def regression_libsvr_modular (svm_c=1, svr_param=0.1, n=100,n_test=100, \ x_range=6,x_range_test=10,noise_var=0.5,width=1, seed=1): from shogun.Features import RegressionLabels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import LibSVR, LIBSVR_NU_SVR, LIBSVR_EPSILON_SVR # reproducable results random.seed(seed) # easy regression data: one dimensional noisy sine wave n=15 n_test=100 x_range_test=10 noise_var=0.5; X=random.rand(1,n)*x_range X_test=array([[float(i)/n_test*x_range_test for i in range(n_test)]]) Y_test=sin(X_test) Y=sin(X)+random.randn(n)*noise_var # shogun representation labels=RegressionLabels(Y[0]) feats_train=RealFeatures(X) feats_test=RealFeatures(X_test) kernel=GaussianKernel(feats_train, feats_train, width) # two svr models: epsilon and nu svr_epsilon=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_EPSILON_SVR) svr_epsilon.train() svr_nu=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_NU_SVR) svr_nu.train() # predictions kernel.init(feats_train, feats_test) out1_epsilon=svr_epsilon.apply().get_labels() out2_epsilon=svr_epsilon.apply(feats_test).get_labels() out1_nu=svr_epsilon.apply().get_labels() out2_nu=svr_epsilon.apply(feats_test).get_labels() return out1_epsilon,out2_epsilon,out1_nu,out2_nu ,kernel
def regression_libsvr_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,\
width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2):
from shogun.Features import Labels, RealFeatures
from shogun.Kernel import GaussianKernel
from shogun.Regression import LibSVR
feats_train = RealFeatures(fm_train)
feats_test = RealFeatures(fm_test)
kernel = GaussianKernel(feats_train, feats_train, width)
labels = Labels(label_train)
svr = LibSVR(C, tube_epsilon, kernel, labels)
svr.set_epsilon(epsilon)
svr.train()
kernel.init(feats_train, feats_test)
out1 = svr.apply().get_labels()
out2 = svr.apply(feats_test).get_labels()
return out1, out2, kernel
def regression_libsvr_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,\ width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2): from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import LibSVR feats_train=RealFeatures(fm_train) feats_test=RealFeatures(fm_test) kernel=GaussianKernel(feats_train, feats_train, width) labels=Labels(label_train) svr=LibSVR(C, tube_epsilon, kernel, labels) svr.set_epsilon(epsilon) svr.train() kernel.init(feats_train, feats_test) out1=svr.apply().get_labels() out2=svr.apply(feats_test).get_labels() return out1,out2,kernel
def libsvr (): print 'LibSVR' from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import LibSVR feats_train=RealFeatures(fm_train) feats_test=RealFeatures(fm_test) width=2.1 kernel=GaussianKernel(feats_train, feats_train, width) C=1 epsilon=1e-5 tube_epsilon=1e-2 labels=Labels(label_train) svr=LibSVR(C, epsilon, kernel, labels) svr.set_tube_epsilon(tube_epsilon) svr.train() kernel.init(feats_train, feats_test) out1=svr.classify().get_labels() out2=svr.classify(feats_test).get_labels()
class SVR:
svr = None
kernel = None
feats_train = None
def __init__(self, traininput,testinput,traintarget,width=0.5,C=1,epsilon=0.1,tube_epsilon=0.1):
train = matrix(traininput, dtype=float64)
test = matrix(testinput, dtype=float64)
label_train = array(traintarget, dtype=float64)
self.feats_train=RealFeatures(train)
feats_test=RealFeatures(test)
trainstart = time.time()
self.kernel=GaussianKernel(self.feats_train, self.feats_train, width)
# self.kernel = PolyKernel(self.feats_train, self.feats_train, 2, False)
labels=Labels(label_train)
self.svr=LibSVR(C, epsilon, self.kernel, labels)
self.svr.set_tube_epsilon(tube_epsilon)
self.svr.train()
trainend = time.time()
print 'SVR train time'
print trainend-trainstart
def svr_req(self,inputs):
feat_inputs = RealFeatures(matrix(inputs, dtype=float64))
teststart = time.time()
self.kernel.init(self.feats_train, feat_inputs)
out = self.svr.classify(feat_inputs).get_labels()
# feat_input0 = RealFeatures(matrix(inputs[0], dtype=float64))
# feat_input1 = RealFeatures(matrix(inputs[1], dtype=float64))
# feat_input2 = RealFeatures(matrix(inputs[2], dtype=float64))
# feat_input3 = RealFeatures(matrix(inputs[3], dtype=float64))
#
# out.append(self.svr.classify(feat_input0).get_labels())
# out.append(self.svr.classify(feat_input1).get_labels())
# out.append(self.svr.classify(feat_input2).get_labels())
# out.append(self.svr.classify(feat_input3).get_labels())
testend = time.time()
print 'SVR query response '
print testend-teststart
return out
def calc_sme(self, testtarget, realtarget):
result = 0.0
for i in range(len(testtarget)):
result += pow((realtarget[i] - testtarget[i]),2)
result /= len(testtarget)
return result
def calc_mape(self, testtarget, realtarget):
result = 0.0
for i in range(len(testtarget)):
result = abs(testtarget[i] - realtarget[i])/testtarget[i]
return result/len(testtarget)
def calc_rsqr(self, testtarget, realtarget):
result_up = 0.0
result_down = 0.0
avg = sum(realtarget)/len(realtarget)
for i in range(len(testtarget)):
result_up += pow((realtarget[i] - testtarget[i]),2)
result_down += (realtarget[i] - avg)
return 1 - (result_up / result_down)
def calc_pred(self, testtarget, realtarget, x):
countx = 0.0
for i in range(len(testtarget)):
if ((testtarget[i]/realtarget[i]) - 1 < (realtarget[i] * (1-x))):
countx += 1
return countx / len(realtarget)
