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提交 809e4e2c authored 作者: Frederic's avatar Frederic
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better autodoc for sparse.

上级 dc42ec01
隐藏空白字符变更
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正在显示 包含 141 行增加 和 225 行删除
theano/sparse/basic.py
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......@@ -1828,20 +1828,6 @@ add_s_d = AddSD()
class StructuredAddSV(gof.op.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.
:param x: Sparse matrix.
:param y: Tensor type vector.
:return: A sparse matrix containing the addition of the vector to
the data of the sparse matrix.
:note: The grad implemented is structured since the op is structured.
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1878,6 +1864,19 @@ class StructuredAddSV(gof.op.Op):
def __str__(self):
return self.__class__.__name__
structured_add_s_v = StructuredAddSV()
"""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 x: Sparse matrix.
:param y: Tensor type vector.
:return: A sparse matrix containing the addition of the vector to
the data of the sparse matrix.
:note: The grad implemented is structured since the op is structured.
"""
def add(x, y):
......@@ -2094,17 +2093,6 @@ mul_s_d = MulSD()
class MulSV(gof.op.Op):
"""Multiplication of sparse matrix by a broadcasted dense vector
element wise.
:param x: Sparse matrix to multiply.
:param y: Tensor broadcastable vector.
:Return: The product x * y element wise.
:note: The grad implemented is regular, i.e. not structured.
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -2152,6 +2140,15 @@ class MulSV(gof.op.Op):
def __str__(self):
return self.__class__.__name__
mul_s_v = MulSV()
"""Multiplication of sparse matrix by a broadcasted dense vector element wise.
:param x: Sparse matrix to multiply.
:param y: Tensor broadcastable vector.
:Return: The product x * y element wise.
:note: The grad implemented is regular, i.e. not structured.
"""
def mul(x, y):
......@@ -2328,172 +2325,161 @@ def __ComparisonSwitch(SS, SD, DS):
class EqualSS(__ComparisonOpSS):
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x==y
"""
def comparison(self, x, y):
return x == y
equal_s_s = EqualSS()
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x==y
"""
class EqualSD(__ComparisonOpSD):
"""
:param x:sparse matrix
:param y:dense matrix
:return: x==y
"""
class EqualSD(__ComparisonOpSD):
def comparison(self, x, y):
return x == y
equal_s_d = EqualSD()
"""
:param x:sparse matrix
:param y:dense matrix
:return: x==y
"""
class NotEqualSS(__ComparisonOpSS):
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x!=y
"""
class NotEqualSS(__ComparisonOpSS):
def comparison(self, x, y):
return x != y
not_equal_s_s = NotEqualSS()
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x!=y
"""
class NotEqualSD(__ComparisonOpSD):
"""
:param x:sparse matrix
:param y:dense matrix
:return: x!=y
"""
class NotEqualSD(__ComparisonOpSD):
def comparison(self, x, y):
return x != y
not_equal_s_d = NotEqualSD()
"""
:param x:sparse matrix
:param y:dense matrix
:return: x!=y
"""
class LessThanSS(__ComparisonOpSS):
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x<y
"""
class LessThanSS(__ComparisonOpSS):
def comparison(self, x, y):
return x < y
less_than_s_s = LessThanSS()
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x<y
"""
class LessThanSD(__ComparisonOpSD):
"""
:param x:sparse matrix
:param y:dense matrix
:return: x<y
"""
class LessThanSD(__ComparisonOpSD):
def comparison(self, x, y):
return x < y
less_than_s_d = LessThanSD()
"""
:param x:sparse matrix
:param y:dense matrix
:return: x<y
"""
class GreaterThanSS(__ComparisonOpSS):
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x>y
"""
class GreaterThanSS(__ComparisonOpSS):
def comparison(self, x, y):
