338 lines
10 KiB
Python
338 lines
10 KiB
Python
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#!/usr/bin/env python
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from ctypes import *
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from ctypes.util import find_library
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from os import path
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import sys
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__all__ = ['libsvm', 'svm_problem', 'svm_parameter',
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'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'C_SVC',
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'EPSILON_SVR', 'LINEAR', 'NU_SVC', 'NU_SVR', 'ONE_CLASS',
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'POLY', 'PRECOMPUTED', 'PRINT_STRING_FUN', 'RBF',
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'SIGMOID', 'c_double', 'svm_model']
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try:
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dirname = path.dirname(path.abspath(__file__))
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if sys.platform == 'win32':
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#libsvm = CDLL(path.join(dirname, r'..\windows\libsvm.dll'))
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libsvm = CDLL(path.join(dirname, r'libsvm-weights-3.20\libsvm.dll'))
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else:
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#libsvm = CDLL(path.join(dirname, '../libsvm.so.2'))
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libsvm = CDLL(path.join(dirname, 'libsvm-weights-3.20/libsvm.so.2'))
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except:
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# For unix the prefix 'lib' is not considered.
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if find_library('svm'):
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libsvm = CDLL(find_library('svm'))
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elif find_library('libsvm'):
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libsvm = CDLL(find_library('libsvm'))
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else:
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raise Exception('LIBSVM library not found.')
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C_SVC = 0
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NU_SVC = 1
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ONE_CLASS = 2
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EPSILON_SVR = 3
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NU_SVR = 4
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LINEAR = 0
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POLY = 1
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RBF = 2
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SIGMOID = 3
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PRECOMPUTED = 4
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PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p)
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def print_null(s):
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return
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def genFields(names, types):
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return list(zip(names, types))
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def fillprototype(f, restype, argtypes):
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f.restype = restype
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f.argtypes = argtypes
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class svm_node(Structure):
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_names = ["index", "value"]
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_types = [c_int, c_double]
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_fields_ = genFields(_names, _types)
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def __str__(self):
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return '%d:%g' % (self.index, self.value)
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def gen_svm_nodearray(xi, feature_max=None, isKernel=None):
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if isinstance(xi, dict):
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index_range = xi.keys()
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elif isinstance(xi, (list, tuple)):
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if not isKernel:
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xi = [0] + xi # idx should start from 1
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index_range = range(len(xi))
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else:
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raise TypeError('xi should be a dictionary, list or tuple')
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if feature_max:
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assert(isinstance(feature_max, int))
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index_range = filter(lambda j: j <= feature_max, index_range)
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if not isKernel:
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index_range = filter(lambda j:xi[j] != 0, index_range)
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index_range = sorted(index_range)
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ret = (svm_node * (len(index_range)+1))()
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ret[-1].index = -1
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for idx, j in enumerate(index_range):
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ret[idx].index = j
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ret[idx].value = xi[j]
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max_idx = 0
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if index_range:
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max_idx = index_range[-1]
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return ret, max_idx
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class svm_problem(Structure):
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_names = ["l", "y", "x", "W"]
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_types = [c_int, POINTER(c_double), POINTER(POINTER(svm_node)), POINTER(c_double)]
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_fields_ = genFields(_names, _types)
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def __init__(self, W, y, x, isKernel=None):
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if len(y) != len(x):
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raise ValueError("len(y) != len(x)")
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if len(W) != 0 and len(W) != len(x):
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raise ValueError("len(W) != len(x)")
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self.l = l = len(y)
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if len(W) == 0:
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W = [1] * l
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max_idx = 0
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x_space = self.x_space = []
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for i, xi in enumerate(x):
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tmp_xi, tmp_idx = gen_svm_nodearray(xi,isKernel=isKernel)
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x_space += [tmp_xi]
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max_idx = max(max_idx, tmp_idx)
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self.n = max_idx
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self.W = (c_double * l)()
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for i, Wi in enumerate(W): self.W[i] = Wi
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self.y = (c_double * l)()
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for i, yi in enumerate(y): self.y[i] = yi
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self.x = (POINTER(svm_node) * l)()
