First commit

liblinear-2.49/python/liblinear/commonutil.py

@@ -0,0 +1,189 @@
+from __future__ import print_function
+from array import array
+import sys
+
+try:
+    import numpy as np
+    import scipy
+    from scipy import sparse
+except:
+    scipy = None
+
+
+__all__ = ['svm_read_problem', 'evaluations', 'csr_find_scale_param', 'csr_scale']
+
+def svm_read_problem(data_source, return_scipy=False):
+    """
+    svm_read_problem(data_source, return_scipy=False) -> [y, x], y: list, x: list of dictionary
+    svm_read_problem(data_source, return_scipy=True)  -> [y, x], y: ndarray, x: csr_matrix
+
+    Read LIBSVM-format data from data_source and return labels y
+    and data instances x.
+    """
+    if scipy != None and return_scipy:
+        prob_y = array('d')
+        prob_x = array('d')
+        row_ptr = array('l', [0])
+        col_idx = array('l')
+    else:
+        prob_y = []
+        prob_x = []
+        row_ptr = [0]
+        col_idx = []
+    indx_start = 1
+
+    if hasattr(data_source, "read"):
+        file = data_source
+    else:
+        file = open(data_source)
+    try:
+        for line in file:
+            line = line.split(None, 1)
+            # In case an instance with all zero features
+            if len(line) == 1: line += ['']
+            label, features = line
+            prob_y.append(float(label))
+            if scipy != None and return_scipy:
+                nz = 0
+                for e in features.split():
+                    ind, val = e.split(":")
+                    if ind == '0':
+                        indx_start = 0
+                    val = float(val)
+                    if val != 0:
+                        col_idx.append(int(ind)-indx_start)
+                        prob_x.append(val)
+                        nz += 1
+                row_ptr.append(row_ptr[-1]+nz)
+            else:
+                xi = {}
+                for e in features.split():
+                    ind, val = e.split(":")
+                    xi[int(ind)] = float(val)
+                prob_x += [xi]
+    except Exception as err_msg:
+        raise err_msg
+    finally:
+        if not hasattr(data_source, "read"):
+            # close file only if it was created by us
+            file.close()
+
+    if scipy != None and return_scipy:
+        prob_y = np.frombuffer(prob_y, dtype='d')
+        prob_x = np.frombuffer(prob_x, dtype='d')
+        col_idx = np.frombuffer(col_idx, dtype='l')
+        row_ptr = np.frombuffer(row_ptr, dtype='l')
+        prob_x = sparse.csr_matrix((prob_x, col_idx, row_ptr))
+    return (prob_y, prob_x)
+
+def evaluations_scipy(ty, pv):
+    """
+    evaluations_scipy(ty, pv) -> (ACC, MSE, SCC)
+    ty, pv: ndarray
+
+    Calculate accuracy, mean squared error and squared correlation coefficient
+    using the true values (ty) and predicted values (pv).
+    """
+    if not (scipy != None and isinstance(ty, np.ndarray) and isinstance(pv, np.ndarray)):
+        raise TypeError("type of ty and pv must be ndarray")
+    if len(ty) != len(pv):
+        raise ValueError("len(ty) must be equal to len(pv)")
+    ACC = 100.0*(ty == pv).mean()
+    MSE = ((ty - pv)**2).mean()
+    l = len(ty)
+    sumv = pv.sum()
+    sumy = ty.sum()
+    sumvy = (pv*ty).sum()
+    sumvv = (pv*pv).sum()
+    sumyy = (ty*ty).sum()
+    with np.errstate(all = 'raise'):
+        try:
+            SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy))
+        except:
+            SCC = float('nan')
+    return (float(ACC), float(MSE), float(SCC))
+
+def evaluations(ty, pv, useScipy = True):
+    """
+    evaluations(ty, pv, useScipy) -> (ACC, MSE, SCC)
+    ty, pv: list, tuple or ndarray
+    useScipy: convert ty, pv to ndarray, and use scipy functions for the evaluation
+
+    Calculate accuracy, mean squared error and squared correlation coefficient
+    using the true values (ty) and predicted values (pv).