return x > y
greater_than_s_s = GreaterThanSS()
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x>y
"""
class GreaterThanSD(__ComparisonOpSD):
"""
:param x:sparse matrix
:param y:dense matrix
:return: x>y
"""
class GreaterThanSD(__ComparisonOpSD):
def comparison(self, x, y):
return x > y
greater_than_s_d = GreaterThanSD()
"""
:param x:sparse matrix
:param y:dense matrix
:return: x>y
"""
class LessEqualSS(__ComparisonOpSS):
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x<=y
"""
class LessEqualSS(__ComparisonOpSS):
def comparison(self, x, y):
return x <= y
less_equal_s_s = LessEqualSS()
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x<=y
"""
class LessEqualSD(__ComparisonOpSD):
"""
:param x:sparse matrix
:param y:dense matrix
:return: x<=y
"""
class LessEqualSD(__ComparisonOpSD):
def comparison(self, x, y):
return x <= y
less_equal_s_d = LessEqualSD()
"""
:param x:sparse matrix
:param y:dense matrix
:return: x<=y
"""
class GreaterEqualSS(__ComparisonOpSS):
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x>=y
"""
class GreaterEqualSS(__ComparisonOpSS):
def comparison(self, x, y):
return x >= y
greater_equal_s_s = GreaterEqualSS()
"""
:param x:first compared sparse matrix
:param y:second compared sparse matrix
:return: x>=y
"""
class GreaterEqualSD(__ComparisonOpSD):
"""
:param x:sparse matrix
:param y:dense matrix
:return: x>=y
"""
class GreaterEqualSD(__ComparisonOpSD):
def comparison(self, x, y):
return x >= y
greater_equal_s_d = GreaterEqualSD()
"""
:param x:sparse matrix
:param y:dense matrix
:return: x>=y
"""
eq = __ComparisonSwitch(equal_s_s, equal_s_d, equal_s_d)
"""
......@@ -2561,19 +2547,7 @@ ge = __ComparisonSwitch(greater_equal_s_s, greater_equal_s_d,
class HStack(gof.op.Op):
"""Stack sparse matrices horizontally (column wise).
:param blocks: Sequence of sparse array of compatible shape.
:param format: String representing the output format. Default
is csc.
:param dtype: Output dtype. Must be specified.
:return: The concatenation of the sparse arrays column wise.
:note: The number of line of the sparse matrix must agree.
:note: The grad implemented is regular, i.e. not structured.
"""
# See doc in instance of this Op or function after this class definition.
def __init__(self, format=None, dtype=None):
if format is None:
self.format = 'csc'
......@@ -2670,19 +2644,7 @@ def hstack(blocks, format=None, dtype=None):
class VStack(HStack):
"""Stack sparse matrices vertically (row wise).
:param blocks: Sequence of sparse array of compatible shape.
:param format: String representing the output format. Default
is csc.
:param dtype: Output dtype. Must be specified.
:return: The concatenation of the sparse arrays row wise.
:note: The number of column of the sparse matrix must agree.
:note: The grad implemented is regular, i.e. not structured.
"""
# See doc in instance of this Op or function after this class definition.
def perform(self, node, block, (out, )):
for b in block:
assert _is_sparse(b)
......@@ -2746,15 +2708,7 @@ def vstack(blocks, format=None, dtype=None):
class Remove0(gof.Op):
"""Remove explicit zeros from a sparse matrix.
:param x: Sparse matrix.
:return: Exactly `x` but with a data attribute
exempt of zeros.
:note: The grad implemented is regular, i.e. not structured.
"""
# See doc in instance of this Op or a function after the class definition.
def __init__(self, inplace=False, *args, **kwargs):
gof.Op.__init__(self, *args, **kwargs)
self.inplace = inplace
......@@ -2792,6 +2746,14 @@ class Remove0(gof.Op):
def infer_shape(self, node, i0_shapes):
return i0_shapes
remove0 = Remove0()
"""Remove explicit zeros from a sparse matrix.
:param x: Sparse matrix.
:return: Exactly `x` but with a data attribute
exempt of zeros.
:note: The grad implemented is regular, i.e. not structured.
