N):
for i_idx in xrange(indptr[j], indptr[j + 1]):
y_data[i_idx] *= s[indices[i_idx]]
z[0] = scipy_sparse.csc_matrix((y_data, indices, indptr), (M, N))
def grad(self, (x, s), (gz,)):
return [row_scale(gz, s), sp_sum(x * gz, axis=1)]
def col_scale(x, s):
if x.format == 'csc':
return ColScaleCSC()(x, s)
elif x.format == 'csr':
return RowScaleCSC()(x.T, s).T
else:
raise NotImplementedError()
def row_scale(x, s):
return col_scale(x.T, s).T
class EnsureSortedIndices(Op):
"""
Remove explicit zeros from a sparse matrix, and resort indices
"""
def __init__(self, inplace):
self.inplace = inplace
if self.inplace:
self.view_map = {0: [0]}
def make_node(self, x):
return gof.Apply(self, [x], [x.type()])
def perform(self, node, inputs, output_storage):
x = inputs[0]
z = output_storage[0]
if self.inplace:
x.sort_indices()
z[0] = x
else:
z[0] = x.sorted_indices()
def grad(self, inputs, output_grad):
return [output_grad[0]]
def infer_shape(self, node, i0_shapes):
return i0_shapes
def __str__(self):
if self.inplace:
return self.__class__.__name__ + "{inplace}"
else:
return self.__class__.__name__ + "{no_inplace}"
ensure_sorted_indices = EnsureSortedIndices(inplace=False)
def clean(x):
return ensure_sorted_indices(remove0(x))
class ConvolutionIndices(Op):
"""Build indices for a sparse CSC matrix that could implement A
(convolve) B.
......
theano/sparse/sandbox/sp2.py
浏览文件 @ 685f5fd8
......@@ -19,6 +19,7 @@ from theano.sparse.basic import (
AddSSData, add_s_s_data,
MulSDCSC, mul_s_d_csc, MulSDCSR, mul_s_d_csr,
Multinomial, multinomial, Poisson, poisson,
Binomial, csr_fbinomial, csc_fbinomial, csr_dbinomial, csc_dbinomial,
structured_monoid,
structured_sigmoid, structured_exp, structured_log, structured_pow,
structured_minimum, structured_maximum, structured_add,
......@@ -35,71 +36,3 @@ from theano.sparse.opt import (
# Alias to maintain compatibility
EliminateZeros = Remove0
eliminate_zeros = remove0
class Binomial(gof.op.Op):
# TODO This op is not an equivalent of numpy.random.binomial. In
# facts, this does not follow a binomial distribution at all.
# To see it, just try with p = 1.
"""Return a sparse matrix having random values from a binomial
density having number of experiment `n` and probability of succes
`p`.
.. warning::
For now, this op does not return a true binomial
distribution. It is a random disposition of ones
in a sparse matrix.
:param n: Tensor scalar representing the number of experiment.
:param p: Tensor scalar representing the probability of success.
:param shape: Tensor vector for the output shape.
:return: A sparse matrix of integers representing the number
of success.
