def RunLARSShogun():
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 = None
if "lambda1" in options:
lambda1 = float(options.pop("lambda1"))
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
with totalTimer:
# Perform LARS.
model = LeastAngleRegression(False)
if lambda1:
model.set_max_l1_norm(lambda1)
model.set_labels(responsesFeat)
model.train(inputFeat)
model.get_w_for_var(model.get_path_size() - 1)
except Exception as e:
return -1
return totalTimer.ElapsedTime()
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 RunLASSOShogun(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:
testSet = np.genfromtxt(self.dataset[1], delimiter=',')
# Get all the parameters.
lambda1 = re.search("-l (\d+)", options)
lambda1 = 0.0 if not lambda1 else int(lambda1.group(1))
# Use the last row of the training set as the responses.
X, y = SplitTrainData(self.dataset)
with totalTimer:
model = LeastAngleRegression(lasso=True)
model.set_max_l1_norm(lambda1)
model.set_labels(RegressionLabels(y))
model.train(RealFeatures(X.T))
except Exception as e:
print(e)
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def RunLASSOShogun():
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:
testSet = np.genfromtxt(self.dataset[1], delimiter=',')
# Get all the parameters.
lambda1 = None
if "lambda1" in options:
lambda1 = float(options.pop("lambda1"))
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
# Use the last row of the training set as the responses.
X, y = SplitTrainData(self.dataset)
with totalTimer:
model = LeastAngleRegression(lasso=True)
if lambda1:
model.set_max_l1_norm(lambda1)
model.set_labels(RegressionLabels(y))
model.train(RealFeatures(X.T))
except Exception as e:
return -1
return totalTimer.ElapsedTime()
def RunLASSOShogun():
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:
testSet = np.genfromtxt(self.dataset[1], delimiter=',')
# Get all the parameters.
lambda1 = None
if "lambda1" in options:
lambda1 = float(options.pop("lambda1"))
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
# Use the last row of the training set as the responses.
X, y = SplitTrainData(self.dataset)
with totalTimer:
model = LeastAngleRegression(lasso=True)
if lambda1:
model.set_max_l1_norm(lambda1)
model.set_labels(RegressionLabels(y))
model.train(RealFeatures(X.T))
except Exception as e:
return -1
return totalTimer.ElapsedTime()
def RunLASSOShogun(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:
testSet = np.genfromtxt(self.dataset[1], delimiter=',')
# Get all the parameters.
lambda1 = re.search("-l (\d+)", options)
lambda1 = 0.0 if not lambda1 else int(lambda1.group(1))
# Use the last row of the training set as the responses.
X, y = SplitTrainData(self.dataset)
with totalTimer:
model = LeastAngleRegression(lasso=True)
model.set_max_l1_norm(lambda1)
model.set_labels(RegressionLabels(y))
model.train(RealFeatures(X.T))
except Exception as e:
print(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
X = Xall[0:ntrain, :]
y = yall[0:ntrain]
Xtest = Xall[ntrain:, :]
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)
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 = None
if "lambda1" in options:
lambda1 = float(options.pop("lambda1"))
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
with totalTimer:
# Perform LARS.
model = LeastAngleRegression(False)
if lambda1:
model.set_max_l1_norm(lambda1)
model.set_labels(responsesFeat)
model.train(inputFeat)
model.get_w_for_var(model.get_path_size() - 1)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
2] + 0.5 * np.random.randn(n)
X = Xall[0:ntrain, :]
y = yall[0:ntrain]
Xtest = Xall[ntrain:, :]
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]):
