lenskit.data.matrix

Classes for working with matrix data.

Attributes

t
M
SPARSE_IDX_EXT_NAME
SPARSE_IDX_LIST_EXT_NAME
SPARSE_ROW_EXT_NAME

Classes

CSRStructure	Representation of the compressed sparse row structure of a sparse matrix,
COOStructure	Representation of the coordinate structure of a sparse matrix, without any
SparseIndexType	Data type for the index field of a sparse row. Indexes are just stored as
SparseIndexListType	Sparse index lists. These are the row type for structure-only sparse
SparseRowType	Data type for sparse rows stored in Arrow. Sparse rows are stored as lists
SparseRowArray	An array of sparse rows (a compressed sparse row matrix).

Functions

fast_col_cooc(…)	Compute column co-occurrances (\(M^{\mathrm{T}}M\)) efficiently.
normalize_matrix(matrix, normalize)	Normalize rows of a matrix.

Module Contents

lenskit.data.matrix.t

lenskit.data.matrix.M

lenskit.data.matrix.SPARSE_IDX_EXT_NAME = 'lenskit.sparse_index'

lenskit.data.matrix.SPARSE_IDX_LIST_EXT_NAME = 'lenskit.sparse_index_list'

lenskit.data.matrix.SPARSE_ROW_EXT_NAME = 'lenskit.sparse_row'

lenskit.data.matrix.fast_col_cooc(rows: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, cols: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, shape: tuple[int, int], *, progress: lenskit.logging.Progress | None = None, include_diagonal: bool = True, ordered: bool = False, dense: Literal[True]) → numpy.ndarray[tuple[int, int], numpy.dtype[numpy.float32]]

lenskit.data.matrix.fast_col_cooc(rows: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, cols: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, shape: tuple[int, int], *, progress: lenskit.logging.Progress | None = None, include_diagonal: bool = True, ordered: bool = False, dense: Literal[False] = False) → scipy.sparse.coo_array

lenskit.data.matrix.fast_col_cooc(rows: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, cols: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, shape: tuple[int, int], *, progress: lenskit.logging.Progress | None = None, include_diagonal: bool = True, ordered: bool = False, dense: bool = False) → Any

Compute column co-occurrances (\(M^{\mathrm{T}}M\)) efficiently.

lenskit.data.matrix.normalize_matrix(matrix, normalize)

Normalize rows of a matrix.

Parameters:

• matrix (scipy.sparse.csr_array | numpy.typing.NDArray[numpy.floating[Any]]) – Sparse or dense matrix to normalize

• normalize (Literal['unit', 'distribution'] | None) – Normalization mode (“unit” for L2, “distribution” for L1)

Returns:

Normalized matrix

Return type:

scipy.sparse.csr_array | numpy.typing.NDArray[numpy.floating[Any]]

Exported Aliases

class lenskit.data.matrix.Progress

Re-exported alias for lenskit.logging.Progress.

lenskit.data.matrix.NPVector

Re-exported alias for lenskit.data.types.NPVector.