"""
# Structured monoid
......@@ -3011,28 +2973,6 @@ def sqrt(x):
class TrueDot(gof.op.Op):
"""Calculate the true dot operation between two matrices.
`TrueDot` is different of `StructuredDot` for sparse matrix
since the grad of `TrueDot` is regular, i.e. not structured.
The parameter `grad_preserves_dense`, controlled by the
constructor, is a boolean flags to controls whether gradients
with respect to inputs are converted to dense matrices when the
corresponding input y is dense (not in a L{SparseVariable} wrapper).
This is generally a good idea when L{Dot} is in the middle of a
larger graph, because the types of gy will match that of y. This
conversion might be inefficient if the gradients are graph outputs
though, hence this mask.
:param x: Sparse matrix for the left operand.
:param y: Sparse or dense matrix for the right operand.
:return: The dot product `x` . `y` in a sparse matrix.
:note:
- The grad implemented is regular, i.e. not structured.
"""
# TODO
# Simplify code by splitting into DotSS and DotSD.
......@@ -3136,14 +3076,15 @@ def true_dot(x, y, grad_preserves_dense=True):
one or all operands are sparse. Supported formats are CSC and CSR.
The output of the operation is sparse.
:param x: Sparse matrix or 2d tensor variable.
:param x: Sparse matrix.
:param y: Sparse matrix or 2d tensor variable.
:param grad_preserves_dense: if True (default), makes the grad of
dense inputs dense. Otherwise the grad is always sparse.
:return: The dot product `x`.`y` in a sparse format.
:note: one of ``x`` or ``y`` must be sparse.
:note:
- The grad implemented is regular, i.e. not structured.
"""
# TODO
# Maybe the triple-transposition formulation
......@@ -3171,21 +3112,7 @@ def true_dot(x, y, grad_preserves_dense=True):
# 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.
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` as a dense matrix.
:note: The grad implemented is structured.
"""
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return (type(self) == type(other))
......@@ -3600,33 +3527,7 @@ def structured_dot_grad(sparse_A, dense_B, ga):
class SamplingDot(gof.op.Op):
"""Operand 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: Sparse matrix in csr format.
:return: A dense matrix containing the dot product of `x` by `y`.T only
where `p` is 1.
:note: The grad implemented is regular, i.e. not structured.
"""
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return type(self) == type(other)
......@@ -3674,25 +3575,36 @@ class SamplingDot(gof.op.Op):
def __str__(self):
return self.__class__.__name__
sampling_dot = SamplingDot()
"""Operand 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.
class Dot(gof.op.Op):
"""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.
.. note::
:param x: sparse or dense matrix variable.
:param y: sparse or dense matrix variable.
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.
:return: The dot product `x`.`y` in a dense format.
:param x: Tensor matrix.
:param y: Tensor matrix.
:param p: Sparse matrix in csr format.
:note: The grad implemented is regular, i.e. not structured.
:note: At least one of `x` or `y` must be a sparse matrix.
:note: When the operation has the form dot(csr_matrix, dense)
the gradient of this operation can be performed inplace
by UsmmCscDense. This leads to significant speed-ups.
"""
:return: A dense matrix containing the dot product of `x` by `y`.T only
where `p` is 1.
:note: The grad implemented is regular, i.e. not structured.
"""
class Dot(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return type(self) == type(other)
......@@ -3790,13 +3702,17 @@ def dot(x, y):
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.
:param x: sparse or dense matrix variable.
:param y: sparse or dense matrix variable.
:return: The dot product `x`.`y` in a dense format.
:note: The grad implemented is regular, i.e. not structured.
:note: At least one of `x` or `y` must be a sparse matrix.
:note: At least one of `x` or `y` must be a sparse matrix.
:note: When the operation has the form dot(csr_matrix, dense)
the gradient of this operation can be performed inplace
by UsmmCscDense. This leads to significant speed-ups.
"""
if hasattr(x, 'getnnz'):
......
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