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from numpy.testing import *
import numpy as N
set_local_path('../..')
from svm.dataset import LibSvmRegressionDataSet, LibSvmTestDataSet
from svm.kernel import *
from svm.predict import *
from svm.regression import *
restore_path()
class test_regression(NumpyTestCase):
def check_basics(self):
Model = LibSvmEpsilonRegressionModel
kernel = LinearKernel()
Model(kernel)
Model(kernel, epsilon=0.1)
Model(kernel, cost=1.0)
model = Model(kernel, shrinking=False)
self.assert_(not model.shrinking)
Model = LibSvmNuRegressionModel
Model(kernel)
Model(kernel, nu=0.5)
model = Model(kernel, 0.5, cache_size=60, tolerance=0.005)
self.assertEqual(model.cache_size, 60)
self.assertAlmostEqual(model.tolerance, 0.005)
def check_epsilon_train(self):
ModelType = LibSvmEpsilonRegressionModel
y = [10., 20., 30., 40.]
x = [N.array([0, 0]),
N.array([0, 1]),
N.array([1, 0]),
N.array([1, 1])]
traindata = LibSvmRegressionDataSet(y, x)
testdata = LibSvmTestDataSet(x)
model = ModelType(LinearKernel(), probability=True)
results = model.fit(traindata)
results.predict(testdata)
results.get_svr_probability()
def _make_basic_datasets(self):
labels = [0, 1.0, 1.0, 2.0]
x = [N.array([0, 0]),
N.array([0, 1]),
N.array([1, 0]),
N.array([1, 1])]
traindata = LibSvmRegressionDataSet(labels, x)
testdata = LibSvmTestDataSet(x)
return traindata, testdata
def _make_basic_kernels(self, gamma):
kernels = [
LinearKernel(),
PolynomialKernel(3, gamma, 0.0),
RBFKernel(gamma)
]
return kernels
def check_epsilon_more(self):
ModelType = LibSvmEpsilonRegressionModel
epsilon = 0.1
cost = 10.0
modelargs = epsilon, cost
expected_ys = [
N.array([0.1, 1.0, 1.0, 1.9]),
N.array([0.24611273, 0.899866638, 0.90006681, 1.90006681]),
N.array([0.1, 1.0, 1.0, 1.9])
]
self._regression_basic(ModelType, modelargs, expected_ys)
def _regression_basic(self, ModelType, modelargs, expected_ys):
traindata, testdata = self._make_basic_datasets()
kernels = self._make_basic_kernels(traindata.gamma)
for kernel, expected_y in zip(kernels, expected_ys):
args = (kernel,) + modelargs
model = ModelType(*args)
results = model.fit(traindata)
predictions = results.predict(testdata)
# use differences instead of assertAlmostEqual due to
# compiler-dependent variations in these values
diff = N.absolute(predictions - expected_y)
self.assert_(N.alltrue(diff < 1e-3))
def check_cross_validate(self):
y = N.random.randn(100)
x = N.random.randn(len(y), 10)
traindata = LibSvmRegressionDataSet(y, x)
kernel = LinearKernel()
model = LibSvmEpsilonRegressionModel(kernel)
nr_fold = 10
mse, scc = model.cross_validate(traindata, nr_fold)
def check_nu_more(self):
ModelType = LibSvmNuRegressionModel
nu = 0.4
cost = 10.0
modelargs = nu, cost
expected_ys = [
N.array([0.0, 1.0, 1.0, 2.0]),
N.array([0.2307521, 0.7691364, 0.76930371, 1.769304]),
N.array([0.0, 1.0, 1.0, 2.0])
]
self._regression_basic(ModelType, modelargs, expected_ys)
def _make_datasets(self):
y1 = N.random.randn(50)
x1 = N.random.randn(len(y1), 10)
y2 = N.random.randn(5)
x2 = N.random.randn(len(y2), x1.shape[1])
trndata1 = LibSvmRegressionDataSet(y1, x1)
trndata2 = LibSvmRegressionDataSet(y2, x2)
refy = N.concatenate([y1, y2])
refx = N.vstack([x1, x2])
trndata = LibSvmRegressionDataSet(refy, refx)
testdata = LibSvmTestDataSet(refx)
return trndata, trndata1, trndata2, testdata
def _make_kernels(self):
def kernelf(x, y):
return N.dot(x, y.T)
def kernelg(x, y):
return -N.dot(x, y.T)
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, trndata1, trndata2, testdata = self._make_datasets()
kernels = self._make_kernels()
for kernel in kernels:
pctrndata1 = trndata1.precompute(kernel)
pctrndata = pctrndata1.combine(trndata2)
models = [
LibSvmEpsilonRegressionModel(kernel, 1.0, 2.0),
LibSvmNuRegressionModel(kernel, 0.4, 0.5)
]
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)
for args in fitargs[1:]:
results = model.fit(*args)
self.assertAlmostEqual(results.rho, refrho)
p = results.predict(testdata)
assert_array_almost_equal(refp, p)
def check_compact(self):
traindata, testdata = self._make_basic_datasets()
kernel = LinearKernel()
model = LibSvmEpsilonRegressionModel(LinearKernel())
results = model.fit(traindata, LibSvmPythonPredictor)
refp = results.predict(testdata)
results.compact()
p = results.predict(testdata)
assert_array_equal(refp, p)
if __name__ == '__main__':
NumpyTest().run()
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