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