"""
def __init__(self, format, dtype):
self.format = format
self.dtype = dtype
def __eq__(self, other):
return ((type(self) == type(other)) and
self.format == other.format and
self.dtype == other.dtype)
def __hash__(self):
return hash(type(self)) ^ hash(self.format) ^ hash(self.dtype)
def make_node(self, n, p, shape):
n = tensor.as_tensor_variable(n)
p = tensor.as_tensor_variable(p)
shape = tensor.as_tensor_variable(shape)
return gof.Apply(self, [n, p, shape], [SparseType(dtype=self.dtype,
format=self.format).make_variable()])
def perform(self, node, (n, p, shape, ), (out, )):
N = n * p * shape[0] * shape[1]
data = numpy.ones(N, dtype=self.dtype)
row = numpy.random.randint(0, shape[0], N)
col = numpy.random.randint(0, shape[1], N)
res = scipy.sparse.coo_matrix((data, (row, col)), shape=shape)
out[0] = getattr(res, 'to' + self.format)()
out[0].data = numpy.ones_like(out[0].data)
def grad(self, (n, p, shape, ), (gz,)):
return None, None, None
def infer_shape(self, node, ins_shapes):
return [(node.inputs[2][0], node.inputs[2][1])]
def __str__(self):
return self.__class__.__name__
csr_fbinomial = Binomial('csr', 'float32')
csc_fbinomial = Binomial('csc', 'float32')
csr_dbinomial = Binomial('csr', 'float64')
csc_dbinomial = Binomial('csc', 'float64')
theano/sparse/sandbox/test_sp.py
浏览文件 @ 685f5fd8
......@@ -18,6 +18,7 @@ from theano.sparse.sandbox import sp
from theano.sparse.tests.test_basic import random_lil
from theano.tests import unittest_tools as utt
from theano.sparse import verify_grad_sparse
from theano.sparse.tests.test_basic import sparse_random_inputs
class TestSP(unittest.TestCase):
......@@ -363,121 +364,6 @@ class TestSP(unittest.TestCase):
utt.verify_grad(d, [kvals])
def test_diag():
m = theano.sparse.csc_matrix()
d = sp.diag(m)
f = theano.function([m], d)
f2 = theano.function([m], d.shape)
for K in 1, 5:
np_matrix = numpy.asarray(numpy.reshape(range(K**2),(K,K)),
dtype=theano.config.floatX)
diag = numpy.diagonal(np_matrix)
sp_matrix = scipy.sparse.csc_matrix(np_matrix)
assert numpy.all(diag == f(sp_matrix))
assert f2(sp_matrix) == diag.shape
def test_square_diagonal():
for K in 1, 5:
d = tensor.ivector()
sd = sp.square_diagonal(d)
f = theano.function([d], sd)
n = numpy.zeros((K,K), dtype='int32')
for i in range(K):
n[i,i] = i
assert numpy.all(n == f(range(K)).toarray())
def test_ensure_sorted_indices():
x = 2000
y = 2000
sparsity = 1000
for i in range(2):
# testing both csc and csr
if i is 0:
# csc
input_tensor = theano.sparse.csc_dmatrix()
sample = scipy.sparse.csc_matrix(random_lil((x,y),'float64',sparsity))
else:
# csr
input_tensor = theano.sparse.csr_dmatrix()
sample = scipy.sparse.csr_matrix(random_lil((x,y),'float64',sparsity))
sort_op = sp.ensure_sorted_indices(input_tensor)
f = theano.function([input_tensor], sort_op)
sorted_scipy = sample.sorted_indices()
sorted_theano = f(sample)
assert numpy.all(sorted_theano.todense() == sorted_scipy.todense())
def test_square_diagonal_grad():
def d(x):
return sp.sp_sum(sp.square_diagonal(x), sparse_grad=True)
utt.verify_grad(d, [[0.0, 0.1, 0.2, 0.3]],
mode=theano.Mode(linker='py', optimizer='fast_compile'))
def test_diag_grad():
def d(x):
sp_x = theano.sparse.csc_from_dense(x)
diag_x = sp.diag(sp_x)
return diag_x.sum()
diag_mat = numpy.zeros((4,4))
for idx in xrange(4):
diag_mat[idx, idx] += idx * 0.1
utt.verify_grad(d, [diag_mat],
mode=theano.Mode(linker='py', optimizer='fast_compile'))
def test_row_scale():
x = theano.sparse.csc_dmatrix()
s = theano.tensor.dvector()
rng = numpy.random.RandomState(8723)
R = 5
C = 8
x_val_dense = numpy.zeros((R, C), dtype='d')
