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Traktor/myenv/Lib/site-packages/sklearn/metrics/_dist_metrics.pyx.tp
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2024-05-26 05:12:46 +02:00

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{{py:
implementation_specific_values = [
# Values are the following ones:
#
# name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
('64', 'float64_t', 'np.float64'),
('32', 'float32_t', 'np.float32')
]
}}
# By Jake Vanderplas (2013) <jakevdp@cs.washington.edu>
# written for the scikit-learn project
# License: BSD
import numpy as np
cimport numpy as cnp
cnp.import_array() # required in order to use C-API
from libc.math cimport fabs, sqrt, exp, pow, cos, sin, asin
from scipy.sparse import csr_matrix, issparse
from ..utils._typedefs cimport float64_t, float32_t, int32_t, intp_t
from ..utils import check_array
from ..utils.fixes import parse_version, sp_base_version
cdef inline double fmax(double a, double b) noexcept nogil:
return max(a, b)
######################################################################
# newObj function
# this is a helper function for pickling
def newObj(obj):
return obj.__new__(obj)
BOOL_METRICS = [
"hamming",
"jaccard",
"dice",
"rogerstanimoto",
"russellrao",
"sokalmichener",
"sokalsneath",
]
if sp_base_version < parse_version("1.11"):
# Deprecated in SciPy 1.9 and removed in SciPy 1.11
BOOL_METRICS += ["kulsinski"]
if sp_base_version < parse_version("1.9"):
# Deprecated in SciPy 1.0 and removed in SciPy 1.9
BOOL_METRICS += ["matching"]
def get_valid_metric_ids(L):
"""Given an iterable of metric class names or class identifiers,
return a list of metric IDs which map to those classes.
Example:
>>> L = get_valid_metric_ids([EuclideanDistance, 'ManhattanDistance'])
>>> sorted(L)
['cityblock', 'euclidean', 'l1', 'l2', 'manhattan']
"""
return [key for (key, val) in METRIC_MAPPING64.items()
if (val.__name__ in L) or (val in L)]
cdef class 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 :meth:`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 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.
"""
@classmethod
def get_metric(cls, metric, dtype=np.float64, **kwargs):
"""Get the given distance metric from the string identifier.
See the docstring of DistanceMetric for a list of available metrics.
Parameters
----------
metric : str 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_obj : instance of the requested metric
An instance of the requested distance metric class.
"""
if dtype == np.float32:
specialized_class = DistanceMetric32
elif dtype == np.float64:
specialized_class = DistanceMetric64
else:
raise ValueError(
f"Unexpected dtype {dtype} provided. Please select a dtype from"
" {np.float32, np.float64}"
)
return specialized_class.get_metric(metric, **kwargs)
{{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
######################################################################
# metric mappings
# These map from metric id strings to class names
METRIC_MAPPING{{name_suffix}} = {
'euclidean': EuclideanDistance{{name_suffix}},
'l2': EuclideanDistance{{name_suffix}},
'minkowski': MinkowskiDistance{{name_suffix}},
'p': MinkowskiDistance{{name_suffix}},
'manhattan': ManhattanDistance{{name_suffix}},
'cityblock': ManhattanDistance{{name_suffix}},
'l1': ManhattanDistance{{name_suffix}},
'chebyshev': ChebyshevDistance{{name_suffix}},
'infinity': ChebyshevDistance{{name_suffix}},
'seuclidean': SEuclideanDistance{{name_suffix}},
'mahalanobis': MahalanobisDistance{{name_suffix}},
'hamming': HammingDistance{{name_suffix}},
'canberra': CanberraDistance{{name_suffix}},
'braycurtis': BrayCurtisDistance{{name_suffix}},
'matching': MatchingDistance{{name_suffix}},
'jaccard': JaccardDistance{{name_suffix}},
'dice': DiceDistance{{name_suffix}},
'kulsinski': KulsinskiDistance{{name_suffix}},
'rogerstanimoto': RogersTanimotoDistance{{name_suffix}},
'russellrao': RussellRaoDistance{{name_suffix}},
'sokalmichener': SokalMichenerDistance{{name_suffix}},
'sokalsneath': SokalSneathDistance{{name_suffix}},
'haversine': HaversineDistance{{name_suffix}},
'pyfunc': PyFuncDistance{{name_suffix}},
}
cdef inline object _buffer_to_ndarray{{name_suffix}}(const {{INPUT_DTYPE_t}}* x, intp_t n):
# Wrap a memory buffer with an ndarray. Warning: this is not robust.
# In particular, if x is deallocated before the returned array goes
# out of scope, this could cause memory errors. Since there is not
# a possibility of this for our use-case, this should be safe.
# Note: this Segfaults unless np.import_array() is called above
# TODO: remove the explicit cast to cnp.intp_t* when cython min version >= 3.0
return cnp.PyArray_SimpleNewFromData(1, <cnp.intp_t*>&n, cnp.NPY_FLOAT64, <void*>x)
cdef {{INPUT_DTYPE_t}} INF{{name_suffix}} = np.inf
######################################################################
# Distance Metric Classes
cdef class DistanceMetric{{name_suffix}}(DistanceMetric):
"""DistanceMetric class
This class provides a uniform interface to fast distance metric
functions. The various metrics can be accessed via the :meth:`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)``
"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 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.
"""
def __cinit__(self):
self.p = 2
self.vec = np.zeros(1, dtype=np.float64, order='C')
self.mat = np.zeros((1, 1), dtype=np.float64, order='C')
self.size = 1
def __reduce__(self):
"""
reduce method used for pickling
"""
return (newObj, (self.__class__,), self.__getstate__())
def __getstate__(self):
"""
get state for pickling
"""
if self.__class__.__name__ == "PyFuncDistance{{name_suffix}}":
return (float(self.p), np.asarray(self.vec), np.asarray(self.mat), self.func, self.kwargs)
return (float(self.p), np.asarray(self.vec), np.asarray(self.mat))
def __setstate__(self, state):
"""
set state for pickling
"""
self.p = state[0]
self.vec = state[1]
self.mat = state[2]
if self.__class__.__name__ == "PyFuncDistance{{name_suffix}}":
self.func = state[3]
self.kwargs = state[4]
self.size = self.vec.shape[0]
@classmethod
def get_metric(cls, metric, **kwargs):
"""Get the given distance metric from the string identifier.
See the docstring of DistanceMetric for a list of available metrics.