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for i, xi in enumerate(self.x_space): self.x[i] = xi
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class svm_parameter(Structure):
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_names = ["svm_type", "kernel_type", "degree", "gamma", "coef0",
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"cache_size", "eps", "C", "nr_weight", "weight_label", "weight",
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"nu", "p", "shrinking", "probability"]
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_types = [c_int, c_int, c_int, c_double, c_double,
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c_double, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double),
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c_double, c_double, c_int, c_int]
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_fields_ = genFields(_names, _types)
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def __init__(self, options = None):
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if options == None:
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options = ''
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self.parse_options(options)
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def __str__(self):
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s = ''
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attrs = svm_parameter._names + list(self.__dict__.keys())
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values = map(lambda attr: getattr(self, attr), attrs)
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for attr, val in zip(attrs, values):
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s += (' %s: %s\n' % (attr, val))
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s = s.strip()
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return s
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def set_to_default_values(self):
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self.svm_type = C_SVC;
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self.kernel_type = RBF
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self.degree = 3
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self.gamma = 0
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self.coef0 = 0
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self.nu = 0.5
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self.cache_size = 100
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self.C = 1
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self.eps = 0.001
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self.p = 0.1
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self.shrinking = 1
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self.probability = 0
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self.nr_weight = 0
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self.weight_label = (c_int*0)()
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self.weight = (c_double*0)()
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self.cross_validation = False
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self.nr_fold = 0
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self.print_func = cast(None, PRINT_STRING_FUN)
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def parse_options(self, options):
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if isinstance(options, list):
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argv = options
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elif isinstance(options, str):
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argv = options.split()
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else:
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raise TypeError("arg 1 should be a list or a str.")
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self.set_to_default_values()
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self.print_func = cast(None, PRINT_STRING_FUN)
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weight_label = []
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weight = []
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i = 0
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while i < len(argv):
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if argv[i] == "-s":
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i = i + 1
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self.svm_type = int(argv[i])
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elif argv[i] == "-t":
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i = i + 1
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self.kernel_type = int(argv[i])
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elif argv[i] == "-d":
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i = i + 1
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self.degree = int(argv[i])
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elif argv[i] == "-g":
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i = i + 1
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self.gamma = float(argv[i])
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elif argv[i] == "-r":
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i = i + 1
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self.coef0 = float(argv[i])
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elif argv[i] == "-n":
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i = i + 1
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self.nu = float(argv[i])
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elif argv[i] == "-m":
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i = i + 1
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self.cache_size = float(argv[i])
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elif argv[i] == "-c":
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i = i + 1
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self.C = float(argv[i])
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elif argv[i] == "-e":
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i = i + 1
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self.eps = float(argv[i])
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elif argv[i] == "-p":
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i = i + 1
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self.p = float(argv[i])
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elif argv[i] == "-h":
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i = i + 1
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self.shrinking = int(argv[i])
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elif argv[i] == "-b":
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i = i + 1
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self.probability = int(argv[i])
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elif argv[i] == "-q":
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self.print_func = PRINT_STRING_FUN(print_null)
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elif argv[i] == "-v":
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i = i + 1
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self.cross_validation = 1
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self.nr_fold = int(argv[i])
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if self.nr_fold < 2:
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raise ValueError("n-fold cross validation: n must >= 2")
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elif argv[i].startswith("-w"):
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i = i + 1
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self.nr_weight += 1
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nr_weight = self.nr_weight
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weight_label += [int(argv[i-1][2:])]
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weight += [float(argv[i])]
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else:
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raise ValueError("Wrong options")