for idx in [(0, 0), (4, 1), (2, 1), (3, 3), (4, 4), (3, 7), (2, 7)]:
x_val_dense.__setitem__(idx, rng.randn())
x_val = scipy.sparse.csc_matrix(x_val_dense)
s_val = rng.randn(R)
f = theano.function([x, s], sp.row_scale(x, s))
# print 'A', f(x_val, s_val).toarray()
# print 'B', (x_val_dense.T * s_val).T
assert numpy.all(f(x_val, s_val).toarray() == (x_val_dense.T * s_val).T)
verify_grad_sparse(sp.row_scale, [x_val, s_val], structured=False)
def test_col_scale():
x = theano.sparse.csc_dmatrix()
s = theano.tensor.dvector()
rng = numpy.random.RandomState(8723)
R = 5
C = 8
x_val_dense = numpy.zeros((R, C), dtype='d')
for idx in [(0, 0), (4, 1), (2, 1), (3, 3), (4, 4), (3, 7), (2, 7)]:
x_val_dense.__setitem__(idx, rng.randn())
x_val = scipy.sparse.csc_matrix(x_val_dense)
s_val = rng.randn(C)
f = theano.function([x, s], sp.col_scale(x, s))
# print 'A', f(x_val, s_val).toarray()
# print 'B', (x_val_dense * s_val)
assert numpy.all(f(x_val, s_val).toarray() == (x_val_dense * s_val))
verify_grad_sparse(sp.col_scale, [x_val, s_val], structured=False)
if __name__ == '__main__':
if 0:
test_remove0()
......
theano/sparse/tests/test_basic.py
浏览文件 @ 685f5fd8
......@@ -33,11 +33,13 @@ from theano.sparse import (
Dot, Usmm, UsmmCscDense, sp_ones_like, GetItemScalar,
SparseFromDense,
Cast, HStack, VStack, AddSSData, add_s_s_data,
Poisson, poisson, Multinomial, multinomial,
Poisson, poisson, Binomial, Multinomial, multinomial,
structured_sigmoid, structured_exp, structured_log,
structured_pow, structured_minimum, structured_maximum, structured_add,
MulSV, mul_s_v, StructuredAddSV, structured_add_s_v,
SamplingDot, sampling_dot)
SamplingDot, sampling_dot,
Diag, diag, SquareDiagonal, square_diagonal,
EnsureSortedIndices, ensure_sorted_indices, clean)
from theano.tests import unittest_tools as utt
from theano.tensor.basic import _allclose
......@@ -72,6 +74,47 @@ def random_lil(shape, dtype, nnz):
return rval
def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5):
"""Return a tuple containing everything needed to
perform a test.
If `out_dtype` is `None`, theano.config.floatX is
used.
:param format: Sparse format.
:param shape: Shape of data.
:param n: Number of variable.
:param out_dtype: dtype of output.
:param p: Sparsity proportion.
:return: (variable, data) where both `variable`
and `data` are list.
"""
if out_dtype is None:
out_dtype = theano.config.floatX
assert 0 <= p and p <= 1
assert len(shape) == 2
assert out_dtype in sparse.all_dtypes
def _rand():
where = numpy.random.binomial(1, p, size=shape).astype('int8')
if out_dtype in sparse.discrete_dtypes:
value = numpy.random.randint(20, size=shape).astype(out_dtype)
else:
value = numpy.random.random(shape).astype(out_dtype)
return where * value
variable = [getattr(theano.sparse, format + '_matrix')(dtype=out_dtype)
for k in range(n)]
data = [getattr(scipy.sparse, format + '_matrix')(_rand())
for k in range(n)]
return (variable, data)
class T_verify_grad_sparse(unittest.TestCase):
class FailOp(gof.op.Op):
def __init__(self, structured):
......@@ -1329,71 +1372,285 @@ def test_size():
check()
def test_sp_sum():
from theano.sparse import SpSum
# TODO: test both grad.
rng = numpy.random.RandomState(42)
from theano.sparse.basic import SparseFromDense,DenseFromSparse
cases = [("csc", scipy.sparse.csc_matrix), ("csr", scipy.sparse.csr_matrix)]
for format, cast in cases:
#print 'format: %(format)s' % locals()
x = theano.sparse.SparseType(format=format,
dtype=theano.config.floatX)()
x_data = numpy.arange(20).reshape(5,4).astype(theano.config.floatX)
# Sum on all axis
#print 'sum on all axis...'