Parameters
----------
metric : str or class name
The distance metric to use
**kwargs
additional arguments will be passed to the requested metric
"""
if isinstance(metric, DistanceMetric{{name_suffix}}):
return metric
if callable(metric):
return PyFuncDistance{{name_suffix}}(metric, **kwargs)
# Map the metric string ID to the metric class
if isinstance(metric, type) and issubclass(metric, DistanceMetric{{name_suffix}}):
pass
else:
try:
metric = METRIC_MAPPING{{name_suffix}}[metric]
except:
raise ValueError("Unrecognized metric '%s'" % metric)
# In Minkowski special cases, return more efficient methods
if metric is MinkowskiDistance{{name_suffix}}:
p = kwargs.pop('p', 2)
w = kwargs.pop('w', None)
if p == 1 and w is None:
return ManhattanDistance{{name_suffix}}(**kwargs)
elif p == 2 and w is None:
return EuclideanDistance{{name_suffix}}(**kwargs)
elif np.isinf(p) and w is None:
return ChebyshevDistance{{name_suffix}}(**kwargs)
else:
return MinkowskiDistance{{name_suffix}}(p, w, **kwargs)
else:
return metric(**kwargs)
def __init__(self):
if self.__class__ is DistanceMetric{{name_suffix}}:
raise NotImplementedError("DistanceMetric{{name_suffix}} is an abstract class")
def _validate_data(self, X):
"""Validate the input data.
This should be overridden in a base class if a specific input format
is required.
"""
return
cdef {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
"""Compute the distance between vectors x1 and x2
This should be overridden in a base class.
"""
return -999
cdef {{INPUT_DTYPE_t}} rdist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
"""Compute the rank-preserving surrogate distance between vectors x1 and x2.
This can optionally be overridden in a base class.
The rank-preserving 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.
"""
return self.dist(x1, x2, size)
cdef int pdist(
self,
const {{INPUT_DTYPE_t}}[:, ::1] X,
{{INPUT_DTYPE_t}}[:, ::1] D,
) except -1:
"""Compute the pairwise distances between points in X"""
cdef intp_t i1, i2
for i1 in range(X.shape[0]):
for i2 in range(i1, X.shape[0]):
D[i1, i2] = self.dist(&X[i1, 0], &X[i2, 0], X.shape[1])
D[i2, i1] = D[i1, i2]
return 0
cdef int cdist(
self,
const {{INPUT_DTYPE_t}}[:, ::1] X,
const {{INPUT_DTYPE_t}}[:, ::1] Y,
{{INPUT_DTYPE_t}}[:, ::1] D,
) except -1:
"""Compute the cross-pairwise distances between arrays X and Y"""
cdef intp_t i1, i2
if X.shape[1] != Y.shape[1]:
raise ValueError('X and Y must have the same second dimension')
for i1 in range(X.shape[0]):
for i2 in range(Y.shape[0]):
D[i1, i2] = self.dist(&X[i1, 0], &Y[i2, 0], X.shape[1])
return 0
cdef {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
"""Compute the distance between vectors x1 and x2 represented
under the CSR format.
This must be overridden in a subclass.
Notes
-----
0. The implementation of this method in subclasses must be robust to the
presence of explicit zeros in the CSR representation.
1. The `data` arrays are passed using pointers to be able to support an
alternative representation of the CSR data structure for supporting
fused sparse-dense datasets pairs with minimum overhead.
See the explanations in `SparseDenseDatasetsPair.__init__`.
2. An alternative signature would be:
cdef {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
) except -1 nogil:
Where callers would use slicing on the original CSR data and indices
memoryviews:
x1_start = X1_csr.indices_ptr[i]
x1_end = X1_csr.indices_ptr[i+1]
x2_start = X2_csr.indices_ptr[j]
x2_end = X2_csr.indices_ptr[j+1]
self.dist_csr(
&x1_data[x1_start],
x1_indices[x1_start:x1_end],
&x2_data[x2_start],
x2_indices[x2_start:x2_end],
)
Yet, slicing on memoryview slows down execution as it takes the GIL.
See: https://github.com/scikit-learn/scikit-learn/issues/17299
Hence, to avoid slicing the data and indices arrays of the sparse
matrices containing respectively x1 and x2 (namely x{1,2}_{data,indices})
are passed as well as their indices pointers (namely x{1,2}_{start,end}).
3. For reference about the CSR format, see section 3.4 of
Saad, Y. (2003), Iterative Methods for Sparse Linear Systems, SIAM.
https://www-users.cse.umn.edu/~saad/IterMethBook_2ndEd.pdf
"""
return -999
cdef {{INPUT_DTYPE_t}} rdist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
"""Distance between rows of CSR matrices x1 and x2.
This can optionally be overridden in a subclass.
The rank-preserving 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.
Notes
-----
The implementation of this method in subclasses must be robust to the
presence of explicit zeros in the CSR representation.
More information about the motives for this method signature is given
in the docstring of dist_csr.
"""
return self.dist_csr(
x1_data,
x1_indices,
x2_data,
x2_indices,
x1_start,
x1_end,
x2_start,
x2_end,
size,
)
cdef int pdist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t[::1] x1_indices,
const int32_t[::1] x1_indptr,
const intp_t size,
{{INPUT_DTYPE_t}}[:, ::1] D,
) except -1 nogil:
"""Pairwise distances between rows in CSR matrix X.
Note that this implementation is twice faster than cdist_csr(X, X)
because it leverages the symmetry of the problem.
"""
cdef:
intp_t i1, i2
intp_t n_x1 = x1_indptr.shape[0] - 1
intp_t x1_start, x1_end, x2_start, x2_end
for i1 in range(n_x1):
x1_start = x1_indptr[i1]
x1_end = x1_indptr[i1 + 1]
for i2 in range(i1, n_x1):
x2_start = x1_indptr[i2]
x2_end = x1_indptr[i2 + 1]
D[i1, i2] = D[i2, i1] = self.dist_csr(
x1_data,
&x1_indices[0],
x1_data,
&x1_indices[0],
x1_start,
x1_end,
x2_start,
x2_end,
size,
)
return 0
cdef int cdist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t[::1] x1_indices,
const int32_t[::1] x1_indptr,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t[::1] x2_indices,
const int32_t[::1] x2_indptr,
const intp_t size,
{{INPUT_DTYPE_t}}[:, ::1] D,
) except -1 nogil:
"""Compute the cross-pairwise distances between arrays X and Y
represented in the CSR format."""
cdef:
intp_t i1, i2
intp_t n_x1 = x1_indptr.shape[0] - 1
intp_t n_x2 = x2_indptr.shape[0] - 1
intp_t x1_start, x1_end, x2_start, x2_end
for i1 in range(n_x1):
x1_start = x1_indptr[i1]
x1_end = x1_indptr[i1 + 1]
for i2 in range(n_x2):
x2_start = x2_indptr[i2]
x2_end = x2_indptr[i2 + 1]
D[i1, i2] = self.dist_csr(
x1_data,
&x1_indices[0],
x2_data,
&x2_indices[0],
x1_start,
x1_end,
x2_start,
x2_end,
size,
)
return 0
cdef {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
"""Convert the rank-preserving surrogate distance to the distance"""
return rdist
cdef {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
"""Convert the distance to the rank-preserving surrogate distance"""
return dist
def rdist_to_dist(self, rdist):
"""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
----------
rdist : double
Surrogate distance.
