8000 MNT Use cimport numpy as cnp for sklearn/tree (#23315) · scikit-learn/scikit-learn@3850935 · GitHub
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RMeliglemaitrethomasjpfan
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MNT Use cimport numpy as cnp for sklearn/tree (#23315)
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
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8 files changed

+95
-105
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8 files changed

+95
-105
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‎sklearn/tree/_criterion.pxd

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@@ -9,9 +9,6 @@
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# See _criterion.pyx for implementation details.
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import numpy as np
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cimport numpy as np
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from ._tree cimport DTYPE_t # Type of X
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from ._tree cimport DOUBLE_t # Type of y, sample_weight
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from ._tree cimport SIZE_t # Type for indices and counters

‎sklearn/tree/_criterion.pyx

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@@ -17,8 +17,8 @@ from libc.string cimport memset
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from libc.math cimport fabs
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import numpy as np
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cimport numpy as np
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np.import_array()
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cimport numpy as cnp
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cnp.import_array()
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from numpy.math cimport INFINITY
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from scipy.special.cython_special cimport xlogy
@@ -197,7 +197,7 @@ cdef class ClassificationCriterion(Criterion):
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"""Abstract criterion for classification."""
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def __cinit__(self, SIZE_t n_outputs,
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np.ndarray[SIZE_t, ndim=1] n_classes):
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cnp.ndarray[SIZE_t, ndim=1] n_classes):
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"""Initialize attributes for this criterion.
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Parameters
@@ -874,8 +874,8 @@ cdef class MAE(RegressionCriterion):
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MAE = (1 / n)*(\sum_i |y_i - f_i|), where y_i is the true
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value and f_i is the predicted value."""
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cdef np.ndarray left_child
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cdef np.ndarray right_child
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cdef cnp.ndarray left_child
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cdef cnp.ndarray right_child
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cdef DOUBLE_t[::1] node_medians
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def __cinit__(self, SIZE_t n_outputs, SIZE_t n_samples):

‎sklearn/tree/_splitter.pxd

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# See _splitter.pyx for details.
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import numpy as np
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cimport numpy as np
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from ._criterion cimport Criterion
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from ._tree cimport DTYPE_t # Type of X

‎sklearn/tree/_splitter.pyx

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@@ -19,8 +19,6 @@ from libc.string cimport memcpy
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from libc.string cimport memset
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import numpy as np
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cimport numpy as np
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np.import_array()
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from scipy.sparse import csc_matrix
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‎sklearn/tree/_tree.pxd

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# See _tree.pyx for details.
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import numpy as np
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cimport numpy as np
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cimport numpy as cnp
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ctypedef np.npy_float32 DTYPE_t # Type of X
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ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight
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ctypedef np.npy_intp SIZE_t # Type for indices and counters
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ctypedef np.npy_int32 INT32_t # Signed 32 bit integer
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ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer
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ctypedef cnp.npy_float32 DTYPE_t # Type of X
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ctypedef cnp.npy_float64 DOUBLE_t # Type of y, sample_weight
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ctypedef cnp.npy_intp SIZE_t # Type for indices and counters
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ctypedef cnp.npy_int32 INT32_t # Signed 32 bit integer
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ctypedef cnp.npy_uint32 UINT32_t # Unsigned 32 bit integer
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from ._splitter cimport Splitter
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from ._splitter cimport SplitRecord
@@ -62,14 +62,14 @@ cdef class Tree:
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cdef int _resize(self, SIZE_t capacity) nogil except -1
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cdef int _resize_c(self, SIZE_t capacity=*) nogil except -1
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cdef np.ndarray _get_value_ndarray(self)
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cdef np.ndarray _get_node_ndarray(self)
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cdef cnp.ndarray _get_value_ndarray(self)
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cdef cnp.ndarray _get_node_ndarray(self)
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cpdef np.ndarray predict(self, object X)
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cpdef cnp.ndarray predict(self, object X)
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cpdef np.ndarray apply(self, object X)
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cdef np.ndarray _apply_dense(self, object X)
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cdef np.ndarray _apply_sparse_csr(self, object X)
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cpdef cnp.ndarray apply(self, object X)
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cdef cnp.ndarray _apply_dense(self, object X)
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cdef cnp.ndarray _apply_sparse_csr(self, object X)
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cpdef object decision_path(self, object X)
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cdef object _decision_path_dense(self, object X)
@@ -98,6 +98,6 @@ cdef class TreeBuilder:
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cdef SIZE_t max_depth # Maximal tree depth
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cdef double min_impurity_decrease # Impurity threshold for early stopping
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cpdef build(self, Tree tree, object X, np.ndarray y,
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np.ndarray sample_weight=*)
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cdef _check_input(self, object X, np.ndarray y, np.ndarray sample_weight)
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cpdef build(self, Tree tree, object X, cnp.ndarray y,
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cnp.ndarray sample_weight=*)
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cdef _check_input(self, object X, cnp.ndarray y, cnp.ndarray sample_weight)

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