def apply(self):
kernel = ["linear", "poly", "rbf", "sigmoid"][self.kernel_type]
common_args = dict(kernel=kernel,
degree=self.degree,
gamma=self.gamma,
coef0=self.coef0,
tol=self.tol,
preprocessors=self.preprocessors)
if self.svrtype == OWSVMRegression.Epsilon_SVR:
learner = SVRLearner(C=self.epsilon_C,
epsilon=self.epsilon,
**common_args)
else:
learner = NuSVRLearner(C=self.nu_C, nu=self.nu, **common_args)
learner.name = self.learner_name
predictor = None
sv = None
if self.data is not None:
self.error(0)
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
predictor = learner(self.data)
predictor.name = self.learner_name
sv = self.data[predictor.skl_model.support_]
self.send("Learner", learner)
self.send("Predictor", predictor)
self.send("Support vectors", sv)
def test_SVR(self):
nrows, ncols = 200, 5
X = np.random.rand(nrows, ncols)
y = X.dot(np.random.rand(ncols))
data = Table(X, y)
learn = SVRLearner(kernel='rbf', gamma=0.1)
res = CrossValidation(data, [learn], k=2)
self.assertLess(RMSE(res)[0], 0.15)
def test_LinearSVR(self):
nrows, ncols = 200, 5
X = np.random.rand(nrows, ncols)
y = X.dot(np.random.rand(ncols))
data = Table(X, y)
learn = SVRLearner()
cv = CrossValidation(k=2)
res = cv(data, [learn])
self.assertLess(RMSE(res)[0], 0.15)
def test_SVR(self):
nrows, ncols = 200, 5
X = np.random.rand(nrows, ncols)
y = X.dot(np.random.rand(ncols))
data = Table.from_numpy(None, X, y)
learn = SVRLearner(kernel='rbf', gamma=0.1)
cv = CrossValidation(k=2)
res = cv(data, [learn])
self.assertLess(RMSE(res)[0], 0.15)
def create_learner(self):
kernel = ["linear", "poly", "rbf", "sigmoid"][self.kernel_type]
common_args = dict(kernel=kernel,
degree=self.degree,
gamma=self.gamma,
coef0=self.coef0,
tol=self.tol,
preprocessors=self.preprocessors)
if self.svrtype == OWSVMRegression.Epsilon_SVR:
return SVRLearner(C=self.epsilon_C,
epsilon=self.epsilon,
**common_args)
else:
return NuSVRLearner(C=self.nu_C, nu=self.nu, **common_args)