KNeighborsClassifier#

class pyts.classification.KNeighborsClassifier(n_neighbors: int = 1, weights: Literal['uniform', 'distance'] | Callable[[...], NDArray[float64]] = 'uniform', algorithm: Literal['auto', 'ball_tree', 'kd_tree', 'brute'] = 'auto', leaf_size: int = 30, p: int = 2, metric: str | Callable[[...], float] = 'minkowski', metric_params: dict[str, Any] | None = None, n_jobs: int = 1, **kwargs: Any)[source]#

k-nearest neighbors classifier.

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.

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’.

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.

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 pyts.classification import KNeighborsClassifier
>>> from pyts.datasets import load_gunpoint
>>> X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)
>>> clf = KNeighborsClassifier()
>>> clf.fit(X_train, y_train)
KNeighborsClassifier(...)
>>> clf.score(X_test, y_test)
0.91...

Methods

`__init__`([n_neighbors, weights, algorithm, ...])
`fit`(X, y)	Fit the model according to the given training data.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict the class labels for the provided data.
`predict_proba`(X)	Return probability estimates for the test data X.
`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_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

__init__(n_neighbors: int = 1, weights: Literal['uniform', 'distance'] | Callable[[...], NDArray[float64]] = 'uniform', algorithm: Literal['auto', 'ball_tree', 'kd_tree', 'brute'] = 'auto', leaf_size: int = 30, p: int = 2, metric: str | Callable[[...], float] = 'minkowski', metric_params: dict[str, Any] | None = None, n_jobs: int = 1, **kwargs: Any) → None[source]#

fit(X: ArrayLike, y: ArrayLike) → Self[source]#

Fit the model according to the given training data.

Parameters:

Xarray-like, shape = (n_samples, n_timestamps): Training vector.
yarray-like, shape = (n_samples,): Class labels for each data sample.

Returns:

selfobject

predict(X: ArrayLike) → NDArray[Any][source]#

Predict the class labels for the provided data.

Parameters:

Xarray-like, shape = (n_samples, n_timestamps): Test samples.

Returns:

y_predarray-like, shape = (n_samples,): Class labels for each data sample.

predict_proba(X: ArrayLike) → NDArray[float64][source]#

Return probability estimates for the test data X.

Parameters:

Xarray-like, shape = (n_samples, n_features): Test samples.

Returns:

parray, shape = (n_samples, n_classes): Probability estimates.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → KNeighborsClassifier#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.