sklearn.neighbors.DistanceMetric

class sklearn.neighbors.DistanceMetric

DistanceMetric class

This class provides a uniform interface to fast distance metric functions. The various metrics can be accessed via the get_metric class method and the metric string identifier (see below).

Examples

>>> from sklearn.neighbors import DistanceMetric
>>> dist = DistanceMetric.get_metric('euclidean')
>>> X = [[0, 1, 2],
         [3, 4, 5]]
>>> dist.pairwise(X)
array([[ 0.        ,  5.19615242],
       [ 5.19615242,  0.        ]])

Available Metrics

The following lists the string metric identifiers and the associated distance metric classes:

Metrics intended for real-valued vector spaces:

identifier

class name

args

distance function

“euclidean”

EuclideanDistance

sqrt(sum((x - y)^2))

“manhattan”

ManhattanDistance

sum(|x - y|)

“chebyshev”

ChebyshevDistance

max(|x - y|)

“minkowski”

MinkowskiDistance

p

sum(|x - y|^p)^(1/p)

“wminkowski”

WMinkowskiDistance

p, w

sum(|w * (x - y)|^p)^(1/p)

“seuclidean”

SEuclideanDistance

V

sqrt(sum((x - y)^2 / V))

“mahalanobis”

MahalanobisDistance

V or VI

sqrt((x - y)' V^-1 (x - y))

Metrics intended for two-dimensional vector spaces: Note that the haversine distance metric requires data in the form of [latitude, longitude] and both inputs and outputs are in units of radians.

identifier

class name

distance function

“haversine”

HaversineDistance

2 arcsin(sqrt(sin^2(0.5*dx) + cos(x1)cos(x2)sin^2(0.5*dy)))

Metrics intended for integer-valued vector spaces: Though intended for integer-valued vectors, these are also valid metrics in the case of real-valued vectors.

identifier

class name

distance function

“hamming”

HammingDistance

N_unequal(x, y) / N_tot

“canberra”

CanberraDistance

sum(|x - y| / (|x| + |y|))

“braycurtis”

BrayCurtisDistance

sum(|x - y|) / (sum(|x|) + sum(|y|))

Metrics intended for boolean-valued vector spaces: Any nonzero entry is evaluated to “True”. In the listings below, the following abbreviations are used:

  • N : number of dimensions

  • NTT : number of dims in which both values are True

  • NTF : number of dims in which the first value is True, second is False

  • NFT : number of dims in which the first value is False, second is True

  • NFF : number of dims in which both values are False

  • NNEQ : number of non-equal dimensions, NNEQ = NTF + NFT

  • NNZ : number of nonzero dimensions, NNZ = NTF + NFT + NTT

identifier

class name

distance function

“jaccard”

JaccardDistance

NNEQ / NNZ

“matching”

MatchingDistance

NNEQ / N

“dice”

DiceDistance

NNEQ / (NTT + NNZ)

“kulsinski”

KulsinskiDistance

(NNEQ + N - NTT) / (NNEQ + N)

“rogerstanimoto”

RogersTanimotoDistance

2 * NNEQ / (N + NNEQ)

“russellrao”

RussellRaoDistance

NNZ / N

“sokalmichener”

SokalMichenerDistance

2 * NNEQ / (N + NNEQ)

“sokalsneath”

SokalSneathDistance

NNEQ / (NNEQ + 0.5 * NTT)

User-defined distance:

identifier

class name

args

“pyfunc”

PyFuncDistance

func

Here func is a function which takes two one-dimensional numpy arrays, and returns a distance. Note that in order to be used within the BallTree, the distance must be a true metric: i.e. it must satisfy the following properties

  1. Non-negativity: d(x, y) >= 0

  2. Identity: d(x, y) = 0 if and only if x == y

  3. Symmetry: d(x, y) = d(y, x)

  4. Triangle Inequality: d(x, y) + d(y, z) >= d(x, z)

Because of the Python object overhead involved in calling the python function, this will be fairly slow, but it will have the same scaling as other distances.

Methods

dist_to_rdist

Convert the true distance to the reduced distance.

get_metric

Get the given distance metric from the string identifier.

pairwise

Compute the pairwise distances between X and Y

rdist_to_dist

Convert the Reduced distance to the true distance.

dist_to_rdist()

Convert the true distance to the reduced distance.

The reduced distance, defined for some metrics, is a computationally more efficient measure which preserves the rank of the true distance. For example, in the Euclidean distance metric, the reduced distance is the squared-euclidean distance.

get_metric()

Get the given distance metric from the string identifier.

See the docstring of DistanceMetric for a list of available metrics.

Parameters
metricstring or class name

The distance metric to use

**kwargs

additional arguments will be passed to the requested metric

pairwise()

Compute the pairwise distances between X and Y

This is a convenience routine for the sake of testing. For many metrics, the utilities in scipy.spatial.distance.cdist and scipy.spatial.distance.pdist will be faster.

Parameters
Xarray-like

Array of shape (Nx, D), representing Nx points in D dimensions.

Yarray-like (optional)

Array of shape (Ny, D), representing Ny points in D dimensions. If not specified, then Y=X.

Returns
——-
distndarray

The shape (Nx, Ny) array of pairwise distances between points in X and Y.

rdist_to_dist()

Convert the Reduced distance to the true distance.

The reduced distance, defined for some metrics, is a computationally more efficient measure which preserves the rank of the true distance. For example, in the Euclidean distance metric, the reduced distance is the squared-euclidean distance.