ShapeletLearningClassifierTslearn

class 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)[source]

Learning Time Series Shapelets Classifier, from tslearn.

Direct interface to tslearn.shapelets.shapelets.LearningShapelets.

Learning Time-Series Shapelets was originally presented in [1].

Parameters:

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.

Attributes:

shapelets_numpy.ndarray of objects, each object being a time series: Set of time-series shapelets.
shapelets_as_time_series_numpy.ndarray of shape (n_shapelets, sz_shp, d): where sz_shp is the maximum of all shapelet sizes Set of time-series shapelets formatted as a tslearn time series dataset.
transformer_model_keras.Model: Transforms an input dataset of timeseries into distances to the learned shapelets.
locator_model_keras.Model: Returns the indices where each of the shapelets can be found (minimal distance) within each of the timeseries of the input dataset.
model_keras.Model: Directly predicts the class probabilities for the input timeseries.
history_dict: Dictionary of losses and metrics recorded during fit.

References

[1]

Grabocka et al. Learning Time-Series Shapelets. SIGKDD 2014.

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series classifier to training data.
`fit_predict`(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
`fit_predict_proba`(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`predict_proba`(X)	Predicts labels probabilities for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y)	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.