KNeighborsTimeSeriesClassifier#

class KNeighborsTimeSeriesClassifier(n_neighbors=1, weights='uniform', distance='dtw', distance_params=None, **kwargs)[source]#

KNN Time Series Classifier.

An adapted version of the scikit-learn KNeighborsClassifier to work with time series data.

Necessary changes required for time series data:

calls to X.shape in kneighbors, predict and predict_proba. In the base class, these methods contain:

n_samples, _ = X.shape

This however assumes that data must be 2d (a set of multivariate time series is 3d). Therefore these methods needed to be overridden to change this call to the following to support 3d data:

n_samples = X.shape[0]
check array has been disabled. This method allows nd data via an

argument in the method header. However, there: seems to be no way to set this in the classifier and allow it to propagate down to the method. Therefore, this method has been temporarily disabled (and then re-enabled). It is unclear how to fix this issue without either writing a new classifier from scratch or changing the scikit-learn implementation. TO-DO: find permanent resolution to this issue (raise as an issue on sklearn GitHub?)

Parameters

n_neighborsint, set k for knn (default =1)
weightsstring or callable function, optional, default ==’ uniform’: mechanism for weighting a vote, one of: ‘uniform’, ‘distance’ or a callable function
algorithmsearch method for neighbours {‘auto’, ‘ball_tree’,
‘kd_tree’, ‘brute’}: default = ‘brute’
distancedistance measure for time series: {‘dtw’,’ddtw’,
‘wdtw’,’lcss’,’erp’,’msm’,’twe’}: default =’dtw’
distance_paramsdictionary for metric parameters: default = None

Attributes

is_fitted: Whether fit has been called.

Examples

>>> from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(return_X_y=True, split="train")
>>> X_test, y_test = load_unit_test(return_X_y=True, split="test")
>>> classifier = KNeighborsTimeSeriesClassifier(distance="euclidean")
>>> classifier.fit(X_train, y_train)
KNeighborsTimeSeriesClassifier(...)
>>> y_pred = classifier.predict(X_test)

Methods

`check_is_fitted`()	Check if the estimator has been fitted.
`clone_tags`(estimator[, tag_names])	clone/mirror tags from another estimator as dynamic override.
`create_test_instance`([parameter_set])	Construct Estimator instance if possible.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y, **kwargs)	Override fit is required to sort out the multiple inheritance.
`get_class_tag`(tag_name[, tag_value_default])	Get tag value from estimator class (only class tags).
`get_class_tags`()	Get class tags from estimator class and all its parent classes.
`get_params`([deep])	Get parameters for this estimator.
`get_tag`(tag_name[, tag_value_default, …])	Get tag value from estimator class and dynamic tag overrides.
`get_tags`()	Get tags from estimator class and dynamic tag overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composite.
`kneighbors`(X[, n_neighbors, return_distance])	Find the K-neighbors of a point.
`kneighbors_graph`([X, n_neighbors, mode])	Compute the (weighted) graph of k-Neighbors for points in X.
`predict`(X, **kwargs)	Predict wrapper.
`predict_proba`(X, **kwargs)	Predict proba wrapper.
`reset`()	Reset the object to a clean post-init state.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of this estimator.
`set_tags`(**tag_dict)	Set dynamic tags to given values.

fit(X, y, **kwargs)[source]#: Override fit is required to sort out the multiple inheritance.

kneighbors(X, n_neighbors=None, return_distance=True)[source]#

Find the K-neighbors of a point.

Returns indices of and distances to the neighbors of each point.

Parameters

Xsktime-format pandas dataframe with shape([n_cases,n_dimensions]),
or numpy ndarray with shape([n_cases,n_readings,n_dimensions])
y{array-like, sparse matrix}: Target values of shape = [n_samples]
n_neighborsint: Number of neighbors to get (default is the value passed to the constructor).
return_distanceboolean, optional. Defaults to True.: If False, distances will not be returned

Returns

distarray: Array representing the lengths to points, only present if return_distance=True
indarray: Indices of the nearest points in the population matrix.

predict(X, **kwargs) → numpy.ndarray[source]#: Predict wrapper.

predict_proba(X, **kwargs) → numpy.ndarray[source]#: Predict proba wrapper.

classmethod get_test_params(parameter_set='default')[source]#

Return testing parameter settings for the estimator.

Parameters

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set. For classifiers, a “default” set of parameters should be provided for general testing, and a “results_comparison” set for comparing against previously recorded results if the general set does not produce suitable probabilities to compare against.

Returns

paramsdict or list of dict, default={}: Parameters to create testing instances of the class. Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params.

check_is_fitted()[source]#

Check if the estimator has been fitted.

Raises

NotFittedError: If the estimator has not been fitted yet.

clone_tags(estimator, tag_names=None)[source]#

clone/mirror tags from another estimator as dynamic override.

Parameters

estimatorestimator inheriting from :class:BaseEstimator
tag_namesstr or list of str, default = None: Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns

Self: Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct Estimator instance if possible.

Parameters

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns

instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')[source]#

Create list of all test instances and a list of names for them.

Parameters

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns

objslist of instances of cls: i-th instance is cls(**cls.get_test_params()[i])
nameslist of str, same length as objs: i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}
parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get tag value from estimator class (only class tags).

Parameters

tag_namestr: Name of tag value.
tag_value_defaultany type: Default/fallback value if tag is not found.

Returns

tag_value: Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()[source]#

Get class tags from estimator class and all its parent classes.

Returns

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict: Parameter names mapped to their values.

get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

Get tag value from estimator class and dynamic tag overrides.

Parameters

tag_namestr: Name of tag to be retrieved
tag_value_defaultany type, optional; default=None: Default/fallback value if tag is not found
raise_errorbool: whether a ValueError is raised when the tag is not found

Returns

tag_value: Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises

ValueError if raise_error is True i.e. if tag_name is not in self.get_tags(
).keys()

get_tags()[source]#

Get tags from estimator class and dynamic tag overrides.

Returns

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

is_composite()[source]#

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns

composite: bool, whether self contains a parameter which is BaseObject

property is_fitted[source]#: Whether fit has been called.

kneighbors_graph(X=None, n_neighbors=None, mode='connectivity')[source]#

Compute the (weighted) graph of k-Neighbors for points in X.

Parameters

Xarray-like of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’, default=None: The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. For metric='precomputed' the shape should be (n_queries, n_indexed). Otherwise the shape should be (n_queries, n_features).
n_neighborsint, default=None: Number of neighbors for each sample. The default is the value passed to the constructor.
mode{‘connectivity’, ‘distance’}, default=’connectivity’: Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class.

Returns

Asparse-matrix of shape (n_queries, n_samples_fit): n_samples_fit is the number of samples in the fitted data. A[i, j] gives the weight of the edge connecting i to j. The matrix is of CSR format.