# tslearn.neighbors.KNeighborsTimeSeriesRegressor¶

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

Classifier implementing the k-nearest neighbors vote for Time Series.

Parameters: n_neighbors : int (default: 5) Number of nearest neighbors to be considered for the decision. weights : str 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 : one of the metrics allowed for KNeighborsTimeSeries class (default: ‘dtw’) Metric to be used at the core of the nearest neighbor procedure metric_params : dict or None (default: None) Dictionnary 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_jobs : int 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. See scikit-learns’ Glossary for more details. verbose : int, 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. Glossary for more details.

Examples

>>> clf = KNeighborsTimeSeriesRegressor(n_neighbors=2, metric="dtw")
>>> clf.fit([[1, 2, 3], [1, 1.2, 3.2], [3, 2, 1]],
...         y=[0.1, 0.1, 1.1]).predict([[1, 2.2, 3.5]])
array([0.1])
>>> clf = KNeighborsTimeSeriesRegressor(n_neighbors=2,
...                                     metric="dtw",
...                                     n_jobs=2)
>>> clf.fit([[1, 2, 3], [1, 1.2, 3.2], [3, 2, 1]],
...         y=[0.1, 0.1, 1.1]).predict([[1, 2.2, 3.5]])
array([0.1])
>>> clf = KNeighborsTimeSeriesRegressor(n_neighbors=2,
...                                     metric="dtw",
...                                     metric_params={
...                                         "itakura_max_slope": 2.},
...                                     n_jobs=2)
>>> clf.fit([[1, 2, 3], [1, 1.2, 3.2], [3, 2, 1]],
...         y=[0.1, 0.1, 1.1]).predict([[1, 2.2, 3.5]])
array([0.1])


Methods

 fit(X, y) Fit the model using X as training data and y as target values get_params([deep]) Get parameters for this estimator. kneighbors([X, n_neighbors, return_distance]) Finds the K-neighbors of a point. kneighbors_graph([X, n_neighbors, mode]) Computes the (weighted) graph of k-Neighbors for points in X predict(X) Predict the target for the provided data score(X, y[, sample_weight]) Return the coefficient of determination $$R^2$$ of the prediction. set_params(**params) Set the parameters of this estimator.
fit(X, y)[source]

Fit the model using X as training data and y as target values

Parameters: X : array-like, shape (n_ts, sz, d) Training data. y : array-like, shape (n_ts, ) or (n_ts, dim_y) Target values. KNeighborsTimeSeriesRegressor The fitted estimator
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters: deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. params : dict Parameter names mapped to their values.
kneighbors(X=None, n_neighbors=None, return_distance=True)[source]

Finds the K-neighbors of a point.

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

Parameters: X : array-like, shape (n_ts, sz, d) The query time series. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. n_neighbors : int Number of neighbors to get (default is the value passed to the constructor). return_distance : boolean, optional. Defaults to True. If False, distances will not be returned dist : array Array representing the distance to points, only present if return_distance=True ind : array Indices of the nearest points in the population matrix.
kneighbors_graph(X=None, n_neighbors=None, mode='connectivity')[source]

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

Parameters: X : array-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_neighbors : int, 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 Euclidean distance between points. A : sparse-matrix of shape (n_queries, n_samples_fit) n_samples_fit is the number of samples in the fitted data A[i, j] is assigned the weight of edge that connects i to j. The matrix is of CSR format.

NearestNeighbors.radius_neighbors_graph

Examples

>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=2)
>>> neigh.fit(X)
NearestNeighbors(n_neighbors=2)
>>> A = neigh.kneighbors_graph(X)
>>> A.toarray()
array([[1., 0., 1.],
[0., 1., 1.],
[1., 0., 1.]])

predict(X)[source]

Predict the target for the provided data

Parameters: X : array-like, shape (n_ts, sz, d) Test samples. array, shape = (n_ts, ) or (n_ts, dim_y) Array of predicted targets
score(X, y, sample_weight=None)[source]

Return the coefficient of determination $$R^2$$ of the prediction.

The coefficient $$R^2$$ is defined as $$(1 - \frac{u}{v})$$, where $$u$$ is the residual sum of squares ((y_true - y_pred) ** 2).sum() and $$v$$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $$R^2$$ score of 0.0.

Parameters: X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator. y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X. sample_weight : array-like of shape (n_samples,), default=None Sample weights. score : float $$R^2$$ of self.predict(X) wrt. y.

Notes

The $$R^2$$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params : dict Estimator parameters. self : estimator instance Estimator instance.