You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
442 lines
14 KiB
Python
442 lines
14 KiB
Python
5 years ago
|
#!/usr/bin/env python
|
||
|
|
|
||
|
|
from __future__ import unicode_literals
|
||
|
|
from __future__ import absolute_import
|
||
|
|
from __future__ import division
|
||
|
|
|
||
|
|
from builtins import str, bytes, int
|
||
|
|
from builtins import object, range
|
||
|
|
from builtins import map, zip, filter
|
||
|
|
|
||
|
|
from ctypes import *
|
||
|
|
from ctypes.util import find_library
|
||
|
|
from os import path
|
||
|
|
import sys
|
||
|
|
|
||
|
|
try:
|
||
|
|
import scipy
|
||
|
|
from scipy import sparse
|
||
|
|
except:
|
||
|
|
scipy = None
|
||
|
|
sparse = None
|
||
|
|
|
||
|
|
__all__ = ['liblinear', 'feature_node', 'gen_feature_nodearray', 'problem',
|
||
|
|
'parameter', 'model', 'toPyModel', 'L2R_LR', 'L2R_L2LOSS_SVC_DUAL',
|
||
|
|
'L2R_L2LOSS_SVC', 'L2R_L1LOSS_SVC_DUAL', 'MCSVM_CS',
|
||
|
|
'L1R_L2LOSS_SVC', 'L1R_LR', 'L2R_LR_DUAL', 'L2R_L2LOSS_SVR',
|
||
|
|
'L2R_L2LOSS_SVR_DUAL', 'L2R_L1LOSS_SVR_DUAL', 'print_null']
|
||
|
|
|
||
|
|
try:
|
||
|
|
dirname = path.dirname(path.abspath(__file__))
|
||
|
|
if sys.platform == 'win32':
|
||
|
|
liblinear = CDLL(path.join(dirname, 'windows\liblinear-2.20\liblinear.dll'))
|
||
|
|
else:
|
||
|
|
liblinear = CDLL(path.join(dirname, 'macos/liblinear-2.20/liblinear.so.3'))
|
||
|
|
except:
|
||
|
|
# For unix the prefix 'lib' is not considered.
|
||
|
|
if find_library('linear'):
|
||
|
|
liblinear = CDLL(find_library('linear'))
|
||
|
|
elif find_library('liblinear'):
|
||
|
|
liblinear = CDLL(find_library('liblinear'))
|
||
|
|
else:
|
||
|
|
libsvm = CDLL(path.join(path.dirname(__file__), 'ubuntu/liblinear-2.20/liblinear.so.3'))
|
||
|
|
|
||
|
|
L2R_LR = 0
|
||
|
|
L2R_L2LOSS_SVC_DUAL = 1
|
||
|
|
L2R_L2LOSS_SVC = 2
|
||
|
|
L2R_L1LOSS_SVC_DUAL = 3
|
||
|
|
MCSVM_CS = 4
|
||
|
|
L1R_L2LOSS_SVC = 5
|
||
|
|
L1R_LR = 6
|
||
|
|
L2R_LR_DUAL = 7
|
||
|
|
L2R_L2LOSS_SVR = 11
|
||
|
|
L2R_L2LOSS_SVR_DUAL = 12
|
||
|
|
L2R_L1LOSS_SVR_DUAL = 13
|
||
|
|
|
||
|
|
PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p)
|
||
|
|
|
||
|
|
|
||
|
|
def print_null(s):
|
||
|
|
return
|
||
|
|
|
||
|
|
|
||
|
|
def genFields(names, types):
|
||
|
|
return list(zip(names, types))
|
||
|
|
|
||
|
|
|
||
|
|
def fillprototype(f, restype, argtypes):
|
||
|
|
f.restype = restype
|
||
|
|
f.argtypes = argtypes
|
||
|
|
|
||
|
|
|
||
|
|
class feature_node(Structure):
|
||
|
|
_names = ["index", "value"]
|
||
|
|
_types = [c_int, c_double]
|
||
|
|
_fields_ = genFields(_names, _types)
|
||
|
|
|
||
|
|
def __str__(self):
|
||
|
|
return '%d:%g' % (self.index, self.value)
|
||
|
|
|
||
|
|
|
||
|
|
def gen_feature_nodearray(xi, feature_max=None):
|
||
|
|
if feature_max:
|
||
|
|
assert(isinstance(feature_max, int))
|
||
|
|
|
||
|
|
xi_shift = 0 # ensure correct indices of xi
|
||
|
|
if scipy and isinstance(xi, tuple) and len(xi) == 2\
|
||
|
|
and isinstance(xi[0], scipy.ndarray) and isinstance(xi[1], scipy.ndarray): # for a sparse vector
|
||
|
|
index_range = xi[0] + 1 # index starts from 1
|
||
|
|
if feature_max:
|
||
|
|
index_range = index_range[scipy.where(index_range <= feature_max)]
|
||
|
|
elif scipy and isinstance(xi, scipy.ndarray):
|
||
|
|
xi_shift = 1
|
||
|
|
index_range = xi.nonzero()[0] + 1 # index starts from 1
|
||
|
|
if feature_max:
|
||
|
|
index_range = index_range[scipy.where(index_range <= feature_max)]
|
||
|
|
elif isinstance(xi, (dict, list, tuple)):
|
||
|
|
if isinstance(xi, dict):
|
||
|
|
index_range = xi.keys()
|
||
|
|
elif isinstance(xi, (list, tuple)):
|
||
|
|
xi_shift = 1
|
||
|
|
index_range = range(1, len(xi) + 1)
|
||
|
|
index_range = list(filter(lambda j: xi[j - xi_shift] != 0, index_range))
|
||
|
|
|
||
|
|
if feature_max:
|
||
|
|
index_range = list(filter(lambda j: j <= feature_max, index_range))
|
||
|
|
index_range = sorted(index_range)
|
||
|
|
else:
|
||
|
|
raise TypeError('xi should be a dictionary, list, tuple, 1-d numpy array, or tuple of (index, data)')
|
||
|
|
|
||
|
|
ret = (feature_node * (len(index_range) + 2))()
|
||
|
|
ret[-1].index = -1 # for bias term
|
||
|
|
ret[-2].index = -1
|
||
|
|
|
||
|
|
if scipy and isinstance(xi, tuple) and len(xi) == 2\
|
||
|
|
and isinstance(xi[0], scipy.ndarray) and isinstance(xi[1], scipy.ndarray): # for a sparse vector
|
||
|
|
for idx, j in enumerate(index_range):
|
||
|
|
ret[idx].index = j
|
||
|
|
ret[idx].value = (xi[1])[idx]
|
||
|
|
else:
|
||
|
|
for idx, j in enumerate(index_range):
|
||
|
|
ret[idx].index = j
|
||
|
|
ret[idx].value = xi[j - xi_shift]
|
||
|
|
|
||
|
|
max_idx = 0
|
||
|
|
if len(index_range) > 0:
|
||
|
|
max_idx = index_range[-1]
|
||
|
|
return ret, max_idx
|
||
|
|
|
||
|
|
try:
|
||
|
|
from numba import jit
|
||
|
|
jit_enabled = True
|
||
|
|
except:
|
||
|
|
jit = lambda x: x
|
||
|
|
jit_enabled = False
|
||
|
|
|
||
|
|
|
||
|
|
@jit
|
||
|
|
def csr_to_problem_jit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr):
|
||
|
|
for i in range(l):
|
||
|
|
b1,e1 = x_rowptr[i], x_rowptr[i + 1]
|
||
|
|
b2,e2 = prob_rowptr[i], prob_rowptr[i + 1] - 2
|
||
|
|
for j in range(b1,e1):
|
||
|
|
prob_ind[j - b1 + b2] = x_ind[j] + 1
|
||
|
|
prob_val[j - b1 + b2] = x_val[j]
|
||
|
|
|
||
|
|
|
||
|
|
def csr_to_problem_nojit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr):
|
||
|
|
for i in range(l):
|
||
|
|
x_slice = slice(x_rowptr[i], x_rowptr[i + 1])
|
||
|
|
prob_slice = slice(prob_rowptr[i], prob_rowptr[i + 1] - 2)
|
||
|
|
prob_ind[prob_slice] = x_ind[x_slice] + 1
|
||
|
|
prob_val[prob_slice] = x_val[x_slice]
|
||
|
|
|
||
|
|
|
||
|
|
def csr_to_problem(x, prob):
|
||
|
|
# Extra space for termination node and (possibly) bias term
|
||
|
|
x_space = prob.x_space = scipy.empty((x.nnz + x.shape[0] * 2), dtype=feature_node)
|
||
|
|
prob.rowptr = x.indptr.copy()
|
||
|
|
prob.rowptr[1:] += 2 * scipy.arange(1,x.shape[0] + 1)
|
||
|
|
prob_ind = x_space["index"]
|
||
|
|
prob_val = x_space["value"]
|
||
|
|
prob_ind[:] = -1
|
||
|
|
if jit_enabled:
|
||
|
|
csr_to_problem_jit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr)
|
||
|
|
else:
|
||
|
|
csr_to_problem_nojit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr)
|
||
|
|
|
||
|
|
|
||
|
|
class problem(Structure):
|
||
|
|
_names = ["l", "n", "y", "x", "bias"]
|
||
|
|
_types = [c_int, c_int, POINTER(c_double), POINTER(POINTER(feature_node)), c_double]
|
||
|
|
_fields_ = genFields(_names, _types)
|
||
|
|
|
||
|
|
def __init__(self, y, x, bias = -1):
|
||
|
|
if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, scipy.ndarray))):
|
||
|
|
raise TypeError("type of y: {0} is not supported!".format(type(y)))
|
||
|
|
|
||
|
|
if isinstance(x, (list, tuple)):
|
||
|
|
if len(y) != len(x):
|
||
|
|
raise ValueError("len(y) != len(x)")
|
||
|
|
elif scipy != None and isinstance(x, (scipy.ndarray, sparse.spmatrix)):
|
||
|
|
if len(y) != x.shape[0]:
|
||
|
|
raise ValueError("len(y) != len(x)")
|
||
|
|
if isinstance(x, scipy.ndarray):
|
||
|
|
x = scipy.ascontiguousarray(x) # enforce row-major
|
||
|
|
if isinstance(x, sparse.spmatrix):
|
||
|
|
x = x.tocsr()
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
raise TypeError("type of x: {0} is not supported!".format(type(x)))
|
||
|
|
self.l = l = len(y)
|
||
|
|
self.bias = -1
|
||
|
|
|
||
|
|
max_idx = 0
|
||
|
|
x_space = self.x_space = []
|
||
|
|
if scipy != None and isinstance(x, sparse.csr_matrix):
|
||
|
|
csr_to_problem(x, self)
|
||
|
|
max_idx = x.shape[1]
|
||
|
|
else:
|
||
|
|
for i, xi in enumerate(x):
|
||
|
|
tmp_xi, tmp_idx = gen_feature_nodearray(xi)
|
||
|
|
x_space += [tmp_xi]
|
||
|
|
max_idx = max(max_idx, tmp_idx)
|
||
|
|
self.n = max_idx
|
||
|
|
|
||
|
|
self.y = (c_double * l)()
|
||
|
|
if scipy != None and isinstance(y, scipy.ndarray):
|
||
|
|
scipy.ctypeslib.as_array(self.y, (self.l,))[:] = y
|
||
|
|
else:
|
||
|
|
for i, yi in enumerate(y):
|
||
|
|
self.y[i] = yi
|
||
|
|
|
||
|
|
self.x = (POINTER(feature_node) * l)()
|
||
|
|
if scipy != None and isinstance(x, sparse.csr_matrix):
|
||
|
|
base = addressof(self.x_space.ctypes.data_as(POINTER(feature_node))[0])
|
||
|
|
x_ptr = cast(self.x, POINTER(c_uint64))
|
||
|
|
x_ptr = scipy.ctypeslib.as_array(x_ptr,(self.l,))
|
||
|
|
x_ptr[:] = self.rowptr[:-1] * sizeof(feature_node) + base
|
||
|
|
else:
|
||
|
|
for i, xi in enumerate(self.x_space):
|
||
|
|
self.x[i] = xi
|
||
|
|
|
||
|
|
self.set_bias(bias)
|
||
|
|
|
||
|
|
def set_bias(self, bias):
|
||
|
|
if self.bias == bias:
|
||
|
|
return
|
||
|
|
if bias >= 0 and self.bias < 0:
|
||
|
|
self.n += 1
|
||
|
|
node = feature_node(self.n, bias)
|
||
|
|
if bias < 0 and self.bias >= 0:
|
||
|
|
self.n -= 1
|
||
|
|
node = feature_node(-1, bias)
|
||
|
|
|
||
|
|
if isinstance(self.x_space, list):
|
||
|
|
for xi in self.x_space:
|
||
|
|
xi[-2] = node
|
||
|
|
else:
|
||
|
|
self.x_space["index"][self.rowptr[1:] - 2] = node.index
|
||
|
|
self.x_space["value"][self.rowptr[1:] - 2] = node.value
|
||
|
|
|
||
|
|
self.bias = bias
|
||
|
|
|
||
|
|
|
||
|
|
class parameter(Structure):
|
||
|
|
_names = ["solver_type", "eps", "C", "nr_weight", "weight_label", "weight", "p", "init_sol"]
|
||
|
|
_types = [c_int, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double), c_double, POINTER(c_double)]
|
||
|
|
_fields_ = genFields(_names, _types)
|
||
|
|
|
||
|
|
def __init__(self, options = None):
|
||
|
|
if options == None:
|
||
|
|
options = ''
|
||
|
|
self.parse_options(options)
|
||
|
|
|
||
|
|
def __str__(self):
|
||
|
|
s = ''
|
||
|
|
attrs = parameter._names + list(self.__dict__.keys())
|
||
|
|
values = list(map(lambda attr: getattr(self, attr), attrs))
|
||
|
|
for attr, val in zip(attrs, values):
|
||
|
|
s += (' %s: %s\n' % (attr, val))
|
||
|
|
s = s.strip()
|
||
|
|
|
||
|
|
return s
|
||
|
|
|
||
|
|
def set_to_default_values(self):
|
||
|
|
self.solver_type = L2R_L2LOSS_SVC_DUAL
|
||
|
|
self.eps = float('inf')
|
||
|
|
self.C = 1
|
||
|
|
self.p = 0.1
|
||
|
|
self.nr_weight = 0
|
||
|
|
self.weight_label = None
|
||
|
|
self.weight = None
|
||
|
|
self.init_sol = None
|
||
|
|
self.bias = -1
|
||
|
|
self.flag_cross_validation = False
|
||
|
|
self.flag_C_specified = False
|
||
|
|
self.flag_solver_specified = False
|
||
|
|
self.flag_find_C = False
|
||
|
|
self.nr_fold = 0
|
||
|
|
self.print_func = cast(None, PRINT_STRING_FUN)
|
||
|
|
|
||
|
|
def parse_options(self, options):
|
||
|
|
if isinstance(options, list):
|
||
|
|
argv = options
|
||
|
|
elif isinstance(options, str):
|
||
|
|
argv = options.split()
|
||
|
|
else:
|
||
|
|
raise TypeError("arg 1 should be a list or a str.")
|
||
|
|
self.set_to_default_values()
|
||
|
|
self.print_func = cast(None, PRINT_STRING_FUN)
|
||
|
|
weight_label = []
|
||
|
|
weight = []
|
||
|
|
|
||
|
|
i = 0
|
||
|
|
while i < len(argv):
|
||
|
|
if argv[i] == "-s":
|
||
|
|
i = i + 1
|
||
|
|
self.solver_type = int(argv[i])
|
||
|
|
self.flag_solver_specified = True
|
||
|
|
elif argv[i] == "-c":
|
||
|
|
i = i + 1
|
||
|
|
self.C = float(argv[i])
|
||
|
|
self.flag_C_specified = True
|
||
|
|
elif argv[i] == "-p":
|
||
|
|
i = i + 1
|
||
|
|
self.p = float(argv[i])
|
||
|
|
elif argv[i] == "-e":
|
||
|
|
i = i + 1
|
||
|
|
self.eps = float(argv[i])
|
||
|
|
elif argv[i] == "-B":
|
||
|
|
i = i + 1
|
||
|
|
self.bias = float(argv[i])
|
||
|
|
elif argv[i] == "-v":
|
||
|
|
i = i + 1
|
||
|
|
self.flag_cross_validation = 1
|
||
|
|
self.nr_fold = int(argv[i])
|
||
|
|
if self.nr_fold < 2:
|
||
|
|
raise ValueError("n-fold cross validation: n must >= 2")
|
||
|
|
elif argv[i].startswith("-w"):
|
||
|
|
i = i + 1
|
||
|
|
self.nr_weight += 1
|
||
|
|
weight_label += [int(argv[i - 1][2:])]
|
||
|
|
weight += [float(argv[i])]
|
||
|
|
elif argv[i] == "-q":
|
||
|
|
self.print_func = PRINT_STRING_FUN(print_null)
|
||
|
|
elif argv[i] == "-C":
|
||
|
|
self.flag_find_C = True
|
||
|
|
|
||
|
|
else:
|
||
|
|
raise ValueError("Wrong options")
|
||
|
|
i += 1
|
||
|
|
|
||
|
|
liblinear.set_print_string_function(self.print_func)
|
||
|
|
self.weight_label = (c_int * self.nr_weight)()
|
||
|
|
self.weight = (c_double * self.nr_weight)()
|
||
|
|
for i in range(self.nr_weight):
|
||
|
|
self.weight[i] = weight[i]
|
||
|
|
self.weight_label[i] = weight_label[i]
|
||
|
|
|
||
|
|
# default solver for parameter selection is L2R_L2LOSS_SVC
|
||
|
|
if self.flag_find_C:
|
||
|
|
if not self.flag_cross_validation:
|
||
|
|
self.nr_fold = 5
|
||
|
|
if not self.flag_solver_specified:
|
||
|
|
self.solver_type = L2R_L2LOSS_SVC
|
||
|
|
self.flag_solver_specified = True
|
||
|
|
elif self.solver_type not in [L2R_LR, L2R_L2LOSS_SVC]:
|
||
|
|
raise ValueError("Warm-start parameter search only available for -s 0 and -s 2")
|
||
|
|
|
||
|
|
if self.eps == float('inf'):
|
||
|
|
if self.solver_type in [L2R_LR, L2R_L2LOSS_SVC]:
|
||
|
|
self.eps = 0.01
|
||
|
|
elif self.solver_type in [L2R_L2LOSS_SVR]:
|
||
|
|
self.eps = 0.001
|
||
|
|
elif self.solver_type in [L2R_L2LOSS_SVC_DUAL, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L2R_LR_DUAL]:
|
||
|
|
self.eps = 0.1
|
||
|
|
elif self.solver_type in [L1R_L2LOSS_SVC, L1R_LR]:
|
||
|
|
self.eps = 0.01
|
||
|
|
elif self.solver_type in [L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL]:
|
||
|
|
self.eps = 0.1
|
||
|
|
|
||
|
|
|
||
|
|
class model(Structure):
|
||
|
|
_names = ["param", "nr_class", "nr_feature", "w", "label", "bias"]
|
||
|
|
_types = [parameter, c_int, c_int, POINTER(c_double), POINTER(c_int), c_double]
|
||
|
|
_fields_ = genFields(_names, _types)
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
self.__createfrom__ = 'python'
|
||
|
|
|
||
|
|
def __del__(self):
|
||
|
|
# free memory created by C to avoid memory leak
|
||
|
|
if hasattr(self, '__createfrom__') and self.__createfrom__ == 'C':
|
||
|
|
liblinear.free_and_destroy_model(pointer(self))
|
||
|
|
|
||
|
|
def get_nr_feature(self):
|
||
|
|
return liblinear.get_nr_feature(self)
|
||
|
|
|
||
|
|
def get_nr_class(self):
|
||
|
|
return liblinear.get_nr_class(self)
|
||
|
|
|
||
|
|
def get_labels(self):
|
||
|
|
nr_class = self.get_nr_class()
|
||
|
|
labels = (c_int * nr_class)()
|
||
|
|
liblinear.get_labels(self, labels)
|
||
|
|
return labels[:nr_class]
|
||
|
|
|
||
|
|
def get_decfun_coef(self, feat_idx, label_idx=0):
|
||
|
|
return liblinear.get_decfun_coef(self, feat_idx, label_idx)
|
||
|
|
|
||
|
|
def get_decfun_bias(self, label_idx=0):
|
||
|
|
return liblinear.get_decfun_bias(self, label_idx)
|
||
|
|
|
||
|
|
def get_decfun(self, label_idx=0):
|
||
|
|
w = [liblinear.get_decfun_coef(self, feat_idx, label_idx) for feat_idx in range(1, self.nr_feature + 1)]
|
||
|
|
b = liblinear.get_decfun_bias(self, label_idx)
|
||
|
|
return (w, b)
|
||
|
|
|
||
|
|
def is_probability_model(self):
|
||
|
|
return (liblinear.check_probability_model(self) == 1)
|
||
|
|
|
||
|
|
def is_regression_model(self):
|
||
|
|
return (liblinear.check_regression_model(self) == 1)
|
||
|
|
|
||
|
|
|
||
|
|
def toPyModel(model_ptr):
|
||
|
|
"""
|
||
|
|
toPyModel(model_ptr) -> model
|
||
|
|
|
||
|
|
Convert a ctypes POINTER(model) to a Python model
|
||
|
|
"""
|
||
|
|
if bool(model_ptr) == False:
|
||
|
|
raise ValueError("Null pointer")
|
||
|
|
m = model_ptr.contents
|
||
|
|
m.__createfrom__ = 'C'
|
||
|
|
return m
|
||
|
|
|
||
|
|
fillprototype(liblinear.train, POINTER(model), [POINTER(problem), POINTER(parameter)])
|
||
|
|
fillprototype(liblinear.find_parameter_C, None, [POINTER(problem), POINTER(parameter), c_int, c_double, c_double, POINTER(c_double), POINTER(c_double)])
|
||
|
|
fillprototype(liblinear.cross_validation, None, [POINTER(problem), POINTER(parameter), c_int, POINTER(c_double)])
|
||
|
|
|
||
|
|
fillprototype(liblinear.predict_values, c_double, [POINTER(model), POINTER(feature_node), POINTER(c_double)])
|
||
|
|
fillprototype(liblinear.predict, c_double, [POINTER(model), POINTER(feature_node)])
|
||
|
|
fillprototype(liblinear.predict_probability, c_double, [POINTER(model), POINTER(feature_node), POINTER(c_double)])
|
||
|
|
|
||
|
|
fillprototype(liblinear.save_model, c_int, [c_char_p, POINTER(model)])
|
||
|
|
fillprototype(liblinear.load_model, POINTER(model), [c_char_p])
|
||
|
|
|
||
|
|
fillprototype(liblinear.get_nr_feature, c_int, [POINTER(model)])
|
||
|
|
fillprototype(liblinear.get_nr_class, c_int, [POINTER(model)])
|
||
|
|
fillprototype(liblinear.get_labels, None, [POINTER(model), POINTER(c_int)])
|
||
|
|
fillprototype(liblinear.get_decfun_coef, c_double, [POINTER(model), c_int, c_int])
|
||
|
|
fillprototype(liblinear.get_decfun_bias, c_double, [POINTER(model), c_int])
|
||
|
|
|
||
|
|
fillprototype(liblinear.free_model_content, None, [POINTER(model)])
|
||
|
|
fillprototype(liblinear.free_and_destroy_model, None, [POINTER(POINTER(model))])
|
||
|
|
fillprototype(liblinear.destroy_param, None, [POINTER(parameter)])
|
||
|
|
fillprototype(liblinear.check_parameter, c_char_p, [POINTER(problem), POINTER(parameter)])
|
||
|
|
fillprototype(liblinear.check_probability_model, c_int, [POINTER(model)])
|
||
|
|
fillprototype(liblinear.check_regression_model, c_int, [POINTER(model)])
|
||
|
|
fillprototype(liblinear.set_print_string_function, None, [CFUNCTYPE(None, c_char_p)])
|