First commit
This commit is contained in:
465
libsvm-3.36/python/libsvm/svm.py
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465
libsvm-3.36/python/libsvm/svm.py
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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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from glob import glob
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from enum import IntEnum
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import sys
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try:
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import numpy as np
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import scipy
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from scipy import sparse
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except:
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scipy = None
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if sys.version_info[0] < 3:
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range = xrange
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from itertools import izip as zip
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__all__ = ['libsvm', 'svm_problem', 'svm_parameter',
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'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'svm_forms',
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'PRINT_STRING_FUN', 'kernel_names', 'c_double', 'svm_model']
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try:
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dirname = path.dirname(path.abspath(__file__))
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dynamic_lib_name = 'clib.cp*'
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path_to_so = glob(path.join(dirname, dynamic_lib_name))[0]
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libsvm = CDLL(path_to_so)
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except:
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try:
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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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else:
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libsvm = CDLL(path.join(dirname, '../../libsvm.so.4'))
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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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class svm_forms(IntEnum):
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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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class kernel_names(IntEnum):
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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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# In multi-threading, all threads share the same memory space of
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# the dynamic library (libsvm). Thus, we use a module-level
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# variable to keep a reference to ctypes print_null, preventing
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# python from garbage collecting it in thread B while thread A
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# still needs it. Check the usage of svm_set_print_string_function()
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# in LIBSVM README for details.
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ctypes_print_null = PRINT_STRING_FUN(print_null)
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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 __init__(self, index=-1, value=0):
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self.index, self.value = index, value
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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=False):
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if feature_max:
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assert(isinstance(feature_max, int))
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xi_shift = 0 # ensure correct indices of xi
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if scipy and isinstance(xi, tuple) and len(xi) == 2\
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and isinstance(xi[0], np.ndarray) and isinstance(xi[1], np.ndarray): # for a sparse vector
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if not isKernel:
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index_range = xi[0] + 1 # index starts from 1
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else:
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index_range = xi[0] # index starts from 0 for precomputed kernel
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if feature_max:
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index_range = index_range[np.where(index_range <= feature_max)]
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elif scipy and isinstance(xi, np.ndarray):
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if not isKernel:
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xi_shift = 1
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index_range = xi.nonzero()[0] + 1 # index starts from 1
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else:
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index_range = np.arange(0, len(xi)) # index starts from 0 for precomputed kernel
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if feature_max:
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index_range = index_range[np.where(index_range <= feature_max)]
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elif isinstance(xi, (dict, list, tuple)):
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if isinstance(xi, dict):
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index_range = sorted(xi.keys())
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elif isinstance(xi, (list, tuple)):
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if not isKernel:
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xi_shift = 1
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index_range = range(1, len(xi) + 1) # index starts from 1
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else:
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index_range = range(0, len(xi)) # index starts from 0 for precomputed kernel
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if feature_max:
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index_range = list(filter(lambda j: j <= feature_max, index_range))
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if not isKernel:
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index_range = list(filter(lambda j:xi[j-xi_shift] != 0, index_range))
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else:
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raise TypeError('xi should be a dictionary, list, tuple, 1-d numpy array, or tuple of (index, data)')
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ret = (svm_node*(len(index_range)+1))()
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ret[-1].index = -1
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if scipy and isinstance(xi, tuple) and len(xi) == 2\
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and isinstance(xi[0], np.ndarray) and isinstance(xi[1], np.ndarray): # for a sparse vector
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# since xi=(indices, values), we must sort them simultaneously.
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for idx, arg in enumerate(np.argsort(index_range)):
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ret[idx].index = index_range[arg]
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ret[idx].value = (xi[1])[arg]
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else:
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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 - xi_shift]
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max_idx = 0
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if len(index_range) > 0:
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max_idx = index_range[-1]
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return ret, max_idx
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try:
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from numba import jit
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jit_enabled = True
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except:
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# We need to support two cases: when jit is called with no arguments, and when jit is called with
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# a keyword argument.
