kedro_datasets.svmlight.SVMLightDataset

class kedro_datasets.svmlight.SVMLightDataset(*, filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)

SVMLightDataset loads/saves data from/to a svmlight/libsvm file using an underlying filesystem (e.g.: local, S3, GCS). It uses sklearn functions dump_svmlight_file to save and load_svmlight_file to load a file.

Data is loaded as a tuple of features and labels. Labels is NumPy array, and features is Compressed Sparse Row matrix.

This format is a text-based format, with one sample per line. It does not store zero valued features hence it is suitable for sparse datasets.

This format is used as the default format for both svmlight and the libsvm command line programs.

Example usage for the YAML API:

svm_dataset:
  type: svmlight.SVMLightDataset
  filepath: data/01_raw/location.svm
  load_args:
    zero_based: False
  save_args:
    zero_based: False

cars:
  type: svmlight.SVMLightDataset
  filepath: gcs://your_bucket/cars.svm
  fs_args:
    project: my-project
  credentials: my_gcp_credentials
  load_args:
    zero_based: False
  save_args:
    zero_based: False

Example usage for the Python API:

from kedro_datasets.svmlight import SVMLightDataset
import numpy as np

# Features and labels.
data = (np.array([[0, 1], [2, 3.14159]]), np.array([7, 3]))

dataset = SVMLightDataset(filepath=tmp_path / "test.svm")
dataset.save(data)
reloaded_features, reloaded_labels = dataset.load()
assert (data[0] == reloaded_features).all()
assert (data[1] == reloaded_labels).all()

Attributes

DEFAULT_FS_ARGS
DEFAULT_LOAD_ARGS
DEFAULT_SAVE_ARGS

Methods

exists()	Checks whether a dataset's output already exists by calling the provided _exists() method.
from_config(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
load()	Loads data by delegation to the provided load method.
release()	Release any cached data.
resolve_load_version()	Compute the version the dataset should be loaded with.
resolve_save_version()	Compute the version the dataset should be saved with.
save(data)	Saves data by delegation to the provided save method.
to_config()	Converts the dataset instance into a dictionary-based configuration for serialization.

DEFAULT_FS_ARGS: dict[str, Any] = {'open_args_load': {'mode': 'rb'}, 'open_args_save': {'mode': 'wb'}}

DEFAULT_LOAD_ARGS: dict[str, Any] = {}

DEFAULT_SAVE_ARGS: dict[str, Any] = {}

__init__(*, filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)

Creates a new instance of SVMLightDataset to load/save data from a svmlight/libsvm file.

Parameters:

• filepath (str) – Filepath in POSIX format to a text file prefixed with a protocol like s3://. If prefix is not provided, file protocol (local filesystem) will be used. The prefix should be any protocol supported by fsspec.

• load_args (Optional[dict[str, Any]]) – Arguments passed on to load_svmlight_file. See the details in https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_svmlight_file.html

• save_args (Optional[dict[str, Any]]) – Arguments passed on to dump_svmlight_file. See the details in https://scikit-learn.org/stable/modules/generated/sklearn.datasets.dump_svmlight_file.html

• version (Optional[Version]) – If specified, should be an instance of kedro.io.core.Version. If its load attribute is None, the latest version will be loaded. If its save attribute is None, save version will be autogenerated.

• credentials (Optional[dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {“token”: None}.

• fs_args (Optional[dict[str, Any]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} for GCSFileSystem). All defaults are preserved, except mode, which is set to rb when loading and to wb when saving.

• metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

exists()

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:

bool

Returns:

Flag indicating whether the output already exists.

Raises:

DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)

Create a dataset instance using the configuration provided.

Parameters:

• name (str) – Data set name.

• config (dict[str, Any]) – Data set config dictionary.

• load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

• save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()

Loads data by delegation to the provided load method.

Return type:

tuple[csr_matrix, ndarray]

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

release()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

resolve_load_version()

Compute the version the dataset should be loaded with.

Return type:

Optional[str]

resolve_save_version()

Compute the version the dataset should be saved with.

Return type:

Optional[str]

save(data)

Saves data by delegation to the provided save method.

Parameters:

data (tuple[ndarray | csr_matrix, ndarray]) – the value to be saved by provided save method.

Raises:

• DatasetError – when underlying save method raises error.

• FileNotFoundError – when save method got file instead of dir, on Windows.

• NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None

to_config()

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:

dict[str, Any]

Returns:

A dictionary containing the dataset’s type and initialization arguments.