ShapeletLearningClassifierTslearn

Learning Time Series Shapelets Classifier, from tslearn.

Quickstart

from sktime.classification.shapelet_based import ShapeletLearningClassifierTslearn

estimator = ShapeletLearningClassifierTslearn(n_shapelets_per_size=None, max_iter=10000, batch_size=256, optimizer='sgd', weight_regularizer=0.0, shapelet_length=0.15, total_lengths=3, max_size=None, scale=False, verbose=0, random_state=None)

Parameters(11)

n_shapelets_per_size: dict (default: None)

Dictionary giving, for each shapelet size (key), the number of such shapelets to be trained (value). If None, grabocka_params_to_shapelet_size_dict is used and the size used to compute is that of the shortest time series passed at fit time.

max_iter: int (default: 10,000)

Number of training epochs.

batch_size: int (default: 256)

Batch size to be used.

optimizer: str or keras.optimizers.Optimizer (default: “sgd”)

keras optimizer to use for training.

weight_regularizer: float or None (default: 0.)

Strength of the L2 regularizer to use for training the classification (softmax) layer. If 0, no regularization is performed.

shapelet_length: float (default: 0.15)

The length of the shapelets, expressed as a fraction of the time series length. Used only if n_shapelets_per_size is None.

total_lengths: int (default: 3)

The number of different shapelet lengths. Will extract shapelets of length i * shapelet_length for i in [1, total_lengths] Used only if n_shapelets_per_size is None.

max_size: int or None (default: None)

Maximum size for time series to be fed to the model. If None, it is set to the size (number of timestamps) of the training time series.

scale: bool (default: False)

Whether input data should be scaled for each feature of each time series to lie in the [0-1] interval. Default for this parameter is set to False in version 0.4 to ensure backward compatibility, but is likely to change in a future version.

verbose: {0, 1, 2} (default: 0)

keras verbose level.

random_stateint or None, optional (default: None)

The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If None, the random number generator is the RandomState instance used by np.random.

References

1. [1 ] Grabocka et al. Learning Time-Series Shapelets. SIGKDD 2014.