+    """
+    if scipy != None and useScipy:
+        return evaluations_scipy(np.asarray(ty), np.asarray(pv))
+    if len(ty) != len(pv):
+        raise ValueError("len(ty) must be equal to len(pv)")
+    total_correct = total_error = 0
+    sumv = sumy = sumvv = sumyy = sumvy = 0
+    for v, y in zip(pv, ty):
+        if y == v:
+            total_correct += 1
+        total_error += (v-y)*(v-y)
+        sumv += v
+        sumy += y
+        sumvv += v*v
+        sumyy += y*y
+        sumvy += v*y
+    l = len(ty)
+    ACC = 100.0*total_correct/l
+    MSE = total_error/l
+    try:
+        SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy))
+    except:
+        SCC = float('nan')
+    return (float(ACC), float(MSE), float(SCC))
+
+def csr_find_scale_param(x, lower=-1, upper=1):
+    assert isinstance(x, sparse.csr_matrix)
+    assert lower < upper
+    l, n = x.shape
+    feat_min = x.min(axis=0).toarray().flatten()
+    feat_max = x.max(axis=0).toarray().flatten()
+    coef = (feat_max - feat_min) / (upper - lower)
+    coef[coef != 0] = 1.0 / coef[coef != 0]
+
+    # (x - ones(l,1) * feat_min') * diag(coef) + lower
+    # = x * diag(coef) - ones(l, 1) * (feat_min' * diag(coef)) + lower
+    # = x * diag(coef) + ones(l, 1) * (-feat_min' * diag(coef) + lower)
+    # = x * diag(coef) + ones(l, 1) * offset'
+    offset = -feat_min * coef + lower
+    offset[coef == 0] = 0
+
+    if sum(offset != 0) * l > 3 * x.getnnz():
+        print(
+            "WARNING: The #nonzeros of the scaled data is at least 2 times larger than the original one.\n"
+            "If feature values are non-negative and sparse, set lower=0 rather than the default lower=-1.",
+            file=sys.stderr)
+
+    return {'coef':coef, 'offset':offset}
+
+def csr_scale(x, scale_param):
+    assert isinstance(x, sparse.csr_matrix)
+
+    offset = scale_param['offset']
+    coef = scale_param['coef']
+    assert len(coef) == len(offset)
+
+    l, n = x.shape
+
+    if not n == len(coef):
+        print("WARNING: The dimension of scaling parameters and feature number do not match.", file=sys.stderr)
+        coef = coef.resize(n) # zeros padded if n > len(coef)
+        offset = offset.resize(n)
+
+    # scaled_x = x * diag(coef) + ones(l, 1) * offset'
+    offset = sparse.csr_matrix(offset.reshape(1, n))
+    offset = sparse.vstack([offset] * l, format='csr', dtype=x.dtype)
+    scaled_x = x.dot(sparse.diags(coef, 0, shape=(n, n))) + offset
+
+    if scaled_x.getnnz() > x.getnnz():
+        print(
+            "WARNING: original #nonzeros %d\n" % x.getnnz() +
+            "       > new      #nonzeros %d\n" % scaled_x.getnnz() +
+            "If feature values are non-negative and sparse, get scale_param by setting lower=0 rather than the default lower=-1.",
+            file=sys.stderr)
+
+    return scaled_x

liblinear-2.49/python/liblinear/liblinear.py

@@ -0,0 +1,479 @@
+from ctypes import *
+from ctypes.util import find_library
+from os import path
+from glob import glob
+import sys
+from enum import IntEnum
+try:
+    import numpy as np
+    import scipy
+    from scipy import sparse
+except:
+    scipy = None
+
+if sys.version_info[0] < 3:
+    range = xrange
+    from itertools import izip as zip
+
+__all__ = ['liblinear', 'feature_node', 'gen_feature_nodearray', 'problem',
+           'parameter', 'model', 'toPyModel', 'solver_names',
+           'print_null']
+
+try:
+    dirname = path.dirname(path.abspath(__file__))
+    dynamic_lib_name = 'clib.cp*'
+    path_to_so = glob(path.join(dirname, dynamic_lib_name))[0]
+    liblinear = CDLL(path_to_so)
+except:
+    try :
+        if sys.platform == 'win32':
+            liblinear = CDLL(path.join(dirname, r'..\..\windows\liblinear.dll'))
+        else:
+            liblinear = CDLL(path.join(dirname, '../../liblinear.so.6'))
+    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:
+            raise Exception('LIBLINEAR library not found.')
+
+class solver_names(IntEnum):
+    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
+    ONECLASS_SVM = 21
+
+PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p)
+def print_null(s):
+    return
+
+# In multi-threading, all threads share the same memory space of
+# the dynamic library (liblinear). Thus, we use a module-level
+# variable to keep a reference to ctypes print_null, preventing
+# python from garbage collecting it in thread B while thread A
+# still needs it. Check the usage of svm_set_print_string_function()
+# in LIBLINEAR README for details.
