neighbors.NearestNeighbors (uses cuML)¶

class pai4sk.neighbors.NearestNeighbors(n_neighbors=5, use_gpu=True, verbose=False, should_downcast=True, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None, handle=None, **kwargs)¶

Unsupervised learner for implementing neighbor searches.

If cuml is installed and input data is cudf dataframe, then pai4sk will try to use the accelerated KNN algorithm from cuML. Otherwise, scikit-learn’s KNN algorithm will be used.

cuML in pai4sk is currently supported only without MPI. | If KNN from cuML is run, then the return values from the APIs will be cudf dataframe and cudf Series objects instead of the return types of scikit-learn API.

Read more in the User Guide.

Parameters

n_neighbors (int, optional (default = 5)) – Number of neighbors to use by default for kneighbors() queries.
radius (float, optional (default = 1.0)) – Range of parameter space to use by default for radius_neighbors() queries.
algorithm ({'auto', 'ball_tree', 'kd_tree', 'brute', 'cuml'}, optional, default is '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.

auto or brute: If cuml is installed and input data is cudf dataframe, then pai4sk
will try to use KNN algorithm from RAPIDS cuML if possible. Otherwise NearestNeighbors from scikit-learn will be used.

cuml: If cuml is installed and input data is cudf dataframe, then pai4sk
will try to use KNN algorithm from RAPIDS cuML if possible. Otherwise throws an error.

cuML in pai4sk is currently supported only without MPI.
use_gpu (boolean, Default is True) – If True, cuML will use all GPUs. Applicable only for cuML.
handle (cuml.Handle, Default is None) – The cumlHandle resources to use. If it is None, a new one is created just for this class. Applicable only for cuML.
leaf_size (int, 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.
metric (string 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.
p (integer, optional (default = 2)) – Parameter for the Minkowski metric from pai4sk.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_params (dict, optional (default = None)) – Additional keyword arguments for the metric function.
n_jobs (int 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.
should_downcast (Bool (default=True)) – Currently only single precision is supported in the underlying undex. Setting this to true will allow single-precision input arrays to be automatically downcasted to single precision. Default = True. Applicable for cuML only.
verbose (Bool (default=False)) – Verbose output. Applicable for cuML only.

Examples

>>> import numpy as np
>>> from pai4sk.neighbors import NearestNeighbors
>>> samples = [[0, 0, 2], [1, 0, 0], [0, 0, 1]]

>>> neigh = NearestNeighbors(2, 0.4)
>>> neigh.fit(samples)  
NearestNeighbors(...)

>>> neigh.kneighbors([[0, 0, 1.3]], 2, return_distance=False)
... 
array([[2, 0]]...)

>>> nbrs = neigh.radius_neighbors([[0, 0, 1.3]], 0.4, return_distance=False)
>>> np.asarray(nbrs[0][0])
array(2)