Returns
-------
double
True distance.
"""
return rdist
def dist_to_rdist(self, dist):
"""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
----------
dist : double
True distance.
Returns
-------
double
Surrogate distance.
"""
return dist
def _pairwise_dense_dense(self, X, Y):
cdef const {{INPUT_DTYPE_t}}[:, ::1] Xarr
cdef const {{INPUT_DTYPE_t}}[:, ::1] Yarr
cdef {{INPUT_DTYPE_t}}[:, ::1] Darr
Xarr = np.asarray(X, dtype={{INPUT_DTYPE}}, order='C')
self._validate_data(Xarr)
if X is Y:
Darr = np.empty((Xarr.shape[0], Xarr.shape[0]), dtype={{INPUT_DTYPE}}, order='C')
self.pdist(Xarr, Darr)
else:
Yarr = np.asarray(Y, dtype={{INPUT_DTYPE}}, order='C')
self._validate_data(Yarr)
Darr = np.empty((Xarr.shape[0], Yarr.shape[0]), dtype={{INPUT_DTYPE}}, order='C')
self.cdist(Xarr, Yarr, Darr)
return np.asarray(Darr)
def _pairwise_sparse_sparse(self, X: csr_matrix , Y: csr_matrix):
cdef:
intp_t n_X, n_features
const {{INPUT_DTYPE_t}}[::1] X_data
const int32_t[::1] X_indices
const int32_t[::1] X_indptr
intp_t n_Y
const {{INPUT_DTYPE_t}}[::1] Y_data
const int32_t[::1] Y_indices
const int32_t[::1] Y_indptr
{{INPUT_DTYPE_t}}[:, ::1] Darr
X_csr = X.tocsr()
n_X, n_features = X_csr.shape
X_data = np.asarray(X_csr.data, dtype={{INPUT_DTYPE}})
X_indices = np.asarray(X_csr.indices, dtype=np.int32)
X_indptr = np.asarray(X_csr.indptr, dtype=np.int32)
if X is Y:
Darr = np.empty((n_X, n_X), dtype={{INPUT_DTYPE}}, order='C')
self.pdist_csr(
x1_data=&X_data[0],
x1_indices=X_indices,
x1_indptr=X_indptr,
size=n_features,
D=Darr,
)
else:
Y_csr = Y.tocsr()
n_Y, _ = Y_csr.shape
Y_data = np.asarray(Y_csr.data, dtype={{INPUT_DTYPE}})
Y_indices = np.asarray(Y_csr.indices, dtype=np.int32)
Y_indptr = np.asarray(Y_csr.indptr, dtype=np.int32)
Darr = np.empty((n_X, n_Y), dtype={{INPUT_DTYPE}}, order='C')
self.cdist_csr(
x1_data=&X_data[0],
x1_indices=X_indices,
x1_indptr=X_indptr,
x2_data=&Y_data[0],
x2_indices=Y_indices,
x2_indptr=Y_indptr,
size=n_features,
D=Darr,
)
return np.asarray(Darr)
def _pairwise_sparse_dense(self, X: csr_matrix, Y):
cdef:
intp_t n_X = X.shape[0]
intp_t n_features = X.shape[1]
const {{INPUT_DTYPE_t}}[::1] X_data = np.asarray(
X.data, dtype={{INPUT_DTYPE}},
)
const int32_t[::1] X_indices = np.asarray(
X.indices, dtype=np.int32,
)
const int32_t[::1] X_indptr = np.asarray(
X.indptr, dtype=np.int32,
)
const {{INPUT_DTYPE_t}}[:, ::1] Y_data = np.asarray(
Y, dtype={{INPUT_DTYPE}}, order="C",
)
intp_t n_Y = Y_data.shape[0]
const int32_t[::1] Y_indices = (
np.arange(n_features, dtype=np.int32)
)
{{INPUT_DTYPE_t}}[:, ::1] Darr = np.empty((n_X, n_Y), dtype={{INPUT_DTYPE}}, order='C')
intp_t i1, i2
intp_t x1_start, x1_end
{{INPUT_DTYPE_t}} * x2_data
with nogil:
# Use the exact same adaptation for CSR than in SparseDenseDatasetsPair
# for supporting the sparse-dense case with minimal overhead.
# Note: at this point this method is only a convenience method
# used in the tests via the DistanceMetric.pairwise method.
# Therefore, there is no need to attempt parallelization of those
# nested for-loops.
# Efficient parallel computation of pairwise distances can be
# achieved via the PairwiseDistances class instead. The latter
# internally calls into vector-wise distance computation from
# the DistanceMetric subclass while benefiting from the generic
# Cython/OpenMP parallelization template for the generic pairwise
# distance + reduction computational pattern.
for i1 in range(n_X):
x1_start = X_indptr[i1]
x1_end = X_indptr[i1 + 1]
for i2 in range(n_Y):
x2_data = &Y_data[0, 0] + i2 * n_features
Darr[i1, i2] = self.dist_csr(
x1_data=&X_data[0],
x1_indices=&X_indices[0],
x2_data=x2_data,
x2_indices=&Y_indices[0],
x1_start=x1_start,
x1_end=x1_end,
x2_start=0,
x2_end=n_features,
size=n_features,
)
return np.asarray(Darr)
def _pairwise_dense_sparse(self, X, Y: csr_matrix):
# We could have implemented this method using _pairwise_dense_sparse by
# swapping argument and by transposing the results, but this would
# have come with an extra copy to ensure C-contiguity of the result.
cdef:
intp_t n_X = X.shape[0]
intp_t n_features = X.shape[1]
const {{INPUT_DTYPE_t}}[:, ::1] X_data = np.asarray(
X, dtype={{INPUT_DTYPE}}, order="C",
)
const int32_t[::1] X_indices = np.arange(
n_features, dtype=np.int32,
)
intp_t n_Y = Y.shape[0]
const {{INPUT_DTYPE_t}}[::1] Y_data = np.asarray(
Y.data, dtype={{INPUT_DTYPE}},
)
const int32_t[::1] Y_indices = np.asarray(
Y.indices, dtype=np.int32,
)
const int32_t[::1] Y_indptr = np.asarray(
Y.indptr, dtype=np.int32,
)
{{INPUT_DTYPE_t}}[:, ::1] Darr = np.empty((n_X, n_Y), dtype={{INPUT_DTYPE}}, order='C')
intp_t i1, i2
{{INPUT_DTYPE_t}} * x1_data
intp_t x2_start, x2_end
with nogil:
