10000 MNT Centralize common cython compiler directives (#21512) · glemaitre/scikit-learn@c7238c8 · GitHub
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MNT Centralize common cython compiler directives (scikit-learn#21512)
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‎sklearn/_build_utils/__init__.py

Lines changed: 8 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -76,7 +76,14 @@ def cythonize_extensions(top_path, config):
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compile_time_env={
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"SKLEARN_OPENMP_PARALLELISM_ENABLED": sklearn._OPENMP_SUPPORTED
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},
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compiler_directives={"language_level": 3},
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compiler_directives={
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"language_level": 3,
81+
"boundscheck": False,
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"wraparound": False,
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"initializedcheck": False,
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"nonecheck": False,
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"cdivision": True,
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},
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)
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‎sklearn/_isotonic.pyx

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@@ -3,8 +3,6 @@
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# Uses the pool adjacent violators algorithm (PAVA), with the
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# enhancement of searching for the longest decreasing subsequence to
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# pool at each step.
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#
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# cython: boundscheck=False, wraparound=False, cdivision=True
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import numpy as np
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cimport numpy as np

‎sklearn/cluster/_dbscan_inner.pyx

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@@ -1,8 +1,6 @@
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# Fast inner loop for DBSCAN.
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# Author: Lars Buitinck
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# License: 3-clause BSD
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#
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# cython: boundscheck=False, wraparound=False
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cimport cython
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from libcpp.vector cimport vector

‎sklearn/cluster/_hierarchical_fast.pyx

Lines changed: 3 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -8,9 +8,6 @@ ctypedef np.float64_t DOUBLE
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ctypedef np.npy_intp INTP
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ctypedef np.int8_t INT8
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# Numpy must be initialized. When using numpy from C or Cython you must
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# _always_ do that, or you will have segfaults
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np.import_array()
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from ..metrics._dist_metrics cimport DistanceMetric
@@ -32,9 +29,6 @@ from numpy.math cimport INFINITY
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###############################################################################
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# Utilities for computing the ward momentum
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35-
@cython.boundscheck(False)
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@cython.wraparound(False)
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@cython.cdivision(True)
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def compute_ward_dist(np.ndarray[DOUBLE, ndim=1, mode='c'] m_1,
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np.ndarray[DOUBLE, ndim=2, mode='c'] m_2,
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np.ndarray[INTP, ndim=1, mode='c'] coord_row,
@@ -101,8 +95,6 @@ def _hc_get_descendent(INTP node, children, INTP n_leaves):
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return descendent
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def hc_get_heads(np.ndarray[INTP, ndim=1] parents, copy=True):
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"""Returns the heads of the forest, as defined by parents.
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@@ -135,8 +127,6 @@ def hc_get_heads(np.ndarray[INTP, ndim=1] parents, copy=True):
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return parents
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def _get_parents(nodes, heads, np.ndarray[INTP, ndim=1] parents,
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np.ndarray[INT8, ndim=1, mode='c'] not_visited):
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"""Returns the heads of the given nodes, as defined by parents.
@@ -176,8 +166,6 @@ def _get_parents(nodes, heads, np.ndarray[INTP, ndim=1] parents,
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# as keys and edge weights as values.
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def max_merge(IntFloatDict a, IntFloatDict b,
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np.ndarray[ITYPE_t, ndim=1] mask,
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ITYPE_t n_a, ITYPE_t n_b):
@@ -231,8 +219,6 @@ def max_merge(IntFloatDict a, IntFloatDict b,
231219
return out_obj
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234-
@cython.boundscheck(False)
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@cython.wraparound(False)
236222
def average_merge(IntFloatDict a, IntFloatDict b,
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np.ndarray[ITYPE_t, ndim=1] mask,
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ITYPE_t n_a, ITYPE_t n_b):
@@ -302,7 +288,6 @@ cdef class WeightedEdge:
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self.a = a
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self.b = b
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@cython.nonecheck(False)
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def __richcmp__(self, WeightedEdge other, int op):
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"""Cython-specific comparison method.
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@@ -348,8 +333,6 @@ cdef class UnionFind(object):
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self.size = np.hstack((np.ones(N, dtype=ITYPE),
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np.zeros(N - 1, dtype=ITYPE)))
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351-
@cython.boundscheck(False)
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@cython.nonecheck(False)
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cdef void union(self, ITYPE_t m, ITYPE_t n):
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self.parent[m] = self.next_label
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self.parent[n] = self.next_label
@@ -358,8 +341,7 @@ cdef class UnionFind(object):
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359342
return
360343

