sklearn.metrics.DistanceMetric

class sklearn.metrics.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.metrics 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, w

sum(w * |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))

Deprecated since version 1.1: WMinkowskiDistance is deprecated in version 1.1 and will be removed in version 1.3. Use MinkowskiDistance instead. Note that in MinkowskiDistance, the weights are applied to the absolute differences already raised to the p power. This is different from WMinkowskiDistance where weights are applied to the absolute differences before raising to the p power. The deprecation aims to remain consistent with SciPy 1.8 convention.

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

(N - NTT) / 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 rank-preserving surrogate 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 rank-preserving surrogate distance to the distance.
dist_to_rdist()

Convert the true distance to the rank-preserving surrogate distance.

The surrogate distance is any measure that yields the same rank as the distance, but is more efficient to compute. For example, the rank-preserving surrogate distance of the Euclidean metric is the squared-euclidean distance.

Parameters:
distdouble

True distance.

Returns:
double

Surrogate distance.

get_metric()

Get the given distance metric from the string identifier.

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

Parameters:
metricstr 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:
Xndarray or CSR matrix of shape (n_samples_X, n_features)

Input data.

Yndarray or CSR matrix of shape (n_samples_Y, n_features)

Input data. If not specified, then Y=X.

Returns:
distndarray of shape (n_samples_X, n_samples_Y)

The distance matrix of pairwise distances between points in X and Y.

rdist_to_dist()

Convert the rank-preserving surrogate distance to the distance.

The surrogate distance is any measure that yields the same rank as the distance, but is more efficient to compute. For example, the rank-preserving surrogate distance of the Euclidean metric is the squared-euclidean distance.

Parameters:
rdistdouble

Surrogate distance.

Returns:
double

True distance.