# Use the exact same adaptation for CSR than in SparseDenseDatasetsPair
# for supporting the dense-sparse case with minimal overhead.
# Note: at this point this method is only a convenience method
# used in the tests via the DistanceMetric.pairwise method.
# Therefore, there is no need to attempt parallelization of those
# nested for-loops.
# Efficient parallel computation of pairwise distances can be
# achieved via the PairwiseDistances class instead. The latter
# internally calls into vector-wise distance computation from
# the DistanceMetric subclass while benefiting from the generic
# Cython/OpenMP parallelization template for the generic pairwise
# distance + reduction computational pattern.
for i1 in range(n_X):
x1_data = &X_data[0, 0] + i1 * n_features
for i2 in range(n_Y):
x2_start = Y_indptr[i2]
x2_end = Y_indptr[i2 + 1]
Darr[i1, i2] = self.dist_csr(
x1_data=x1_data,
x1_indices=&X_indices[0],
x2_data=&Y_data[0],
x2_indices=&Y_indices[0],
x1_start=0,
x1_end=n_features,
x2_start=x2_start,
x2_end=x2_end,
size=n_features,
)
return np.asarray(Darr)
def pairwise(self, X, Y=None):
"""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
----------
X : ndarray or CSR matrix of shape (n_samples_X, n_features)
Input data.
Y : ndarray or CSR matrix of shape (n_samples_Y, n_features)
Input data.
If not specified, then Y=X.
Returns
-------
dist : ndarray of shape (n_samples_X, n_samples_Y)
The distance matrix of pairwise distances between points in X and Y.
"""
X = check_array(X, accept_sparse=['csr'])
if Y is None:
Y = X
else:
Y = check_array(Y, accept_sparse=['csr'])
X_is_sparse = issparse(X)
Y_is_sparse = issparse(Y)
if not X_is_sparse and not Y_is_sparse:
return self._pairwise_dense_dense(X, Y)
if X_is_sparse and Y_is_sparse:
return self._pairwise_sparse_sparse(X, Y)
if X_is_sparse and not Y_is_sparse:
return self._pairwise_sparse_dense(X, Y)
return self._pairwise_dense_sparse(X, Y)
#------------------------------------------------------------
# Euclidean Distance
# d = sqrt(sum(x_i^2 - y_i^2))
cdef class EuclideanDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Euclidean Distance metric
.. math::
D(x, y) = \sqrt{ \sum_i (x_i - y_i) ^ 2 }
"""
def __init__(self):
self.p = 2
cdef inline {{INPUT_DTYPE_t}} dist(self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
return euclidean_dist{{name_suffix}}(x1, x2, size)
cdef inline {{INPUT_DTYPE_t}} rdist(self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
return euclidean_rdist{{name_suffix}}(x1, x2, size)
cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
return sqrt(rdist)
cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
return dist * dist
def rdist_to_dist(self, rdist):
return np.sqrt(rdist)
def dist_to_rdist(self, dist):
return dist ** 2
cdef inline {{INPUT_DTYPE_t}} rdist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
float64_t d = 0.0
float64_t unsquared = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
unsquared = x1_data[i1] - x2_data[i2]
d = d + (unsquared * unsquared)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
unsquared = x1_data[i1]
d = d + (unsquared * unsquared)
i1 = i1 + 1
else:
unsquared = x2_data[i2]
d = d + (unsquared * unsquared)
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
unsquared = x2_data[i2]
d = d + (unsquared * unsquared)
i2 = i2 + 1
else:
while i1 < x1_end:
unsquared = x1_data[i1]
d = d + (unsquared * unsquared)
i1 = i1 + 1
return d
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
return sqrt(
self.rdist_csr(
x1_data,
x1_indices,
x2_data,
x2_indices,
x1_start,
x1_end,
x2_start,
x2_end,
size,
))
#------------------------------------------------------------
# SEuclidean Distance
# d = sqrt(sum((x_i - y_i2)^2 / v_i))
cdef class SEuclideanDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Standardized Euclidean Distance metric
.. math::
D(x, y) = \sqrt{ \sum_i \frac{ (x_i - y_i) ^ 2}{V_i} }
"""
def __init__(self, V):
self.vec = np.asarray(V, dtype=np.float64)
self.size = self.vec.shape[0]
self.p = 2
def _validate_data(self, X):
if X.shape[1] != self.size:
raise ValueError('SEuclidean dist: size of V does not match')
cdef inline {{INPUT_DTYPE_t}} rdist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef float64_t tmp, d=0
cdef intp_t j
for j in range(size):
tmp = x1[j] - x2[j]
d += (tmp * tmp / self.vec[j])
return d
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
return sqrt(self.rdist(x1, x2, size))
cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
return sqrt(rdist)
cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
return dist * dist
def rdist_to_dist(self, rdist):
return np.sqrt(rdist)
def dist_to_rdist(self, dist):
return dist ** 2
cdef inline {{INPUT_DTYPE_t}} rdist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
float64_t d = 0.0
float64_t unsquared = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
unsquared = x1_data[i1] - x2_data[i2]
d = d + (unsquared * unsquared) / self.vec[ix1]
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
unsquared = x1_data[i1]
d = d + (unsquared * unsquared) / self.vec[ix1]
i1 = i1 + 1
else:
unsquared = x2_data[i2]
d = d + (unsquared * unsquared) / self.vec[ix2]
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
ix2 = x2_indices[i2]
unsquared = x2_data[i2]
d = d + (unsquared * unsquared) / self.vec[ix2]
i2 = i2 + 1
else:
while i1 < x1_end:
ix1 = x1_indices[i1]
unsquared = x1_data[i1]
d = d + (unsquared * unsquared) / self.vec[ix1]
i1 = i1 + 1
return d
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
return sqrt(
self.rdist_csr(
x1_data,
x1_indices,
x2_data,
x2_indices,
x1_start,
x1_end,
x2_start,
x2_end,
size,
))
#------------------------------------------------------------
# Manhattan Distance
# d = sum(abs(x_i - y_i))
cdef class ManhattanDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Manhattan/City-block Distance metric
.. math::
D(x, y) = \sum_i |x_i - y_i|
"""
def __init__(self):
self.p = 1
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef float64_t d = 0
cdef intp_t j
for j in range(size):
d += fabs(x1[j] - x2[j])
return d
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
{{INPUT_DTYPE_t}} d = 0.0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
d = d + fabs(x1_data[i1] - x2_data[i2])
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
d = d + fabs(x1_data[i1])
i1 = i1 + 1
else:
d = d + fabs(x2_data[i2])
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
d = d + fabs(x2_data[i2])
i2 = i2 + 1
else:
while i1 < x1_end:
d = d + fabs(x1_data[i1])
i1 = i1 + 1
return d
#------------------------------------------------------------
# Chebyshev Distance
# d = max_i(abs(x_i - y_i))
cdef class ChebyshevDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
"""Chebyshev/Infinity Distance
.. math::
D(x, y) = max_i (|x_i - y_i|)
Examples
--------
>>> from sklearn.metrics.dist_metrics import DistanceMetric
>>> dist = DistanceMetric.get_metric('chebyshev')
>>> X = [[0, 1, 2],
... [3, 4, 5]]
>>> Y = [[-1, 0, 1],
... [3, 4, 5]]
>>> dist.pairwise(X, Y)
array([[1.732..., 5.196...],
[6.928..., 0.... ]])
"""
def __init__(self):
self.p = INF{{name_suffix}}
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef float64_t d = 0
cdef intp_t j
for j in range(size):
d = fmax(d, fabs(x1[j] - x2[j]))
return d
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
float64_t d = 0.0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
d = fmax(d, fabs(x1_data[i1] - x2_data[i2]))
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
d = fmax(d, fabs(x1_data[i1]))
i1 = i1 + 1
else:
d = fmax(d, fabs(x2_data[i2]))
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
d = fmax(d, fabs(x2_data[i2]))
i2 = i2 + 1
else:
while i1 < x1_end:
d = fmax(d, fabs(x1_data[i1]))
i1 = i1 + 1
return d
#------------------------------------------------------------
# Minkowski Distance
cdef class MinkowskiDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Minkowski Distance
.. math::
D(x, y) = {||u-v||}_p
when w is None.
