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 knearest 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 userdefined 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 crossdistance 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 hyperparameters 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 crossdistance matrix computations. Ignored if the crossdistance matrix cannot be computed using parallelization.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See scikitlearns’ 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 Kneighbors of a point. kneighbors_graph
([X, n_neighbors, mode])Computes the (weighted) graph of kNeighbors 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 : arraylike, shape (n_ts, sz, d)
Training data.
 y : arraylike, shape (n_ts, ) or (n_ts, dim_y)
Target values.
Returns:  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.
Returns:  params : dict
Parameter names mapped to their values.

kneighbors
(X=None, n_neighbors=None, return_distance=True)[source]¶ Finds the Kneighbors of a point.
Returns indices of and distances to the neighbors of each point.
Parameters:  X : arraylike, 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
Returns:  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 kNeighbors for points in X
Parameters:  X : arraylike 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.
Returns:  A : sparsematrix 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.
See also
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 : arraylike, shape (n_ts, sz, d)
Test samples.
Returns:  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 : arraylike 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)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator. y : arraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weight : arraylike of shape (n_samples,), default=None
Sample weights.
Returns:  score : float
\(R^2\) of
self.predict(X)
wrt. y.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).

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.
Returns:  self : estimator instance
Estimator instance.