KNeighborsTimeSeriesClassifierTslearn

class KNeighborsTimeSeriesClassifierTslearn(n_neighbors=5, weights='uniform', metric='dtw', metric_params=None, n_jobs=None, verbose=0)[source]

K-nearest neighbors Time Series Classifier, from tslearn.

Direct interface to tslearn.neighbors.KNeighborsTimeSeriesClassifier.

Parameters:

n_neighborsint (default: 5)

Number of nearest neighbors to be considered for the decision.

weightsstr or callable, optional (default: ‘uniform’)

Weight function used in prediction. Possible values:

‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.
‘distance’ : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.
[callable] : a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights.

metric{‘dtw’, ‘softdtw’, ‘ctw’, ‘euclidean’, ‘sqeuclidean’, ‘cityblock’, ‘sax’}

default: ‘dtw’. Metric to be used at the core of the nearest neighbor procedure. When 'sax' is provided as a metric, the data is expected to be normalized such that each time series has zero mean and unit variance. 'euclidean', 'sqeuclidean', 'cityblock' are described in scipy.spatial.distance doc.

metric_paramsdict or None (default: None)

Dictionary of metric parameters. For metrics that accept parallelization of the cross-distance matrix computations, n_jobs and verbose keys passed in metric_params are overridden by the n_jobs and verbose arguments. For 'sax' metric, these are hyper-parameters to be passed at the creation of the SymbolicAggregateApproximation object.

n_jobsint or None, optional (default=None)

The number of jobs to run in parallel for cross-distance matrix computations. Ignored if the cross-distance matrix cannot be computed using parallelization. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

verboseint, optional (default=0)

The verbosity level: if non zero, progress messages are printed. Above 50, the output is sent to stdout. The frequency of the messages increases with the verbosity level. If it more than 10, all iterations are reported.

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.classification.distance_based import (
...     KNeighborsTimeSeriesClassifierTslearn
... )
>>> 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 = KNeighborsTimeSeriesClassifierTslearn(
...     n_neighbors=5,
...     weights="uniform",
...     metric="dtw",
...     metric_params=None,
...     n_jobs=None,
...     verbose=0,
... )
>>> clf.fit(X_train, y_train)
KNeighborsTimeSeriesClassifierTslearn(...)
>>> 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.