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from itertools import izip
from numpy.testing import *
import numpy as N
set_local_path('../..')
from svm.classification import *
from svm.dataset import LibSvmClassificationDataSet, LibSvmTestDataSet
from svm.kernel import *
from svm.predict import *
restore_path()
class test_classification(NumpyTestCase):
def check_basics(self):
kernel = LinearKernel()
# C-SVC
ModelType = LibSvmCClassificationModel
ModelType(kernel)
ModelType(kernel, cost=1.0)
weights = [(2, 10.0), (1, 20.0), (0, 30.0)]
ModelType(kernel, weights=weights)
ModelType(kernel, 1.0, weights)
ModelType(kernel, cost=1.0, weights=weights)
# nu-SVC
ModelType = LibSvmNuClassificationModel
ModelType(kernel)
ModelType(kernel, nu=0.5)
ModelType(kernel, weights=weights)
ModelType(kernel, 0.5, weights)
def _make_basic_datasets(self):
labels = [0, 1, 1, 2]
x = [N.array([0, 0]),
N.array([0, 1]),
N.array([1, 0]),
N.array([1, 1])]
traindata = LibSvmClassificationDataSet(labels, x)
testdata = LibSvmTestDataSet(x)
return traindata, testdata
def check_c_basics(self):
traindata, testdata = self._make_basic_datasets()
kernel = RBFKernel(traindata.gamma)
model = LibSvmCClassificationModel(kernel)
results = model.fit(traindata)
p = results.predict(testdata)
assert_array_equal(p, [1, 1, 1, 1])
results.predict_values(testdata)
def _make_basic_kernels(self, gamma):
kernels = [
LinearKernel(),
PolynomialKernel(3, gamma, 0.0),
RBFKernel(gamma)
]
return kernels
def _classify_basic(self, ModelType,
modelargs, expected_rhos, expected_ps):
traindata, testdata = self._make_basic_datasets()
kernels = self._make_basic_kernels(traindata.gamma)
for kernel, expected_rho, expected_p in \
zip(kernels, expected_rhos, expected_ps):
args = (kernel,) + modelargs
model = ModelType(*args)
results = model.fit(traindata)
self.assertEqual(results.labels, [0, 1, 2])
# decimal=4 due to compiler-dependent variations in rho
assert_array_almost_equal(results.rho, expected_rho, decimal=4)
p = N.array(results.predict(testdata))
assert_array_equal(p, expected_p)
def check_c_more(self):
cost = 10.0
weights = [(1, 10.0)]
modelargs = cost, weights
expected_rhos = [[-0.999349, -1.0, -3.0],
[0.375, -1.0, -1.153547],
[0.671181, 0.0, -0.671133]]
expected_ps = [[0, 1, 1, 2], [1, 1, 1, 2], [0, 1, 1, 2]]
self._classify_basic(LibSvmCClassificationModel,
modelargs, expected_rhos, expected_ps)
def check_c_probability(self):
traindata, testdata = self._make_basic_datasets()
nu = 0.5
cost = 10.0
weights = [(1, 10.0)]
kernels = self._make_basic_kernels(traindata.gamma)
models = [
(LibSvmCClassificationModel, (cost, weights)),
(LibSvmNuClassificationModel, (nu, weights))
]
for ModelType, modelargs in models:
for kernel in kernels:
args = (kernel,) + modelargs
kwargs = {'probability' : True}
model = ModelType(*args, **kwargs)
results = model.fit(traindata)
results.predict_probability(testdata)
def check_cross_validate(self):
labels = ([-1] * 50) + ([1] * 50)
x = N.random.randn(len(labels), 10)
traindata = LibSvmClassificationDataSet(labels, x)
kernel = LinearKernel()
model = LibSvmCClassificationModel(kernel)
nr_fold = 10
pcorr = model.cross_validate(traindata, nr_fold)
# XXX check cross-validation with and without probability
# output enabled
def check_nu_basics(self):
