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"""
Wrapper for liblinear
Author: fabian.pedregosa@inria.fr
"""
import numpy as np
cimport numpy as cnp
from ..utils._cython_blas cimport _dot, _axpy, _scal, _nrm2
include "_liblinear.pxi"
cnp.import_array()
def train_wrap(
object X,
const cnp.float64_t[::1] Y,
bint is_sparse,
int solver_type,
double eps,
double bias,
double C,
const cnp.float64_t[:] class_weight,
int max_iter,
unsigned random_seed,
double epsilon,
const cnp.float64_t[::1] sample_weight
):
cdef parameter *param
cdef problem *problem
cdef model *model
cdef char_const_ptr error_msg
cdef int len_w
cdef bint X_has_type_float64 = X.dtype == np.float64
cdef char * X_data_bytes_ptr
cdef const cnp.float64_t[::1] X_data_64
cdef const cnp.float32_t[::1] X_data_32
cdef const cnp.int32_t[::1] X_indices
cdef const cnp.int32_t[::1] X_indptr
if is_sparse:
X_indices = X.indices
X_indptr = X.indptr
if X_has_type_float64:
X_data_64 = X.data
X_data_bytes_ptr = <char *> &X_data_64[0]
else:
X_data_32 = X.data
X_data_bytes_ptr = <char *> &X_data_32[0]
problem = csr_set_problem(
X_data_bytes_ptr,
X_has_type_float64,
<char *> &X_indices[0],
<char *> &X_indptr[0],
(<cnp.int32_t>X.shape[0]),
(<cnp.int32_t>X.shape[1]),
(<cnp.int32_t>X.nnz),
bias,
<char *> &sample_weight[0],
<char *> &Y[0]
)
else:
X_as_1d_array = X.reshape(-1)
if X_has_type_float64:
X_data_64 = X_as_1d_array
X_data_bytes_ptr = <char *> &X_data_64[0]
else:
X_data_32 = X_as_1d_array
X_data_bytes_ptr = <char *> &X_data_32[0]
problem = set_problem(
X_data_bytes_ptr,
X_has_type_float64,
(<cnp.int32_t>X.shape[0]),
(<cnp.int32_t>X.shape[1]),
(<cnp.int32_t>np.count_nonzero(X)),
bias,
<char *> &sample_weight[0],
<char *> &Y[0]
)
cdef cnp.int32_t[::1] class_weight_label = np.arange(class_weight.shape[0], dtype=np.intc)
param = set_parameter(
solver_type,
eps,
C,
class_weight.shape[0],
<char *> &class_weight_label[0] if class_weight_label.size > 0 else NULL,
<char *> &class_weight[0] if class_weight.size > 0 else NULL,
max_iter,
random_seed,
epsilon
)
error_msg = check_parameter(problem, param)
if error_msg:
free_problem(problem)
free_parameter(param)
raise ValueError(error_msg)
cdef BlasFunctions blas_functions
blas_functions.dot = _dot[double]
blas_functions.axpy = _axpy[double]
blas_functions.scal = _scal[double]
blas_functions.nrm2 = _nrm2[double]
# early return
with nogil:
model = train(problem, param, &blas_functions)
# FREE
free_problem(problem)
free_parameter(param)
# destroy_param(param) don't call this or it will destroy class_weight_label and class_weight
# coef matrix holder created as fortran since that's what's used in liblinear
cdef cnp.float64_t[::1, :] w
cdef int nr_class = get_nr_class(model)
cdef int labels_ = nr_class
if nr_class == 2:
labels_ = 1
cdef cnp.int32_t[::1] n_iter = np.zeros(labels_, dtype=np.intc)
get_n_iter(model, <int *> &n_iter[0])
cdef int nr_feature = get_nr_feature(model)
if bias > 0:
nr_feature = nr_feature + 1
if nr_class == 2 and solver_type != 4: # solver is not Crammer-Singer
w = np.empty((1, nr_feature), order='F')
copy_w(&w[0, 0], model, nr_feature)
else:
len_w = (nr_class) * nr_feature
w = np.empty((nr_class, nr_feature), order='F')
copy_w(&w[0, 0], model, len_w)
free_and_destroy_model(&model)
return w.base, n_iter.base
def set_verbosity_wrap(int verbosity):
"""
Control verbosity of libsvm library
"""
set_verbosity(verbosity)
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