MNT cleanup Cython imports in tree module (scikit-learn#33947)

Author: lorentzenchr

sklearn/tree/_criterion.pyx

@@ -1,9 +1,9 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
+from libc.math cimport INFINITY
 from libc.string cimport memcpy
 from libc.string cimport memset
-from libc.math cimport INFINITY
 
 import numpy as np
 cimport numpy as cnp

sklearn/tree/_partitioner.pxd

@@ -6,7 +6,7 @@
 from cython cimport floating
 
 from sklearn.utils._typedefs cimport (
-    float32_t, float64_t, int8_t, int32_t, intp_t, uint8_t, uint32_t
+    float32_t, float64_t, int32_t, intp_t, uint8_t
 )
 from sklearn.tree._splitter cimport SplitRecord
 

sklearn/tree/_partitioner.pyx

@@ -11,7 +11,7 @@ and sparse data stored in a Compressed Sparse Column (CSC) format.
 # SPDX-License-Identifier: BSD-3-Clause
 
 from cython cimport final
-from libc.math cimport isnan, log2
+from libc.math cimport INFINITY, isnan, log2
 from libc.stdlib cimport qsort
 from libc.string cimport memcpy, memmove
 
@@ -26,9 +26,6 @@ from sklearn.tree._splitter cimport SplitRecord
 # in SparsePartitioner
 cdef float32_t EXTRACT_NNZ_SWITCH = 0.1
 
-# Allow for 32 bit float comparisons
-cdef float32_t INFINITY_32t = np.inf
-
 
 @final
 cdef class DensePartitioner:
@@ -138,8 +135,8 @@ cdef class DensePartitioner:
             intp_t p
             float32_t current_feature_value
             intp_t[::1] samples = self.samples
-            float32_t min_feature_value = INFINITY_32t
-            float32_t max_feature_value = -INFINITY_32t
+            float32_t min_feature_value = INFINITY
+            float32_t max_feature_value = -INFINITY
             float32_t[::1] feature_values = self.feature_values
             intp_t n_missing = 0
             bint seen_non_missing = False

sklearn/tree/_splitter.pyx

@@ -20,9 +20,9 @@ of splitting strategies:
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
+from libc.math cimport INFINITY
 from libc.string cimport memcpy
 
-from sklearn.utils._typedefs cimport int8_t
 from sklearn.tree._criterion cimport Criterion
 from sklearn.tree._partitioner cimport (
     FEATURE_THRESHOLD, DensePartitioner, SparsePartitioner,
@@ -41,9 +41,6 @@ ctypedef fused Partitioner:
     SparsePartitioner
 
 
-cdef float64_t INFINITY = np.inf
-
-
 cdef inline void _init_split(SplitRecord* self, intp_t start_pos) noexcept nogil:
     self.impurity_left = INFINITY
     self.impurity_right = INFINITY

sklearn/tree/_tree.pxd

@@ -6,10 +6,11 @@
 import numpy as np
 cimport numpy as cnp
 
-from sklearn.utils._typedefs cimport float32_t, float64_t, intp_t, int32_t, uint8_t, uint32_t
+from sklearn.utils._typedefs cimport (
+    float32_t, float64_t, intp_t, int32_t, uint8_t, uint32_t
+)
+from sklearn.tree._splitter cimport Splitter, SplitRecord
 
-from sklearn.tree._splitter cimport Splitter
-from sklearn.tree._splitter cimport SplitRecord
 
 cdef struct Node:
     # Base storage structure for the nodes in a Tree object

sklearn/tree/_tree.pyx

@@ -3,11 +3,11 @@
 
 from cpython cimport Py_INCREF, PyObject, PyTypeObject
 
+from libc.math cimport INFINITY, isnan
 from libc.stdlib cimport free
 from libc.string cimport memcpy
 from libc.string cimport memset
 from libc.stdint cimport INTPTR_MAX
-from libc.math cimport isnan
 from libcpp.vector cimport vector
 from libcpp.algorithm cimport pop_heap
 from libcpp.algorithm cimport push_heap
@@ -42,7 +42,6 @@ cdef extern from "numpy/arrayobject.h":
 from numpy import float32 as DTYPE
 from numpy import float64 as DOUBLE
 
-cdef float64_t INFINITY = np.inf
 cdef float64_t EPSILON = np.finfo('double').eps
 
 # Some handy constants (BestFirstTreeBuilder)

sklearn/tree/_utils.pxd

@@ -6,7 +6,7 @@
 cimport numpy as cnp
 from sklearn.tree._tree cimport Node
 from sklearn.neighbors._quad_tree cimport Cell
-from sklearn.utils._typedefs cimport float32_t, float64_t, intp_t, uint8_t, int32_t, uint32_t
+from sklearn.utils._typedefs cimport float32_t, float64_t, intp_t, uint8_t, uint32_t
 
 
 cdef enum: