def libsvm ():
print 'LibSVM'
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import GaussianKernel
from shogun.Evaluation import PerformanceMeasures
from shogun.Classifier import LibSVM
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
width=2.1
kernel=GaussianKernel(feats_train, feats_train, width)
C=1
epsilon=1e-5
labels=Labels(label_train_twoclass)
svm=LibSVM(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.train()
#kernel.init(feats_train, feats_test)
output = svm.classify(feats_test)#.get_labels()
#output_vector = output.get_labels()
out=svm.classify().get_labels()
testerr=mean(sign(out)!=testlab)
print testerr
def classifier_libsvm_modular(fm_train_real=traindat,
fm_test_real=testdat,
label_train_twoclass=label_traindat,
width=2.1,
C=1,
epsilon=1e-5):
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import GaussianKernel
from shogun.Classifier import LibSVM
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
kernel = GaussianKernel(feats_train, feats_train, width)
labels = Labels(label_train_twoclass)
svm = LibSVM(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.train()
kernel.init(feats_train, feats_test)
labels = svm.classify().get_labels()
supportvectors = sv_idx = svm.get_support_vectors()
alphas = svm.get_alphas()
predictions = svm.classify()
return predictions, svm, predictions.get_labels()
def libsvm():
print 'LibSVM'
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import GaussianKernel
from shogun.Evaluation import PerformanceMeasures
from shogun.Classifier import LibSVM
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
width = 2.1
kernel = GaussianKernel(feats_train, feats_train, width)
C = 1
epsilon = 1e-5
labels = Labels(label_train_twoclass)
svm = LibSVM(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.train()
#kernel.init(feats_train, feats_test)
output = svm.classify(feats_test) #.get_labels()
#output_vector = output.get_labels()
out = svm.classify().get_labels()
testerr = mean(sign(out) != testlab)
print testerr
def kernel_combined_custom_poly_modular(fm_train_real=traindat,
fm_test_real=testdat,
fm_label_twoclass=label_traindat):
from shogun.Features import CombinedFeatures, RealFeatures, Labels
from shogun.Kernel import CombinedKernel, PolyKernel, CustomKernel
from shogun.Classifier import LibSVM
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(fm_train_real)
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(fm_train_real)
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10, 2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = Labels(fm_label_twoclass)
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(fm_test_real)
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(fm_test_real)
feats_pred.append_feature_obj(subkfeats_test)
subkernel = PolyKernel(10, 2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_pred)
svm.set_kernel(kernel)
svm.classify()
km_train = kernel.get_kernel_matrix()
return km_train, kernel
def kernel_combined_custom_poly_modular(fm_train_real = traindat,fm_test_real = testdat,fm_label_twoclass=label_traindat):
from shogun.Features import CombinedFeatures, RealFeatures, Labels
from shogun.Kernel import CombinedKernel, PolyKernel, CustomKernel
from shogun.Classifier import LibSVM
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(fm_train_real)
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(fm_train_real)
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10,2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = Labels(fm_label_twoclass)
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(fm_test_real)
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(fm_test_real)
feats_pred.append_feature_obj(subkfeats_test)
subkernel = PolyKernel(10, 2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_pred)
svm.set_kernel(kernel)
svm.classify()
km_train=kernel.get_kernel_matrix()
return km_train,kernel
def classifier_libsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1,epsilon=1e-5): from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import LibSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=Labels(label_train_twoclass) svm=LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) labels = svm.classify().get_labels() supportvectors = sv_idx=svm.get_support_vectors() alphas=svm.get_alphas() predictions = svm.classify() return predictions, svm, predictions.get_labels()
def classifier_custom_kernel_modular(C=1, dim=7):
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import CustomKernel
from shogun.Classifier import LibSVM
from numpy import diag, ones, sign
from numpy.random import rand, seed
seed((C, dim))
lab = sign(2 * rand(dim) - 1)
data = rand(dim, dim)
symdata = data * data.T + diag(ones(dim))
kernel = CustomKernel()
kernel.set_full_kernel_matrix_from_full(data)
labels = Labels(lab)
svm = LibSVM(C, kernel, labels)
svm.train()
predictions = svm.classify()
out = svm.classify().get_labels()
return svm, out
def classifier_custom_kernel_modular(C=1,dim=7):
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import CustomKernel
from shogun.Classifier import LibSVM
from numpy import diag,ones,sign
from numpy.random import rand,seed
seed((C,dim))
lab=sign(2*rand(dim) - 1)
data=rand(dim, dim)
symdata=data*data.T + diag(ones(dim))
kernel=CustomKernel()
kernel.set_full_kernel_matrix_from_full(data)
labels=Labels(lab)
svm=LibSVM(C, kernel, labels)
svm.train()
predictions =svm.classify()
out=svm.classify().get_labels()
return svm,out
def libsvm (): print 'LibSVM' from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import LibSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) width=2.1 kernel=GaussianKernel(feats_train, feats_train, width) C=1 epsilon=1e-5 labels=Labels(label_train_twoclass) svm=LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) svm.classify().get_labels() sv_idx=svm.get_support_vectors() alphas=svm.get_alphas()
def bench_shogun(X, y, T, valid):
#
# .. Shogun ..
#
from shogun.Classifier import LibSVM
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import GaussianKernel
start = datetime.now()
feat = RealFeatures(X.T)
feat_test = RealFeatures(T.T)
labels = Labels(y.astype(np.float64))
kernel = GaussianKernel(feat, feat, sigma)
shogun_svm = LibSVM(1., kernel, labels)
shogun_svm.train()
dec_func = shogun_svm.classify(feat_test).get_labels()
score = np.mean(np.sign(dec_func) == valid)
return score, datetime.now() - start
def bench_shogun(X, y, T, valid):
#
# .. Shogun ..
#
from shogun.Classifier import LibSVM
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import GaussianKernel
start = datetime.now()
feat = RealFeatures(X.T)
feat_test = RealFeatures(T.T)
labels = Labels(y.astype(np.float64))
kernel = GaussianKernel(feat, feat, sigma)
shogun_svm = LibSVM(1., kernel, labels)
shogun_svm.train()
dec_func = shogun_svm.classify(feat_test).get_labels()
score = np.mean(np.sign(dec_func) == valid)
return score, datetime.now() - start
svm = LibSVM(1000.0, gk, labels)
svm.train()
lda=LDA(1,features,labels)
lda.train()
## plot points
subplot(211)
plot(pos[0,:], pos[1,:], "r.")
