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_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,)

Examples using tslearn.datasets.CachedDatasets

k-NN search

k-NN search

k-NN search
Hyper-parameter tuning of a Pipeline with KNeighborsTimeSeriesClassifier

Hyper-parameter tuning of a Pipeline with KNeighborsTimeSeriesClassifier

Hyper-parameter tuning of a Pipeline with KNeighborsTimeSeriesClassifier
KShape

KShape

KShape
Kernel k-means

Kernel k-means

Kernel k-means
Barycenters

Barycenters

Barycenters
Soft-DTW weighted barycenters

Soft-DTW weighted barycenters

Soft-DTW weighted barycenters
k-means

k-means

k-means
SVM and GAK

SVM and GAK

SVM and GAK
Learning Shapelets

Learning Shapelets

Learning Shapelets
Aligning discovered shapelets with timeseries

Aligning discovered shapelets with timeseries

Aligning discovered shapelets with timeseries
Learning Shapelets: decision boundaries in 2D distance space

Learning Shapelets: decision boundaries in 2D distance space

Learning Shapelets: decision boundaries in 2D distance space
Model Persistence

Model Persistence

Model Persistence
Distance and Matrix Profiles

Distance and Matrix Profiles

Distance and Matrix Profiles