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Restore wrong modified python modular examples
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@lisitsyn
lisitsyn committed Aug 30, 2011
1 parent 03ee3f8 commit 9580eb8
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41 changes: 21 additions & 20 deletions examples/undocumented/python_modular/classifier_domainadaptationsvm_modular.py
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Expand Up @@ -14,8 +14,7 @@
'CGACGTAGTCGTAGCCCCA',
'CAAAAAAAAAAAAAAAATA',
'CGACGGGGGGGGGGGCGTA']
label_traindna = NArray.to_na([-1.0]*5 + [1.0]*5)

label_traindna = numpy.array(5*[-1.0] + 5*[1.0])
testdna = ['AGCACGTACGTAGCTCGAT',
'AGACGTAGTCGTAGTCGTA',
'CAACGGGGGGGGGGTCGTA',
Expand All @@ -26,7 +25,7 @@
'CGACGTAGTCCCAGCCCCA',
'CAAAAAAAAAAAACCAATA',
'CGACGGCCGGGGGGGCGTA']
label_testdna = NArray.to_na([-1.0]*5 + [1.0]*5)
label_testdna = numpy.array(5*[-1.0] + 5*[1.0])


traindna2 = ['AGACAGTCAGTCGATAGCT',
Expand All @@ -39,8 +38,7 @@
'AGCAGTCGTAGTCGAAAAC',
'ACCCCCCCCCCCCCCCCTC',
'AGCAGGGGGGGGGGGAGTC']
label_traindna2 = NArray.to_na([-1.0]*5 + [1.0]*5)

label_traindna2 = numpy.array(5*[-1.0] + 5*[1.0])
testdna2 = ['CGACAGTCAGTCGATAGCT',
'CGCAGTCGTAGTCGTAGTC',
'ACCAGGGGGGGGGGTAGTC',
Expand All @@ -51,36 +49,39 @@
'AGCAGTCGTAAACGAAAAC',
'ACCCCCCCCCCCCAACCTC',
'AGCAGGAAGGGGGGGAGTC']
label_testdna2 = NArray.to_na([-1.0]*5 + [1.0]*5)
label_testdna2 = numpy.array(5*[-1.0] + 5*[1.0])

parameter_list = [[traindna,testdna,label_traindna,label_testdna,traindna2,label_traindna2,
testdna2,label_testdna2,1,3],[traindna,testdna,label_traindna,label_testdna,traindna2,label_traindna2,
parameter_list = [[traindna,testdna,label_traindna,label_testdna,traindna2,label_traindna2, \
testdna2,label_testdna2,1,3],[traindna,testdna,label_traindna,label_testdna,traindna2,label_traindna2, \
testdna2,label_testdna2,2,5]]

def classifier_domainadaptationsvm_modular(fm_train_dna=traindna, fm_test_dna=testdna, label_train_dna=label_traindna, label_test_dna=label_testdna, fm_train_dna2=traindna2,fm_test_dna2=testdna2, label_train_dna2=label_traindna2, label_test_dna2=label_testdna2, c=1, degree=3)
def classifier_domainadaptationsvm_modular(fm_train_dna=traindna,fm_test_dna=testdna, \
label_train_dna=label_traindna, \
label_test_dna=label_testdna,fm_train_dna2=traindna2,fm_test_dna2=testdna2, \
label_train_dna2=label_traindna2,label_test_dna2=label_testdna2,C=1,degree=3):




feats_train = Modshogun::StringCharFeatures.new(fm_train_dna, Modshogun::DNA)
feats_test = Modshogun::StringCharFeatures.new(fm_test_dna, Modshogun::DNA)
kernel = Modshogun::WeightedDegreeStringKernel.new(feats_train, feats_train, degree)
labels = Modshogun::Labels.new(label_train_dna)
svm = Modshogun::SVMLight.new(c, kernel, labels)
feats_train = StringCharFeatures(fm_train_dna, DNA)
feats_test = StringCharFeatures(fm_test_dna, DNA)
kernel = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels = Labels(label_train_dna)
svm = SVMLight(C, kernel, labels)
svm.train()
#svm.io.set_loglevel(MSG_DEBUG)

#####################################

#print "obtaining DA SVM from previously trained SVM"

feats_train2 = Modshogun::StringCharFeatures.new(fm_train_dna, Modshogun::DNA)
feats_test2 = Modshogun::StringCharFeatures.new(fm_test_dna, Modshogun::DNA)
kernel2 = Modshogun::WeightedDegreeStringKernel.new(feats_train, feats_train, degree)
labels2 = Modshogun::Labels.new(label_train_dna)
feats_train2 = StringCharFeatures(fm_train_dna, DNA)
feats_test2 = StringCharFeatures(fm_test_dna, DNA)
kernel2 = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels2 = Labels(label_train_dna)

