KNeighborsRegressor#

class skfda.ml.regression.KNeighborsRegressor(*, n_neighbors: int = 5, weights: typing_extensions.Literal[uniform, distance] | Callable[[ndarray[Any, dtype[float64]]], ndarray[Any, dtype[float64]]] = 'uniform', algorithm: typing_extensions.Literal[auto, ball_tree, kd_tree, brute] = 'auto', leaf_size: int = 30, metric: typing_extensions.Literal[precomputed], n_jobs: int | None = None)[source]#
class skfda.ml.regression.KNeighborsRegressor(*, n_neighbors: int = 5, weights: typing_extensions.Literal[uniform, distance] | Callable[[ndarray[Any, dtype[float64]]], ndarray[Any, dtype[float64]]] = 'uniform', algorithm: typing_extensions.Literal[auto, ball_tree, kd_tree, brute] = 'auto', leaf_size: int = 30, n_jobs: int | None = None)
class skfda.ml.regression.KNeighborsRegressor(*, n_neighbors: int = 5, weights: typing_extensions.Literal[uniform, distance] | Callable[[ndarray[Any, dtype[float64]]], ndarray[Any, dtype[float64]]] = 'uniform', algorithm: typing_extensions.Literal[auto, ball_tree, kd_tree, brute] = 'auto', leaf_size: int = 30, metric: Metric[Input] = l2_distance, n_jobs: int | None = None)

Regression based on k-nearest neighbors.

Regression with scalar, multivariate or functional response.

The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.

Parameters:
  • n_neighbors (int) – Number of neighbors to use by default for kneighbors() queries.

  • weights (WeightsType) –

    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 (AlgorithmType) –

    Algorithm used to compute the nearest neighbors:

    • ’ball_tree’ will use sklearn.neighbors.BallTree.

    • ’brute’ will use a brute-force search.

    • ’auto’ will attempt to decide the most appropriate algorithm based on the values passed to fit() method.

  • leaf_size (int) – 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.

  • metric (Literal['precomputed'] | Metric[Input]) – The distance metric to use for the tree. The default metric is the L2 distance. See the documentation of the metrics module for a list of available metrics.

  • n_jobs (int | None) – The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. Doesn’t affect fit() method.

Examples

Firstly, we will create a toy dataset with gaussian-like samples shifted.

>>> from skfda.ml.regression import KNeighborsRegressor
>>> from skfda.datasets import make_multimodal_samples
>>> from skfda.datasets import make_multimodal_landmarks
>>> y = make_multimodal_landmarks(
...     n_samples=30,
...     std=0.5,
...     random_state=0,
... )
>>> y_train = y.flatten()
>>> X_train = make_multimodal_samples(
...     n_samples=30,
...     std=0.5,
...     random_state=0,
... )
>>> X_test = make_multimodal_samples(
...     n_samples=5,
...     std=0.05,
...     random_state=0,
... )

We will fit a K-Nearest Neighbors regressor to regress a scalar response.

>>> neigh = KNeighborsRegressor()
>>> neigh.fit(X_train, y_train)
KNeighborsRegressor(...)

We can predict the modes of new samples

>>> neigh.predict(X_test).round(2) # Predict test data
array([ 0.38, 0.14, 0.27, 0.52, 0.38])

Now we will create a functional response to train the model

>>> y_train = 5 * X_train + 1
>>> y_train
FDataGrid(...)

We train the estimator with the functional response

>>> neigh.fit(X_train, y_train)
KNeighborsRegressor(...)

And predict the responses as in the first case.

>>> neigh.predict(X_test)
FDataGrid(...)

Notes

See Nearest Neighbors in the sklearn online documentation for a discussion of the choice of algorithm and leaf_size.

This class wraps the sklearn regressor sklearn.neighbors.KNeighborsRegressor.

Warning

Regarding the Nearest Neighbors algorithms, if it is found that two neighbors, neighbor k+1 and k, have identical distances but different labels, the results will depend on the ordering of the training data.

https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm

Methods

fit(X, y)

Fit the model using X as training data and y as responses.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

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)

Predict the target for the provided data.

score(X, y[, sample_weight])

Return the coefficient of determination of the prediction.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.

fit(X, y)[source]#

Fit the model using X as training data and y as responses.