+ctypes_print_null = PRINT_STRING_FUN(print_null)
+
+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], np.ndarray) and isinstance(xi[1], np.ndarray): # for a sparse vector
+        index_range = xi[0] + 1 # index starts from 1
+        if feature_max:
+            index_range = index_range[np.where(index_range <= feature_max)]
+    elif scipy and isinstance(xi, np.ndarray):
+        xi_shift = 1
+        index_range = xi.nonzero()[0] + 1 # index starts from 1
+        if feature_max:
+            index_range = index_range[np.where(index_range <= feature_max)]
+    elif isinstance(xi, (dict, list, tuple)):
+        if isinstance(xi, dict):
+            index_range = sorted(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))
+    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], np.ndarray) and isinstance(xi[1], np.ndarray): # for a sparse vector
+        # since xi=(indices, values), we must sort them simultaneously.
+        for idx, arg in enumerate(np.argsort(index_range)):
+            ret[idx].index = index_range[arg]
+            ret[idx].value = (xi[1])[arg]
+    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:
+    # We need to support two cases: when jit is called with no arguments, and when jit is called with
+    # a keyword argument.
+    def jit(func=None, *args, **kwargs):
+        if func is None:
+            # This handles the case where jit is used with parentheses: @jit(nopython=True)
+            return lambda x: x
+        else:
+            # This handles the case where jit is used without parentheses: @jit
+            return func
+    jit_enabled = False
+
+@jit(nopython=True)
+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):
+    if not x.has_sorted_indices:
+        x.sort_indices()
+
+    # Extra space for termination node and (possibly) bias term
+    x_space = prob.x_space = np.empty((x.nnz+x.shape[0]*2), dtype=feature_node)
+    # rowptr has to be a 64bit integer because it will later be used for pointer arithmetic,
+    # which overflows when the added pointer points to an address that is numerically high.
+    prob.rowptr = x.indptr.astype(np.int64, copy=True)
+    prob.rowptr[1:] += 2*np.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, np.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, (np.ndarray, sparse.spmatrix)):
+            if len(y) != x.shape[0]:
+                raise ValueError("len(y) != len(x)")
+            if isinstance(x, np.ndarray):
+                x = np.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, np.ndarray):
+            np.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 = np.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
+
+    def copy(self):
+        prob_copy = problem.__new__(problem)
+        for key in problem._names + list(vars(self)):
+            setattr(prob_copy, key, getattr(self, key))
+        return prob_copy
+
+
+class parameter(Structure):
+    _names = ["solver_type", "eps", "C", "nr_weight", "weight_label",
+              "weight", "p", "nu", "init_sol", "regularize_bias",
+              "w_recalc"]
+    _types = [c_int, c_double, c_double, c_int, POINTER(c_int),
+              POINTER(c_double), c_double, c_double, POINTER(c_double), c_int,c_bool]
+    _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 = 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 = solver_names.L2R_L2LOSS_SVC_DUAL
+        self.eps = float('inf')
+        self.C = 1
+        self.p = 0.1
+        self.nu = 0.5
+        self.nr_weight = 0
+        self.weight_label = None
+        self.weight = None
+        self.init_sol = None
+        self.bias = -1
+        self.regularize_bias = 1
+        self.w_recalc = False
+        self.flag_cross_validation = False
+        self.flag_C_specified = False
+        self.flag_p_specified = False
+        self.flag_solver_specified = False
+        self.flag_find_parameters = 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 = solver_names(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])
+                self.flag_p_specified = True
+            elif argv[i] == "-n":
+                i = i + 1
+                self.nu = 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 = ctypes_print_null
+            elif argv[i] == "-C":
+                self.flag_find_parameters = True
+            elif argv[i] == "-R":
+                self.regularize_bias = 0
