def RunLinearRegressionShogun(q):
totalTimer = Timer()
# Load input dataset.
# If the dataset contains two files then the second file is the responses
# file.
try:
Log.Info("Loading dataset", self.verbose)
if len(self.dataset) == 2:
X = np.genfromtxt(self.dataset[0], delimiter=',')
y = np.genfromtxt(self.dataset[1], delimiter=',')
else:
X = np.genfromtxt(self.dataset, delimiter=',')
y = X[:, (X.shape[1] - 1)]
X = X[:,:-1]
with totalTimer:
# Perform linear regression.
model = LeastSquaresRegression(RealFeatures(X.T), RegressionLabels(y))
model.train()
b = model.get_w()
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def RunLinearRegressionShogun(q):
totalTimer = Timer()
# Load input dataset.
# If the dataset contains two files then the second file is the responses
# file.
try:
Log.Info("Loading dataset", self.verbose)
if len(self.dataset) == 2:
X = np.genfromtxt(self.dataset[0], delimiter=',')
y = np.genfromtxt(self.dataset[1], delimiter=',')
else:
X = np.genfromtxt(self.dataset, delimiter=',')
y = X[:, (X.shape[1] - 1)]
X = X[:, :-1]
with totalTimer:
# Perform linear regression.
model = LeastSquaresRegression(RealFeatures(X.T),
RegressionLabels(y))
model.train()
b = model.get_w()
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def regression_least_squares_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6): from shogun.Features import RegressionLabels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import LeastSquaresRegression ls=LeastSquaresRegression(RealFeatures(traindat), RegressionLabels(label_train)) ls.train() out = ls.apply(RealFeatures(fm_test)).get_labels() return out,ls
def regression_least_squares_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6): from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import LeastSquaresRegression ls=LeastSquaresRegression(RealFeatures(traindat), Labels(label_train)) ls.train() out = ls.apply(RealFeatures(fm_test)).get_labels() return out,ls
for i in xrange(p):
X[:, i] -= np.mean(X[:, i])
X[:, i] /= np.linalg.norm(X[:, i])
y -= np.mean(y)
# train LASSO
LeastAngleRegression = LeastAngleRegression()
LeastAngleRegression.set_labels(RegressionLabels(y))
LeastAngleRegression.train(RealFeatures(X.T))
# train ordinary LSR
if use_ridge:
lsr = LinearRidgeRegression(0.01, RealFeatures(X.T), Labels(y))
lsr.train()
else:
lsr = LeastSquaresRegression()
lsr.set_labels(RegressionLabels(y))
lsr.train(RealFeatures(X.T))
# gather LASSO path
path = np.zeros((p, LeastAngleRegression.get_path_size()))
for i in xrange(path.shape[1]):
path[:, i] = LeastAngleRegression.get_w(i)
# apply on training data
mse_train = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_train.shape[0]):
LeastAngleRegression.switch_w(i)
ypred = LeastAngleRegression.apply(RealFeatures(X.T)).get_labels()
mse_train[i] = np.dot(ypred - y, ypred - y) / y.shape[0]
ypred = lsr.apply(RealFeatures(X.T)).get_labels()
