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)