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import numpy as np
from scipy import sparse
from ..utils._cython_blas cimport _dot
from ..utils._typedefs cimport float64_t, int32_t, intp_t
cdef extern from *:
ctypedef char* const_char_p "const char*"
################################################################################
# Includes
cdef extern from "_svm_cython_blas_helpers.h":
ctypedef double (*dot_func)(int, const double*, int, const double*, int)
cdef struct BlasFunctions:
dot_func dot
cdef extern from "svm.h":
cdef struct svm_csr_node
cdef struct svm_csr_model
cdef struct svm_parameter
cdef struct svm_csr_problem
char *svm_csr_check_parameter(svm_csr_problem *, svm_parameter *)
svm_csr_model *svm_csr_train(svm_csr_problem *, svm_parameter *, int *, BlasFunctions *) nogil
void svm_csr_free_and_destroy_model(svm_csr_model** model_ptr_ptr)
cdef extern from "libsvm_sparse_helper.c":
# this file contains methods for accessing libsvm 'hidden' fields
svm_csr_problem * csr_set_problem (
char *, intp_t *, char *, intp_t *, char *, char *, char *, int)
svm_csr_model *csr_set_model(svm_parameter *param, int nr_class,
char *SV_data, intp_t *SV_indices_dims,
char *SV_indices, intp_t *SV_intptr_dims,
char *SV_intptr,
char *sv_coef, char *rho, char *nSV,
char *probA, char *probB)
svm_parameter *set_parameter (int , int , int , double, double ,
double , double , double , double,
double, int, int, int, char *, char *, int,
int)
void copy_sv_coef (char *, svm_csr_model *)
void copy_n_iter (char *, svm_csr_model *)
void copy_support (char *, svm_csr_model *)
void copy_intercept (char *, svm_csr_model *, intp_t *)
int copy_predict (char *, svm_csr_model *, intp_t *, char *, BlasFunctions *)
int csr_copy_predict_values (intp_t *data_size, char *data, intp_t *index_size,
char *index, intp_t *intptr_size, char *size,
svm_csr_model *model, char *dec_values, int nr_class, BlasFunctions *)
int csr_copy_predict (intp_t *data_size, char *data, intp_t *index_size,
char *index, intp_t *intptr_size, char *size,
svm_csr_model *model, char *dec_values, BlasFunctions *) nogil
int csr_copy_predict_proba (intp_t *data_size, char *data, intp_t *index_size,
char *index, intp_t *intptr_size, char *size,
svm_csr_model *model, char *dec_values, BlasFunctions *) nogil
int copy_predict_values(char *, svm_csr_model *, intp_t *, char *, int, BlasFunctions *)
int csr_copy_SV (char *values, intp_t *n_indices,
char *indices, intp_t *n_indptr, char *indptr,
svm_csr_model *model, int n_features)
intp_t get_nonzero_SV (svm_csr_model *)
void copy_nSV (char *, svm_csr_model *)
void copy_probA (char *, svm_csr_model *, intp_t *)
void copy_probB (char *, svm_csr_model *, intp_t *)
intp_t get_l (svm_csr_model *)
intp_t get_nr (svm_csr_model *)
int free_problem (svm_csr_problem *)
int free_model (svm_csr_model *)
int free_param (svm_parameter *)
int free_model_SV(svm_csr_model *model)
void set_verbosity(int)
def libsvm_sparse_train (int n_features,
const float64_t[::1] values,
const int32_t[::1] indices,
const int32_t[::1] indptr,
const float64_t[::1] Y,
int svm_type, int kernel_type, int degree, double gamma,
double coef0, double eps, double C,
const float64_t[::1] class_weight,
const float64_t[::1] sample_weight,
double nu, double cache_size, double p, int
shrinking, int probability, int max_iter,
int random_seed):
"""
Wrap svm_train from libsvm using a scipy.sparse.csr matrix
Work in progress.
Parameters
----------
n_features : number of features.
XXX: can we retrieve this from any other parameter ?
X : array-like, dtype=float, size=[N, D]
Y : array, dtype=float, size=[N]
target vector
...
Notes
-------------------
See sklearn.svm.predict for a complete list of parameters.
