Added multitask python examples

shogun-toolbox · Jul 17, 2012 · 197ebdf · 197ebdf
1 parent dfb929e
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Showing 4 changed files with 116 additions and 3 deletions.
diff --git a/examples/undocumented/python_modular/transfer_multitask_l1l2_logistic_regression.py b/examples/undocumented/python_modular/transfer_multitask_l1l2_logistic_regression.py
@@ -0,0 +1,37 @@
+from numpy import array,hstack
+from numpy.random import seed, rand
+from tools.load import LoadMatrix
+lm=LoadMatrix()
+
+traindat = lm.load_numbers('../data/fm_train_real.dat')
+testdat = lm.load_numbers('../data/fm_test_real.dat')
+label_traindat = lm.load_labels('../data/label_train_twoclass.dat')
+
+parameter_list = [[traindat,testdat,label_traindat]]
+
+def transfer_multitask_l1l2_logistic_regression(fm_train=traindat,fm_test=testdat,label_train=label_traindat):
+
+	from modshogun import BinaryLabels, RealFeatures, IndexBlock, IndexBlockGroup, MultitaskL1L2LogisticRegression
+
+	features = RealFeatures(hstack((traindat,traindat)))
+	labels = BinaryLabels(hstack((label_train,label_train)))
+
+	n_vectors = features.get_num_vectors()
+	task_one = IndexBlock(0,n_vectors/2)
+	task_two = IndexBlock(n_vectors/2,n_vectors)
+	task_group = IndexBlockGroup()
+	task_group.add_block(task_one)
+	task_group.add_block(task_two)
+
+	mtlr = MultitaskL1L2LogisticRegression(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
+
+if __name__=='__main__':
+	print('TransferMultitaskL1L2LogisticRegression')
+	transfer_multitask_l1l2_logistic_regression(*parameter_list[0])
diff --git a/...ar/transfer_multitask_group_regression.py → ...sfer_multitask_leastsquares_regression.py b/...ar/transfer_multitask_group_regression.py → ...sfer_multitask_leastsquares_regression.py
@@ -9,7 +9,7 @@
 
 parameter_list = [[traindat,testdat,label_traindat]]
 
-def transfer_multitask_group_regression(fm_train=traindat,fm_test=testdat,label_train=label_traindat):
+def transfer_multitask_leastsquares_regression(fm_train=traindat,fm_test=testdat,label_train=label_traindat):
 
 	from modshogun import RegressionLabels, RealFeatures, IndexBlock, IndexBlockGroup, MultitaskLSRegression
 
@@ -24,11 +24,13 @@ def transfer_multitask_group_regression(fm_train=traindat,fm_test=testdat,label_
 	task_group.add_block(task_two)
 
 	mtlsr = MultitaskLSRegression(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
 
 if __name__=='__main__':
-	print('TransferMultitaskGroupRegression')
-	transfer_multitask_group_regression(*parameter_list[0])
+	print('TransferMultitaskLeastSquaresRegression')
+	transfer_multitask_leastsquares_regression(*parameter_list[0])
diff --git a/examples/undocumented/python_modular/transfer_multitask_logistic_regression.py b/examples/undocumented/python_modular/transfer_multitask_logistic_regression.py
@@ -0,0 +1,37 @@
+from numpy import array,hstack
+from numpy.random import seed, rand
+from tools.load import LoadMatrix
+lm=LoadMatrix()
+
+traindat = lm.load_numbers('../data/fm_train_real.dat')
+testdat = lm.load_numbers('../data/fm_test_real.dat')
+label_traindat = lm.load_labels('../data/label_train_twoclass.dat')
+
+parameter_list = [[traindat,testdat,label_traindat]]
+
+def transfer_multitask_logistic_regression(fm_train=traindat,fm_test=testdat,label_train=label_traindat):
+
+	from modshogun import BinaryLabels, RealFeatures, IndexBlock, IndexBlockGroup, MultitaskLogisticRegression
+
+	features = RealFeatures(hstack((traindat,traindat)))
+	labels = BinaryLabels(hstack((label_train,label_train)))
+
+	n_vectors = features.get_num_vectors()
+	task_one = IndexBlock(0,n_vectors/2)
+	task_two = IndexBlock(n_vectors/2,n_vectors)
+	task_group = IndexBlockGroup()
+	task_group.add_block(task_one)
+	task_group.add_block(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_regression().get_labels()
+
+	return out
+
+if __name__=='__main__':
+	print('TransferMultitaskLogisticRegression')
+	transfer_multitask_logistic_regression(*parameter_list[0])
diff --git a/examples/undocumented/python_modular/transfer_multitask_trace_logistic_regression.py b/examples/undocumented/python_modular/transfer_multitask_trace_logistic_regression.py
@@ -0,0 +1,37 @@
+from numpy import array,hstack
+from numpy.random import seed, rand
+from tools.load import LoadMatrix
+lm=LoadMatrix()
+
+traindat = lm.load_numbers('../data/fm_train_real.dat')
+testdat = lm.load_numbers('../data/fm_test_real.dat')
+label_traindat = lm.load_labels('../data/label_train_twoclass.dat')
+
+parameter_list = [[traindat,testdat,label_traindat]]
+
+def transfer_multitask_trace_logistic_regression(fm_train=traindat,fm_test=testdat,label_train=label_traindat):
+
+	from modshogun import BinaryLabels, RealFeatures, IndexBlock, IndexBlockGroup, MultitaskTraceLogisticRegression
+
+	features = RealFeatures(hstack((traindat,traindat)))
+	labels = BinaryLabels(hstack((label_train,label_train)))
+
+	n_vectors = features.get_num_vectors()
+	task_one = IndexBlock(0,n_vectors/2)
+	task_two = IndexBlock(n_vectors/2,n_vectors)
+	task_group = IndexBlockGroup()
+	task_group.add_block(task_one)
+	task_group.add_block(task_two)
+
+	mtlr = MultitaskTraceLogisticRegression(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
+
+if __name__=='__main__':
+	print('TransferMultitaskTraceLogisticRegression')
+	transfer_multitask_trace_logistic_regression(*parameter_list[0])