+            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_parameters:
+            if not self.flag_cross_validation:
+                self.nr_fold = 5
+            if not self.flag_solver_specified:
+                self.solver_type = solver_names.L2R_L2LOSS_SVC
+                self.flag_solver_specified = True
+            elif self.solver_type not in [solver_names.L2R_LR, solver_names.L2R_L2LOSS_SVC, solver_names.L2R_L2LOSS_SVR]:
+                raise ValueError("Warm-start parameter search only available for -s 0, -s 2 and -s 11")
+
+        if self.eps == float('inf'):
+            if self.solver_type in [solver_names.L2R_LR, solver_names.L2R_L2LOSS_SVC]:
+                self.eps = 0.01
+            elif self.solver_type in [solver_names.L2R_L2LOSS_SVR]:
+                self.eps = 0.0001
+            elif self.solver_type in [solver_names.L2R_L2LOSS_SVC_DUAL, solver_names.L2R_L1LOSS_SVC_DUAL, solver_names.MCSVM_CS, solver_names.L2R_LR_DUAL]:
+                self.eps = 0.1
+            elif self.solver_type in [solver_names.L1R_L2LOSS_SVC, solver_names.L1R_LR]:
+                self.eps = 0.01
+            elif self.solver_type in [solver_names.L2R_L2LOSS_SVR_DUAL, solver_names.L2R_L1LOSS_SVR_DUAL]:
+                self.eps = 0.1
+            elif self.solver_type in [solver_names.ONECLASS_SVM]:
+                self.eps = 0.01
+
+class model(Structure):
+    _names = ["param", "nr_class", "nr_feature", "w", "label", "bias", "rho"]
+    _types = [parameter, c_int, c_int, POINTER(c_double), POINTER(c_int), c_double, 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_rho(self):
+        return liblinear.get_decfun_rho(self)
+
+    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)]
+        if self.is_oneclass_model():
+            rho = self.get_decfun_rho()
+            return (w, -rho)
+        else:
+            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 is_oneclass_model(self):
+        return (liblinear.check_oneclass_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_parameters, None, [POINTER(problem), POINTER(parameter), c_int, c_double, c_double, POINTER(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.get_decfun_rho, c_double, [POINTER(model)])
+
+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.check_oneclass_model, c_int, [POINTER(model)])
+fillprototype(liblinear.set_print_string_function, None, [CFUNCTYPE(None, c_char_p)])

liblinear-2.49/python/liblinear/liblinearutil.py

@@ -0,0 +1,285 @@
+import os, sys
+from .liblinear import *
+from .liblinear import __all__ as liblinear_all
+from .commonutil import *
+from .commonutil import __all__ as common_all
+from ctypes import c_double
+
+try:
+    import numpy as np
+    import scipy
+    from scipy import sparse
+except:
+    scipy = None
+
+if sys.version_info[0] < 3:
+    range = xrange
+    from itertools import izip as zip
+    _cstr = lambda s: s.encode("utf-8") if isinstance(s,unicode) else str(s)
+else:
+    _cstr = lambda s: bytes(s, "utf-8")
+
+__all__ = ['load_model', 'save_model', 'train', 'predict'] + liblinear_all + common_all
+
+
+def load_model(model_file_name):
+    """
+    load_model(model_file_name) -> model
+
+    Load a LIBLINEAR model from model_file_name and return.
+    """
+    model = liblinear.load_model(_cstr(model_file_name))
+    if not model:
+        print("can't open model file %s" % model_file_name)
+        return None
+    model = toPyModel(model)
+    return model
+
+def save_model(model_file_name, model):
+    """
+    save_model(model_file_name, model) -> None
+
+    Save a LIBLINEAR model to the file model_file_name.
+    """
+    liblinear.save_model(_cstr(model_file_name), model)
+
+def train(arg1, arg2=None, arg3=None):
+    """
+    train(y, x [, options]) -> model | ACC
+
+    y: a list/tuple/ndarray of l true labels (type must be int/double).
+
+    x: 1. a list/tuple of l training instances. Feature vector of
+          each training instance is a list/tuple or dictionary.
+
+       2. an l * n numpy ndarray or scipy spmatrix (n: number of features).
+
+    train(prob [, options]) -> model | ACC
+    train(prob, param) -> model | ACC
+
+    Train a model from data (y, x) or a problem prob using
+    'options' or a parameter param.
+
+    If '-v' is specified in 'options' (i.e., cross validation)
+    either accuracy (ACC) or mean-squared error (MSE) is returned.
