Tensors¶
Dense Tensors¶
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class
arrow::Tensor¶ Subclassed by arrow::NumericTensor< TYPE >
Public Functions
Constructor with no dimension names or strides, data assumed to be row-major.
Constructor with non-negative strides.
Constructor with non-negative strides and dimension names.
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int64_t
size() const¶ Total number of value cells in the tensor.
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bool
is_mutable() const¶ Return true if the underlying data buffer is mutable.
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bool
is_contiguous() const¶ Either row major or column major.
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bool
is_row_major() const¶ AKA “C order”.
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bool
is_column_major() const¶ AKA “Fortran order”.
Public Static Functions
Create a Tensor with full parameters.
This factory function will return Status::Invalid when the parameters are inconsistent
- Parameters
[in] type: The data type of the tensor values[in] data: The buffer of the tensor content[in] shape: The shape of the tensor[in] strides: The strides of the tensor (if this is empty, the data assumed to be row-major)[in] dim_names: The names of the tensor dimensions
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int64_t
CalculateValueOffset(const std::vector<int64_t> &strides, const std::vector<int64_t> &index)¶ Return the offset of the given index on the given strides.
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template<typename
TYPE>
classarrow::NumericTensor: public arrow::Tensor¶ Public Functions
Constructor with non-negative strides and dimension names.
Constructor with no dimension names or strides, data assumed to be row-major.
Constructor with non-negative strides.
Public Static Functions
Create a NumericTensor with full parameters.
This factory function will return Status::Invalid when the parameters are inconsistent
- Parameters
[in] data: The buffer of the tensor content[in] shape: The shape of the tensor[in] strides: The strides of the tensor (if this is empty, the data assumed to be row-major)[in] dim_names: The names of the tensor dimensions
Sparse Tensors¶
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enum
arrow::SparseTensorFormat::type¶ EXPERIMENTAL: The index format type of SparseTensor.
Values:
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enumerator
COO¶ Coordinate list (COO) format.
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enumerator
CSR¶ Compressed sparse row (CSR) format.
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enumerator
CSC¶ Compressed sparse column (CSC) format.
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enumerator
CSF¶ Compressed sparse fiber (CSF) format.
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enumerator
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class
arrow::SparseIndex¶ EXPERIMENTAL: The base class for the index of a sparse tensor.
SparseIndex describes where the non-zero elements are within a SparseTensor.
There are several ways to represent this. The format_id is used to distinguish what kind of representation is used. Each possible value of format_id must have only one corresponding concrete subclass of SparseIndex.
Subclassed by arrow::internal::SparseIndexBase< SparseIndexType >, arrow::internal::SparseIndexBase< SparseCOOIndex >, arrow::internal::SparseIndexBase< SparseCSCIndex >, arrow::internal::SparseIndexBase< SparseCSFIndex >, arrow::internal::SparseIndexBase< SparseCSRIndex >
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class
arrow::SparseCOOIndex: public arrow::internal::SparseIndexBase<SparseCOOIndex>¶ EXPERIMENTAL: The index data for a COO sparse tensor.
A COO sparse index manages the location of its non-zero values by their coordinates.
Public Functions
Construct SparseCOOIndex from column-major NumericTensor.
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const std::shared_ptr<Tensor> &
indices() const¶ Return a tensor that has the coordinates of the non-zero values.
The returned tensor is a N x D tensor where N is the number of non-zero values and D is the number of dimensions in the logical data. The column at index
iis a D-tuple of coordinates indicating that the logical value at those coordinates should be found at physical indexi.
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int64_t
non_zero_length() const override¶ Return the number of non zero values in the sparse tensor related to this sparse index.
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bool
is_canonical() const¶ Return whether a sparse tensor index is canonical, or not.
If a sparse tensor index is canonical, it is sorted in the lexicographical order, and the corresponding sparse tensor doesn’t have duplicated entries.
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std::string
ToString() const override¶ Return a string representation of the sparse index.
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bool
Equals(const SparseCOOIndex &other) const¶ Return whether the COO indices are equal.
Public Static Functions
Make SparseCOOIndex from a coords tensor and canonicality.
Make SparseCOOIndex from a coords tensor with canonicality auto-detection.
Make SparseCOOIndex from raw properties with canonicality auto-detection.
Make SparseCOOIndex from raw properties.
Make SparseCOOIndex from sparse tensor’s shape properties and data with canonicality auto-detection.
The indices_data should be in row-major (C-like) order. If not, use the raw properties constructor.
Make SparseCOOIndex from sparse tensor’s shape properties and data.
The indices_data should be in row-major (C-like) order. If not, use the raw properties constructor.
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class
SparseCSRIndex: public arrow::internal::SparseCSXIndex<SparseCSRIndex, internal::SparseMatrixCompressedAxis::ROW>¶ EXPERIMENTAL: The index data for a CSR sparse matrix.
A CSR sparse index manages the location of its non-zero values by two vectors.
The first vector, called indptr, represents the range of the rows; the i-th row spans from indptr[i] to indptr[i+1] in the corresponding value vector. So the length of an indptr vector is the number of rows + 1.
The other vector, called indices, represents the column indices of the corresponding non-zero values. So the length of an indices vector is same as the number of non-zero-values.
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class
arrow::SparseTensor¶ EXPERIMENTAL: The base class of sparse tensor container.
Subclassed by arrow::SparseTensorImpl< SparseIndexType >
Public Functions
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std::shared_ptr<Buffer>
data() const¶ Return a buffer that contains the value vector of the sparse tensor.
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const uint8_t *
raw_data() const¶ Return an immutable raw data pointer.
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uint8_t *
raw_mutable_data() const¶ Return a mutable raw data pointer.
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const std::vector<int64_t> &
shape() const¶ Return a shape vector of the sparse tensor.
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const std::shared_ptr<SparseIndex> &
sparse_index() const¶ Return a sparse index of the sparse tensor.
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int
ndim() const¶ Return a number of dimensions of the sparse tensor.
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const std::vector<std::string> &
dim_names() const¶ Return a vector of dimension names.
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const std::string &
dim_name(int i) const¶ Return the name of the i-th dimension.
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int64_t
size() const¶ Total number of value cells in the sparse tensor.
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bool
is_mutable() const¶ Return true if the underlying data buffer is mutable.
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int64_t
non_zero_length() const¶ Total number of non-zero cells in the sparse tensor.
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bool
Equals(const SparseTensor &other, const EqualOptions& = EqualOptions::Defaults()) const¶ Return whether sparse tensors are equal.
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Result<std::shared_ptr<Tensor>>
ToTensor(MemoryPool *pool) const¶ Return dense representation of sparse tensor as tensor.
The returned Tensor has row-major order (C-like).
Status-return version of ToTensor().
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std::shared_ptr<Buffer>
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template<typename
SparseIndexType>
classarrow::SparseTensorImpl: public arrow::SparseTensor¶ EXPERIMENTAL: Concrete sparse tensor implementation classes with sparse index type.
Public Functions
Construct a sparse tensor from physical data buffer and logical index.
Construct an empty sparse tensor.
Public Static Functions
Create a SparseTensor with full parameters.
Create a sparse tensor from a dense tensor.
The dense tensor is re-encoded as a sparse index and a physical data buffer for the non-zero value.
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using
arrow::SparseCOOTensor= SparseTensorImpl<SparseCOOIndex>¶ EXPERIMENTAL: Type alias for COO sparse tensor.
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using
arrow::SparseCSRMatrix= SparseTensorImpl<SparseCSRIndex>¶ EXPERIMENTAL: Type alias for CSR sparse matrix.