sklearn.metrics
.DistanceMetric¶
- class sklearn.metrics.DistanceMetric¶
Uniform interface for fast distance metric functions.
The
DistanceMetric
class provides a convenient way to compute pairwise distances between samples. It supports various distance metrics, such as Euclidean distance, Manhattan distance, and more.The
pairwise
method can be used to compute pairwise distances between samples in the input arrays. It returns a distance matrix representing the distances between all pairs of samples.The
get_metric
method allows you to retrieve a specific metric using its string identifier.Examples
>>> from sklearn.metrics import DistanceMetric >>> dist = DistanceMetric.get_metric('euclidean') >>> X = [[1, 2], [3, 4], [5, 6]] >>> Y = [[7, 8], [9, 10]] >>> dist.pairwise(X,Y) array([[7.81..., 10.63...] [5.65..., 8.48...] [1.41..., 4.24...]])
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)
“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
(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 propertiesNon-negativity: d(x, y) >= 0
Identity: d(x, y) = 0 if and only if x == y
Symmetry: d(x, y) = d(y, x)
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
Get the given distance metric from the string identifier.
- 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 string identifier or class name of the desired distance metric. See the documentation of the
DistanceMetric
class for a list of available metrics.- dtype{np.float32, np.float64}, default=np.float64
The data type of the input on which the metric will be applied. This affects the precision of the computed distances. By default, it is set to
np.float64
.- **kwargs
Additional keyword arguments that will be passed to the requested metric. These arguments can be used to customize the behavior of the specific metric.
- Returns:
- metric_objinstance of the requested metric
An instance of the requested distance metric class.