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WIP: enforce contiguous arrays to optimize construction

This commit is contained in:
Gilles Louppe 2013-07-18 13:57:10 +02:00
parent cc3f2cf124
commit bbdccca354
5 changed files with 199 additions and 178 deletions
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6
sklearn/ensemble/forest.py
View File

@ -253,8 +253,10 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble,
# Precompute some data
X, y = check_arrays(X, y, sparse_format="dense")
if (getattr(X, "dtype", None) != DTYPE or X.ndim != 2):
X = array2d(X, dtype=DTYPE)
if ((getattr(X, "dtype", None) != DTYPE) or
(X.ndim != 2) or
(not X.flags.contiguous)):
X = array2d(X, dtype=DTYPE, order="C")
n_samples, self.n_features_ = X.shape

20
sklearn/ensemble/gradient_boosting.py
View File

@ -496,8 +496,7 @@ class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble)):
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features. Use fortran-style
to avoid memory copies.
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values (integers in classification, real numbers in
@ -511,9 +510,14 @@ class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble)):
Returns self.
"""
# Check input
X, y = check_arrays(X, y, sparse_format='dense')
X = np.asfortranarray(X, dtype=DTYPE)
y = np.ravel(y, order='C')
X, y = check_arrays(X, y, sparse_format="dense")
if ((getattr(X, "dtype", None) != DTYPE) or
(X.ndim != 2) or
(not X.flags.contiguous)):
X = array2d(X, dtype=DTYPE, order="C")
y = np.ravel(y, order="C")
# Check parameters
n_samples, n_features = X.shape
@ -816,8 +820,7 @@ class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin):
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features. Use fortran-style
to avoid memory copies.
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values (integers in classification, real numbers in
@ -1048,8 +1051,7 @@ class GradientBoostingRegressor(BaseGradientBoosting, RegressorMixin):
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features. Use fortran-style
to avoid memory copies.
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values (integers in classification, real numbers in

