RadiusNeighborsClassifier#

class skfda.ml.classification.RadiusNeighborsClassifier(*, radius: float = 1.0, weights: Literal['uniform', 'distance'] | Callable[[ndarray[Any, dtype[float64]]], ndarray[Any, dtype[float64]]] = 'uniform', algorithm: Literal['auto', 'ball_tree', 'kd_tree', 'brute'] = 'auto', leaf_size: int = 30, metric: Literal['precomputed'], outlier_label: int | str | Sequence[int] | Sequence[str] | None = None, n_jobs: int | None = None)[source]#

class skfda.ml.classification.RadiusNeighborsClassifier(*, radius: float = 1.0, weights: Literal['uniform', 'distance'] | Callable[[ndarray[Any, dtype[float64]]], ndarray[Any, dtype[float64]]] = 'uniform', algorithm: Literal['auto', 'ball_tree', 'kd_tree', 'brute'] = 'auto', leaf_size: int = 30, outlier_label: int | str | Sequence[int] | Sequence[str] | None = None, n_jobs: int | None = None)

class skfda.ml.classification.RadiusNeighborsClassifier(*, radius: float = 1.0, weights: Literal['uniform', 'distance'] | Callable[[ndarray[Any, dtype[float64]]], ndarray[Any, dtype[float64]]] = 'uniform', algorithm: Literal['auto', 'ball_tree', 'kd_tree', 'brute'] = 'auto', leaf_size: int = 30, metric: Metric[Input] = l2_distance, outlier_label: int | str | Sequence[int] | Sequence[str] | None = None, n_jobs: int | None = None)

Classifier implementing a vote among neighbors within a given radius.

Parameters:

radius (float) – Range of parameter space to use by default for radius_neighbors() 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.
outlier_label (OutlierLabelType) – Label, which is given for outlier samples (samples with no neighbors on given radius). If set to None, ValueError is raised, when outlier is detected.
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.

Examples

Firstly, we will create a toy dataset with 2 classes.

>>> 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)
>>> y = 15*[0] + 15*[1]

We will fit a Radius Nearest Neighbors classifier.

>>> from skfda.ml.classification import RadiusNeighborsClassifier
>>> neigh = RadiusNeighborsClassifier(radius=.3)
>>> neigh.fit(fd, y)
RadiusNeighborsClassifier(...radius=0.3...)

We can predict the class of new samples.

>>> neigh.predict(fd[::2]) # Predict labels for even samples
array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])

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 classifier sklearn.neighbors.RadiusNeighborsClassifier.

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.
`predict`(X)	Predict the class labels for the provided data.
`predict_proba`(X)	Calculate probability estimates for the test data X.
`radius_neighbors`([X, radius, return_distance])	Find the neighbors within a given radius of a fdatagrid.
`radius_neighbors_graph`([X, radius, mode])	Compute the (weighted) graph of Neighbors for points in 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])	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 (TargetClassification) – Training data. FData with the training respones (functional response case) or array matrix with length n_samples in the multivariate response case.
self (SelfTypeClassifier) –

Returns:

Self.

Return type:

SelfTypeClassifier

Note

This method adds the attribute classes_ to the classifier.

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

predict(X)[source]#

Predict the class labels for the provided data.

Parameters:: X (Input) – Test samples or array (n_query, n_indexed) if metric == ‘precomputed’.
Returns:: Array of shape [n_samples] or [n_samples, n_outputs] with class labels for each data sample.
Return type:: TargetClassification

Notes

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

predict_proba(X)[source]#

Calculate probability estimates for the test data X.

Parameters:: X (Input) – FDataGrid with the test samples or array (n_query, n_indexed) if metric == ‘precomputed’.
Returns:: The class probabilities of the input samples. Classes are ordered by lexicographic order.
Return type:: ndarray[Any, dtype[float64]]

radius_neighbors(X=None, radius=None, *, return_distance=True)[source]#

Find the neighbors within a given radius of a fdatagrid.

Return the indices and distances of each point from the dataset lying in a ball with size radius around the points of the query array. Points lying on the boundary are included in the results. The result points are not necessarily sorted by distance to their query point.

Parameters:

X (Input | None) – Sample or samples whose neighbors will be returned. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.
radius (float | None) – Limiting distance of neighbors to return. (default is the value passed to the constructor).
return_distance (bool) – Defaults to True. If False, distances will not be returned.

Returns:

distarray of arrays representing the: distances to each point, only present if return_distance=True. The distance values are computed according to the metric constructor parameter.
(array, shape (n_samples,): An array of arrays of indices of the: approximate nearest points from the population matrix that lie within a ball of size radius around the query points.

Return type:

(array, shape (n_samples)

Examples

Firstly, we will create a toy dataset

>>> from skfda.datasets import make_gaussian_process
>>> X = make_gaussian_process(
...    n_samples=30,
...    random_state=0,
... )

We will fit a Nearest Neighbors estimator.

>>> from skfda.ml.clustering import NearestNeighbors
>>> neigh = NearestNeighbors(radius=0.7)
>>> neigh.fit(X)
NearestNeighbors(...)

Now we can query the neighbors in a given radius.

>>> distances, index = neigh.radius_neighbors(X)
>>> index[0]
array([ 0,  1,  8, 18]...)

>>> distances[0].round(2)
array([ 0.  ,  0.58,  0.41,  0.68])

Examples using `skfda.ml.classification.RadiusNeighborsClassifier`#

Radius neighbors classification

RadiusNeighborsClassifier#

Examples using skfda.ml.classification.RadiusNeighborsClassifier#

Examples using `skfda.ml.classification.RadiusNeighborsClassifier`#