tslearn.neighbors.KNeighborsTimeSeriesClassifier

class tslearn.neighbors.KNeighborsTimeSeriesClassifier(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.

Notes

The training data are saved to disk if this model is serialized and may result in a large model file if the training dataset is large.

Examples

>>> clf = KNeighborsTimeSeriesClassifier(n_neighbors=2, metric="dtw")
>>> clf.fit([[1, 2, 3], [1, 1.2, 3.2], [3, 2, 1]],
...         y=[0, 0, 1]).predict([[1, 2.2, 3.5]])
array([0])
>>> clf = KNeighborsTimeSeriesClassifier(n_neighbors=2,
...                                      metric="dtw",
...                                      n_jobs=2)
>>> clf.fit([[1, 2, 3], [1, 1.2, 3.2], [3, 2, 1]],
...         y=[0, 0, 1]).predict([[1, 2.2, 3.5]])
array([0])
>>> clf = KNeighborsTimeSeriesClassifier(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, 0, 1]).predict([[1, 2.2, 3.5]])
array([0])

Methods

fit(self, X, y) Fit the model using X as training data and y as target values
from_hdf5(path) Load model from a HDF5 file.
from_json(path) Load model from a JSON file.
from_pickle(path) Load model from a pickle file.
get_params(self[, deep]) Get parameters for this estimator.
kneighbors(self[, X, n_neighbors, …]) Finds the K-neighbors of a point.
kneighbors_graph(self[, X, n_neighbors, mode]) Computes the (weighted) graph of k-Neighbors for points in X
predict(self, X) Predict the class labels for the provided data
predict_proba(self, X) Predict the class probabilities for the provided data
score(self, X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.
set_params(self, **params) Set the parameters of this estimator.
to_hdf5(self, path) Save model to a HDF5 file.
to_json(self, path) Save model to a JSON file.
to_pickle(self, path) Save model to a pickle file.
fit(self, 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, )

Target values.

Returns:
KNeighborsTimeSeriesClassifier

The fitted estimator

classmethod from_hdf5(path)[source]

Load model from a HDF5 file. Requires h5py http://docs.h5py.org/

Parameters:
path : str

Full path to file.

Returns:
Model instance
classmethod from_json(path)[source]

Load model from a JSON file.

Parameters:
path : str

Full path to file.

Returns:
Model instance
classmethod from_pickle(path)[source]

Load model from a pickle file.

Parameters:
path : str

Full path to file.

Returns:
Model instance
get_params(self, 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 : mapping of string to any

Parameter names mapped to their values.

kneighbors(self, 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

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

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

Parameters:
X : array-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’

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.

n_neighbors : int

Number of neighbors for each sample. (default is value passed to the constructor).

mode : {‘connectivity’, ‘distance’}, optional

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 : sparse graph in CSR format, 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.

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(self, X)[source]

Predict the class labels for the provided data

Parameters:
X : array-like, shape (n_ts, sz, d)

Test samples.

Returns:
array, shape = (n_ts, )

Array of predicted class labels

predict_proba(self, X)[source]

Predict the class probabilities for the provided data

Parameters:
X : array-like, shape (n_ts, sz, d)

Test samples.

Returns:
array, shape = (n_ts, n_classes)

Array of predicted class probabilities

score(self, X, y, sample_weight=None)[source]

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:
X : array-like of shape (n_samples, n_features)

Test samples.

y : array-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights.

Returns:
score : float

Mean accuracy of self.predict(X) wrt. y.

set_params(self, **params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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 : object

Estimator instance.

to_hdf5(self, path)[source]

Save model to a HDF5 file. Requires h5py http://docs.h5py.org/

Parameters:
path : str

Full file path. File must not already exist.

Raises:
FileExistsError

If a file with the same path already exists.

to_json(self, path)[source]

Save model to a JSON file.

Parameters:
path : str

Full file path.

to_pickle(self, path)[source]

Save model to a pickle file.

Parameters:
path : str

Full file path.