def _set_operation(a, b, set_operation, validate_indices=True):
"""Compute set operation of elements in last dimension of `a` and `b`.
All but the last dimension of `a` and `b` must match.
Args:
a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
must be sorted in row-major order.
b: `Tensor` or `SparseTensor` of the same type as `a`. Must be
`SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
sorted in row-major order.
set_operation: String indicating set operaiton. See
SetOperationOp::SetOperationFromContext for valid values.
validate_indices: Whether to validate the order and range of sparse indices
in `a` and `b`.
Returns:
A `SparseTensor` with the same rank as `a` and `b`, and all but the last
dimension the same. Elements along the last dimension contain the results
of the set operation.
Raises:
TypeError: If inputs are invalid types.
ValueError: If `a` is sparse and `b` is dense.
"""
a = tensor_util.convert_to_tensor_or_sparse_tensor(a, name="a")
if a.dtype.base_dtype not in _VALID_DTYPES:
raise TypeError("'a' invalid dtype %s." % a.dtype)
b = tensor_util.convert_to_tensor_or_sparse_tensor(b, name="b")
if b.dtype.base_dtype != a.dtype.base_dtype:
raise TypeError("Types don't match, %s vs %s." % (a.dtype, b.dtype))
# pylint: disable=protected-access
if isinstance(a, sparse_tensor.SparseTensor):
if isinstance(b, sparse_tensor.SparseTensor):
indices, values, shape = _set_ops.sparse_to_sparse_set_operation(
a.indices, a.values, a.shape, b.indices, b.values, b.shape,
set_operation, validate_indices)
else:
raise ValueError(
"Sparse,Dense is not supported, but Dense,Sparse is. "
"Please flip the order of your inputs.")
elif isinstance(b, sparse_tensor.SparseTensor):
indices, values, shape = _set_ops.dense_to_sparse_set_operation(
a, b.indices, b.values, b.shape, set_operation, validate_indices)
else:
indices, values, shape = _set_ops.dense_to_dense_set_operation(
a, b, set_operation, validate_indices)
# pylint: enable=protected-access
return sparse_tensor.SparseTensor(indices, values, shape)
def _set_operation(a, b, set_operation, validate_indices=True):
"""Compute set operation of elements in last dimension of `a` and `b`.
All but the last dimension of `a` and `b` must match.
Args:
a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
must be sorted in row-major order.
b: `Tensor` or `SparseTensor` of the same type as `a`. Must be
`SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
sorted in row-major order.
set_operation: String indicating set operaiton. See
SetOperationOp::SetOperationFromContext for valid values.
validate_indices: Whether to validate the order and range of sparse indices
in `a` and `b`.
Returns:
A `SparseTensor` with the same rank as `a` and `b`, and all but the last
dimension the same. Elements along the last dimension contain the results
of the set operation.
Raises:
TypeError: If inputs are invalid types.
ValueError: If `a` is sparse and `b` is dense.
"""
a = tensor_util.convert_to_tensor_or_sparse_tensor(a, name="a")
if a.dtype.base_dtype not in _VALID_DTYPES:
raise TypeError("'a' invalid dtype %s." % a.dtype)
b = tensor_util.convert_to_tensor_or_sparse_tensor(b, name="b")
if b.dtype.base_dtype != a.dtype.base_dtype:
raise TypeError("Types don't match, %s vs %s." % (a.dtype, b.dtype))
# pylint: disable=protected-access
if isinstance(a, ops.SparseTensor):
if isinstance(b, ops.SparseTensor):
indices, values, shape = _set_ops.sparse_to_sparse_set_operation(
a.indices, a.values, a.shape, b.indices, b.values, b.shape,
set_operation, validate_indices)
else:
raise ValueError("Sparse,Dense is not supported, but Dense,Sparse is. "
"Please flip the order of your inputs.")
elif isinstance(b, ops.SparseTensor):
indices, values, shape = _set_ops.dense_to_sparse_set_operation(
a, b.indices, b.values, b.shape, set_operation, validate_indices)
else:
indices, values, shape = _set_ops.dense_to_dense_set_operation(
a, b, set_operation, validate_indices)
# pylint: enable=protected-access
return ops.SparseTensor(indices, values, shape)
def set_size(a, validate_indices=True):
"""Compute number of unique elements along last dimension of `a`.
Args:
a: `SparseTensor`, with indices sorted in row-major order.
validate_indices: Whether to validate the order and range of sparse indices
in `a`.
Returns:
For `a` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st
`n-1` dimensions as `a`. Each value is the number of unique elements in
the corresponding `[0...n-1]` dimension of `a`.
Raises:
TypeError: If `a` is an invalid types.
"""
a = tensor_util.convert_to_tensor_or_sparse_tensor(a, name="a")
if not isinstance(a, ops.SparseTensor):
raise TypeError("Expected `SparseTensor`, got %s." % a)
if a.values.dtype.base_dtype not in _VALID_DTYPES:
raise TypeError("Invalid dtype %s." % a.values.dtype)
# pylint: disable=protected-access
return _set_ops.set_size(a.indices, a.values, a.shape, validate_indices)
def set_size(a, validate_indices=True):
"""Compute number of unique elements along last dimension of `a`.
Args:
a: `SparseTensor`, with indices sorted in row-major order.
validate_indices: Whether to validate the order and range of sparse indices
in `a`.
Returns:
`int32` `Tensor` of set sizes. For `a` ranked `n`, this is a `Tensor` with
rank `n-1`, and the same 1st `n-1` dimensions as `a`. Each value is the
number of unique elements in the corresponding `[0...n-1]` dimension of `a`.
Raises:
TypeError: If `a` is an invalid types.
"""
a = tensor_util.convert_to_tensor_or_sparse_tensor(a, name="a")
if not isinstance(a, ops.SparseTensor):
raise TypeError("Expected `SparseTensor`, got %s." % a)
if a.values.dtype.base_dtype not in _VALID_DTYPES:
raise TypeError("Invalid dtype %s." % a.values.dtype)
# pylint: disable=protected-access
return _set_ops.set_size(a.indices, a.values, a.shape, validate_indices)
