ShapeDTW

class ShapeDTW(n_neighbors=1, subsequence_length=30, shape_descriptor_function='raw', shape_descriptor_functions=None, metric_params=None, n_splits=10)[source]

ShapeDTW classifier.

ShapeDTW[1] works by initially extracting a set of subsequences describing local neighbourhoods around each data point in a time series. These subsequences are then passed into a shape descriptor function that transforms these local neighbourhoods into a new representation. This new representation is then sent into DTW with 1-NN.

Parameters:

n_neighborsint, int, set k for knn (default =1).

subsequence_lengthint, defines the length of the

subsequences(default=sqrt(n_timepoints)).

shape_descriptor_functionstring, defines the function to describe

the set of subsequences (default = ‘raw’).

The possible shape descriptor functions are as follows:

‘raw’use the raw subsequence as the
shape descriptor function.
- params = None
‘paa’use PAA as the shape descriptor function.
- params = num_intervals_paa (default=8)
‘dwt’use DWT (Discrete Wavelet Transform)
as the shape descriptor function.
- params = num_levels_dwt (default=3)
‘slope’use the gradient of each subsequence
fitted by a total least squares regression as the shape descriptor function.
- params = num_intervals_slope (default=8)
‘derivative’use the derivative of each subsequence
as the shape descriptor function.
- params = None
‘hog1d’use a histogram of gradients in one
dimension as the shape descriptor function.
- params = num_intervals_hog1d
  
  (default=2)
  
  = num_bins_hod1d
  (default=8)
  
  = scaling_factor_hog1d
  (default=0.1)
‘compound’use a combination of two shape
descriptors simultaneously.
- params = weighting_factor
  
  (default=None)
  Defines how to scale values of a shape descriptor. If a value is not given, this value is tuned by 10-fold cross-validation on the training data.

shape_descriptor_functionsstring list, only applicable when the

shape_descriptor_function is set to ‘compound’. Use a list of shape descriptor functions at the same time. (default = [‘raw’,’derivative’])

metric_paramsdictionary for metric parameters

(default = None).

n_splitsint, number of splits for cross-validation

(default = 10). Used for finding the weighting_factor if ‘shape_descriptor_function’ is set to ‘compound’ and weighting_factor is not given in ‘metric_params’.

Attributes:

is_fitted: Whether fit has been called.

Notes

[1]

Jiaping Zhao and Laurent Itti, “shapeDTW: Shape Dynamic Time Warping”, Pattern Recognition, 74, pp 171-184, 2018 http://www.sciencedirect.com/science/article/pii/S0031320317303710,

Examples

>>> from sktime.classification.distance_based import ShapeDTW
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train")
>>> X_test, y_test = load_unit_test(split="test")
>>> clf = ShapeDTW(n_neighbors=1,
...     subsequence_length=30,
...     shape_descriptor_function="raw",
...     shape_descriptor_functions=None,
...     metric_params=None,
... )
>>> clf.fit(X_train, y_train)
ShapeDTW(...)
>>> y_pred = clf.predict(X_test)

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.