+
+    options:
+        -s type : set type of solver (default 1)
+          for multi-class classification
+             0 -- L2-regularized logistic regression (primal)
+             1 -- L2-regularized L2-loss support vector classification (dual)
+             2 -- L2-regularized L2-loss support vector classification (primal)
+             3 -- L2-regularized L1-loss support vector classification (dual)
+             4 -- support vector classification by Crammer and Singer
+             5 -- L1-regularized L2-loss support vector classification
+             6 -- L1-regularized logistic regression
+             7 -- L2-regularized logistic regression (dual)
+          for regression
+            11 -- L2-regularized L2-loss support vector regression (primal)
+            12 -- L2-regularized L2-loss support vector regression (dual)
+            13 -- L2-regularized L1-loss support vector regression (dual)
+          for outlier detection
+            21 -- one-class support vector machine (dual)
+        -c cost : set the parameter C (default 1)
+        -p epsilon : set the epsilon in loss function of SVR (default 0.1)
+        -e epsilon : set tolerance of termination criterion
+            -s 0 and 2
+                |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,
+                where f is the primal function, (default 0.01)
+            -s 11
+                |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001)
+            -s 1, 3, 4, 7, and 21
+                Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21)
+            -s 5 and 6
+                |f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf,
+                where f is the primal function (default 0.01)
+            -s 12 and 13
+                |f'(alpha)|_1 <= eps |f'(alpha0)|,
+                where f is the dual function (default 0.1)
+        -B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
+        -R : not regularize the bias; must with -B 1 to have the bias; DON'T use this unless you know what it is
+            (for -s 0, 2, 5, 6, 11)"
+        -wi weight: weights adjust the parameter C of different classes (see README for details)
+        -v n: n-fold cross validation mode
+        -C : find parameters (C for -s 0, 2 and C, p for -s 11)
+        -q : quiet mode (no outputs)
+    """
+    prob, param = None, None
+    if isinstance(arg1, (list, tuple)) or (scipy and isinstance(arg1, np.ndarray)):
+        assert isinstance(arg2, (list, tuple)) or (scipy and isinstance(arg2, (np.ndarray, sparse.spmatrix)))
+        y, x, options = arg1, arg2, arg3
+        prob = problem(y, x)
+        param = parameter(options)
+    elif isinstance(arg1, problem):
+        prob = arg1
+        if isinstance(arg2, parameter):
+            param = arg2
+        else:
+            param = parameter(arg2)
+    if prob == None or param == None :
+        raise TypeError("Wrong types for the arguments")
+
+    prob.set_bias(param.bias)
+    liblinear.set_print_string_function(param.print_func)
+    err_msg = liblinear.check_parameter(prob, param)
+    if err_msg :
+        raise ValueError('Error: %s' % err_msg)
+
+    if param.flag_find_parameters:
+        nr_fold = param.nr_fold
+        best_C = c_double()
+        best_p = c_double()
+        best_score = c_double()
+        if param.flag_C_specified:
+            start_C = param.C
+        else:
+            start_C = -1.0
+        if param.flag_p_specified:
+            start_p = param.p
+        else:
+            start_p = -1.0
+        liblinear.find_parameters(prob, param, nr_fold, start_C, start_p, best_C, best_p, best_score)
+        if param.solver_type in [solver_names.L2R_LR, solver_names.L2R_L2LOSS_SVC]:
+            print("Best C = %g  CV accuracy = %g%%\n"% (best_C.value, 100.0*best_score.value))
+        elif param.solver_type in [solver_names.L2R_L2LOSS_SVR]:
+            print("Best C = %g Best p = %g  CV MSE = %g\n"% (best_C.value, best_p.value, best_score.value))
+        return best_C.value,best_p.value,best_score.value
+
+
+    elif param.flag_cross_validation:
+        l, nr_fold = prob.l, param.nr_fold
+        target = (c_double * l)()
+        liblinear.cross_validation(prob, param, nr_fold, target)
+        ACC, MSE, SCC = evaluations(prob.y[:l], target[:l])
+        if param.solver_type in [solver_names.L2R_L2LOSS_SVR, solver_names.L2R_L2LOSS_SVR_DUAL, solver_names.L2R_L1LOSS_SVR_DUAL]:
+            print("Cross Validation Mean squared error = %g" % MSE)
+            print("Cross Validation Squared correlation coefficient = %g" % SCC)
+            return MSE
+        else:
+            print("Cross Validation Accuracy = %g%%" % ACC)
+            return ACC
+    else:
+        m = liblinear.train(prob, param)
+        m = toPyModel(m)
+
+        return m
+
+def predict(y, x, m, options=""):
+    """
+    predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals)
+
+    y: a list/tuple/ndarray of l true labels (type must be int/double).
+       It is used for calculating the accuracy. Use [] if true labels are
+       unavailable.
