def transfer_multitask_leastsquares_regression(fm_train=traindat,
fm_test=testdat,
label_train=label_traindat):
from modshogun import RegressionLabels, RealFeatures, Task, TaskGroup
try:
from modshogun import MultitaskLeastSquaresRegression
except ImportError:
print("MultitaskLeastSquaresRegression not available")
exit(0)
features = RealFeatures(traindat)
labels = RegressionLabels(label_train)
n_vectors = features.get_num_vectors()
task_one = Task(0, n_vectors // 2)
task_two = Task(n_vectors // 2, n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
mtlsr = MultitaskLeastSquaresRegression(0.1, features, labels, task_group)
mtlsr.set_regularization(1) # use regularization ratio
mtlsr.set_tolerance(1e-2) # use 1e-2 tolerance
mtlsr.train()
mtlsr.set_current_task(0)
out = mtlsr.apply_regression().get_labels()
return out
def transfer_multitask_leastsquares_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
from modshogun import RegressionLabels, RealFeatures, Task, TaskGroup
try:
from modshogun import MultitaskLeastSquaresRegression
except ImportError:
print("MultitaskLeastSquaresRegression not available")
exit(0)
features = RealFeatures(traindat)
labels = RegressionLabels(label_train)
n_vectors = features.get_num_vectors()
task_one = Task(0,n_vectors//2)
task_two = Task(n_vectors//2,n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
mtlsr = MultitaskLeastSquaresRegression(0.1,features,labels,task_group)
mtlsr.set_regularization(1) # use regularization ratio
mtlsr.set_tolerance(1e-2) # use 1e-2 tolerance
mtlsr.train()
mtlsr.set_current_task(0)
out = mtlsr.apply_regression().get_labels()
return out
def transfer_multitask_clustered_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup, MSG_DEBUG
try:
from modshogun import MultitaskClusteredLogisticRegression
except ImportError:
print("MultitaskClusteredLogisticRegression not available")
exit()
features = RealFeatures(hstack((traindat,sin(traindat),cos(traindat))))
labels = BinaryLabels(hstack((label_train,label_train,label_train)))
n_vectors = features.get_num_vectors()
task_one = Task(0,n_vectors//3)
task_two = Task(n_vectors//3,2*n_vectors//3)
task_three = Task(2*n_vectors//3,n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
task_group.append_task(task_three)
mtlr = MultitaskClusteredLogisticRegression(1.0,100.0,features,labels,task_group,2)
#mtlr.io.set_loglevel(MSG_DEBUG)
mtlr.set_tolerance(1e-3) # use 1e-2 tolerance
mtlr.set_max_iter(100)
mtlr.train()
mtlr.set_current_task(0)
#print mtlr.get_w()
out = mtlr.apply_regression().get_labels()
return out
def transfer_multitask_l12_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup
try:
from modshogun import MultitaskL12LogisticRegression
except ImportError:
print("MultitaskL12LogisticRegression not available")
exit(0)
features = RealFeatures(hstack((traindat,traindat)))
labels = BinaryLabels(hstack((label_train,label_train)))
n_vectors = features.get_num_vectors()
task_one = Task(0,n_vectors//2)
task_two = Task(n_vectors//2,n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
mtlr = MultitaskL12LogisticRegression(0.1,0.1,features,labels,task_group)
mtlr.set_tolerance(1e-2) # use 1e-2 tolerance
mtlr.set_max_iter(10)
mtlr.train()
mtlr.set_current_task(0)
out = mtlr.apply_regression().get_labels()
return out
def transfer_multitask_l12_logistic_regression(fm_train=traindat,
fm_test=testdat,
label_train=label_traindat):
from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup
try:
from modshogun import MultitaskL12LogisticRegression
except ImportError:
print("MultitaskL12LogisticRegression not available")
exit(0)
features = RealFeatures(hstack((traindat, traindat)))
labels = BinaryLabels(hstack((label_train, label_train)))
n_vectors = features.get_num_vectors()
task_one = Task(0, n_vectors // 2)
task_two = Task(n_vectors // 2, n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
mtlr = MultitaskL12LogisticRegression(0.1, 0.1, features, labels,
task_group)
mtlr.set_tolerance(1e-2) # use 1e-2 tolerance
mtlr.set_max_iter(10)
mtlr.train()
mtlr.set_current_task(0)
out = mtlr.apply_regression().get_labels()
return out
def transfer_multitask_group_regression(fm_train=traindat,fm_test=testdat,label_train=label_traindat): from modshogun import RegressionLabels, RealFeatures, Task, TaskGroup, MultitaskLSRegression features = RealFeatures(traindat) labels = RegressionLabels(label_train) n_vectors = features.get_num_vectors() task_one = Task(0,n_vectors/2) task_two = Task(n_vectors/2,n_vectors) task_group = TaskGroup() task_group.add_task(task_one) task_group.add_task(task_two) mtlsr = MultitaskLSRegression(0.1,features,labels,task_group) mtlsr.train() mtlsr.set_current_task(0) out = mtlsr.apply_regression().get_labels() return out
def transfer_multitask_group_regression(fm_train=traindat,
fm_test=testdat,
label_train=label_traindat):
from modshogun import RegressionLabels, RealFeatures, Task, TaskGroup, MultitaskLSRegression
features = RealFeatures(traindat)
labels = RegressionLabels(label_train)
n_vectors = features.get_num_vectors()
task_one = Task(0, n_vectors / 2)
task_two = Task(n_vectors / 2, n_vectors)
task_group = TaskGroup()
task_group.add_task(task_one)
task_group.add_task(task_two)
mtlsr = MultitaskLSRegression(0.1, features, labels, task_group)
mtlsr.train()
mtlsr.set_current_task(0)
out = mtlsr.apply_regression().get_labels()
return out
def transfer_multitask_clustered_logistic_regression(fm_train=traindat,
fm_test=testdat,
label_train=label_traindat
):
from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup, MultitaskClusteredLogisticRegression, MSG_DEBUG
features = RealFeatures(hstack((traindat, sin(traindat), cos(traindat))))
labels = BinaryLabels(hstack((label_train, label_train, label_train)))
n_vectors = features.get_num_vectors()
task_one = Task(0, n_vectors // 3)
task_two = Task(n_vectors // 3, 2 * n_vectors // 3)
task_three = Task(2 * n_vectors // 3, n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
task_group.append_task(task_three)
mtlr = MultitaskClusteredLogisticRegression(1.0, 100.0, features, labels,
task_group, 2)
#mtlr.io.set_loglevel(MSG_DEBUG)
mtlr.set_tolerance(1e-3) # use 1e-2 tolerance
mtlr.set_max_iter(100)
mtlr.train()
mtlr.set_current_task(0)
#print mtlr.get_w()
out = mtlr.apply_regression().get_labels()
return out
def transfer_multitask_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat): from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup, MultitaskLogisticRegression features = RealFeatures(hstack((traindat,traindat))) labels = BinaryLabels(hstack((label_train,label_train))) n_vectors = features.get_num_vectors() task_one = Task(0,n_vectors/2) task_two = Task(n_vectors/2,n_vectors) task_group = TaskGroup() task_group.append_task(task_one) task_group.append_task(task_two) mtlr = MultitaskLogisticRegression(0.1,features,labels,task_group) mtlr.set_regularization(1) # use regularization ratio mtlr.set_tolerance(1e-2) # use 1e-2 tolerance mtlr.train() mtlr.set_current_task(0) out = mtlr.apply().get_labels() return out
