bo-graduation/nltk-book/pattern-master/pattern/vector/svm/libsvm.py

#!/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

__all__ = ['libsvm', 'svm_problem', 'svm_parameter',
           'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'C_SVC',
           'EPSILON_SVR', 'LINEAR', 'NU_SVC', 'NU_SVR', 'ONE_CLASS',
           'POLY', 'PRECOMPUTED', 'PRINT_STRING_FUN', 'RBF',
           'SIGMOID', 'c_double', 'svm_model']

try:
	dirname = path.dirname(path.abspath(__file__))
	if sys.platform == 'win32':
		libsvm = CDLL(path.join(dirname, 'windows\libsvm-3.22\libsvm.dll'))
	else:
		libsvm = CDLL(path.join(dirname, 'macos/libsvm-3.22/libsvm.so.2'))

except:
# For unix the prefix 'lib' is not considered.
	if find_library('svm'):
		libsvm = CDLL(find_library('svm'))
	elif find_library('libsvm'):
		libsvm = CDLL(find_library('libsvm'))
	else:
		libsvm = CDLL(path.join(path.dirname(__file__), 'ubuntu/libsvm-3.22/libsvm.so.2'))


C_SVC = 0
NU_SVC = 1
ONE_CLASS = 2
EPSILON_SVR = 3
NU_SVR = 4

LINEAR = 0
POLY = 1
RBF = 2
SIGMOID = 3
PRECOMPUTED = 4

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 svm_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_svm_nodearray(xi, feature_max=None, isKernel=None):
	if isinstance(xi, dict):
		index_range = xi.keys()
	elif isinstance(xi, (list, tuple)):
		if not isKernel:
			xi = [0] + xi  # idx should start from 1
		index_range = range(len(xi))
	else:
		raise TypeError('xi should be a dictionary, list or tuple')

	if feature_max:
		assert(isinstance(feature_max, int))
		index_range = list(filter(lambda j: j <= feature_max, index_range))
	if not isKernel:
		index_range = list(filter(lambda j:xi[j] != 0, index_range))

	index_range = sorted(index_range)
	ret = (svm_node * (len(index_range) + 1))()
	ret[-1].index = -1
	for idx, j in enumerate(index_range):
		ret[idx].index = j
		ret[idx].value = xi[j]
	max_idx = 0
	if index_range:
		max_idx = index_range[-1]
	return ret, max_idx


class svm_problem(Structure):
	_names = ["l", "y", "x"]
	_types = [c_int, POINTER(c_double), POINTER(POINTER(svm_node))]
	_fields_ = genFields(_names, _types)

	def __init__(self, y, x, isKernel=None):
		if len(y) != len(x):
			raise ValueError("len(y) != len(x)")
		self.l = l = len(y)

		max_idx = 0
		x_space = self.x_space = []
		for i, xi in enumerate(x):
			tmp_xi, tmp_idx = gen_svm_nodearray(xi,isKernel=isKernel)
			x_space += [tmp_xi]
			max_idx = max(max_idx, tmp_idx)
		self.n = max_idx

		self.y = (c_double * l)()
		for i, yi in enumerate(y):
			self.y[i] = yi

		self.x = (POINTER(svm_node) * l)()
		for i, xi in enumerate(self.x_space):
			self.x[i] = xi


class svm_parameter(Structure):
	_names = ["svm_type", "kernel_type", "degree", "gamma", "coef0",
			"cache_size", "eps", "C", "nr_weight", "weight_label", "weight",
			"nu", "p", "shrinking", "probability"]
	_types = [c_int, c_int, c_int, c_double, c_double,
			c_double, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double),
			c_double, c_double, c_int, c_int]
	_fields_ = genFields(_names, _types)

	def __init__(self, options = None):
		if options == None:
			options = ''
		self.parse_options(options)

	def __str__(self):
		s = ''
		attrs = svm_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.svm_type = C_SVC
		self.kernel_type = RBF
		self.degree = 3
		self.gamma = 0
		self.coef0 = 0
		self.nu = 0.5
		self.cache_size = 100
		self.C = 1
		self.eps = 0.001
		self.p = 0.1
		self.shrinking = 1
		self.probability = 0
		self.nr_weight = 0
		self.weight_label = None
		self.weight = None
		self.cross_validation = 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.svm_type = int(argv[i])
			elif argv[i] == "-t":
				i = i + 1
				self.kernel_type = int(argv[i])
			elif argv[i] == "-d":
				i = i + 1
				self.degree = int(argv[i])
			elif argv[i] == "-g":
				i = i + 1
				self.gamma = float(argv[i])
			elif argv[i] == "-r":
				i = i + 1
				self.coef0 = float(argv[i])
			elif argv[i] == "-n":
				i = i + 1
				self.nu = float(argv[i])
			elif argv[i] == "-m":
				i = i + 1
				self.cache_size = float(argv[i])
			elif argv[i] == "-c":
				i = i + 1
				self.C = float(argv[i])
			elif argv[i] == "-e":
				i = i + 1
				self.eps = float(argv[i])
			elif argv[i] == "-p":
				i = i + 1
				self.p = float(argv[i])
			elif argv[i] == "-h":
				i = i + 1
				self.shrinking = int(argv[i])
			elif argv[i] == "-b":
				i = i + 1
				self.probability = int(argv[i])
			elif argv[i] == "-q":
				self.print_func = PRINT_STRING_FUN(print_null)
			elif argv[i] == "-v":
				i = i + 1
				self.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])]
			else:
				raise ValueError("Wrong options")
			i += 1

