def regression_linear_ridge_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6): from shogun.Features import Labels, RealFeatures from shogun.Regression import LinearRidgeRegression rr=LinearRidgeRegression(tau, RealFeatures(traindat), Labels(label_train)) rr.train() out = rr.apply(RealFeatures(fm_test)).get_labels() return out,rr
def regression_linear_ridge_modular(fm_train=traindat,
fm_test=testdat,
label_train=label_traindat,
tau=1e-6):
from shogun.Features import RegressionLabels, RealFeatures
from shogun.Regression import LinearRidgeRegression
rr = LinearRidgeRegression(tau, RealFeatures(traindat),
RegressionLabels(label_train))
rr.train()
out = rr.apply(RealFeatures(fm_test)).get_labels()
return out, rr
ytest = yall[ntrain:]
# preprocess data
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(Labels(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(Labels(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)
ytest = yall[ntrain:]
# preprocess data
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)
ytest = yall[ntrain:]
# preprocess data
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)
evaluator = MeanSquaredError()
# apply on training data
mse_train = np.zeros(LeastAngleRegression.get_path_size())
