`sklearn.neighbors`.KNeighborsTransformer¶

class sklearn.neighbors.KNeighborsTransformer(*, mode='distance', n_neighbors=5, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=1)[source]¶

Transform X into a (weighted) graph of k nearest neighbors.

The transformed data is a sparse graph as returned by kneighbors_graph.

See also

kneighbors_graph: Compute the weighted graph of k-neighbors for points in X.
RadiusNeighborsTransformer: Transform X into a weighted graph of neighbors nearer than a radius.

Examples

>>> from sklearn.datasets import load_wine
>>> from sklearn.neighbors import KNeighborsTransformer
>>> X, _ = load_wine(return_X_y=True)
>>> X.shape
(178, 13)
>>> transformer = KNeighborsTransformer(n_neighbors=5, mode='distance')
>>> X_dist_graph = transformer.fit_transform(X)
>>> X_dist_graph.shape
(178, 178)

Methods

`fit`(X[, y])	Fit the k-nearest neighbors transformer from the training dataset.
`fit_transform`(X[, y])	Fit to data, then transform it.
`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.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Compute the (weighted) graph of Neighbors for points in X.

fit(X, y=None)[source]¶

Fit the k-nearest neighbors transformer from the training dataset.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric=’precomputed’: Training data.
yIgnored: Not used, present for API consistency by convention.

Returns

selfKNeighborsTransformer: The fitted k-nearest neighbors transformer.

fit_transform(X, y=None)[source]¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xarray-like of shape (n_samples, n_features): Training set.
yIgnored: Not used, present for API consistency by convention.

Returns

Xtsparse matrix of shape (n_samples, n_samples): Xt[i, j] is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit. The matrix is of CSR format.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters

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

Returns

paramsdict: Parameter names mapped to their values.

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

Xarray-like, 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.
n_neighborsint, default=None: Number of neighbors required for each sample. The default is the value passed to the constructor.
return_distancebool, default=True: Whether or not to return the distances.

Returns

neigh_distndarray of shape (n_queries, n_neighbors): Array representing the lengths to points, only present if return_distance=True.
neigh_indndarray of shape (n_queries, n_neighbors): Indices of the nearest points in the population matrix.

Examples

In the following example, we construct a NearestNeighbors 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 sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=1)
>>> neigh.fit(samples)
NearestNeighbors(n_neighbors=1)
>>> print(neigh.kneighbors([[1., 1., 1.]]))
(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)
array([[1],
       [2]]...)

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

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

Parameters

Xarray-like 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_neighborsint, 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 distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class.

Returns

Asparse-matrix of shape (n_queries, n_samples_fit): n_samples_fit is the number of samples in the fitted data. A[i, j] gives the weight of the edge connecting i to j. The matrix is of CSR format.

Examples using `sklearn.neighbors.KNeighborsTransformer`¶

sklearn.neighbors.KNeighborsTransformer¶

Examples using sklearn.neighbors.KNeighborsTransformer¶

`sklearn.neighbors`.KNeighborsTransformer¶

Examples using `sklearn.neighbors.KNeighborsTransformer`¶