		libsvm.svm_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]


class svm_model(Structure):
	_names = ['param', 'nr_class', 'l', 'SV', 'sv_coef', 'rho',
			'probA', 'probB', 'sv_indices', 'label', 'nSV', 'free_sv']
	_types = [svm_parameter, c_int, c_int, POINTER(POINTER(svm_node)),
			POINTER(POINTER(c_double)), POINTER(c_double),
			POINTER(c_double), POINTER(c_double), POINTER(c_int),
			POINTER(c_int), POINTER(c_int), c_int]
	_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':
			libsvm.svm_free_and_destroy_model(pointer(self))

	def get_svm_type(self):
		return libsvm.svm_get_svm_type(self)

	def get_nr_class(self):
		return libsvm.svm_get_nr_class(self)

	def get_svr_probability(self):
		return libsvm.svm_get_svr_probability(self)

	def get_labels(self):
		nr_class = self.get_nr_class()
		labels = (c_int * nr_class)()
		libsvm.svm_get_labels(self, labels)
		return labels[:nr_class]

	def get_sv_indices(self):
		total_sv = self.get_nr_sv()
		sv_indices = (c_int * total_sv)()
		libsvm.svm_get_sv_indices(self, sv_indices)
		return sv_indices[:total_sv]

	def get_nr_sv(self):
		return libsvm.svm_get_nr_sv(self)

	def is_probability_model(self):
		return (libsvm.svm_check_probability_model(self) == 1)

	def get_sv_coef(self):
		return [tuple(self.sv_coef[j][i] for j in xrange(self.nr_class - 1))
				for i in xrange(self.l)]

	def get_SV(self):
		result = []
		for sparse_sv in self.SV[:self.l]:
			row = dict()

			i = 0
			while True:
				row[sparse_sv[i].index] = sparse_sv[i].value
				if sparse_sv[i].index == -1:
					break
				i += 1

			result.append(row)
		return result


def toPyModel(model_ptr):
	"""
	toPyModel(model_ptr) -> svm_model

	Convert a ctypes POINTER(svm_model) to a Python svm_model
	"""
	if bool(model_ptr) == False:
		raise ValueError("Null pointer")
	m = model_ptr.contents
	m.__createfrom__ = 'C'
	return m

fillprototype(libsvm.svm_train, POINTER(svm_model), [POINTER(svm_problem), POINTER(svm_parameter)])
fillprototype(libsvm.svm_cross_validation, None, [POINTER(svm_problem), POINTER(svm_parameter), c_int, POINTER(c_double)])

fillprototype(libsvm.svm_save_model, c_int, [c_char_p, POINTER(svm_model)])
fillprototype(libsvm.svm_load_model, POINTER(svm_model), [c_char_p])

fillprototype(libsvm.svm_get_svm_type, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_nr_class, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_labels, None, [POINTER(svm_model), POINTER(c_int)])
fillprototype(libsvm.svm_get_sv_indices, None, [POINTER(svm_model), POINTER(c_int)])
fillprototype(libsvm.svm_get_nr_sv, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_svr_probability, c_double, [POINTER(svm_model)])

fillprototype(libsvm.svm_predict_values, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])
fillprototype(libsvm.svm_predict, c_double, [POINTER(svm_model), POINTER(svm_node)])
fillprototype(libsvm.svm_predict_probability, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])

fillprototype(libsvm.svm_free_model_content, None, [POINTER(svm_model)])
fillprototype(libsvm.svm_free_and_destroy_model, None, [POINTER(POINTER(svm_model))])
fillprototype(libsvm.svm_destroy_param, None, [POINTER(svm_parameter)])

fillprototype(libsvm.svm_check_parameter, c_char_p, [POINTER(svm_problem), POINTER(svm_parameter)])
fillprototype(libsvm.svm_check_probability_model, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_set_print_string_function, None, [PRINT_STRING_FUN])