z = theano.sparse.sp_sum(x)
assert z.type.broadcastable == ()
f = theano.function([x], z)
x_val = cast(x_data)
out = f(x_val)
expected = x_val.sum()
assert out == expected
# Sum on axis 0
#print 'sum on axis 0...'
z = theano.sparse.sp_sum(x, axis=0)
assert z.type.broadcastable == (False,)
f = theano.function([x], z)
x_val = cast(x_data)
out = f(x_val)
expected = x_val.sum(axis=0)
assert (out == expected).all()
# Sum on axis 1
#print 'sum on axis 1...'
z = theano.sparse.sp_sum(x, axis=1)
assert z.type.broadcastable == (False,)
f = theano.function([x], z)
x_val = cast(x_data)
out = f(x_val)
expected = numpy.asarray(x_val.sum(axis=1)).reshape(x_val.shape[0])
assert (out == expected).all()
# Sparse gradient on Sum on all axis
# unfinished, and suspended until verify_grad get fixed
if False:
# print 'grad on sum on all axis...'
def fun(x):
## verify_grad does not handle sparse data, so here's some casting as a workaround.
# x is a dense matrix: make it sparse
sparse_var = SparseFromDense(format)(x)
# apply op
dense_sum = theano.sparse.SpSum(axis=None, sparse_grad=False)(sparse_var)
return dense_sum
# cast back to dense so that verify_grad can work
dense_sum = theano.sparse.DenseFromSparse()(sparse_sum)
return dense_sum
x_val = x_data.copy()
# print type(x_val)
import pdb;pdb.set_trace()
tensor.verify_grad(fun, [x_val], rng=rng)
#utt.verify_grad(SpSum(axis=None), [x_val])
# print 'ok'
class ColScaleCSCTester(utt.InferShapeTester):
def setUp(self):
super(ColScaleCSCTester, self).setUp()
self.op = sparse.col_scale
def test_op(self):
for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector())
data.append(numpy.random.random(10))
f = theano.function(variable, self.op(*variable))
tested = f(*data)
x, s = data[0].toarray(), data[1][numpy.newaxis, :]
expected = x * s
assert tested.format == format
assert numpy.allclose(tested.toarray(), expected)
def test_infer_shape(self):
for format, cls in [('csc', sparse.ColScaleCSC),
('csr', sparse.RowScaleCSC)]:
variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector())
data.append(numpy.random.random(10))
self._compile_and_check(variable,
[self.op(*variable)],
data,
cls)
def test_grad(self):
for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector())
data.append(numpy.random.random(10))
verify_grad_sparse(self.op, data, structured=True)
class RowScaleCSCTester(utt.InferShapeTester):
def setUp(self):
super(RowScaleCSCTester, self).setUp()
self.op = sparse.row_scale
def test_op(self):
for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector())
data.append(numpy.random.random(8))
f = theano.function(variable, self.op(*variable))
tested = f(*data)
x, s = data[0].toarray(), data[1][:, numpy.newaxis]
expected = x * s
assert tested.format == format
assert numpy.allclose(tested.toarray(), expected)
def test_infer_shape(self):
for format, cls in [('csc', sparse.RowScaleCSC),
('csr', sparse.ColScaleCSC)]:
variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector())
data.append(numpy.random.random(8))
self._compile_and_check(variable,
[self.op(*variable)],
data,
cls)
def test_grad(self):
for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector())
data.append(numpy.random.random(8))
verify_grad_sparse(self.op, data, structured=True)
class SpSumTester(utt.InferShapeTester):
possible_axis = [None, 0, 1]
def setUp(self):
super(SpSumTester, self).setUp()
self.op_class = sparse.SpSum
self.op = sparse.sp_sum
def test_op(self):
for format in sparse.sparse_formats:
for axis in self.possible_axis:
variable, data = sparse_random_inputs(format,
shape=(10, 10))
z = theano.sparse.sp_sum(*variable, axis=axis)
if axis == None:
assert z.type.broadcastable == ()
else:
assert z.type.broadcastable == (False, )
f = theano.function(variable, self.op(*variable, axis=axis))
tested = f(*data)
expected = data[0].todense().sum(axis).ravel()
assert numpy.allclose(tested, expected)
def test_infer_shape(self):
for format in sparse.sparse_formats:
for axis in self.possible_axis:
variable, data = sparse_random_inputs(format,
shape=(10, 10))