"""
cdef svm_parameter *param
cdef svm_csr_problem *problem
cdef svm_csr_model *model
cdef const_char_p error_msg
if len(sample_weight) == 0:
sample_weight = np.ones(Y.shape[0], dtype=np.float64)
else:
assert sample_weight.shape[0] == indptr.shape[0] - 1, \
"sample_weight and X have incompatible shapes: " + \
"sample_weight has %s samples while X has %s" % \
(sample_weight.shape[0], indptr.shape[0] - 1)
# we should never end up here with a precomputed kernel matrix,
# as this is always dense.
assert(kernel_type != 4)
# set libsvm problem
problem = csr_set_problem(
<char *> &values[0],
<intp_t *> indices.shape,
<char *> &indices[0],
<intp_t *> indptr.shape,
<char *> &indptr[0],
<char *> &Y[0],
<char *> &sample_weight[0],
kernel_type,
)
cdef int32_t[::1] \
class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
# set parameters
param = set_parameter(
svm_type,
kernel_type,
degree,
gamma,
coef0,
nu,
cache_size,
C,
eps,
p,
shrinking,
probability,
<int> 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,
)
# check parameters
if (param == NULL or problem == NULL):
raise MemoryError("Seems we've run out of memory")
error_msg = svm_csr_check_parameter(problem, param)
if error_msg:
free_problem(problem)
free_param(param)
raise ValueError(error_msg)
cdef BlasFunctions blas_functions
blas_functions.dot = _dot[double]
# call svm_train, this does the real work
cdef int fit_status = 0
with nogil:
model = svm_csr_train(problem, param, &fit_status, &blas_functions)
cdef intp_t SV_len = get_l(model)
cdef intp_t n_class = get_nr(model)
cdef int[::1] n_iter
n_iter = np.empty(max(1, n_class * (n_class - 1) // 2), dtype=np.intc)
copy_n_iter(<char *> &n_iter[0], model)
# copy model.sv_coef
# we create a new array instead of resizing, otherwise
# it would not erase previous information
cdef float64_t[::1] sv_coef_data
sv_coef_data = np.empty((n_class-1)*SV_len, dtype=np.float64)
copy_sv_coef (<char *> &sv_coef_data[0] if sv_coef_data.size > 0 else NULL, model)
cdef int32_t[::1] support
support = np.empty(SV_len, dtype=np.int32)
copy_support(<char *> &support[0] if support.size > 0 else NULL, model)
# copy model.rho into the intercept
# the intercept is just model.rho but with sign changed
cdef float64_t[::1]intercept
intercept = np.empty(n_class*(n_class-1)//2, dtype=np.float64)
copy_intercept (<char *> &intercept[0], model, <intp_t *> intercept.shape)
# copy model.SV
# we erase any previous information in SV
# TODO: custom kernel
cdef intp_t nonzero_SV
nonzero_SV = get_nonzero_SV (model)
cdef float64_t[::1] SV_data
cdef int32_t[::1] SV_indices, SV_indptr
SV_data = np.empty(nonzero_SV, dtype=np.float64)
SV_indices = np.empty(nonzero_SV, dtype=np.int32)
SV_indptr = np.empty(<intp_t>SV_len + 1, dtype=np.int32)
csr_copy_SV(
<char *> &SV_data[0] if SV_data.size > 0 else NULL,
<intp_t *> SV_indices.shape,
<char *> &SV_indices[0] if SV_indices.size > 0 else NULL,
<intp_t *> SV_indptr.shape,
<char *> &SV_indptr[0] if SV_indptr.size > 0 else NULL,
model,
n_features,
)
support_vectors_ = sparse.csr_matrix(
(SV_data, SV_indices, SV_indptr), (SV_len, n_features)
)
# copy model.nSV
# TODO: do only in classification
cdef int32_t[::1]n_class_SV
n_class_SV = np.empty(n_class, dtype=np.int32)
copy_nSV(<char *> &n_class_SV[0], model)
# # copy probabilities
cdef float64_t[::1] probA, probB
if probability != 0:
if svm_type < 2: # SVC and NuSVC
probA = np.empty(n_class*(n_class-1)//2, dtype=np.float64)
probB = np.empty(n_class*(n_class-1)//2, dtype=np.float64)
copy_probB(<char *> &probB[0], model, <intp_t *> probB.shape)
else:
probA = np.empty(1, dtype=np.float64)
probB = np.empty(0, dtype=np.float64)
copy_probA(<char *> &probA[0], model, <intp_t *> probA.shape)
else:
probA = np.empty(0, dtype=np.float64)
probB = np.empty(0, dtype=np.float64)
svm_csr_free_and_destroy_model (&model)
free_problem(problem)
free_param(param)
return (
support.base,
support_vectors_,
sv_coef_data.base,
intercept.base,
n_class_SV.base,
probA.base,
probB.base,
fit_status,
n_iter.base,
)
def libsvm_sparse_predict (const float64_t[::1] T_data,
const int32_t[::1] T_indices,
const int32_t[::1] T_indptr,
const float64_t[::1] SV_data,
const int32_t[::1] SV_indices,
const int32_t[::1] SV_indptr,
const float64_t[::1] sv_coef,
const float64_t[::1]
intercept, int svm_type, int kernel_type, int
degree, double gamma, double coef0, double
eps, double C,
const float64_t[:] class_weight,
double nu, double p, int
shrinking, int probability,
const int32_t[::1] nSV,
const float64_t[::1] probA,
const float64_t[::1] probB):
"""
Predict values T given a model.