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def jit(func=None, *args, **kwargs):
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if func is None:
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# This handles the case where jit is used with parentheses: @jit(nopython=True)
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return lambda x: x
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else:
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# This handles the case where jit is used without parentheses: @jit
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return func
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jit_enabled = False
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@jit(nopython=True)
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def csr_to_problem_jit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr, indx_start):
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for i in range(l):
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b1,e1 = x_rowptr[i], x_rowptr[i+1]
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b2,e2 = prob_rowptr[i], prob_rowptr[i+1]-1
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for j in range(b1,e1):
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prob_ind[j-b1+b2] = x_ind[j]+indx_start
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prob_val[j-b1+b2] = x_val[j]
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def csr_to_problem_nojit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr, indx_start):
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for i in range(l):
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x_slice = slice(x_rowptr[i], x_rowptr[i+1])
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prob_slice = slice(prob_rowptr[i], prob_rowptr[i+1]-1)
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prob_ind[prob_slice] = x_ind[x_slice]+indx_start
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prob_val[prob_slice] = x_val[x_slice]
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def csr_to_problem(x, prob, isKernel):
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if not x.has_sorted_indices:
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x.sort_indices()
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# Extra space for termination node and (possibly) bias term
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x_space = prob.x_space = np.empty((x.nnz+x.shape[0]), dtype=svm_node)
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# rowptr has to be a 64bit integer because it will later be used for pointer arithmetic,
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# which overflows when the added pointer points to an address that is numerically high.
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prob.rowptr = x.indptr.astype(np.int64, copy=True)
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prob.rowptr[1:] += np.arange(1,x.shape[0]+1)
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prob_ind = x_space["index"]
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prob_val = x_space["value"]
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prob_ind[:] = -1
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if not isKernel:
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indx_start = 1 # index starts from 1
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else:
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indx_start = 0 # index starts from 0 for precomputed kernel
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if jit_enabled:
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csr_to_problem_jit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr, indx_start)
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else:
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csr_to_problem_nojit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr, indx_start)
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class svm_problem(Structure):
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_names = ["l", "y", "x"]
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_types = [c_int, POINTER(c_double), POINTER(POINTER(svm_node))]
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_fields_ = genFields(_names, _types)
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def __init__(self, y, x, isKernel=False):
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if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, np.ndarray))):
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raise TypeError("type of y: {0} is not supported!".format(type(y)))
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if isinstance(x, (list, tuple)):
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if len(y) != len(x):
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raise ValueError("len(y) != len(x)")
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elif scipy != None and isinstance(x, (np.ndarray, sparse.spmatrix)):
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if len(y) != x.shape[0]:
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raise ValueError("len(y) != len(x)")
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if isinstance(x, np.ndarray):
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x = np.ascontiguousarray(x) # enforce row-major
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if isinstance(x, sparse.spmatrix):
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x = x.tocsr()
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pass
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else:
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raise TypeError("type of x: {0} is not supported!".format(type(x)))
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self.l = l = len(y)
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max_idx = 0
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x_space = self.x_space = []
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if scipy != None and isinstance(x, sparse.csr_matrix):
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csr_to_problem(x, self, isKernel)
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max_idx = x.shape[1]
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else:
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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.y = (c_double * l)()
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if scipy != None and isinstance(y, np.ndarray):
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np.ctypeslib.as_array(self.y, (self.l,))[:] = y
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else:
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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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if scipy != None and isinstance(x, sparse.csr_matrix):
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base = addressof(self.x_space.ctypes.data_as(POINTER(svm_node))[0])
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x_ptr = cast(self.x, POINTER(c_uint64))
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x_ptr = np.ctypeslib.as_array(x_ptr,(self.l,))
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x_ptr[:] = self.rowptr[:-1]*sizeof(svm_node)+base
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else:
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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 = svm_forms.C_SVC;
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self.kernel_type = kernel_names.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 = None
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self.weight = None
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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 = svm_forms(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 = kernel_names(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 = ctypes_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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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', 'prob_density_marks', 'sv_indices',
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'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_double),
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POINTER(c_int), 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(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 range(self.nr_class - 1))
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for i in range(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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if sparse_sv[i].index == -1:
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break
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row[sparse_sv[i].index] = sparse_sv[i].value
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i += 1
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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])
|
||||
Reference in New Issue
Block a user