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(csr, *[, 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

sum(self, **kwargs)

Sum the values in a numerical array.

take(self, indices)

Select values from an array.

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)

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.

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

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(csr, *, values=True, large=None)#

Create a sparse row array from a SciPy sparse matrix.

Parameters:

  • csr (sps.csr_array[Any, tuple[int, int]]) – 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.

Returns:

The sparse row array.

Return type:

SparseRowArray

to_scipy()#

Convert this sparse row array to a SciPy sparse array.

Return type:

sps.csr_array[Any, tuple[int, int]]

to_torch()#

Convert this sparse row array to a Torch sparse tensor.

Return type:

Tensor

property dimension: int#

Get the number of columns in the sparse matrix.

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