333
sklearn/tree/_tree.c
View File

@ -1,4 +1,4 @@
/* Generated by Cython 0.20dev on Thu Jul 18 08:49:01 2013 */
/* Generated by Cython 0.20dev on Thu Jul 18 10:24:33 2013 */
#define PY_SSIZE_T_CLEAN
#ifndef CYTHON_USE_PYLONG_INTERNALS
@ -1320,11 +1320,12 @@ static void __Pyx_WriteUnraisable(const char *name, int clineno,
static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1)
#define __Pyx_BufPtrCContig2d(type, buf, i0, s0, i1, s1) ((type)((char*)buf + i0 * s0) + i1)
static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); /*proto*/
static void __Pyx_RaiseBufferFallbackError(void); /*proto*/
#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1)
#define __Pyx_BufPtrStrided3d(type, buf, i0, s0, i1, s1, i2, s2) (type)((char*)buf + i0 * s0 + i1 * s1 + i2 * s2)
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/
@ -6985,7 +6986,7 @@ static PyObject *__pyx_pf_7sklearn_4tree_5_tree_8Splitter_6__setstate__(CYTHON_U
/* "sklearn/tree/_tree.pyx":818
* pass
*
* cdef void init(self, np.ndarray[DTYPE_t, ndim=2] X, # <<<<<<<<<<<<<<
* cdef void init(self, np.ndarray[DTYPE_t, ndim=2, mode="c"] X, # <<<<<<<<<<<<<<
* np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
* DOUBLE_t* sample_weight):
*/
@ -7024,7 +7025,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_8Splitter_init(struct __pyx_obj_7sklea
__pyx_pybuffernd_y.rcbuffer = &__pyx_pybuffer_y;
{
__Pyx_BufFmt_StackElem __pyx_stack[1];
if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)__pyx_v_X, &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 2, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 818; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)__pyx_v_X, &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_C_CONTIGUOUS, 2, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 818; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
__pyx_pybuffernd_X.diminfo[0].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_X.diminfo[0].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[0]; __pyx_pybuffernd_X.diminfo[1].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[1]; __pyx_pybuffernd_X.diminfo[1].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[1];
{
@ -7922,7 +7923,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
* cdef SIZE_t* features = self.features
* cdef SIZE_t n_features = self.n_features # <<<<<<<<<<<<<<
*
* cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
* cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.n_features;
__pyx_v_n_features = __pyx_t_3;
@ -7930,7 +7931,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
/* "sklearn/tree/_tree.pyx":907
* cdef SIZE_t n_features = self.n_features
*
* cdef np.ndarray[DTYPE_t, ndim=2] X = self.X # <<<<<<<<<<<<<<
* cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X # <<<<<<<<<<<<<<
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
*/
@ -7938,7 +7939,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
__Pyx_INCREF(__pyx_t_1);
{
__Pyx_BufFmt_StackElem __pyx_stack[1];
if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)((PyArrayObject *)__pyx_t_1), &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 2, 0, __pyx_stack) == -1)) {
if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)((PyArrayObject *)__pyx_t_1), &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_C_CONTIGUOUS, 2, 0, __pyx_stack) == -1)) {
__pyx_v_X = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); __pyx_pybuffernd_X.rcbuffer->pybuffer.buf = NULL;
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 907; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
} else {__pyx_pybuffernd_X.diminfo[0].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_X.diminfo[0].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[0]; __pyx_pybuffernd_X.diminfo[1].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[1]; __pyx_pybuffernd_X.diminfo[1].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[1];
@ -7949,7 +7950,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
/* "sklearn/tree/_tree.pyx":908
*
* cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
* cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
* cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<<
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef unsigned int* random_state = &self.rand_r_state
@ -7958,7 +7959,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
__pyx_v_max_features = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":909
* cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
* cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<<
* cdef unsigned int* random_state = &self.rand_r_state
@ -8127,7 +8128,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
__pyx_t_6 = __pyx_v_current_feature;
__pyx_t_7 = (__pyx_v_samples[__pyx_v_p]);
__pyx_t_8 = __pyx_v_current_feature;
__pyx_t_9 = (((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_5, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_6, __pyx_pybuffernd_X.diminfo[1].strides)) <= ((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_7, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_8, __pyx_pybuffernd_X.diminfo[1].strides)) + 1.e-7)) != 0);
__pyx_t_9 = (((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_5, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_6, __pyx_pybuffernd_X.diminfo[1].strides)) <= ((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_7, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_8, __pyx_pybuffernd_X.diminfo[1].strides)) + 1.e-7)) != 0);
__pyx_t_10 = __pyx_t_9;
} else {
__pyx_t_10 = __pyx_t_4;
@ -8274,7 +8275,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
__pyx_t_12 = __pyx_v_current_feature;
__pyx_t_13 = (__pyx_v_samples[__pyx_v_p]);
__pyx_t_14 = __pyx_v_current_feature;
__pyx_v_current_threshold = (((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_11, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_12, __pyx_pybuffernd_X.diminfo[1].strides)) + (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_14, __pyx_pybuffernd_X.diminfo[1].strides))) / 2.0);
__pyx_v_current_threshold = (((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_11, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_12, __pyx_pybuffernd_X.diminfo[1].strides)) + (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_14, __pyx_pybuffernd_X.diminfo[1].strides))) / 2.0);
/* "sklearn/tree/_tree.pyx":972
*
@ -8285,7 +8286,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
*/
__pyx_t_15 = (__pyx_v_samples[__pyx_v_p]);
__pyx_t_16 = __pyx_v_current_feature;
__pyx_t_9 = ((__pyx_v_current_threshold == (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_15, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_16, __pyx_pybuffernd_X.diminfo[1].strides))) != 0);
__pyx_t_9 = ((__pyx_v_current_threshold == (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_15, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_16, __pyx_pybuffernd_X.diminfo[1].strides))) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":973
@ -8297,7 +8298,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
*/