Here is the more general expanded expression for the weighted case:
.. math::
D(x, y) = [\sum_i w_i *|x_i - y_i|^p] ^ (1/p)
Parameters
----------
p : float
The order of the p-norm of the difference (see above).
.. versionchanged:: 1.4.0
Minkowski distance allows `p` to be `0<p<1`.
w : (N,) array-like (optional)
The weight vector.
Minkowski Distance requires p > 0 and finite.
When :math:`p \in (0,1)`, it isn't a true metric but is permissible when
the triangular inequality isn't necessary.
For p = infinity, use ChebyshevDistance.
Note that for p=1, ManhattanDistance is more efficient, and for
p=2, EuclideanDistance is more efficient.
"""
def __init__(self, p, w=None):
if p <= 0:
raise ValueError("p must be greater than 0")
elif np.isinf(p):
raise ValueError("MinkowskiDistance requires finite p. "
"For p=inf, use ChebyshevDistance.")
self.p = p
if w is not None:
w_array = check_array(
w, ensure_2d=False, dtype=np.float64, input_name="w"
)
if (w_array < 0).any():
raise ValueError("w cannot contain negative weights")
self.vec = w_array
self.size = self.vec.shape[0]
else:
self.vec = np.asarray([], dtype=np.float64)
self.size = 0
def _validate_data(self, X):
if self.size > 0 and X.shape[1] != self.size:
raise ValueError("MinkowskiDistance: the size of w must match "
f"the number of features ({X.shape[1]}). "
f"Currently len(w)={self.size}.")
cdef inline {{INPUT_DTYPE_t}} rdist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef float64_t d=0
cdef intp_t j
cdef bint has_w = self.size > 0
if has_w:
for j in range(size):
d += (self.vec[j] * pow(fabs(x1[j] - x2[j]), self.p))
else:
for j in range(size):
d += (pow(fabs(x1[j] - x2[j]), self.p))
return d
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
return pow(self.rdist(x1, x2, size), 1. / self.p)
cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
return pow(rdist, 1. / self.p)
cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
return pow(dist, self.p)
def rdist_to_dist(self, rdist):
return rdist ** (1. / self.p)
def dist_to_rdist(self, dist):
return dist ** self.p
cdef inline {{INPUT_DTYPE_t}} rdist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
float64_t d = 0.0
bint has_w = self.size > 0
if has_w:
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
d = d + (self.vec[ix1] * pow(fabs(
x1_data[i1] - x2_data[i2]
), self.p))
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
d = d + (self.vec[ix1] * pow(fabs(x1_data[i1]), self.p))
i1 = i1 + 1
else:
d = d + (self.vec[ix2] * pow(fabs(x2_data[i2]), self.p))
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
ix2 = x2_indices[i2]
d = d + (self.vec[ix2] * pow(fabs(x2_data[i2]), self.p))
i2 = i2 + 1
else:
while i1 < x1_end:
ix1 = x1_indices[i1]
d = d + (self.vec[ix1] * pow(fabs(x1_data[i1]), self.p))
i1 = i1 + 1
return d
else:
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
d = d + (pow(fabs(
x1_data[i1] - x2_data[i2]
), self.p))
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
d = d + (pow(fabs(x1_data[i1]), self.p))
i1 = i1 + 1
else:
d = d + (pow(fabs(x2_data[i2]), self.p))
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
d = d + (pow(fabs(x2_data[i2]), self.p))
i2 = i2 + 1
else:
while i1 < x1_end:
d = d + (pow(fabs(x1_data[i1]), self.p))
i1 = i1 + 1
return d
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
return pow(
self.rdist_csr(
x1_data,
x1_indices,
x2_data,
x2_indices,
x1_start,
x1_end,
x2_start,
x2_end,
size,
),
1 / self.p
)
#------------------------------------------------------------
# Mahalanobis Distance
# d = sqrt( (x - y)^T V^-1 (x - y) )
cdef class MahalanobisDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
"""Mahalanobis Distance
.. math::
D(x, y) = \sqrt{ (x - y)^T V^{-1} (x - y) }
Parameters
----------
V : array-like
Symmetric positive-definite covariance matrix.
The inverse of this matrix will be explicitly computed.
VI : array-like
optionally specify the inverse directly. If VI is passed,
then V is not referenced.