Parameters:
  • X (Input) – Training data. FDataGrid with the training data or array matrix with shape [n_samples, n_samples] if metric=’precomputed’.

  • y (TargetRegression) – Training data. FData with the training respones (functional response case) or array matrix with length n_samples in the multivariate response case.

  • self (SelfTypeRegressor) –

Returns:

Self.

Return type:

SelfTypeRegressor

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)#

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 – Parameter names mapped to their values.

Return type:

dict

kneighbors(X=None, 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:
  • X (Input | None) – FDatagrid with the query functions or matrix (n_query, n_indexed) if metric == ‘precomputed’. 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 | None) – Number of neighbors to get (default is the value passed to the constructor).

  • return_distance (bool) – Defaults to True. If False, distances will not be returned.

Returns:

array

Array representing the lengths to points, only present if return_distance=True

indarray

Indices of the nearest points in the population matrix.

Return type:

dist

Examples

Firstly, we will create a toy dataset.

>>> from skfda.datasets import make_sinusoidal_process
>>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0)
>>> fd2 = make_sinusoidal_process(phase_mean=1.8, error_std=0.,
...                               phase_std=.25, random_state=0)
>>> fd = fd1.concatenate(fd2)

We will fit a Nearest Neighbors estimator

>>> from skfda.ml.clustering import NearestNeighbors
>>> neigh = NearestNeighbors()
>>> neigh.fit(fd)
NearestNeighbors(...)

Now we can query the k-nearest neighbors.

>>> distances, index = neigh.kneighbors(fd[:2])
>>> index # Index of k-neighbors of samples 0 and 1
array([[ 0,  7,  6, 11,  2],...)
>>> distances.round(2) # Distances to k-neighbors
array([[ 0.  ,  0.28,  0.29,  0.29,  0.3 ],
       [ 0.  ,  0.27,  0.28,  0.29,  0.3 ]])

Notes

This method wraps the corresponding sklearn routine in the module sklearn.neighbors.

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

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

Parameters:
  • X (Input | None) – FDatagrid with the query functions or matrix (n_query, n_indexed) if metric == ‘precomputed’. 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 | None) – Number of neighbors to get (default is the value passed to the constructor).

  • mode (Literal['connectivity', 'distance']) – Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are distance between points.

Returns:

Sparse matrix in CSR format, shape = [n_samples, 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.

Return type:

csr_matrix

Examples

Firstly, we will create a toy dataset.

>>> from skfda.datasets import make_sinusoidal_process
>>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0)
>>> fd2 = make_sinusoidal_process(phase_mean=1.8, error_std=0.,
...                               phase_std=.25, random_state=0)
>>> fd = fd1.concatenate(fd2)

We will fit a Nearest Neighbors estimator.

>>> from skfda.ml.clustering import NearestNeighbors
>>> neigh = NearestNeighbors()
>>> neigh.fit(fd)
NearestNeighbors(...)

Now we can obtain the graph of k-neighbors of a sample.

>>> graph = neigh.kneighbors_graph(fd[0])
>>> print(graph)
  (0, 0)    1.0
  (0, 7)    1.0
  (0, 6)    1.0
  (0, 11)   1.0
  (0, 2)    1.0

Notes

This method wraps the corresponding sklearn routine in the module sklearn.neighbors.

predict(X)[source]#

Predict the target for the provided data.

Parameters:

X (Input) – FDataGrid with the test samples or array (n_query, n_indexed) if metric == ‘precomputed’.

Returns:

array of shape = [n_samples] or [n_samples, n_outputs] or FData containing as many samples as X.

Return type:

TargetRegression

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

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) 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.

Returns:

score\(R^2\) of self.predict(X) w.r.t. y.

Return type:

float

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)#

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.

Return type:

estimator instance

set_score_request(*, sample_weight='$UNCHANGED$')#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

  • self (KNeighborsRegressor) –

Returns:

self – The updated object.

Return type:

object

Examples using skfda.ml.regression.KNeighborsRegressor#

Neighbors Functional Regression

Neighbors Functional Regression

Neighbors Scalar Regression

Neighbors Scalar Regression