traindata, testdata = self._make_basic_datasets()
kernel = RBFKernel(traindata.gamma)
model = LibSvmNuClassificationModel(kernel)
results = model.fit(traindata)
p = results.predict(testdata)
assert_array_equal(p, [0, 1, 1, 2])
v = results.predict_values(testdata)
def check_nu_more(self):
nu = 0.5
weights = [(1, 10.0)]
modelargs = nu, weights
expected_rhos = [[-1.0, -1.0, -3.0],
[-1.0, -1.0, -1.15384846],
[0.6712142, 0.0, -0.6712142]]
expected_ps = [[0, 1, 1, 2]] * 3
self._classify_basic(LibSvmNuClassificationModel,
modelargs, expected_rhos, expected_ps)
def _make_datasets(self):
labels1 = N.random.random_integers(0, 2, 100)
x1 = N.random.randn(len(labels1), 10)
labels2 = N.random.random_integers(0, 2, 10)
x2 = N.random.randn(len(labels2), x1.shape[1])
trndata1 = LibSvmClassificationDataSet(labels1, x1)
trndata2 = LibSvmClassificationDataSet(labels2, x2)
reflabels = N.concatenate([labels1, labels2])
refx = N.vstack([x1, x2])
trndata = LibSvmClassificationDataSet(reflabels, refx)
testdata = LibSvmTestDataSet(refx)
return trndata, testdata, trndata1, trndata2
def _make_kernels(self):
def kernelf(x, y, dot):
return dot(x, y)
def kernelg(x, y, dot):
return -dot(x, y)
kernels = [LinearKernel()]
#kernels += [RBFKernel(gamma)
# for gamma in [-0.1, 0.2, 0.3]]
#kernels += [PolynomialKernel(degree, gamma, coef0)
# for degree, gamma, coef0 in
# [(1, 0.1, 0.0), (2, -0.2, 1.3), (3, 0.3, -0.3)]]
#kernels += [SigmoidKernel(gamma, coef0)
# for gamma, coef0 in [(0.2, -0.5), (-0.5, 1.5)]]
#kernels += [CustomKernel(f) for f in [kernelf, kernelg]]
return kernels
def check_all(self):
trndata, testdata, trndata1, trndata2 = self._make_datasets()
kernels = self._make_kernels()
weights = [(0, 2.0), (1, 5.0), (2, 3.0)]
for kernel in kernels:
pctrndata1 = trndata1.precompute(kernel)
pctrndata = pctrndata1.combine(trndata2)
models = [
LibSvmCClassificationModel(kernel, 2.0, weights, True),
LibSvmNuClassificationModel(kernel, 0.3, weights, True)
]
fitargs = []
# CustomKernel needs a precomputed dataset
if not isinstance(kernel, CustomKernel):
fitargs += [
(trndata, LibSvmPredictor),
#(trndata, LibSvmPythonPredictor),
]
fitargs += [
(pctrndata, LibSvmPredictor),
#(pctrndata, LibSvmPythonPredictor)
]
for model in models:
refresults = model.fit(*fitargs[0])
refrho = refresults.rho
refp = refresults.predict(testdata)
refv = refresults.predict_values(testdata)
refpp = refresults.predict_probability(testdata)
for args in fitargs[1:]:
results = model.fit(*args)
assert_array_almost_equal(results.rho, refrho)
p = results.predict(testdata)
assert_array_almost_equal(refp, p)
v = results.predict_values(testdata)
for v, refv in zip(v, refv):
for key, value in refv.iteritems():
self.assertAlmostEqual(v[key], value)
try:
pp = results.predict_probability(testdata)
# XXX there are slight differences between
# precomputed and normal kernels here
#for (lbl, p), (reflbl, refp) in zip(pp, refpp):
# self.assertEqual(lbl, reflbl)
# assert_array_almost_equal(p, refp)
except NotImplementedError:
self.assert_(fitargs[-1] is LibSvmPythonPredictor)
def check_python_predict(self):
traindata, testdata = self._make_basic_datasets()
kernel = LinearKernel()
cost = 10.0
weights = [(1, 10.0)]
