CachedDatasets#

class tslearn.datasets.CachedDatasets[source]#

A convenience class to access cached time series datasets.

Note, that these cached datasets are statically included into tslearn and are distinct from the ones in UCR_UEA_datasets.

When using the Trace dataset, please cite [1].

See also

UCR_UEA_datasets: Provides more datasets and supports caching.

References

[1]

A. Bagnall, J. Lines, W. Vickers and E. Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com

Methods

`list_datasets`()	List cached datasets.
`load_dataset`(dataset_name)	Load a cached dataset from its name.

list_datasets()[source]#

List cached datasets.

Returns:

list of str:: A list of names of all cached (univariate and multivariate) dataset namas.

Examples

>>> from tslearn.datasets import UCR_UEA_datasets
>>> _ = UCR_UEA_datasets().load_dataset("Trace")
>>> cached = UCR_UEA_datasets().list_cached_datasets()
>>> "Trace" in cached
True

load_dataset(dataset_name)[source]#

Load a cached dataset from its name.

Parameters:

dataset_namestr: Name of the dataset. Should be in the list returned by list_datasets().

Returns:

numpy.ndarray of shape (n_ts_train, sz, d) or None: Training time series. None if unsuccessful.
numpy.ndarray of integers with shape (n_ts_train, ) or None: Training labels. None if unsuccessful.
numpy.ndarray of shape (n_ts_test, sz, d) or None: Test time series. None if unsuccessful.
numpy.ndarray of integers with shape (n_ts_test, ) or None: Test labels. None if unsuccessful.

Raises:

IOError: If the dataset does not exist or cannot be read.

Examples

>>> data_loader = CachedDatasets()
>>> X_train, y_train, X_test, y_test = data_loader.load_dataset(
...                                        "Trace")
>>> print(X_train.shape)
(100, 275, 1)
>>> print(y_train.shape)
(100,)