"""
cdef float64_t[::1] buffer
def __init__(self, V=None, VI=None):
if VI is None:
if V is None:
raise ValueError("Must provide either V or VI "
"for Mahalanobis distance")
VI = np.linalg.inv(V)
if VI.ndim != 2 or VI.shape[0] != VI.shape[1]:
raise ValueError("V/VI must be square")
self.mat = np.asarray(VI, dtype=np.float64, order='C')
self.size = self.mat.shape[0]
# We need to create a buffer to store the vectors' coordinates' differences
self.buffer = np.zeros(self.size, dtype=np.float64)
def __setstate__(self, state):
super().__setstate__(state)
self.size = self.mat.shape[0]
self.buffer = np.zeros(self.size, dtype=np.float64)
def _validate_data(self, X):
if X.shape[1] != self.size:
raise ValueError('Mahalanobis dist: size of V does not match')
cdef inline {{INPUT_DTYPE_t}} rdist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef float64_t tmp, d = 0
cdef intp_t i, j
# compute (x1 - x2).T * VI * (x1 - x2)
for i in range(size):
self.buffer[i] = x1[i] - x2[i]
for i in range(size):
tmp = 0
for j in range(size):
tmp += self.mat[i, j] * self.buffer[j]
d += tmp * self.buffer[i]
return d
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
return sqrt(self.rdist(x1, x2, size))
cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
return sqrt(rdist)
cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
return dist * dist
def rdist_to_dist(self, rdist):
return np.sqrt(rdist)
def dist_to_rdist(self, dist):
return dist ** 2
cdef inline {{INPUT_DTYPE_t}} rdist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
float64_t tmp, d = 0.0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
self.buffer[ix1] = x1_data[i1] - x2_data[i2]
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
self.buffer[ix1] = x1_data[i1]
i1 = i1 + 1
else:
self.buffer[ix2] = - x2_data[i2]
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
ix2 = x2_indices[i2]
self.buffer[ix2] = - x2_data[i2]
i2 = i2 + 1
else:
while i1 < x1_end:
ix1 = x1_indices[i1]
self.buffer[ix1] = x1_data[i1]
i1 = i1 + 1
for i in range(size):
tmp = 0
for j in range(size):
tmp += self.mat[i, j] * self.buffer[j]
d += tmp * self.buffer[i]
return d
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
return sqrt(
self.rdist_csr(
x1_data,
x1_indices,
x2_data,
x2_indices,
x1_start,
x1_end,
x2_start,
x2_end,
size,
))
#------------------------------------------------------------
# Hamming Distance
# d = N_unequal(x, y) / N_tot
cdef class HammingDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Hamming Distance
Hamming distance is meant for discrete-valued vectors, though it is
a valid metric for real-valued vectors.
.. math::
D(x, y) = \frac{1}{N} \sum_i \delta_{x_i, y_i}
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef int n_unequal = 0
cdef intp_t j
for j in range(size):
if x1[j] != x2[j]:
n_unequal += 1
return float(n_unequal) / size
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
float64_t d = 0.0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
d += (x1_data[i1] != x2_data[i2])
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
d += (x1_data[i1] != 0)
i1 = i1 + 1
else:
d += (x2_data[i2] != 0)
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
d += (x2_data[i2] != 0)
i2 = i2 + 1
else:
while i1 < x1_end:
d += (x1_data[i1] != 0)
i1 = i1 + 1
d /= size
return d
#------------------------------------------------------------
# Canberra Distance
# D(x, y) = sum[ abs(x_i - y_i) / (abs(x_i) + abs(y_i)) ]
cdef class CanberraDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Canberra Distance
Canberra distance is meant for discrete-valued vectors, though it is
a valid metric for real-valued vectors.
.. math::
D(x, y) = \sum_i \frac{|x_i - y_i|}{|x_i| + |y_i|}
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef float64_t denom, d = 0
cdef intp_t j
for j in range(size):
denom = fabs(x1[j]) + fabs(x2[j])
if denom > 0:
d += fabs(x1[j] - x2[j]) / denom
return d
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
float64_t d = 0.0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
d += (
fabs(x1_data[i1] - x2_data[i2]) /
(fabs(x1_data[i1]) + fabs(x2_data[i2]))
)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
d += 1.
i1 = i1 + 1
else:
d += 1.
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
d += 1.
i2 = i2 + 1
else:
while i1 < x1_end:
d += 1.
i1 = i1 + 1
return d
#------------------------------------------------------------
# Bray-Curtis Distance
# D(x, y) = sum[abs(x_i - y_i)] / sum[abs(x_i) + abs(y_i)]
cdef class BrayCurtisDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Bray-Curtis Distance
Bray-Curtis distance is meant for discrete-valued vectors, though it is
a valid metric for real-valued vectors.
.. math::
D(x, y) = \frac{\sum_i |x_i - y_i|}{\sum_i(|x_i| + |y_i|)}
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef float64_t num = 0, denom = 0
cdef intp_t j
for j in range(size):
num += fabs(x1[j] - x2[j])
denom += fabs(x1[j]) + fabs(x2[j])
if denom > 0:
return num / denom
else:
return 0.0
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
float64_t num = 0.0
float64_t denom = 0.0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
num += fabs(x1_data[i1] - x2_data[i2])
denom += fabs(x1_data[i1]) + fabs(x2_data[i2])
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
num += fabs(x1_data[i1])
denom += fabs(x1_data[i1])
i1 = i1 + 1
else:
num += fabs(x2_data[i2])
denom += fabs(x2_data[i2])
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
num += fabs(x1_data[i1])
denom += fabs(x1_data[i1])
i2 = i2 + 1
else:
while i1 < x1_end:
num += fabs(x2_data[i2])
denom += fabs(x2_data[i2])
i1 = i1 + 1
return num / denom
#------------------------------------------------------------
# Jaccard Distance (boolean)
# D(x, y) = N_unequal(x, y) / N_nonzero(x, y)
cdef class JaccardDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Jaccard Distance
Jaccard Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
D(x, y) = (N_TF + N_FT) / (N_TT + N_TF + N_FT)
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef int tf1, tf2, n_eq = 0, nnz = 0
cdef intp_t j
for j in range(size):
tf1 = x1[j] != 0
tf2 = x2[j] != 0
nnz += (tf1 or tf2)
n_eq += (tf1 and tf2)
# Based on https://github.com/scipy/scipy/pull/7373
# When comparing two all-zero vectors, scipy>=1.2.0 jaccard metric
# was changed to return 0, instead of nan.
if nnz == 0:
return 0
return (nnz - n_eq) * 1.0 / nnz
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
intp_t tf1, tf2, n_tt = 0, nnz = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
tf1 = x1_data[i1] != 0
tf2 = x2_data[i2] != 0
if ix1 == ix2:
nnz += (tf1 or tf2)
n_tt += (tf1 and tf2)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
nnz += tf1
i1 = i1 + 1
else:
nnz += tf2
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
tf2 = x2_data[i2] != 0
nnz += tf2
i2 = i2 + 1
else:
while i1 < x1_end:
