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. Read more in the User Guide.  New in version 0.22.   Parameters 
 
mode{‘distance’, ‘connectivity’}, default=’distance’ 

Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, and ‘distance’ will return the distances between neighbors according to the given metric.  
n_neighborsint, default=5 

Number of neighbors for each sample in the transformed sparse graph. For compatibility reasons, as each sample is considered as its own neighbor, one extra neighbor will be computed when mode == ‘distance’. In this case, the sparse graph contains (n_neighbors + 1) neighbors.  
algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’ 

Algorithm used to compute the nearest neighbors:  ‘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.  
leaf_sizeint, 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.  
metricstr or callable, default=’minkowski’ 

metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy’s metrics, but is less efficient than passing the metric name as a string. Distance matrices are not supported. Valid values for metric are:  from scikit-learn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’] from scipy.spatial.distance: [‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’]  See the documentation for scipy.spatial.distance for details on these metrics.  
pint, default=2 

Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. 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.  
metric_paramsdict, default=None 

Additional keyword arguments for the metric function.  
n_jobsint, default=1 

The number of parallel jobs to run for neighbors search. If -1, then the number of jobs is set to the number of CPU cores.    Attributes 
 
effective_metric_str or callable 

The distance metric used. It will be same as the metric parameter or a synonym of it, e.g. ‘euclidean’ if the metric parameter set to ‘minkowski’ and p parameter set to 2.  
effective_metric_params_dict 

Additional keyword arguments for the metric function. For most metrics will be same with metric_params parameter, but may also contain the p parameter value if the effective_metric_ attribute is set to ‘minkowski’.  
n_samples_fit_int 

Number of samples in the fitted data.     Examples >>> from sklearn.manifold import Isomap
>>> from sklearn.neighbors import KNeighborsTransformer
>>> from sklearn.pipeline import make_pipeline
>>> estimator = make_pipeline(
...     KNeighborsTransformer(n_neighbors=5, mode='distance'),
...     Isomap(neighbors_algorithm='precomputed'))
 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]) Finds the K-neighbors of a point.  
kneighbors_graph([X, n_neighbors, mode]) Computes the (weighted) graph of k-Neighbors for points in X  
set_params(**params) Set the parameters of this estimator.  
transform(X) Computes 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.    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 
  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]
 
Finds 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]
 
Computes 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 Euclidean distance between points.    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] is assigned the weight of edge that connects i to j. The matrix is of CSR format.      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.]])
 
  
set_params(**params) [source]
 
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 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(X) [source]
 
Computes the (weighted) graph of Neighbors for points in X  Parameters 
 
Xarray-like of shape (n_samples_transform, n_features) 

Sample data.    Returns 
 
Xtsparse matrix of shape (n_samples_transform, n_samples_fit) 

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.     
 
 Examples using sklearn.neighbors.KNeighborsTransformer
 
  Release Highlights for scikit-learn 0.22  

  Caching nearest neighbors  

  Approximate nearest neighbors in TSNE