__pyx_t_17 = (__pyx_v_samples[(__pyx_v_p - 1)]);
__pyx_t_18 = __pyx_v_current_feature;
__pyx_v_current_threshold = (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_17, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_18, __pyx_pybuffernd_X.diminfo[1].strides));
__pyx_v_current_threshold = (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_17, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_18, __pyx_pybuffernd_X.diminfo[1].strides));
goto __pyx_L12;
}
__pyx_L12:;
@ -8432,7 +8433,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
*/
__pyx_t_3 = (__pyx_v_samples[__pyx_v_p]);
__pyx_t_19 = __pyx_v_best_feature;
__pyx_t_9 = (((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_3, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_19, __pyx_pybuffernd_X.diminfo[1].strides)) <= __pyx_v_best_threshold) != 0);
__pyx_t_9 = (((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_3, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_19, __pyx_pybuffernd_X.diminfo[1].strides)) <= __pyx_v_best_threshold) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":994
@ -8512,7 +8513,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
* feature[0] = best_feature
* threshold[0] = best_threshold # <<<<<<<<<<<<<<
*
* cdef void sort(np.ndarray[DTYPE_t, ndim=2] X, SIZE_t current_feature,
* cdef void sort(np.ndarray[DTYPE_t, ndim=2, mode="c"] X, SIZE_t current_feature,
*/
(__pyx_v_threshold[0]) = __pyx_v_best_threshold;
@ -8536,7 +8537,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
/* "sklearn/tree/_tree.pyx":1008
* threshold[0] = best_threshold
*
* cdef void sort(np.ndarray[DTYPE_t, ndim=2] X, SIZE_t current_feature, # <<<<<<<<<<<<<<
* cdef void sort(np.ndarray[DTYPE_t, ndim=2, mode="c"] X, SIZE_t current_feature, # <<<<<<<<<<<<<<
* SIZE_t* samples, SIZE_t length):
* """In-place sorting of samples[start:end] using
*/
@ -8572,7 +8573,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_sort(PyArrayObject *__pyx_v_X, __pyx_t
__pyx_pybuffernd_X.rcbuffer = &__pyx_pybuffer_X;
{
__Pyx_BufFmt_StackElem __pyx_stack[1];
if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)__pyx_v_X, &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 2, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1008; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)__pyx_v_X, &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_C_CONTIGUOUS, 2, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1008; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
__pyx_pybuffernd_X.diminfo[0].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_X.diminfo[0].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[0]; __pyx_pybuffernd_X.diminfo[1].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[1]; __pyx_pybuffernd_X.diminfo[1].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[1];
@ -8695,7 +8696,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_sort(PyArrayObject *__pyx_v_X, __pyx_t
*/
__pyx_t_2 = __pyx_v_tmp;
__pyx_t_3 = __pyx_v_current_feature;
__pyx_v_tmp_value = (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_2, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_3, __pyx_pybuffernd_X.diminfo[1].strides));
__pyx_v_tmp_value = (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_2, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_3, __pyx_pybuffernd_X.diminfo[1].strides));
/* "sklearn/tree/_tree.pyx":1031
*
@ -8747,7 +8748,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_sort(PyArrayObject *__pyx_v_X, __pyx_t
__pyx_t_5 = __pyx_v_current_feature;
__pyx_t_6 = (__pyx_v_samples[__pyx_v_child]);
__pyx_t_7 = __pyx_v_current_feature;
__pyx_t_8 = (((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_4, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_5, __pyx_pybuffernd_X.diminfo[1].strides)) > (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_6, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_7, __pyx_pybuffernd_X.diminfo[1].strides))) != 0);
__pyx_t_8 = (((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_4, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_5, __pyx_pybuffernd_X.diminfo[1].strides)) > (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_6, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_7, __pyx_pybuffernd_X.diminfo[1].strides))) != 0);
__pyx_t_9 = __pyx_t_8;
} else {
__pyx_t_9 = __pyx_t_1;
@ -8775,7 +8776,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_sort(PyArrayObject *__pyx_v_X, __pyx_t
*/
__pyx_t_10 = (__pyx_v_samples[__pyx_v_child]);
__pyx_t_11 = __pyx_v_current_feature;
__pyx_t_9 = (((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_10, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_11, __pyx_pybuffernd_X.diminfo[1].strides)) > __pyx_v_tmp_value) != 0);
__pyx_t_9 = (((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_10, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_11, __pyx_pybuffernd_X.diminfo[1].strides)) > __pyx_v_tmp_value) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1040
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__pyx_t_5 = __pyx_t_4;
}
__pyx_v_is_leaf = __pyx_t_4;
__pyx_v_is_leaf = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":1497
* (n_node_samples < 2 * self.min_samples_leaf))
@ -12590,12 +12605,12 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
* if not is_leaf:
*/
if (!__pyx_v_is_leaf) {
__pyx_t_4 = (__pyx_v_impurity == 0.0);
__pyx_t_6 = __pyx_t_4;
__pyx_t_5 = (__pyx_v_impurity == 0.0);
__pyx_t_4 = __pyx_t_5;
} else {
__pyx_t_6 = __pyx_v_is_leaf;
__pyx_t_4 = __pyx_v_is_leaf;
}
__pyx_v_is_leaf = __pyx_t_6;
__pyx_v_is_leaf = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":1500
* is_leaf = is_leaf or (impurity == 0.0)
@ -12604,8 +12619,8 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
* splitter.node_split(&pos, &feature, &threshold)
* is_leaf = is_leaf or (pos >= end)
*/
__pyx_t_6 = ((!(__pyx_v_is_leaf != 0)) != 0);
if (__pyx_t_6) {
__pyx_t_4 = ((!(__pyx_v_is_leaf != 0)) != 0);
if (__pyx_t_4) {
/* "sklearn/tree/_tree.pyx":1501
*
@ -12624,12 +12639,12 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
* node_id = self._add_node(parent, is_left, is_leaf, feature,
*/
if (!__pyx_v_is_leaf) {
__pyx_t_6 = (__pyx_v_pos >= __pyx_v_end);
__pyx_t_4 = __pyx_t_6;
__pyx_t_4 = (__pyx_v_pos >= __pyx_v_end);
__pyx_t_5 = __pyx_t_4;
} else {
__pyx_t_4 = __pyx_v_is_leaf;
__pyx_t_5 = __pyx_v_is_leaf;
}
__pyx_v_is_leaf = __pyx_t_4;
__pyx_v_is_leaf = __pyx_t_5;
goto __pyx_L10;
}
__pyx_L10:;
@ -12650,8 +12665,8 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
* # Don't store value for internal nodes
* splitter.node_value(self.value + node_id * self.value_stride)
*/
__pyx_t_4 = (__pyx_v_is_leaf != 0);
if (__pyx_t_4) {
__pyx_t_5 = (__pyx_v_is_leaf != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":1509
* if is_leaf:
@ -12672,8 +12687,8 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
* stack_capacity *= 2
* stack = <SIZE_t*> realloc(stack, stack_capacity * sizeof(SIZE_t))
*/
__pyx_t_4 = (((__pyx_v_stack_n_values + 10) > __pyx_v_stack_capacity) != 0);
if (__pyx_t_4) {
__pyx_t_5 = (((__pyx_v_stack_n_values + 10) > __pyx_v_stack_capacity) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":1513
* else:
@ -12833,7 +12848,7 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
__Pyx_XDECREF(__pyx_t_1);
__Pyx_XDECREF(__pyx_t_2);
__Pyx_XDECREF(__pyx_t_3);
__Pyx_XDECREF(__pyx_t_5);
__Pyx_XDECREF(__pyx_t_7);
__Pyx_AddTraceback("sklearn.tree._tree.Tree.build", __pyx_clineno, __pyx_lineno, __pyx_filename);
__pyx_r = 0;
__pyx_L0:;