plot(neg[0,:], neg[1,:], "b.")
grid(True)
title('Data',size=10)
# plot PRC for SVM
subplot(223)
PRC_evaluation=PRCEvaluation()
PRC_evaluation.evaluate(svm.classify(),labels)
PRC = PRC_evaluation.get_PRC()
plot(PRC[:,0], PRC[:,1])
fill_between(PRC[:,0],PRC[:,1],0,alpha=0.1)
text(0.55,mean(PRC[:,1])/3,'auPRC = %.5f' % PRC_evaluation.get_auPRC())
grid(True)
xlabel('Precision')
ylabel('Recall')
title('LibSVM (Gaussian kernel, C=%.3f) PRC curve' % svm.get_C1(),size=10)
# plot PRC for LDA
subplot(224)
PRC_evaluation.evaluate(lda.classify(),labels)
PRC = PRC_evaluation.get_PRC()
plot(PRC[:,0], PRC[:,1])
fill_between(PRC[:,0],PRC[:,1],0,alpha=0.1)
epsilon = 1e-5
tube_epsilon = 1e-2
svm = LibSVM()
svm.set_C(C, C)
svm.set_epsilon(epsilon)
svm.set_tube_epsilon(tube_epsilon)
for i in xrange(3):
data_train = random.rand(num_feats, num_vec)
data_test = random.rand(num_feats, num_vec)
feats_train = RealFeatures(data_train)
feats_test = RealFeatures(data_test)
labels = Labels(random.rand(num_vec).round() * 2 - 1)
svm.set_kernel(LinearKernel(size_cache, scale))
svm.set_labels(labels)
kernel = svm.get_kernel()
print "kernel cache size: %s" % (kernel.get_cache_size())
kernel.init(feats_test, feats_test)
svm.train()
kernel.init(feats_train, feats_test)
print svm.classify().get_labels()
#kernel.remove_lhs_and_rhs()
#import pdb
#pdb.set_trace()
tube_epsilon=1e-2 svm=LibSVM() svm.set_C(C, C) svm.set_epsilon(epsilon) svm.set_tube_epsilon(tube_epsilon) for i in xrange(3): data_train=random.rand(num_feats, num_vec) data_test=random.rand(num_feats, num_vec) feats_train=RealFeatures(data_train) feats_test=RealFeatures(data_test) labels=Labels(random.rand(num_vec).round()*2-1) svm.set_kernel(LinearKernel(size_cache, scale)) svm.set_labels(labels) kernel=svm.get_kernel() print "kernel cache size: %s" % (kernel.get_cache_size()) kernel.init(feats_test, feats_test) svm.train() kernel.init(feats_train, feats_test) print svm.classify().get_labels() #kernel.remove_lhs_and_rhs() #import pdb #pdb.set_trace()
inner_sum = inner_sum + alpha * kv.kernel(sv_id, idx)
inner.append(inner_sum)
#general case
linterm_manual[idx] = B * tmp_lab[idx] * inner_sum - 1.0
################
# compare pre-svms
assert (presvm_liblinear.get_bias() == 0.0)
assert (presvm_libsvm.get_bias() == 0.0)
tmp_out = presvm_liblinear.classify(feat).get_labels()
tmp_out2 = presvm_libsvm.classify(feat).get_labels()
# compare outputs
for i in xrange(N):
try:
assert (abs(inner[i] - tmp_out[i]) <= 0.001)
assert (abs(inner[i] - tmp_out2[i]) <= 0.001)
except Exception, message:
print "difference in outputs: (%.4f, %.4f, %.4f)" % (tmp_out[i],
tmp_out2[i])
###############
# compare to LibSVM
dasvm_manual_libsvm = LibSVM(1.0, wdk, lab)
inner.append(inner_sum)
#general case
linterm_manual[idx] = B *tmp_lab[idx] * inner_sum - 1.0
################
# compare pre-svms
assert(presvm_liblinear.get_bias() == 0.0)
assert(presvm_libsvm.get_bias() == 0.0)
tmp_out = presvm_liblinear.classify(feat).get_labels()
tmp_out2 = presvm_libsvm.classify(feat).get_labels()
# compare outputs
for i in xrange(N):
try:
assert(abs(inner[i]-tmp_out[i])<= 0.001)
assert(abs(inner[i]-tmp_out2[i])<= 0.001)
except Exception, message:
print "difference in outputs: (%.4f, %.4f, %.4f)" % (tmp_out[i], tmp_out2[i])
###############
# compare to LibSVM
from numpy import * from numpy.random import rand from shogun.Features import RealFeatures, Labels from shogun.Kernel import CustomKernel from shogun.Classifier import LibSVM C=1 dim=7 lab=sign(2*rand(dim) - 1) data=rand(dim, dim) symdata=data*data.T kernel=CustomKernel() kernel.set_full_kernel_matrix_from_full(data) labels=Labels(lab) svm=LibSVM(C, kernel, labels) svm.train() out=svm.classify().get_labels()