self._compile_and_check(variable,
[self.op(*variable, axis=axis)],
data,
self.op_class)
def test_grad(self):
for format in sparse.sparse_formats:
for axis in self.possible_axis:
for struct in [True, False]:
variable, data = sparse_random_inputs(format,
shape=(10, 10))
verify_grad_sparse(
self.op_class(axis=axis, sparse_grad=struct),
data,
structured=struct)
class DiagTester(utt.InferShapeTester):
def setUp(self):
super(DiagTester, self).setUp()
self.op_class = Diag
self.op = diag
def test_op(self):
for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format,
shape=(10, 10))
z = self.op(*variable)
assert z.type.broadcastable == (False, )
f = theano.function(variable, z)
tested = f(*data)
expected = data[0].toarray().diagonal()
assert numpy.allclose(tested, expected)
def test_infer_shape(self):
for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format,
shape=(10, 10))
self._compile_and_check(variable,
[self.op(*variable)],
data,
self.op_class)
def test_grad(self):
for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format,
shape=(10, 10))
verify_grad_sparse(
self.op,
data,
structured=False)
class SquareDiagonalTester(utt.InferShapeTester):
def setUp(self):
super(SquareDiagonalTester, self).setUp()
self.op_class = SquareDiagonal
self.op = square_diagonal
def test_op(self):
for format in sparse.sparse_formats:
for size in range(5, 9):
variable = [tensor.vector()]
data = [numpy.random.random(size)]
f = theano.function(variable, self.op(*variable))
tested = f(*data).toarray()
expected = numpy.diag(*data)
assert numpy.allclose(tested, expected)
assert tested.dtype == expected.dtype
assert tested.shape == expected.shape
def test_infer_shape(self):
for format in sparse.sparse_formats:
for size in range(5, 9):
variable = [tensor.vector()]
data = [numpy.random.random(size)]
self._compile_and_check(variable,
[self.op(*variable)],
data,
self.op_class)
def test_grad(self):
for format in sparse.sparse_formats:
for size in range(5, 9):
variable = [tensor.vector()]
data = [numpy.random.random(size)]
verify_grad_sparse(
self.op,
data,
structured=False)
class EnsureSortedIndicesTester(utt.InferShapeTester):
def setUp(self):
super(EnsureSortedIndicesTester, self).setUp()
self.op_class = EnsureSortedIndices
self.op = ensure_sorted_indices
def test_op(self):
for format in sparse.sparse_formats:
for shape in zip(range(5, 9), range(3, 7)[::-1]):
variable, data = sparse_random_inputs(format, shape=shape)
f = theano.function(variable, self.op(*variable))
tested = f(*data).toarray()
expected = data[0].sorted_indices().toarray()
assert numpy.allclose(tested, expected)
def test_infer_shape(self):
for format in sparse.sparse_formats:
for shape in zip(range(5, 9), range(3, 7)[::-1]):
variable, data = sparse_random_inputs(format, shape=shape)
self._compile_and_check(variable,
[self.op(*variable)],
data,
self.op_class)
def test_grad(self):
for format in sparse.sparse_formats:
for shape in zip(range(5, 9), range(3, 7)[::-1]):
variable, data = sparse_random_inputs(format, shape=shape)
verify_grad_sparse(
self.op,
data,
structured=False)
class CleanTester(utt.InferShapeTester):
def setUp(self):
super(CleanTester, self).setUp()
self.op = clean
def test_op(self):
for format in sparse.sparse_formats:
for shape in zip(range(5, 9), range(3, 7)[::-1]):
variable, data = sparse_random_inputs(format, shape=shape)
data[0][0, 0] = data[0][1, 1] = 0
f = theano.function(variable, self.op(*variable))
tested = f(*data)
expected = data[0]
expected.eliminate_zeros()
assert all(tested.data == expected.data)
assert not all(tested.data == 0)
tested = tested.toarray()
expected = expected.toarray()
assert numpy.allclose(tested, expected)
def test_grad(self):
for format in sparse.sparse_formats:
for shape in zip(range(5, 9), range(3, 7)[::-1]):
variable, data = sparse_random_inputs(format, shape=shape)
verify_grad_sparse(
self.op,
data,
structured=False)
class Remove0Tester(utt.InferShapeTester):
......@@ -1788,8 +2045,8 @@ def _hv_switch(op, expected_function):
:Parameters:
- `op`: HStack or VStack class.
