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MNT Use cimport numpy as cnp for sklearn/tree #23315

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Merged
merged 6 commits into from
May 14, 2022
Merged

MNT Use cimport numpy as cnp for sklearn/tree #23315

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d245239
cimport numpy as cnp
RMeli May 9, 2022
aa490d6
convert overlooked file
RMeli May 10, 2022
58a4824
Merge branch 'main' into 23295-tree
glemaitre May 11, 2022
cf01511
remove unused numpy import
RMeli May 14, 2022
39563fd
fix merge conflicts with PR #23273
RMeli May 14, 2022
07dbb0d
Update sklearn/tree/_splitter.pyx
RMeli May 14, 2022
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3 changes: 0 additions & 3 deletions sklearn/tree/_criterion.pxd
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Original file line number Diff line number Diff line change
Expand Up @@ -9,9 +9,6 @@

# See _criterion.pyx for implementation details.

import numpy as np
cimport numpy as np

from ._tree cimport DTYPE_t # Type of X
from ._tree cimport DOUBLE_t # Type of y, sample_weight
from ._tree cimport SIZE_t # Type for indices and counters
Expand Down
10 changes: 5 additions & 5 deletions sklearn/tree/_criterion.pyx
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Expand Up @@ -17,8 +17,8 @@ from libc.string cimport memset
from libc.math cimport fabs

import numpy as np
cimport numpy as np
np.import_array()
cimport numpy as cnp
cnp.import_array()

from numpy.math cimport INFINITY
from scipy.special.cython_special cimport xlogy
Expand Down Expand Up @@ -197,7 +197,7 @@ cdef class ClassificationCriterion(Criterion):
"""Abstract criterion for classification."""

def __cinit__(self, SIZE_t n_outputs,
np.ndarray[SIZE_t, ndim=1] n_classes):
cnp.ndarray[SIZE_t, ndim=1] n_classes):
"""Initialize attributes for this criterion.

Parameters
Expand Down Expand Up @@ -874,8 +874,8 @@ cdef class MAE(RegressionCriterion):
MAE = (1 / n)*(\sum_i |y_i - f_i|), where y_i is the true
value and f_i is the predicted value."""

cdef np.ndarray left_child
cdef np.ndarray right_child
cdef cnp.ndarray left_child
cdef cnp.ndarray right_child
cdef DOUBLE_t[::1] node_medians

def __cinit__(self, SIZE_t n_outputs, SIZE_t n_samples):
Expand Down
3 changes: 0 additions & 3 deletions sklearn/tree/_splitter.pxd
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Expand Up @@ -9,9 +9,6 @@

# See _splitter.pyx for details.

import numpy as np
cimport numpy as np

from ._criterion cimport Criterion

from ._tree cimport DTYPE_t # Type of X
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2 changes: 0 additions & 2 deletions sklearn/tree/_splitter.pyx
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Expand Up @@ -19,8 +19,6 @@ from libc.string cimport memcpy
from libc.string cimport memset

import numpy as np
cimport numpy as np
np.import_array()

from scipy.sparse import csc_matrix

Expand Down
30 changes: 15 additions & 15 deletions sklearn/tree/_tree.pxd
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Expand Up @@ -11,13 +11,13 @@
# See _tree.pyx for details.

import numpy as np
cimport numpy as np
cimport numpy as cnp

ctypedef np.npy_float32 DTYPE_t # Type of X
ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight
ctypedef np.npy_intp SIZE_t # Type for indices and counters
ctypedef np.npy_int32 INT32_t # Signed 32 bit integer
ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer
ctypedef cnp.npy_float32 DTYPE_t # Type of X
ctypedef cnp.npy_float64 DOUBLE_t # Type of y, sample_weight
ctypedef cnp.npy_intp SIZE_t # Type for indices and counters
ctypedef cnp.npy_int32 INT32_t # Signed 32 bit integer
ctypedef cnp.npy_uint32 UINT32_t # Unsigned 32 bit integer

from ._splitter cimport Splitter
from ._splitter cimport SplitRecord
Expand Down Expand Up @@ -62,14 +62,14 @@ cdef class Tree:
cdef int _resize(self, SIZE_t capacity) nogil except -1
cdef int _resize_c(self, SIZE_t capacity=*) nogil except -1

cdef np.ndarray _get_value_ndarray(self)
cdef np.ndarray _get_node_ndarray(self)
cdef cnp.ndarray _get_value_ndarray(self)
cdef cnp.ndarray _get_node_ndarray(self)

cpdef np.ndarray predict(self, object X)
cpdef cnp.ndarray predict(self, object X)

cpdef np.ndarray apply(self, object X)
cdef np.ndarray _apply_dense(self, object X)
cdef np.ndarray _apply_sparse_csr(self, object X)
cpdef cnp.ndarray apply(self, object X)
cdef cnp.ndarray _apply_dense(self, object X)
cdef cnp.ndarray _apply_sparse_csr(self, object X)

cpdef object decision_path(self, object X)
cdef object _decision_path_dense(self, object X)
Expand Down Expand Up @@ -98,6 +98,6 @@ cdef class TreeBuilder:
cdef SIZE_t max_depth # Maximal tree depth
cdef double min_impurity_decrease # Impurity threshold for early stopping

cpdef build(self, Tree tree, object X, np.ndarray y,
np.ndarray sample_weight=*)
cdef _check_input(self, object X, np.ndarray y, np.ndarray sample_weight)
cpdef build(self, Tree tree, object X, cnp.ndarray y,
cnp.ndarray sample_weight=*)
cdef _check_input(self, object X, cnp.ndarray y, cnp.ndarray sample_weight)
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