8000 DOC Add docstring DistanceMetric class (#26795) · punndcoder28/scikit-learn@7d07f87 · GitHub
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DOC Add docstring DistanceMetric class (scikit-learn#26795)
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‎sklearn/metrics/_dist_metrics.pyx.tp

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@@ -65,6 +65,118 @@ def get_valid_metric_ids(L):
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if (val.__name__ in L) or (val in L)]
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cdef class DistanceMetric:
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"""Uniform interface for fast distance metric functions.
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The `DistanceMetric` class provides a convenient way to compute pairwise distances
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between samples. It supports various distance metrics, such as Euclidean distance,
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Manhattan distance, and more.
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The `pairwise` method can be used to compute pairwise distances between samples in
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the input arrays. It returns a distance matrix representing the distances between
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all pairs of samples.
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The :meth:`get_metric` method allows you to retrieve a specific metric using its
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string identifier.
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Examples
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--------
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>>> from sklearn.metrics import DistanceMetric
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>>> dist = DistanceMetric.get_metric('euclidean')
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>>> X = [[1, 2], [3, 4], [5, 6]]
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>>> Y = [[7, 8], [9, 10]]
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>>> dist.pairwise(X,Y)
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array([[7.81..., 10.63...]
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[5.65..., 8.48...]
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[1.41..., 4.24...]])
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Available Metrics
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The following lists the string metric identifiers and the associated
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distance metric classes:
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**Metrics intended for real-valued vector spaces:**
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============== ==================== ======== ===============================
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identifier class name args distance function
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-------------- -------------------- -------- -------------------------------
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"euclidean" EuclideanDistance - ``sqrt(sum((x - y)^2))``
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"manhattan" ManhattanDistance - ``sum(|x - y|)``
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"chebyshev" ChebyshevDistance - ``max(|x - y|)``
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"minkowski" MinkowskiDistance p, w ``sum(w * |x - y|^p)^(1/p)``
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"seuclidean" SEuclideanDistance V ``sqrt(sum((x - y)^2 / V))``
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"mahalanobis" MahalanobisDistance V or VI ``sqrt((x - y)' V^-1 (x - y))``
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============== ==================== ======== ===============================
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**Metrics intended for two-dimensional vector spaces:** Note that the haversine
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distance metric requires data in the form of [latitude, longitude] and both
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inputs and outputs are in units of radians.
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============ ================== ===============================================================
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identifier class name distance function
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------------ ------------------ ---------------------------------------------------------------
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"haversine" HaversineDistance ``2 arcsin(sqrt(sin^2(0.5*dx) + cos(x1)cos(x2)sin^2(0.5*dy)))``
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============ ================== ===============================================================
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**Metrics intended for integer-valued vector spaces:** Though intended
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for integer-valued vectors, these are also valid metrics in the case of
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real-valued vectors.
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============= ==================== ========================================
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identifier class name distance function
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------------- -------------------- ----------------------------------------
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"hamming" HammingDistance ``N_unequal(x, y) / N_tot``
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"canberra" CanberraDistance ``sum(|x - y| / (|x| + |y|))``
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"braycurtis" BrayCurtisDistance ``sum(|x - y|) / (sum(|x|) + sum(|y|))``
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============= ==================== ========================================
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**Metrics intended for boolean-valued vector spaces:** Any nonzero entry
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is evaluated to "True". In the listings below, the following
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abbreviations are used:
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- N : number of dimensions
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- NTT : number of dims in which both values are True
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- NTF : number of dims in which the first value is True, second is False
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- NFT : number of dims in which the first value is False, second is True
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- NFF : number of dims in which both values are False
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- NNEQ : number of non-equal dimensions, NNEQ = NTF + NFT
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- NNZ : number of nonzero dimensions, NNZ = NTF + NFT + NTT
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================= ======================= ===============================
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identifier class name distance function
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----------------- ----------------------- -------------------------------
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"jaccard" JaccardDistance NNEQ / NNZ
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"matching" MatchingDistance NNEQ / N
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"dice" DiceDistance NNEQ / (NTT + NNZ)
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"kulsinski" KulsinskiDistance (NNEQ + N - NTT) / (NNEQ + N)
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"rogerstanimoto" RogersTanimotoDistance 2 * NNEQ / (N + NNEQ)
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"russellrao" RussellRaoDistance (N - NTT) / N
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"sokalmichener" SokalMichenerDistance 2 * NNEQ / (N + NNEQ)
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"sokalsneath" SokalSneathDistance NNEQ / (NNEQ + 0.5 * NTT)
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================= ======================= ===============================
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**User-defined distance:**
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=========== =============== =======
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identifier class name args
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----------- --------------- -------
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"pyfunc" PyFuncDistance func
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=========== =============== =======
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Here ``func`` is a function which takes two one-dimensional numpy
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arrays, and returns a distance. Note that in order to be used within
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the BallTree, the distance must be a true metric:
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i.e. it must satisfy the following properties
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1) Non-negativity: d(x, y) >= 0
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2) Identity: d(x, y) = 0 if and only if x == y
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3) Symmetry: d(x, y) = d(y, x)
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4) Triangle Inequality: d(x, y) + d(y, z) >= d(x, z)
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Because of the Python object overhead involved in calling the python
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function, this will be fairly slow, but it will have the same
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scaling as other distances.
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"""
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@classmethod
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def get_metric(cls, metric, dtype=np.float64, **kwargs):
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"""Get the given distance metric from the string identifier.
@@ -74,11 +186,24 @@ cdef class DistanceMetric:
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Parameters
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----------
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metric : str or class name
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The distance metric to use
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The string identifier or class name of the desired distance metric.
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See the documentation of the `DistanceMetric` class for a list of
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available metrics.
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dtype : {np.float32, np.float64}, default=np.float64
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The dtype of the data on which the metric will be applied
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The data type of the input on which the metric will be applied.
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This affects the precision of the computed distances.
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By default, it is set to `np.float64`.
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**kwargs
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additional arguments will be passed to the requested metric
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Additional keyword arguments that will be passed to the requested metric.
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These arguments can be used to customize the behavior of the specific
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metric.
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Returns
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-------
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metric_obj : instance of the requested metric
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An instance of the requested distance metric class.
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"""
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if dtype == np.float32:
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specialized_class = DistanceMetric32

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