+
+    x: 1. a list/tuple of l training instances. Feature vector of
+          each training instance is a list/tuple or dictionary.
+
+       2. an l * n numpy ndarray or scipy spmatrix (n: number of features).
+
+    Predict data (y, x) with the SVM model m.
+    options:
+        -b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only
+        -q quiet mode (no outputs)
+
+    The return tuple contains
+    p_labels: a list of predicted labels
+    p_acc: a tuple including  accuracy (for classification), mean-squared
+           error, and squared correlation coefficient (for regression).
+    p_vals: a list of decision values or probability estimates (if '-b 1'
+            is specified). If k is the number of classes, for decision values,
+            each element includes results of predicting k binary-class
+            SVMs. if k = 2 and solver is not MCSVM_CS, only one decision value
+            is returned. For probabilities, each element contains k values
+            indicating the probability that the testing instance is in each class.
+            Note that the order of classes here is the same as 'model.label'
+            field in the model structure.
+    """
+
+    def info(s):
+        print(s)
+
+    if scipy and isinstance(x, np.ndarray):
+        x = np.ascontiguousarray(x) # enforce row-major
+    elif scipy and isinstance(x, sparse.spmatrix):
+        x = x.tocsr()
+    elif not isinstance(x, (list, tuple)):
+        raise TypeError("type of x: {0} is not supported!".format(type(x)))
+
+    if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, np.ndarray))):
+        raise TypeError("type of y: {0} is not supported!".format(type(y)))
+
+    predict_probability = 0
+    argv = options.split()
+    i = 0
+    while i < len(argv):
+        if argv[i] == '-b':
+            i += 1
+            predict_probability = int(argv[i])
+        elif argv[i] == '-q':
+            info = print_null
+        else:
+            raise ValueError("Wrong options")
+        i+=1
+
+    solver_type = m.param.solver_type
+    nr_class = m.get_nr_class()
+    nr_feature = m.get_nr_feature()
+    is_prob_model = m.is_probability_model()
+    bias = m.bias
+    if bias >= 0:
+        biasterm = feature_node(nr_feature+1, bias)
+    else:
+        biasterm = feature_node(-1, bias)
+    pred_labels = []
+    pred_values = []
+
+    if scipy and isinstance(x, sparse.spmatrix):
+        nr_instance = x.shape[0]
+    else:
+        nr_instance = len(x)
+
+    if predict_probability:
+        if not is_prob_model:
+            raise TypeError('probability output is only supported for logistic regression')
+        prob_estimates = (c_double * nr_class)()
+        for i in range(nr_instance):
+            if scipy and isinstance(x, sparse.spmatrix):
+                indslice = slice(x.indptr[i], x.indptr[i+1])
+                xi, idx = gen_feature_nodearray((x.indices[indslice], x.data[indslice]), feature_max=nr_feature)
+            else:
+                xi, idx = gen_feature_nodearray(x[i], feature_max=nr_feature)
+            xi[-2] = biasterm
+            label = liblinear.predict_probability(m, xi, prob_estimates)
+            values = prob_estimates[:nr_class]
+            pred_labels += [label]
+            pred_values += [values]
+    else:
+        if nr_class <= 2:
+            nr_classifier = 1
+        else:
+            nr_classifier = nr_class
+        dec_values = (c_double * nr_classifier)()
+        for i in range(nr_instance):
+            if scipy and isinstance(x, sparse.spmatrix):
+                indslice = slice(x.indptr[i], x.indptr[i+1])
+                xi, idx = gen_feature_nodearray((x.indices[indslice], x.data[indslice]), feature_max=nr_feature)
+            else:
+                xi, idx = gen_feature_nodearray(x[i], feature_max=nr_feature)
+            xi[-2] = biasterm
+            label = liblinear.predict_values(m, xi, dec_values)
+            values = dec_values[:nr_classifier]
+            pred_labels += [label]
+            pred_values += [values]
+
+    if len(y) == 0:
+        y = [0] * nr_instance
+    ACC, MSE, SCC = evaluations(y, pred_labels)
+
+    if m.is_regression_model():
+        info("Mean squared error = %g (regression)" % MSE)
+        info("Squared correlation coefficient = %g (regression)" % SCC)
+    else:
+        info("Accuracy = %g%% (%d/%d) (classification)" % (ACC, int(round(nr_instance*ACC/100)), nr_instance))
+
+    return pred_labels, (ACC, MSE, SCC), pred_values