KNeighborsTimeSeriesClassifierPyts

class KNeighborsTimeSeriesClassifierPyts(n_neighbors=1, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=1)[source]

K-nearest neighbors time series classifier, from pyts.

Direct interface to pyts.classification.KNeighborsClassifier, author of the interfaced class is johannfaouzi.

Parameters:

n_neighborsint, optional (default = 1)

Number of neighbors to use.

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.

algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional

Algorithm used to compute the nearest neighbors. Ignored ff metric is either ‘dtw’, ‘dtw_sakoechiba’, ‘dtw_itakura’, ‘dtw_multiscale’, ‘dtw_fast’ or ‘boss’ (‘brute’ will be used).

Note: fitting on sparse input will override the setting of this parameter, using brute force.

leaf_sizeint, optional (default = 30)

Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.

pinteger, optional (default = 2)

Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

metricstring or DistanceMetric object (default = ‘minkowski’)

The distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class from scikit-learn for a list of available metrics. For Dynamic Time Warping, the available metrics are ‘dtw’, ‘dtw_sakoechiba’, ‘dtw_itakura’, ‘dtw_multiscale’ and ‘dtw_fast’. For BOSS metric, one can use ‘boss’.

metric_paramsdict, optional (default = None)

Additional keyword arguments for the metric function.

n_jobsint, optional (default = 1)

The number of parallel jobs to run for neighbors search. If n_jobs=-1, then the number of jobs is set to the number of CPU cores. Doesn’t affect fit method.

Attributes:

classes_array, shape = (n_classes,): An array of class labels known to the classifier.

Examples

>>> from sktime.classification.distance_based import (
...     KNeighborsTimeSeriesClassifierPyts
... )
>>> 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 = KNeighborsTimeSeriesClassifierPyts(n_neighbors=1,
...     weights="uniform",
...     algorithm="auto",
...     leaf_size=30,
...     p=2,
...     metric="minkowski",
...     metric_params=None,
...     n_jobs=1,
... )
>>> clf.fit(X_train, y_train)
KNeighborsTimeSeriesClassifierPyts(...)
>>> 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.