# we regularize against the previously obtained solution
dasvm = Modshogun::DomainAdaptationSVM.new(c, kernel2, labels2, svm, 1.0)
dasvm = DomainAdaptationSVM(C, kernel2, labels2, svm, 1.0)
dasvm.train()

out = dasvm.apply(feats_test2).get_labels()
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12 changes: 6 additions & 6 deletions examples/undocumented/python_modular/evaluation_meansquarederror_modular.py
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Expand Up @@ -4,20 +4,20 @@

N = 100

NArray.srand(17)
ground_truth = NArray.float(N).randomn
predicted = NArray.float(N).randomn
random.seed(17)
ground_truth = random.randn(N)
predicted = random.randn(N)

parameter_list = [[ground_truth,predicted]]

def evaluation_meansquarederror_modular(ground_truth, predicted):
from shogun.Features import Labels
from shogun.Evaluation import MeanSquaredError

ground_truth_labels = Modshogun::Labels.new(ground_truth)
predicted_labels = Modshogun::Labels.new(predicted)
ground_truth_labels = Labels(ground_truth)
predicted_labels = Labels(predicted)

evaluator = Modshogun::MeanSquaredError.new
evaluator = MeanSquaredError()
mse = evaluator.evaluate(predicted_labels,ground_truth_labels)

return mse
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41 changes: 18 additions & 23 deletions examples/undocumented/python_modular/mkl_multiclass_modular.py
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Expand Up @@ -8,59 +8,54 @@
[ fm_train_real, fm_test_real, label_train_multiclass, 1.2, 1.2, 1e-5, 1, 0.001, 1.5],
[ fm_train_real, fm_test_real, label_train_multiclass, 5, 1.2, 1e-2, 1, 0.001, 2]]

def mkl_multiclass_modular(fm_train_real, fm_test_real, label_train_multiclass, width, c, epsilon, num_threads, mkl_epsilon, mkl_norm)
def mkl_multiclass_modular(fm_train_real, fm_test_real, label_train_multiclass,
width, C, epsilon, num_threads, mkl_epsilon, mkl_norm):

from shogun.Features import CombinedFeatures, RealFeatures, Labels
from shogun.Kernel import CombinedKernel, GaussianKernel, LinearKernel,PolyKernel
from shogun.Classifier import MKLMultiClass

kernel = Modshogun::CombinedKernel.new
feats_train = Modshogun::CombinedFeatures.new
feats_test = Modshogun::CombinedFeatures.new
kernel = CombinedKernel()
feats_train = CombinedFeatures()
feats_test = CombinedFeatures()

subkfeats_train = Modshogun::RealFeatures.new
subkfeats_train.set_feature_matrix(fm_train_real)
subkfeats_test = Modshogun::RealFeatures.new
subkfeats_test.set_feature_matrix(fm_test_real)
subkernel = Modshogun::GaussianKernel.new(10, width)
subkfeats_train = RealFeatures(fm_train_real)
subkfeats_test = RealFeatures(fm_test_real)
subkernel = GaussianKernel(10, width)
feats_train.append_feature_obj(subkfeats_train)
feats_test.append_feature_obj(subkfeats_test)
kernel.append_kernel(subkernel)

subkfeats_train = Modshogun::RealFeatures.new
subkfeats_train.set_feature_matrix(fm_train_real)
subkfeats_test = Modshogun::RealFeatures.new
subkfeats_test.set_feature_matrix(fm_test_real)
subkernel = Modshogun::LinearKernel.new
subkfeats_train = RealFeatures(fm_train_real)
subkfeats_test = RealFeatures(fm_test_real)
subkernel = LinearKernel()
feats_train.append_feature_obj(subkfeats_train)
feats_test.append_feature_obj(subkfeats_test)
kernel.append_kernel(subkernel)

subkfeats_train = Modshogun::RealFeatures.new
subkfeats_train.set_feature_matrix(fm_train_real)
subkfeats_test = Modshogun::RealFeatures.new
subkfeats_test.set_feature_matrix(fm_test_real)
subkernel = Modshogun::PolyKernel.new(10,2)
subkfeats_train = RealFeatures(fm_train_real)
subkfeats_test = RealFeatures(fm_test_real)
subkernel = PolyKernel(10,2)
feats_train.append_feature_obj(subkfeats_train)
feats_test.append_feature_obj(subkfeats_test)
kernel.append_kernel(subkernel)

kernel.init(feats_train, feats_train)

labels = Modshogun::Labels.new(label_train_multiclass)
labels = Labels(label_train_multiclass)

mkl = Modshogun::MKLMultiClass.new(c, kernel, labels)
mkl = MKLMultiClass(C, kernel, labels)

mkl.set_epsilon(epsilon);
mkl.parallel.set_num_threads(num_threads)
mkl.set_mkl_epsilon(mkl_epsilon)
mkl.set_mkl_norm(mkl_norm)

mkl.train
mkl.train()

kernel.init(feats_train, feats_test)

out = mkl.apply.get_labels()
out = mkl.apply().get_labels()
return out

if __name__ == '__main__':
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