12
sklearn/tree/_tree.pyx
View File

@ -815,7 +815,7 @@ cdef class Splitter:
def __setstate__(self, d):
pass
cdef void init(self, np.ndarray[DTYPE_t, ndim=2] X,
cdef void init(self, np.ndarray[DTYPE_t, ndim=2, mode="c"] X,
np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
DOUBLE_t* sample_weight):
"""Initialize the splitter."""
@ -904,7 +904,7 @@ cdef class BestSplitter(Splitter):
cdef SIZE_t* features = self.features
cdef SIZE_t n_features = self.n_features
cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
cdef SIZE_t max_features = self.max_features
cdef SIZE_t min_samples_leaf = self.min_samples_leaf
cdef unsigned int* random_state = &self.rand_r_state
@ -1005,7 +1005,7 @@ cdef class BestSplitter(Splitter):
feature[0] = best_feature
threshold[0] = best_threshold
cdef void sort(np.ndarray[DTYPE_t, ndim=2] X, SIZE_t current_feature,
cdef void sort(np.ndarray[DTYPE_t, ndim=2, mode="c"] X, SIZE_t current_feature,
SIZE_t* samples, SIZE_t length):
"""In-place sorting of samples[start:end] using
X[sample[i], current_feature] as key."""
@ -1066,7 +1066,7 @@ cdef class RandomSplitter(Splitter):
cdef SIZE_t* features = self.features
cdef SIZE_t n_features = self.n_features
cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
cdef SIZE_t max_features = self.max_features
cdef SIZE_t min_samples_leaf = self.min_samples_leaf
cdef unsigned int* random_state = &self.rand_r_state
@ -1427,8 +1427,8 @@ cdef class Tree:
np.ndarray sample_weight=None):
"""Build a decision tree from the training set (X, y)."""
# Prepare data before recursive partitioning
if X.dtype != DTYPE:
X = np.asarray(X, dtype=DTYPE)
if X.dtype != DTYPE or not X.flags.contiguous:
X = np.asarray(X, dtype=DTYPE, order="C")
if y.dtype != DOUBLE or not y.flags.contiguous:
y = np.asarray(y, dtype=DOUBLE, order="C")

6
sklearn/tree/tree.py
View File

@ -129,8 +129,10 @@ class BaseDecisionTree(six.with_metaclass(ABCMeta, BaseEstimator,
X, y = check_arrays(X, y)
random_state = check_random_state(self.random_state)
if (getattr(X, "dtype", None) != DTYPE or X.ndim != 2):
X = array2d(X, dtype=DTYPE)
if ((getattr(X, "dtype", None) != DTYPE) or
(X.ndim != 2) or
(not X.flags.contiguous)):
X = array2d(X, dtype=DTYPE, order="C")
n_samples, self.n_features_ = X.shape
is_classification = isinstance(self, ClassifierMixin)