tslearn.datasets
.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_name : str
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,)