def RunLARSShogun(q):
totalTimer = Timer()
# Load input dataset.
try:
Log.Info("Loading dataset", self.verbose)
inputData = np.genfromtxt(self.dataset[0], delimiter=',')
responsesData = np.genfromtxt(self.dataset[1], delimiter=',')
inputFeat = RealFeatures(inputData.T)
responsesFeat = RegressionLabels(responsesData)
# Get all the parameters.
lambda1 = re.search("-l (\d+)", options)
lambda1 = 0.0 if not lambda1 else int(lambda1.group(1))
with totalTimer:
# Perform LARS.
model = LeastAngleRegression(False)
model.set_max_l1_norm(lambda1)
model.set_labels(responsesFeat)
model.train(inputFeat)
model.get_w(model.get_path_size() - 1)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def RunLARSShogun(q):
totalTimer = Timer()
# Load input dataset.
try:
Log.Info("Loading dataset", self.verbose)
inputData = np.genfromtxt(self.dataset[0], delimiter=',')
responsesData = np.genfromtxt(self.dataset[1], delimiter=',')
inputFeat = RealFeatures(inputData.T)
responsesFeat = RegressionLabels(responsesData)
# Get all the parameters.
lambda1 = re.search("-l (\d+)", options)
lambda1 = 0.0 if not lambda1 else int(lambda1.group(1))
with totalTimer:
# Perform LARS.
model = LeastAngleRegression(False)
model.set_max_l1_norm(lambda1)
model.set_labels(responsesFeat)
model.train(inputFeat)
model.get_w(model.get_path_size() - 1)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
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)
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()
mse_train_lsr = np.dot(ypred - y, ypred - y) / y.shape[0]
# apply on test data
mse_test = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_test.shape[0]):
LeastAngleRegression.switch_w(i)
ypred = LeastAngleRegression.apply(RealFeatures(Xtest.T)).get_labels()
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()
mse_train_lsr = np.dot(ypred - y, ypred - y) / y.shape[0]
# apply on test data
mse_test = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_test.shape[0]):
LeastAngleRegression.switch_w(i)
ypred = LeastAngleRegression.apply(RealFeatures(Xtest.T)).get_labels()