tf1 = x1_data[i1] != 0
nnz += tf1
i1 = i1 + 1
# Based on https://github.com/scipy/scipy/pull/7373
# When comparing two all-zero vectors, scipy>=1.2.0 jaccard metric
# was changed to return 0, instead of nan.
if nnz == 0:
return 0
return (nnz - n_tt) * 1.0 / nnz
#------------------------------------------------------------
# Matching Distance (boolean)
# D(x, y) = n_neq / n
cdef class MatchingDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Matching Distance
Matching Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
D(x, y) = (N_TF + N_FT) / N
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef int tf1, tf2, n_neq = 0
cdef intp_t j
for j in range(size):
tf1 = x1[j] != 0
tf2 = x2[j] != 0
n_neq += (tf1 != tf2)
return n_neq * 1. / size
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
intp_t tf1, tf2, n_neq = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
if ix1 == ix2:
tf1 = x1_data[i1] != 0
tf2 = x2_data[i2] != 0
n_neq += (tf1 != tf2)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
n_neq += (x1_data[i1] != 0)
i1 = i1 + 1
else:
n_neq += (x2_data[i2] != 0)
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
n_neq += (x2_data[i2] != 0)
i2 = i2 + 1
else:
while i1 < x1_end:
n_neq += (x1_data[i1] != 0)
i1 = i1 + 1
return n_neq * 1.0 / size
#------------------------------------------------------------
# Dice Distance (boolean)
# D(x, y) = n_neq / (2 * ntt + n_neq)
cdef class DiceDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Dice Distance
Dice Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
D(x, y) = (N_TF + N_FT) / (2 * N_TT + N_TF + N_FT)
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef int tf1, tf2, n_neq = 0, n_tt = 0
cdef intp_t j
for j in range(size):
tf1 = x1[j] != 0
tf2 = x2[j] != 0
n_tt += (tf1 and tf2)
n_neq += (tf1 != tf2)
return n_neq / (2.0 * n_tt + n_neq)
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
intp_t tf1, tf2, n_tt = 0, n_neq = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
tf1 = x1_data[i1] != 0
tf2 = x2_data[i2] != 0
if ix1 == ix2:
n_tt += (tf1 and tf2)
n_neq += (tf1 != tf2)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
n_neq += tf1
i1 = i1 + 1
else:
n_neq += tf2
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
tf2 = x2_data[i2] != 0
n_neq += tf2
i2 = i2 + 1
else:
while i1 < x1_end:
tf1 = x1_data[i1] != 0
n_neq += tf1
i1 = i1 + 1
return n_neq / (2.0 * n_tt + n_neq)
#------------------------------------------------------------
# Kulsinski Distance (boolean)
# D(x, y) = (ntf + nft - ntt + n) / (n_neq + n)
cdef class KulsinskiDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Kulsinski Distance
Kulsinski Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
D(x, y) = 1 - N_TT / (N + N_TF + N_FT)
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef int tf1, tf2, n_tt = 0, n_neq = 0
cdef intp_t j
for j in range(size):
tf1 = x1[j] != 0
tf2 = x2[j] != 0
n_neq += (tf1 != tf2)
n_tt += (tf1 and tf2)
return (n_neq - n_tt + size) * 1.0 / (n_neq + size)
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
intp_t tf1, tf2, n_tt = 0, n_neq = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
tf1 = x1_data[i1] != 0
tf2 = x2_data[i2] != 0
if ix1 == ix2:
n_tt += (tf1 and tf2)
n_neq += (tf1 != tf2)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
n_neq += tf1
i1 = i1 + 1
else:
n_neq += tf2
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
tf2 = x2_data[i2] != 0
n_neq += tf2
i2 = i2 + 1
else:
while i1 < x1_end:
tf1 = x1_data[i1] != 0
n_neq += tf1
i1 = i1 + 1
return (n_neq - n_tt + size) * 1.0 / (n_neq + size)
#------------------------------------------------------------
# Rogers-Tanimoto Distance (boolean)
# D(x, y) = 2 * n_neq / (n + n_neq)
cdef class RogersTanimotoDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Rogers-Tanimoto Distance
Rogers-Tanimoto Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
D(x, y) = 2 (N_TF + N_FT) / (N + N_TF + N_FT)
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef int tf1, tf2, n_neq = 0
cdef intp_t j
for j in range(size):
tf1 = x1[j] != 0
tf2 = x2[j] != 0
n_neq += (tf1 != tf2)
return (2.0 * n_neq) / (size + n_neq)
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
intp_t tf1, tf2, n_neq = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
tf1 = x1_data[i1] != 0
tf2 = x2_data[i2] != 0
if ix1 == ix2:
n_neq += (tf1 != tf2)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
n_neq += tf1
i1 = i1 + 1
else:
n_neq += tf2
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
tf2 = x2_data[i2] != 0
n_neq += tf2
i2 = i2 + 1
else:
while i1 < x1_end:
tf1 = x1_data[i1] != 0
n_neq += tf1
i1 = i1 + 1
return (2.0 * n_neq) / (size + n_neq)
#------------------------------------------------------------
# Russell-Rao Distance (boolean)
# D(x, y) = (n - ntt) / n
cdef class RussellRaoDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Russell-Rao Distance
Russell-Rao Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
D(x, y) = (N - N_TT) / N
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef int tf1, tf2, n_tt = 0
cdef intp_t j
for j in range(size):
tf1 = x1[j] != 0
tf2 = x2[j] != 0
n_tt += (tf1 and tf2)
return (size - n_tt) * 1. / size
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
intp_t tf1, tf2, n_tt = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
tf1 = x1_data[i1] != 0
tf2 = x2_data[i2] != 0
if ix1 == ix2:
n_tt += (tf1 and tf2)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
i1 = i1 + 1
else:
i2 = i2 + 1
# We don't need to go through all the longest
# vector because tf1 or tf2 will be false
# and thus n_tt won't be increased.
return (size - n_tt) * 1. / size
#------------------------------------------------------------
# Sokal-Michener Distance (boolean)
# D(x, y) = 2 * n_neq / (n + n_neq)
cdef class SokalMichenerDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Sokal-Michener Distance
Sokal-Michener Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
D(x, y) = 2 (N_TF + N_FT) / (N + N_TF + N_FT)
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef int tf1, tf2, n_neq = 0
cdef intp_t j
for j in range(size):
tf1 = x1[j] != 0
tf2 = x2[j] != 0
n_neq += (tf1 != tf2)
return (2.0 * n_neq) / (size + n_neq)
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
intp_t tf1, tf2, n_neq = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
tf1 = x1_data[i1] != 0
tf2 = x2_data[i2] != 0
if ix1 == ix2:
n_neq += (tf1 != tf2)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
n_neq += tf1
i1 = i1 + 1
else:
n_neq += tf2
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
tf2 = x2_data[i2] != 0
n_neq += tf2
i2 = i2 + 1
else:
while i1 < x1_end:
tf1 = x1_data[i1] != 0
n_neq += tf1
i1 = i1 + 1
return (2.0 * n_neq) / (size + n_neq)
#------------------------------------------------------------
# Sokal-Sneath Distance (boolean)
# D(x, y) = n_neq / (0.5 * n_tt + n_neq)
cdef class SokalSneathDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
r"""Sokal-Sneath Distance
Sokal-Sneath Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
D(x, y) = (N_TF + N_FT) / (N_TT / 2 + N_FT + N_TF)
"""
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef int tf1, tf2, n_tt = 0, n_neq = 0
cdef intp_t j
for j in range(size):
tf1 = x1[j] != 0
tf2 = x2[j] != 0
n_neq += (tf1 != tf2)
n_tt += (tf1 and tf2)
return n_neq / (0.5 * n_tt + n_neq)
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
intp_t tf1, tf2, n_tt = 0, n_neq = 0
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
tf1 = x1_data[i1] != 0
tf2 = x2_data[i2] != 0
if ix1 == ix2:
n_tt += (tf1 and tf2)
n_neq += (tf1 != tf2)
i1 = i1 + 1
i2 = i2 + 1
elif ix1 < ix2:
n_neq += tf1
i1 = i1 + 1
else:
n_neq += tf2
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
tf2 = x2_data[i2] != 0
n_neq += tf2
i2 = i2 + 1
else:
while i1 < x1_end:
tf1 = x1_data[i1] != 0
n_neq += tf1
i1 = i1 + 1
return n_neq / (0.5 * n_tt + n_neq)
#------------------------------------------------------------
# Haversine Distance (2 dimensional)
# D(x, y) = 2 arcsin{sqrt[sin^2 ((x1 - y1) / 2)
# + cos(x1) cos(y1) sin^2 ((x2 - y2) / 2)]}
cdef class HaversineDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
"""Haversine (Spherical) Distance
The Haversine distance is the angular distance between two points on
the surface of a sphere. The first distance of each point is assumed
to be the latitude, the second is the longitude, given in radians.
The dimension of the points must be 2:
D(x, y) = 2 arcsin[sqrt{sin^2((x1 - y1) / 2) + cos(x1)cos(y1)sin^2((x2 - y2) / 2)}]
"""
def _validate_data(self, X):
if X.shape[1] != 2:
raise ValueError("Haversine distance only valid "
"in 2 dimensions")
cdef inline {{INPUT_DTYPE_t}} rdist(self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
cdef float64_t sin_0 = sin(0.5 * ((x1[0]) - (x2[0])))
cdef float64_t sin_1 = sin(0.5 * ((x1[1]) - (x2[1])))
return (sin_0 * sin_0 + cos(x1[0]) * cos(x2[0]) * sin_1 * sin_1)
cdef inline {{INPUT_DTYPE_t}} dist(self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
return 2 * asin(sqrt(self.rdist(x1, x2, size)))
cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
return 2 * asin(sqrt(rdist))
cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
cdef float64_t tmp = sin(0.5 * dist)
return tmp * tmp
def rdist_to_dist(self, rdist):
return 2 * np.arcsin(np.sqrt(rdist))
def dist_to_rdist(self, dist):
tmp = np.sin(0.5 * dist)
return tmp * tmp
cdef inline {{INPUT_DTYPE_t}} dist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
return 2 * asin(sqrt(self.rdist_csr(
x1_data,
x1_indices,
x2_data,
x2_indices,
x1_start,
x1_end,
x2_start,
x2_end,
size,
)))
cdef inline {{INPUT_DTYPE_t}} rdist_csr(
self,
const {{INPUT_DTYPE_t}}* x1_data,
const int32_t* x1_indices,
const {{INPUT_DTYPE_t}}* x2_data,
const int32_t* x2_indices,
const int32_t x1_start,
const int32_t x1_end,
const int32_t x2_start,
const int32_t x2_end,
const intp_t size,
) except -1 nogil:
cdef:
intp_t ix1, ix2
intp_t i1 = x1_start
intp_t i2 = x2_start
float64_t x1_0 = 0
float64_t x1_1 = 0
float64_t x2_0 = 0
float64_t x2_1 = 0
float64_t sin_0
float64_t sin_1
while i1 < x1_end and i2 < x2_end:
ix1 = x1_indices[i1]
ix2 = x2_indices[i2]
# Find the components in the 2D vectors to work with
x1_component = ix1 if (x1_start == 0) else ix1 % x1_start
x2_component = ix2 if (x2_start == 0) else ix2 % x2_start
if x1_component == 0:
x1_0 = x1_data[i1]
else:
x1_1 = x1_data[i1]
if x2_component == 0:
x2_0 = x2_data[i2]
else:
x2_1 = x2_data[i2]
i1 = i1 + 1
i2 = i2 + 1
if i1 == x1_end:
while i2 < x2_end:
ix2 = x2_indices[i2]
x2_component = ix2 if (x2_start == 0) else ix2 % x2_start
if x2_component == 0:
x2_0 = x2_data[i2]
else:
x2_1 = x2_data[i2]
i2 = i2 + 1
else:
while i1 < x1_end:
ix1 = x1_indices[i1]
x1_component = ix1 if (x1_start == 0) else ix1 % x1_start
if x1_component == 0:
x1_0 = x1_data[i1]
else:
x1_1 = x1_data[i1]
i1 = i1 + 1
sin_0 = sin(0.5 * (x1_0 - x2_0))
sin_1 = sin(0.5 * (x1_1 - x2_1))
return (sin_0 * sin_0 + cos(x1_0) * cos(x2_0) * sin_1 * sin_1)
#------------------------------------------------------------
# User-defined distance
#
cdef class PyFuncDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
"""PyFunc Distance
A user-defined distance
Parameters
----------
func : function
func should take two numpy arrays as input, and return a distance.
"""
def __init__(self, func, **kwargs):
self.func = func
self.kwargs = kwargs
# in cython < 0.26, GIL was required to be acquired during definition of
# the function and inside the body of the function. This behaviour is not
# allowed in cython >= 0.26 since it is a redundant GIL acquisition. The
# only way to be back compatible is to inherit `dist` from the base class
# without GIL and called an inline `_dist` which acquire GIL.
cdef inline {{INPUT_DTYPE_t}} dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 nogil:
return self._dist(x1, x2, size)
cdef inline {{INPUT_DTYPE_t}} _dist(
self,
const {{INPUT_DTYPE_t}}* x1,
const {{INPUT_DTYPE_t}}* x2,
intp_t size,
) except -1 with gil:
cdef:
object x1arr = _buffer_to_ndarray{{name_suffix}}(x1, size)
object x2arr = _buffer_to_ndarray{{name_suffix}}(x2, size)
d = self.func(x1arr, x2arr, **self.kwargs)
try:
# Cython generates code here that results in a TypeError
# if d is the wrong type.
return d
except TypeError:
raise TypeError("Custom distance function must accept two "
"vectors and return a float.")
{{endfor}}
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