sklearn.metrics
.pairwise_distances_argmin_min¶

sklearn.metrics.
pairwise_distances_argmin_min
(X, Y, axis=1, metric='euclidean', batch_size=500, metric_kwargs=None)¶ Compute minimum distances between one point and a set of points.
This function computes for each row in X, the index of the row of Y which is closest (according to the specified distance). The minimal distances are also returned.
This is mostly equivalent to calling:
 (pairwise_distances(X, Y=Y, metric=metric).argmin(axis=axis),
 pairwise_distances(X, Y=Y, metric=metric).min(axis=axis))
but uses much less memory, and is faster for large arrays.
Parameters: X : {arraylike, sparse matrix}, shape (n_samples1, n_features)
Array containing points.
Y : {arraylike, sparse matrix}, shape (n_samples2, n_features)
Arrays containing points.
axis : int, optional, default 1
Axis along which the argmin and distances are to be computed.
metric : string or callable, default ‘euclidean’
metric to use for distance computation. Any metric from scikitlearn 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 scikitlearn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’]
 from scipy.spatial.distance: [‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’]
See the documentation for scipy.spatial.distance for details on these metrics.
batch_size : integer
To reduce memory consumption over the naive solution, data are processed in batches, comprising batch_size rows of X and batch_size rows of Y. The default value is quite conservative, but can be changed for finetuning. The larger the number, the larger the memory usage.
metric_kwargs : dict, optional
Keyword arguments to pass to specified metric function.
Returns: argmin : numpy.ndarray
Y[argmin[i], :] is the row in Y that is closest to X[i, :].
distances : numpy.ndarray
distances[i] is the distance between the ith row in X and the argmin[i]th row in Y.