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from ctypes import POINTER, c_int, c_double
from itertools import izip, repeat, chain
import numpy as N
from model import LibSvmModel
import libsvm
__all__ = [
'LibSvmCClassificationModel',
'LibSvmNuClassificationModel',
'LibSvmClassificationResults'
]
class LibSvmClassificationResults:
def __init__(self, model, traindataset, kernel, PredictorType):
modelc = model.contents
if modelc.param.svm_type not in [libsvm.C_SVC, libsvm.NU_SVC]:
raise TypeError, '%s is for classification problems' % \
str(self.__class__)
self.nr_class = modelc.nr_class
self.labels = modelc.labels[:self.nr_class]
nrho = self.nr_class * (self.nr_class - 1) / 2
self.rho = modelc.rho[:nrho]
self.nSV = modelc.nSV[:self.nr_class]
sv_coef = N.empty((self.nr_class - 1, modelc.l), dtype=N.float64)
for i, c in enumerate(modelc.sv_coef[:self.nr_class - 1]):
sv_coef[i,:] = c[:modelc.l]
self.sv_coef = sv_coef
self.predictor = PredictorType(model, traindataset, kernel)
def predict(self, dataset):
"""
This function does classification on a test vector x and
returns the label of the predicted class.
"""
if self.predictor.is_compact and dataset.is_array_data():
return [int(x) for x in
self.predictor.predict(dataset.data)]
else:
return [int(self.predictor.predict(x)) for x in dataset]
def predict_values(self, dataset):
"""
This function does classification on a test dataset and
returns decision values.
For training data with nr_class classes, this function returns
nr_class*(nr_class-1)/2 decision values in a dictionary for
each item in the test dataset. The keys of the dictionary are
2-tuples, one for each permutation of two class labels.
"""
n = self.nr_class * (self.nr_class - 1) / 2
def p(vv):
vv = N.atleast_1d(vv)
d = {}
labels = self.labels
for v, (li, lj) in \
izip(vv, chain(*[izip(repeat(x), labels[i+1:])
for i, x in enumerate(labels[:-1])])):
d[li, lj] = v
d[lj, li] = -v
return d
if self.predictor.is_compact and dataset.is_array_data():
vs = self.predictor.predict_values(dataset.data, n)
else:
vs = [self.predictor.predict_values(x, n) for x in dataset]
return [p(v) for v in vs]
def predict_probability(self, dataset):
"""
This function does classification on a test dataset for a
model with probability information.
This function returns a list of 2-tuples. The first item in
each tuple is the label of the class with the highest
probability. The second item is a dictionary that associated
labels with class probabilities.
"""
def p(x):
n = self.nr_class
label, prob_estimates = \
self.predictor.predict_probability(x, self.nr_class)
return int(label), prob_estimates
return [p(x) for x in dataset]
def compact(self):
self.predictor.compact()
class LibSvmClassificationModel(LibSvmModel):
"""
A model for support vector classification.
Classification models can predict a class label, decision values
over all classes or a posterior class probability.
See also:
- Platt. Probabilistic Outputs for Support Vector Machines and
Comparisons to Regularized Likelihood Methods.
- Lin. A Note on Platt's Probabilistic Outputs for Support Vector
Machines.
"""
ResultsType = LibSvmClassificationResults
def __init__(self, kernel, weights, **kwargs):
LibSvmModel.__init__(self, kernel, **kwargs)
if weights is not None:
self.weight_labels = N.empty((len(weights),), dtype=N.intp)
self.weights = N.empty((len(weights),), dtype=N.float64)
weights = weights[:]
weights.sort()
for i, (label, weight) in enumerate(weights):
self.weight_labels[i] = label
self.weights[i] = weight
self.param.nr_weight = len(weights)
self.param.weight_label = \
self.weight_labels.ctypes.data_as(POINTER(c_int))
self.param.weight = \
self.weights.ctypes.data_as(POINTER(c_double))
def cross_validate(self, dataset, nr_fold):
"""
Perform stratified cross-validation to determine the
suitability of chosen model parameters.
Data are separated to nr_fold folds. Each fold is validated
against a model trained using the data from the remaining
(nr_fold-1) folds.
This function returns the percentage of data that was
classified correctly over all the experiments.
"""
problem = dataset._create_svm_problem()
target = N.empty((len(dataset.data),), dtype=N.float64)
tp = target.ctypes.data_as(POINTER(c_double))
libsvm.svm_cross_validation(problem, self.param, nr_fold, tp)
total_correct = 0.
for x, t in zip(dataset.data, target):
if x[0] == int(t):
total_correct += 1
# XXX also return results from folds in a list
return 100.0 * total_correct / len(dataset.data)
class LibSvmCClassificationModel(LibSvmClassificationModel):
"""
A model for C-SV classification.
See also:
- Hsu, et al. A Practical Guide to Support Vector Classification.
- Gunn. Support Vector Machines for Classification and Regression.
- Burges. A Tutorial on Support Vector Machines for Pattern
Recognition.
"""
def __init__(self, kernel,
cost=1.0, weights=None, probability=False, **kwargs):
"""
Parameters:
- `cost`: XXX
- `weights`: XXX
"""
LibSvmClassificationModel.__init__(self, kernel, weights, **kwargs)
self.cost = cost
self.param.svm_type = libsvm.C_SVC
self.param.C = cost
self.param.probability = probability
class LibSvmNuClassificationModel(LibSvmClassificationModel):
"""
A model for nu-SV classification.
See also:
- Chen, et al. A Tutorial on nu-Support Vector Machines.
- Scholkopf, et al. New Support Vector Algorithms.
"""
def __init__(self, kernel,
nu=0.5, weights=None, probability=False, **kwargs):
"""
Parameters:
- `nu`: XXX
- `weights`: XXX
"""
LibSvmClassificationModel.__init__(self, kernel, weights, **kwargs)
self.nu = nu
self.param.svm_type = libsvm.NU_SVC
self.param.nu = nu
self.param.probability = probability
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