skhubness.neighbors.KNeighborsClassifier

class skhubness.neighbors.KNeighborsClassifier(n_neighbors: int = 5, weights: str = 'uniform', algorithm: str = 'auto', algorithm_params: dict = None, hubness: str = None, hubness_params: dict = None, leaf_size: int = 30, p=2, metric='minkowski', metric_params=None, n_jobs=None, verbose: int = 0, **kwargs)[source]

Classifier implementing the k-nearest neighbors vote.

Read more in the scikit-learn User Guide

Parameters
n_neighborsint, optional (default = 5)

Number of neighbors to use by default for kneighbors() queries.

weightsstr 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.

algorithm{‘auto’, ‘hnsw’, ‘lsh’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional

Algorithm used to compute the nearest neighbors:

  • ‘hnsw’ will use HNSW

  • ‘lsh’ will use LSH

  • ‘ball_tree’ will use BallTree

  • ‘kd_tree’ will use KDTree

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

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

Note: fitting on sparse input will override the setting of this parameter, using brute force.

algorithm_paramsdict, optional

Override default parameters of the NN algorithm. For example, with algorithm=’lsh’ and algorithm_params={n_candidates: 100} one hundred approximate neighbors are retrieved with LSH. If parameter hubness is set, the candidate neighbors are further reordered with hubness reduction. Finally, n_neighbors objects are used from the (optionally reordered) candidates.

hubness{‘mutual_proximity’, ‘local_scaling’, ‘dis_sim_local’, None}, optional

Hubness reduction algorithm # TODO add all supported hubness reduction methods

  • ‘mutual_proximity’ or ‘mp’ will use MutualProximity

  • ‘local_scaling’ or ‘ls’ will use LocalScaling

  • ‘dis_sim_local’ or ‘dsl’ will use DisSimLocal

If None, no hubness reduction will be performed (=vanilla kNN).

hubness_params: dict, optional

Override default parameters of the selected hubness reduction algorithm. For example, with hubness=’mp’ and hubness_params={‘method’: ‘normal’} a mutual proximity variant is used, which models distance distributions with independent Gaussians.

leaf_sizeint, optional (default = 30)

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.

pinteger, optional (default = 2)

Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

metricstring or callable, default ‘minkowski’

the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class for a list of available metrics.

metric_paramsdict, optional (default = None)

Additional keyword arguments for the metric function.

n_jobsint or None, optional (default=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. See Glossary for more details. Doesn’t affect fit() method.

Notes

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

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

Examples

>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from skhubness.neighbors import KNeighborsClassifier
>>> neigh = KNeighborsClassifier(n_neighbors=3)
>>> neigh.fit(X, y) # doctest: +ELLIPSIS
KNeighborsClassifier(...)
>>> print(neigh.predict([[1.1]]))
[0]
>>> print(neigh.predict_proba([[0.9]]))
[[0.66666667 0.33333333]]
__init__(self, n_neighbors: int = 5, weights: str = 'uniform', algorithm: str = 'auto', algorithm_params: dict = None, hubness: str = None, hubness_params: dict = None, leaf_size: int = 30, p=2, metric='minkowski', metric_params=None, n_jobs=None, verbose: int = 0, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, n_neighbors, weights, …[, …])

Initialize self.

fit(self, X, y)

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

get_params(self[, deep])

Get parameters for this estimator.

kcandidates(self[, X, n_neighbors, …])

Finds the K-neighbors of a point.

kneighbors(self[, X, n_neighbors, …])

TODO

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 :param X: Test samples.

predict_proba(self, X)

Return probability estimates for the test data X.

score(self, X, y[, sample_weight])

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

set_params(self, \*\*params)

Set the parameters of this estimator.

fit(self, X, y)[source]

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

Parameters
X{array-like, sparse matrix, BallTree, KDTree}

Training data. If array or matrix, shape [n_samples, n_features], or [n_samples, n_samples] if metric=’precomputed’.

y{array-like, sparse matrix}

Target values of shape = [n_samples] or [n_samples, n_outputs]

get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

kcandidates(self, X=None, n_neighbors=None, return_distance=True) → numpy.ndarray[source]

Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point.

Parameters
Xarray-like, shape (n_query, n_features), or (n_query, 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_neighborsint

Number of neighbors to get (default is the value passed to the constructor).

return_distanceboolean, optional. Defaults to True.

If False, distances will not be returned

Returns
distarray

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

indarray

Indices of the nearest points in the population matrix.

Examples

In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who’s the closest point to [1,1,1]

>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from skhubness.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=1)
>>> neigh.fit(samples) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> print(neigh.kneighbors([[1., 1., 1.]])) # doctest: +ELLIPSIS
(array([[0.5]]), array([[2]]))

As you can see, it returns [[0.5]], and [[2]], which means that the element is at distance 0.5 and is the third element of samples (indexes start at 0). You can also query for multiple points:

>>> X = [[0., 1., 0.], [1., 0., 1.]]
>>> neigh.kneighbors(X, return_distance=False) # doctest: +ELLIPSIS
array([[1],
       [2]]...)
kneighbors(self, X=None, n_neighbors=None, return_distance=True)[source]

TODO

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

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

Parameters
Xarray-like, shape (n_query, n_features), or (n_query, 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_neighborsint

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
Asparse 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.

Examples

>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=2)
>>> neigh.fit(X) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> 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 :param X: Test samples. :type X: array-like, shape (n_query, n_features), or (n_query, n_indexed) if metric == ‘precomputed’

Returns

y – Class labels for each data sample.

Return type

array of shape [n_samples] or [n_samples, n_outputs]

predict_proba(self, X)[source]

Return probability estimates for the test data X. :param X: Test samples. :type X: array-like, shape (n_query, n_features), or (n_query, n_indexed) if metric == ‘precomputed’

Returns

p – of such arrays if n_outputs > 1. The class probabilities of the input samples. Classes are ordered by lexicographic order.

Return type

array of shape = [n_samples, n_classes], or a list of n_outputs

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

Returns 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
Xarray-like, shape = (n_samples, n_features)

Test samples.

yarray-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like, shape = [n_samples], optional

Sample weights.

Returns
scorefloat

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.

Returns
self