kedro.extras.datasets.svmlight.SVMLightDataSet¶

class kedro.extras.datasets.svmlight.SVMLightDataSet(filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None)[source]¶

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.extras.datasets.svmlight import SVMLightDataSet
import numpy as np

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

data_set = SVMLightDataSet(filepath="test.svm")
data_set.save(data)
reloaded_features, reloaded_labels = data_set.load()
assert (data[0] == reloaded_features).all()
assert (data[1] == reloaded_labels).all()

Attributes

`DEFAULT_LOAD_ARGS`
`DEFAULT_SAVE_ARGS`

Methods

`exists`()	Checks whether a data set's output already exists by calling the provided _exists() method.
`from_config`(name, config[, load_version, ...])	Create a data set 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.

DEFAULT_LOAD_ARGS: Dict[str, Any] = {}¶

DEFAULT_SAVE_ARGS: Dict[str, Any] = {}¶

exists()¶

Checks whether a data set’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 data set instance using the configuration provided.

Parameters

name – Data set name.
config – Data set config dictionary.
load_version – Version string to be used for load operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.
save_version – Version string to be used for save operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.

Returns

An instance of an AbstractDataset subclass.

Raises

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

load()¶

Loads data by delegation to the provided load method.

Return type: TypeVar(_DO)
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: str | None

resolve_save_version()¶

Compute the version the dataset should be saved with.

Return type: str | None

save(data)¶

Saves data by delegation to the provided save method.

Parameters

data (TypeVar(_DI)) – 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