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i += 1
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libsvm.svm_set_print_string_function(self.print_func)
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self.weight_label = (c_int*self.nr_weight)()
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self.weight = (c_double*self.nr_weight)()
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for i in range(self.nr_weight):
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self.weight[i] = weight[i]
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self.weight_label[i] = weight_label[i]
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class svm_model(Structure):
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_names = ['param', 'nr_class', 'l', 'SV', 'sv_coef', 'rho',
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'probA', 'probB', 'sv_indices', 'label', 'nSV', 'free_sv']
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_types = [svm_parameter, c_int, c_int, POINTER(POINTER(svm_node)),
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POINTER(POINTER(c_double)), POINTER(c_double),
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POINTER(c_double), POINTER(c_double), POINTER(c_int),
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POINTER(c_int), POINTER(c_int), c_int]
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_fields_ = genFields(_names, _types)
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def __init__(self):
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self.__createfrom__ = 'python'
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def __del__(self):
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# free memory created by C to avoid memory leak
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if hasattr(self, '__createfrom__') and self.__createfrom__ == 'C':
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libsvm.svm_free_and_destroy_model(pointer(self))
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def get_svm_type(self):
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return libsvm.svm_get_svm_type(self)
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def get_nr_class(self):
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return libsvm.svm_get_nr_class(self)
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def get_svr_probability(self):
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return libsvm.svm_get_svr_probability(self)
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def get_labels(self):
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nr_class = self.get_nr_class()
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labels = (c_int * nr_class)()
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libsvm.svm_get_labels(self, labels)
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return labels[:nr_class]
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def get_sv_indices(self):
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total_sv = self.get_nr_sv()
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sv_indices = (c_int * total_sv)()
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libsvm.svm_get_sv_indices(self, sv_indices)
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return sv_indices[:total_sv]
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def get_nr_sv(self):
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return libsvm.svm_get_nr_sv(self)
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def is_probability_model(self):
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return (libsvm.svm_check_probability_model(self) == 1)
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def get_sv_coef(self):
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return [tuple(self.sv_coef[j][i] for j in xrange(self.nr_class - 1))
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for i in xrange(self.l)]
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def get_SV(self):
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result = []
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for sparse_sv in self.SV[:self.l]:
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row = dict()
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i = 0
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while True:
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row[sparse_sv[i].index] = sparse_sv[i].value
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if sparse_sv[i].index == -1:
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break
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i += 1
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result.append(row)
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return result
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def toPyModel(model_ptr):
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"""
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toPyModel(model_ptr) -> svm_model
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Convert a ctypes POINTER(svm_model) to a Python svm_model
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"""
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if bool(model_ptr) == False:
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raise ValueError("Null pointer")
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m = model_ptr.contents
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m.__createfrom__ = 'C'
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return m
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fillprototype(libsvm.svm_train, POINTER(svm_model), [POINTER(svm_problem), POINTER(svm_parameter)])
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fillprototype(libsvm.svm_cross_validation, None, [POINTER(svm_problem), POINTER(svm_parameter), c_int, POINTER(c_double)])
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fillprototype(libsvm.svm_save_model, c_int, [c_char_p, POINTER(svm_model)])
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fillprototype(libsvm.svm_load_model, POINTER(svm_model), [c_char_p])
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fillprototype(libsvm.svm_get_svm_type, c_int, [POINTER(svm_model)])
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fillprototype(libsvm.svm_get_nr_class, c_int, [POINTER(svm_model)])
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fillprototype(libsvm.svm_get_labels, None, [POINTER(svm_model), POINTER(c_int)])
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fillprototype(libsvm.svm_get_sv_indices, None, [POINTER(svm_model), POINTER(c_int)])
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fillprototype(libsvm.svm_get_nr_sv, c_int, [POINTER(svm_model)])
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fillprototype(libsvm.svm_get_svr_probability, c_double, [POINTER(svm_model)])
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fillprototype(libsvm.svm_predict_values, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])
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fillprototype(libsvm.svm_predict, c_double, [POINTER(svm_model), POINTER(svm_node)])
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fillprototype(libsvm.svm_predict_probability, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])
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fillprototype(libsvm.svm_free_model_content, None, [POINTER(svm_model)])
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fillprototype(libsvm.svm_free_and_destroy_model, None, [POINTER(POINTER(svm_model))])
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fillprototype(libsvm.svm_destroy_param, None, [POINTER(svm_parameter)])
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fillprototype(libsvm.svm_check_parameter, c_char_p, [POINTER(svm_problem), POINTER(svm_parameter)])
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fillprototype(libsvm.svm_check_probability_model, c_int, [POINTER(svm_model)])
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fillprototype(libsvm.svm_set_print_string_function, None, [PRINT_STRING_FUN])
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