For speed, all real work is done at the C level in function
copy_predict (libsvm_helper.c).
We have to reconstruct model and parameters to make sure we stay
in sync with the python object.
See sklearn.svm.predict for a complete list of parameters.
Parameters
----------
X : array-like, dtype=float
Y : array
target vector
Returns
-------
dec_values : array
predicted values.
"""
cdef float64_t[::1] dec_values
cdef svm_parameter *param
cdef svm_csr_model *model
cdef int32_t[::1] \
class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
cdef int rv
param = set_parameter(
svm_type,
kernel_type,
degree,
gamma,
coef0,
nu,
100.0, # cache size has no effect on predict
C,
eps,
p,
shrinking,
probability,
<int> 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,
-1,
-1, # random seed has no effect on predict either
)
model = csr_set_model(
param, <int> nSV.shape[0],
<char *> &SV_data[0] if SV_data.size > 0 else NULL,
<intp_t *>SV_indices.shape,
<char *> &SV_indices[0] if SV_indices.size > 0 else NULL,
<intp_t *> SV_indptr.shape,
<char *> &SV_indptr[0] if SV_indptr.size > 0 else NULL,
<char *> &sv_coef[0] if sv_coef.size > 0 else NULL,
<char *> &intercept[0],
<char *> &nSV[0],
<char *> &probA[0] if probA.size > 0 else NULL,
<char *> &probB[0] if probB.size > 0 else NULL,
)
# TODO: use check_model
dec_values = np.empty(T_indptr.shape[0]-1)
cdef BlasFunctions blas_functions
blas_functions.dot = _dot[double]
with nogil:
rv = csr_copy_predict(
<intp_t *> T_data.shape,
<char *> &T_data[0],
<intp_t *> T_indices.shape,
<char *> &T_indices[0],
<intp_t *> T_indptr.shape,
<char *> &T_indptr[0],
model,
<char *> &dec_values[0],
&blas_functions,
)
if rv < 0:
raise MemoryError("We've run out of memory")
# free model and param
free_model_SV(model)
free_model(model)
free_param(param)
return dec_values.base
def libsvm_sparse_predict_proba(
const float64_t[::1] T_data,
const int32_t[::1] T_indices,
const int32_t[::1] T_indptr,
const float64_t[::1] SV_data,
const int32_t[::1] SV_indices,
const int32_t[::1] SV_indptr,
const float64_t[::1] sv_coef,
const float64_t[::1]
intercept, int svm_type, int kernel_type, int
degree, double gamma, double coef0, double
eps, double C,
const float64_t[:] class_weight,
double nu, double p, int shrinking, int probability,
const int32_t[::1] nSV,
const float64_t[::1] probA,
const float64_t[::1] probB,
):
"""
Predict values T given a model.
