8000 MAINT float32 support for `DistanceMetric` (#22764) · ogrisel/scikit-learn@48874f4 · GitHub
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MAINT float32 support for DistanceMetric (scikit-learn#22764)
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com>
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‎.gitignore

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@@ -85,3 +85,5 @@ sklearn/utils/_seq_dataset.pxd
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sklearn/utils/_weight_vector.pyx
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sklearn/utils/_weight_vector.pxd
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sklearn/linear_model/_sag_fast.pyx
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sklearn/metrics/_dist_metrics.pyx
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sklearn/metrics/_dist_metrics.pxd

‎sklearn/metrics/_dist_metrics.pxd

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‎sklearn/metrics/_dist_metrics.pxd.tp

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{{py:
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implementation_specific_values = [
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# Values are the following ones:
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#
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# name_suffix, DTYPE_t, DTYPE
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#
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# On the first hand, an empty string is used for `name_suffix`
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# for the float64 case as to still be able to expose the original
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# float64 implementation under the same API, namely `DistanceMetric`.
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#
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# On the other hand, '32' bit is used for `name_suffix` for the float32
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# case to remove ambiguity and use `DistanceMetric32`, which is not
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# publicly exposed.
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#
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# The metric mapping is adapted accordingly to route to the correct
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# implementations.
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#
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# We also use 64bit types as defined in `sklearn.utils._typedefs`
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# to maintain backward compatibility at the symbol level for extra
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# safety.
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#
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('', 'DTYPE_t', 'DTYPE'),
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('32', 'cnp.float32_t', 'np.float32')
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]
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}}
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cimport numpy as cnp
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from libc.math cimport sqrt, exp
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from ..utils._typedefs cimport DTYPE_t, ITYPE_t
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{{for name_suffix, DTYPE_t, DTYPE in implementation_specific_values}}
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######################################################################
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# Inline distance functions
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#
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# We use these for the default (euclidean) case so that they can be
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# inlined. This leads to faster computation for the most common case
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cdef inline DTYPE_t euclidean_dist{{name_suffix}}(
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const {{DTYPE_t}}* x1,
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const {{DTYPE_t}}* x2,
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ITYPE_t size,
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) nogil except -1:
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cdef DTYPE_t tmp, d=0
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cdef cnp.intp_t j
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for j in range(size):
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tmp = <DTYPE_t> (x1[j] - x2[j])
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d += tmp * tmp
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return sqrt(d)
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cdef inline DTYPE_t euclidean_rdist{{name_suffix}}(
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const {{DTYPE_t}}* x1,
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const {{DTYPE_t}}* x2,
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ITYPE_t size,
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) nogil except -1:
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cdef DTYPE_t tmp, d=0
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cdef cnp.intp_t j
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for j in range(size):
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tmp = <DTYPE_t>(x1[j] - x2[j])
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d += tmp * tmp
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return d
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cdef inline DTYPE_t euclidean_dist_to_rdist{{name_suffix}}(const {{DTYPE_t}} dist) nogil except -1:
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return dist * dist
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cdef inline DTYPE_t euclidean_rdist_to_dist{{name_suffix}}(const {{DTYPE_t}} dist) nogil except -1:
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return sqrt(dist)
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######################################################################
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# DistanceMetric base class
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cdef class DistanceMetric{{name_suffix}}:
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# The following attributes are required for a few of the subclasses.
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# we must define them here so that cython's limited polymorphism will work.
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# Because we don't expect to instantiate a lot of these objects, the
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# extra memory overhead of this setup should not be an issue.
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cdef {{DTYPE_t}} p
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cdef {{DTYPE_t}}[::1] vec
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cdef {{DTYPE_t}}[:, ::1] mat
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cdef ITYPE_t size
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cdef object func
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cdef object kwargs
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cdef DTYPE_t dist(self, const {{DTYPE_t}}* x1, const {{DTYPE_t}}* x2,
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ITYPE_t size) nogil except -1
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cdef DTYPE_t rdist(self, const {{DTYPE_t}}* x1, const {{DTYPE_t}}* x2,
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ITYPE_t size) nogil except -1
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cdef int pdist(self, const {{DTYPE_t}}[:, ::1] X, {{DTYPE_t}}[:, ::1] D) except -1
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cdef int cdist(self, const {{DTYPE_t}}[:, ::1] X, const {{DTYPE_t}}[:, ::1] Y,
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{{DTYPE_t}}[:, ::1] D) except -1
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cdef DTYPE_t _rdist_to_dist(self, {{DTYPE_t}} rdist) nogil except -1
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cdef DTYPE_t _dist_to_rdist(self, {{DTYPE_t}} dist) nogil except -1
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{{endfor}}
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######################################################################
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# DatasetsPair base class
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cdef class DatasetsPair:
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cdef DistanceMetric distance_metric
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cdef ITYPE_t n_samples_X(self) nogil
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cdef ITYPE_t n_samples_Y(self) nogil
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cdef DTYPE_t dist(self, ITYPE_t i, ITYPE_t j) nogil
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cdef DTYPE_t surrogate_dist(self, ITYPE_t i, ITYPE_t j) nogil
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cdef class DenseDenseDatasetsPair(DatasetsPair):
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cdef:
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3CF2 const DTYPE_t[:, ::1] X
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const DTYPE_t[:, ::1] Y
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ITYPE_t d

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