model = LibSvmCClassificationModel(kernel, cost, weights)
refresults = model.fit(traindata)
results = model.fit(traindata, LibSvmPythonPredictor)
refv = refresults.predict_values(testdata)
v = results.predict_values(testdata)
self.assertEqual(len(refv), len(v))
for pred, refpred in zip(v, refv):
for key, value in refpred.iteritems():
assert_array_almost_equal(value, pred[key])
refp = refresults.predict(testdata)
p = results.predict(testdata)
assert_array_equal(p, refp)
def xcheck_compact(self):
traindata, testdata = self._make_basic_datasets()
kernel = LinearKernel()
cost = 10.0
weights = [(1, 10.0)]
model = LibSvmCClassificationModel(kernel, cost, weights)
results = model.fit(traindata, LibSvmPythonPredictor)
refvs = results.predict_values(testdata)
results.compact()
vs = results.predict_values(testdata)
for refv, v in zip(refvs, vs):
for key, value in refv.iteritems():
self.assertEqual(value, v[key])
def _make_compact_check_datasets(self):
x = N.random.randn(150, 3)
labels = N.random.random_integers(1, 5, x.shape[0])
traindata = LibSvmClassificationDataSet(labels, x)
xdim, ydim, zdim = 4, 4, x.shape[1]
img = N.random.randn(xdim, ydim, zdim)
testdata1 = LibSvmTestDataSet(img.reshape(xdim*ydim, zdim))
testdata2 = LibSvmTestDataSet(list(img.reshape(xdim*ydim, zdim)))
return traindata, testdata1, testdata2
def check_compact_predict_values(self):
def compare_predict_values(vx, vy):
for pred1, pred2 in izip(vx, vy):
for labels, x in pred1.iteritems():
self.assert_(labels in pred2)
self.assertAlmostEqual(x, pred2[labels])
traindata, testdata1, testdata2 = \
self._make_compact_check_datasets()
kernel = LinearKernel()
model = LibSvmCClassificationModel(kernel)
refresults = model.fit(traindata)
refv1 = refresults.predict_values(testdata1)
refv2 = refresults.predict_values(testdata2)
results = model.fit(traindata, LibSvmPythonPredictor)
v11 = results.predict_values(testdata1)
v12 = results.predict_values(testdata2)
results.compact()
v21 = results.predict_values(testdata1)
v22 = results.predict_values(testdata2)
compare_predict_values(refv1, refv2)
compare_predict_values(refv1, v11)
compare_predict_values(refv1, v12)
compare_predict_values(refv1, v21)
# XXX this test fails
#compare_predict_values(refv1, v22)
def check_compact_predict(self):
traindata, testdata1, testdata2 = \
self._make_compact_check_datasets()
kernel = LinearKernel()
model = LibSvmCClassificationModel(kernel)
refresults = model.fit(traindata)
refp1 = refresults.predict(testdata1)
refp2 = refresults.predict(testdata2)
results = model.fit(traindata, LibSvmPythonPredictor)
p11 = results.predict(testdata1)
p12 = results.predict(testdata2)
results.compact()
p21 = results.predict(testdata1)
p22 = results.predict(testdata2)
self.assertEqual(refp1, refp2)
self.assertEqual(refp1, p11)
self.assertEqual(refp1, p12)
# XXX these tests fail
#self.assertEqual(refp1, p21)
#self.assertEqual(refp1, p22)
def check_no_support_vectors(self):
x = N.array([[10.0, 20.0]])
labels = [1]
traindata = LibSvmClassificationDataSet(labels, x)
kernel = LinearKernel()
model = LibSvmCClassificationModel(kernel)
testdata = LibSvmTestDataSet(x)
self.assertRaises(ValueError, model.fit, traindata)
if __name__ == '__main__':
NumpyTest().run()
|
