lenskit.data.matrix.SparseRowArray

class lenskit.data.matrix.SparseRowArray

Bases: ExtensionArray

An array of sparse rows (a compressed sparse row matrix).

Stability: Internal

This API is at the internal or experimental stability level: it may change at any time, and breaking changes will not necessarily be described in the release notes. See Stability Levels for details.

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
buffers(self)	Return a list of Buffer objects pointing to this array's physical storage.
cast(self[, target_type, safe, options, ...])	Cast array values to another data type
copy_to(self, destination)	Construct a copy of the array with all buffers on destination device.
dictionary_encode(self[, null_encoding])	Compute dictionary-encoded representation of array.
diff(self, Array other)	Compare contents of this array against another one.
drop_null(self)	Remove missing values from an array.
equals(self, Array other)
fill_null(self, fill_value)	See pyarrow.compute.fill_null() for usage.
filter(self, mask, *[, null_selection_behavior])	Select values from an array.
format(self, **kwargs)	DEPRECATED, use pyarrow.Array.to_string
from_array(array[, dimension])	Interpret an Arrow array as a sparse row array, if possible.
from_arrays(offsets, indices[, values, shape])
from_buffers(DataType type, length, buffers)	Construct an Array from a sequence of buffers.
from_pandas(obj[, mask, type])	Convert pandas.Series to an Arrow Array.
from_scipy(matrix, *[, values, large])	Create a sparse row array from a SciPy sparse matrix.
from_storage(BaseExtensionType typ, ...)	Construct ExtensionArray from type and storage array.
get_total_buffer_size(self)	The sum of bytes in each buffer referenced by the array.
index(self, value[, start, end, memory_pool])	Find the first index of a value.
is_nan(self)	Return BooleanArray indicating the NaN values.
is_null(self, *[, nan_is_null])	Return BooleanArray indicating the null values.
is_valid(self)	Return BooleanArray indicating the non-null values.
row_extent(row)	Get the start and end of a row.
row_indices(row)	Get the index array for a compressed sparse row.
slice(self[, offset, length])	Compute zero-copy slice of this array.
sort(self[, order])	Sort the Array
structure()	Get the structure of this matrix (without values).
sum(self, **kwargs)	Sum the values in a numerical array.
take(self, indices)	Select values from an array.
to_coo()	Convert this array to table representing the array in COO format.
to_numpy(self[, zero_copy_only, writable])	Return a NumPy view or copy of this array.
to_pandas(self[, memory_pool, categories, ...])	Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
to_pylist(self, *[, maps_as_pydicts])	Convert to a list of native Python objects.
to_scipy()	Convert this sparse row array to a SciPy sparse array.
to_string(self, *, int indent=2, ...)	Render a "pretty-printed" string representation of the Array.
to_torch()	Convert this sparse row array to a Torch sparse tensor.
tolist(self)	Alias of to_pylist for compatibility with NumPy.
transpose()	Get the transpose of this sparse matrix.
unique(self)	Compute distinct elements in array.
validate(self, *[, full])	Perform validation checks.
value_counts(self)	Compute counts of unique elements in array.
view(self, target_type)	Return zero-copy "view" of array as another data type.

Attributes

device_type	The device type where the array resides.
dimension	Get the number of columns in the sparse matrix.
has_values
indices
is_cpu	Whether the array is CPU-accessible.
nbytes	Total number of bytes consumed by the elements of the array.
null_count
offset	A relative position into another array's data.
offsets
shape
statistics	Statistics of the array.
storage
type
values

classmethod from_array(array, dimension=None)

Interpret an Arrow array as a sparse row array, if possible. Handles legacy layouts without the extension types.

Parameters:

• array (Array) – The array to convert.

• dimension (int | None) – The dimensionality of the sparse rows, if known from an external source.

Return type:

SparseRowArray

classmethod from_scipy(matrix, *, values=True, large=None)

Create a sparse row array from a SciPy sparse matrix.

Parameters:

• csr – The SciPy sparse matrix (in CSR format).

• values (bool) – Whether to include the values or create a structure-only array.

• large (bool | None) – True to force creation of a pa.LargeListArray.

• matrix (sparray)

Returns:

The sparse row array.

Return type:

SparseRowArray

to_scipy()

Convert this sparse row array to a SciPy sparse array.

Return type:

csr_array[Any, tuple[int, int]]

to_torch()

Convert this sparse row array to a Torch sparse tensor.

Return type:

Tensor

to_coo()

Convert this array to table representing the array in COO format.

Return type:

Table

property dimension: int

Get the number of columns in the sparse matrix.

structure()

Get the structure of this matrix (without values).

Return type:

SparseRowArray

transpose()

Get the transpose of this sparse matrix.

Return type:

SparseRowArray

row_extent(row)

Get the start and end of a row.

Parameters:

row (int)

Return type:

tuple[int, int]

row_indices(row)

Get the index array for a compressed sparse row.

Parameters:

row (int)

Return type:

Int32Array