361-
@cython.boundscheck(False)
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@cython.nonecheck(False)
344+
@cython.wraparound(True)
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cdef ITYPE_t fast_find(self, ITYPE_t n):
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cdef ITYPE_t p
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p = n
@@ -371,8 +353,7 @@ cdef class UnionFind(object):
371353
p, self.parent[p] = self.parent[p], n
372354
return n
373355

374-
@cython.boundscheck(False)
375-
@cython.nonecheck(False)
356+
376357
cpdef np.ndarray[DTYPE_t, ndim=2] _single_linkage_label(
377358
np.ndarray[DTYPE_t, ndim=2] L):
378359
"""
@@ -423,6 +404,7 @@ cpdef np.ndarray[DTYPE_t, ndim=2] _single_linkage_label(
423404
return result_arr
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425406

407+
@cython.wraparound(True)
426408
def single_linkage_label(L):
427409
"""
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Convert an linkage array or MST to a tree by labelling clusters at merges.
@@ -452,8 +434,6 @@ def single_linkage_label(L):
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453435

454436
# Implements MST-LINKAGE-CORE from https://arxiv.org/abs/1109.2378
455-
@cython.boundscheck(False)
456-
@cython.nonecheck(False)
457437
def mst_linkage_core(
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const DTYPE_t [:, ::1] raw_data,
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DistanceMetric dist_metric):

‎sklearn/cluster/_k_means_common.pxd

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@@ -1,6 +1,3 @@
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# cython: language_level=3
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3-
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from cython cimport floating
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cimport numpy as np
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‎sklearn/cluster/_k_means_common.pyx

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@@ -1,7 +1,3 @@
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# cython: profile=True, boundscheck=False, wraparound=False, cdivision=True
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# Profiling is enabled by default as the overhead does not seem to be
3-
# measurable on this specific use case.
4-
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# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
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# Olivier Grisel <olivier.grisel@ensta.org>
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# Lars Buitinck

‎sklearn/cluster/_k_means_elkan.pyx

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@@ -1,5 +1,3 @@
1-
# cython: profile=True, boundscheck=False, wraparound=False, cdivision=True
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#
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# Author: Andreas Mueller
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#
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# Licence: BSD 3 clause

‎sklearn/cluster/_k_means_lloyd.pyx

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@@ -1,5 +1,3 @@
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# cython: profile=True, boundscheck=False, wraparound=False, cdivision=True
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#
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# Licence: BSD 3 clause
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53
# TODO: We still need to use ndarrays instead of typed memoryviews when using

‎sklearn/cluster/_k_means_minibatch.pyx

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@@ -1,5 +1,3 @@
1-
# cython: profile=True, boundscheck=False, wraparound=False, cdivision=True
2-
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# TODO: We still need to use ndarrays instead of typed memoryviews when using
42
# fused types and when the array may be read-only (for instance when it's
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# provided by the user). This will be fixed in cython >= 0.3.

‎sklearn/datasets/_svmlight_format_fast.pyx

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@@ -4,8 +4,6 @@
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# Lars Buitinck
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# Olivier Grisel <olivier.grisel@ensta.org>
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# License: BSD 3 clause
7-
#
8-
# cython: boundscheck=False, wraparound=False
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108
import array
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from cpython cimport array

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