"""
cdef float64_t[:, ::1] dec_values
cdef svm_parameter *param
cdef svm_csr_model *model
cdef int32_t[::1] \
class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
param = set_parameter(
svm_type,
kernel_type,
degree,
gamma,
coef0,
nu,
100.0, # cache size has no effect on predict
C,
eps,
p,
shrinking,
probability,
<int> 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,
-1,
-1, # random seed has no effect on predict either
)
model = csr_set_model(
param,
<int> nSV.shape[0],
<char *> &SV_data[0] if SV_data.size > 0 else NULL,
<intp_t *> SV_indices.shape,
<char *> &SV_indices[0] if SV_indices.size > 0 else NULL,
<intp_t *> SV_indptr.shape,
<char *> &SV_indptr[0] if SV_indptr.size > 0 else NULL,
<char *> &sv_coef[0] if sv_coef.size > 0 else NULL,
<char *> &intercept[0],
<char *> &nSV[0],
<char *> &probA[0] if probA.size > 0 else NULL,
<char *> &probB[0] if probB.size > 0 else NULL,
)
# TODO: use check_model
cdef intp_t n_class = get_nr(model)
cdef int rv
dec_values = np.empty((T_indptr.shape[0]-1, n_class), dtype=np.float64)
cdef BlasFunctions blas_functions
blas_functions.dot = _dot[double]
with nogil:
rv = csr_copy_predict_proba(
<intp_t *> T_data.shape,
<char *> &T_data[0],
<intp_t *> T_indices.shape,
<char *> &T_indices[0],
<intp_t *> T_indptr.shape,
<char *> &T_indptr[0],
model,
<char *> &dec_values[0, 0],
&blas_functions,
)
if rv < 0:
raise MemoryError("We've run out of memory")
# free model and param
free_model_SV(model)
free_model(model)
free_param(param)
return dec_values.base
def libsvm_sparse_decision_function(
const float64_t[::1] T_data,
const int32_t[::1] T_indices,
const int32_t[::1] T_indptr,
const float64_t[::1] SV_data,
const int32_t[::1] SV_indices,
const int32_t[::1] SV_indptr,
const float64_t[::1] sv_coef,
const float64_t[::1]
intercept, int svm_type, int kernel_type, int
degree, double gamma, double coef0, double
eps, double C,
const float64_t[:] class_weight,
double nu, double p, int shrinking, int probability,
const int32_t[::1] nSV,
const float64_t[::1] probA,
const float64_t[::1] probB,
):
"""
Predict margin (libsvm name for this is predict_values)
We have to reconstruct model and parameters to make sure we stay
in sync with the python object.
"""
cdef float64_t[:, ::1] dec_values
cdef svm_parameter *param
cdef intp_t n_class
cdef svm_csr_model *model
cdef int32_t[::1] \
class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
param = set_parameter(
svm_type,
kernel_type,
degree,
gamma,
coef0,
nu,
100.0, # cache size has no effect on predict
C,
eps,
p,
shrinking,
probability,
<int> 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,
-1,
-1,
)
model = csr_set_model(
param,
<int> nSV.shape[0],
<char *> &SV_data[0] if SV_data.size > 0 else NULL,
<intp_t *> SV_indices.shape,
<char *> &SV_indices[0] if SV_indices.size > 0 else NULL,
<intp_t *> SV_indptr.shape,
<char *> &SV_indptr[0] if SV_indptr.size > 0 else NULL,
<char *> &sv_coef[0] if sv_coef.size > 0 else NULL,
<char *> &intercept[0],
<char *> &nSV[0],
<char *> &probA[0] if probA.size > 0 else NULL,
<char *> &probB[0] if probB.size > 0 else NULL,
)
if svm_type > 1:
n_class = 1
else:
n_class = get_nr(model)
n_class = n_class * (n_class - 1) // 2
dec_values = np.empty((T_indptr.shape[0] - 1, n_class), dtype=np.float64)
cdef BlasFunctions blas_functions
blas_functions.dot = _dot[double]
if csr_copy_predict_values(
<intp_t *> T_data.shape,
<char *> &T_data[0],
<intp_t *> T_indices.shape,
<char *> &T_indices[0],
<intp_t *> T_indptr.shape,
<char *> &T_indptr[0],
model,
<char *> &dec_values[0, 0],
n_class,
&blas_functions,
) < 0:
raise MemoryError("We've run out of memory")
# free model and param
free_model_SV(model)
free_model(model)
free_param(param)
return dec_values.base
def set_verbosity_wrap(int verbosity):
"""
Control verbosity of libsvm library
"""
set_verbosity(verbosity)
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