- `expected_function`: function from scipy for comparaison.
"""
class XStackTester(_HVStackTester):
op_class = op
......@@ -1885,6 +2142,46 @@ class PoissonTester(utt.InferShapeTester):
self.op_class)
class BinomialTester(utt.InferShapeTester):
n = tensor.scalar()
p = tensor.scalar()
shape = tensor.lvector()
_n = 5
_p = .25
_shape = numpy.asarray([3, 5], dtype='int64')
inputs = [n, p, shape]
_inputs = [_n, _p, _shape]
def setUp(self):
super(BinomialTester, self).setUp()
self.op_class = Binomial
def test_op(self):
for sp_format in sparse.sparse_formats:
for o_type in sparse.float_dtypes:
f = theano.function(
self.inputs,
Binomial(sp_format, o_type)(*self.inputs))
tested = f(*self._inputs)
assert tested.shape == tuple(self._shape)
assert tested.format == sp_format
assert tested.dtype == o_type
assert numpy.allclose(numpy.floor(tested.todense()),
tested.todense())
def test_infer_shape(self):
for sp_format in sparse.sparse_formats:
for o_type in sparse.float_dtypes:
self._compile_and_check(
self.inputs,
[Binomial(sp_format, o_type)(*self.inputs)],
self._inputs,
self.op_class)
class MultinomialTester(utt.InferShapeTester):
p = sparse.csr_matrix()
_p = sp.csr_matrix(numpy.asarray([[0.0, 0.5, 0.0, 0.5],
......
theano/sparse/tests/test_sp2.py
浏览文件 @ 685f5fd8
......@@ -22,45 +22,5 @@ from theano.tests import unittest_tools as utt
from theano.sparse.basic import verify_grad_sparse
class BinomialTester(utt.InferShapeTester):
n = tensor.scalar()
p = tensor.scalar()
shape = tensor.lvector()
_n = 5
_p = .25
_shape = np.asarray([3, 5], dtype='int64')
inputs = [n, p, shape]
_inputs = [_n, _p, _shape]
def setUp(self):
super(BinomialTester, self).setUp()
self.op_class = S2.Binomial
def test_op(self):
for sp_format in sparse.sparse_formats:
for o_type in sparse.float_dtypes:
f = theano.function(
self.inputs,
S2.Binomial(sp_format, o_type)(*self.inputs))
tested = f(*self._inputs)
assert tested.shape == tuple(self._shape)
assert tested.format == sp_format
assert tested.dtype == o_type
assert np.allclose(np.floor(tested.todense()),
tested.todense())
def test_infer_shape(self):
for sp_format in sparse.sparse_formats:
for o_type in sparse.float_dtypes:
self._compile_and_check(
self.inputs,
[S2.Binomial(sp_format, o_type)(*self.inputs)],
self._inputs,
self.op_class)
if __name__ == '__main__':
unittest.main()
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