def RunSVRShogun(): totalTimer = Timer() # Load input dataset. Log.Info("Loading dataset", self.verbose) # Use the last row of the training set as the responses. X, y = SplitTrainData(self.dataset) # Get all the parameters. self.C = 1.0 self.epsilon = 1.0 self.width = 0.1 if "c" in options: self.C = float(options.pop("c")) if "epsilon" in options: self.epsilon = float(options.pop("epsilon")) if "gamma" in options: self.width = np.true_divide(1, float(options.pop("gamma"))) if len(options) > 0: Log.Fatal("Unknown parameters: " + str(options)) raise Exception("unknown parameters") data = RealFeatures(X.T) labels_train = RegressionLabels(y) self.kernel = GaussianKernel(data, data, self.width) try: with totalTimer: # Perform SVR. model = LibSVR(self.C, self.epsilon, self.kernel, labels_train) model.train() except Exception as e: return -1 return totalTimer.ElapsedTime()
def RunSVRShogun(q): totalTimer = Timer() # Load input dataset. Log.Info("Loading dataset", self.verbose) # Use the last row of the training set as the responses. X, y = SplitTrainData(self.dataset) # Get all the parameters. c = re.search("-c (\d+\.\d+)", options) e = re.search("-e (\d+\.\d+)", options) g = re.search("-g (\d+\.\d+)", options) self.C = 1.0 if not c else float(c.group(1)) self.epsilon = 1.0 if not e else float(e.group(1)) g = 10.0 if not g else float(g.group(1)) self.width = np.true_divide(1, g) data = RealFeatures(X.T) labels_train = RegressionLabels(y) self.kernel = GaussianKernel(data, data, self.width) try: with totalTimer: # Perform SVR. model = LibSVR(self.C, self.epsilon, self.kernel, labels_train) model.train() except Exception as e: q.put(-1) return -1 time = totalTimer.ElapsedTime() q.put(time) return time
def runShogunSVRWDKernel(train_xt, train_lt, test_xt, svm_c=1, svr_param=0.1): """ serialize svr with string kernels """ ################################################## # set up svr feats_train = construct_features(train_xt) feats_test = construct_features(test_xt) max_len = len(train_xt[0]) kernel_wdk = WeightedDegreePositionStringKernel(SIZE, 5) shifts_vector = np.ones(max_len, dtype=np.int32) * NUMSHIFTS kernel_wdk.set_shifts(shifts_vector) ######## # set up spectrum use_sign = False kernel_spec_1 = WeightedCommWordStringKernel(SIZE, use_sign) #kernel_spec_2 = WeightedCommWordStringKernel(SIZE, use_sign) ######## # combined kernel kernel = CombinedKernel() kernel.append_kernel(kernel_wdk) kernel.append_kernel(kernel_spec_1) #kernel.append_kernel(kernel_spec_2) # init kernel labels = RegressionLabels(train_lt) # two svr models: epsilon and nu svr_epsilon = LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_EPSILON_SVR) print "Ready to train!" svr_epsilon.train(feats_train) #svr_nu=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_NU_SVR) #svr_nu.train(feats_train) # predictions print "Making 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() ################################################## # serialize to file fEpsilon = open(FNEPSILON, 'w+') #fNu = open(FNNU, 'w+') svr_epsilon.save(fEpsilon) #svr_nu.save(fNu) fEpsilon.close() #fNu.close() ################################################## #return out1_epsilon,out2_epsilon,out1_nu,out2_nu ,kernel return out1_epsilon, out2_epsilon, kernel
def runShogunSVRWDKernel(train_xt, train_lt, test_xt, svm_c=1, svr_param=0.1): """ serialize svr with string kernels """ ################################################## # set up svr feats_train = construct_features(train_xt) feats_test = construct_features(test_xt) max_len = len(train_xt[0]) kernel_wdk = WeightedDegreePositionStringKernel(SIZE, 5) shifts_vector = np.ones(max_len, dtype=np.int32)*NUMSHIFTS kernel_wdk.set_shifts(shifts_vector) ######## # set up spectrum use_sign = False kernel_spec_1 = WeightedCommWordStringKernel(SIZE, use_sign) #kernel_spec_2 = WeightedCommWordStringKernel(SIZE, use_sign) ######## # combined kernel kernel = CombinedKernel() kernel.append_kernel(kernel_wdk) kernel.append_kernel(kernel_spec_1) #kernel.append_kernel(kernel_spec_2) # init kernel labels = RegressionLabels(train_lt) # two svr models: epsilon and nu svr_epsilon=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_EPSILON_SVR) print "Ready to train!" svr_epsilon.train(feats_train) #svr_nu=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_NU_SVR) #svr_nu.train(feats_train) # predictions print "Making 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() ################################################## # serialize to file fEpsilon = open(FNEPSILON, 'w+') #fNu = open(FNNU, 'w+') svr_epsilon.save(fEpsilon) #svr_nu.save(fNu) fEpsilon.close() #fNu.close() ################################################## #return out1_epsilon,out2_epsilon,out1_nu,out2_nu ,kernel return out1_epsilon,out2_epsilon,kernel
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 modshogun import RegressionLabels, RealFeatures from modshogun import GaussianKernel from modshogun 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 (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 modshogun import RegressionLabels, RealFeatures from modshogun import GaussianKernel from modshogun 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 runShogunSVRSpectrumKernel(train_xt, train_lt, test_xt, svm_c=1): """ serialize svr with spectrum kernels """ ################################################## # set up svr charfeat_train = StringCharFeatures(train_xt, DNA) feats_train = StringWordFeatures(DNA) feats_train.obtain_from_char(charfeat_train, K-1, K, GAP, False) preproc=SortWordString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() charfeat_test = StringCharFeatures(test_xt, DNA) feats_test=StringWordFeatures(DNA) feats_test.obtain_from_char(charfeat_test, K-1, K, GAP, False) feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() kernel=CommWordStringKernel(feats_train, feats_train, False) kernel.io.set_loglevel(MSG_DEBUG) # init kernel labels = RegressionLabels(train_lt) # two svr models: epsilon and nu print "Ready to train!" svr_epsilon=LibSVR(svm_c, SVRPARAM, kernel, labels, LIBSVR_EPSILON_SVR) svr_epsilon.io.set_loglevel(MSG_DEBUG) svr_epsilon.train() # predictions print "Making predictions!" out1_epsilon=svr_epsilon.apply(feats_train).get_labels() kernel.init(feats_train, feats_test) out2_epsilon=svr_epsilon.apply(feats_test).get_labels() return out1_epsilon,out2_epsilon,kernel
def runShogunSVRSpectrumKernel(train_xt, train_lt, test_xt, svm_c=1): """ serialize svr with spectrum kernels """ ################################################## # set up svr charfeat_train = StringCharFeatures(train_xt, DNA) feats_train = StringWordFeatures(DNA) feats_train.obtain_from_char(charfeat_train, K - 1, K, GAP, False) preproc = SortWordString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() charfeat_test = StringCharFeatures(test_xt, DNA) feats_test = StringWordFeatures(DNA) feats_test.obtain_from_char(charfeat_test, K - 1, K, GAP, False) feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() kernel = CommWordStringKernel(feats_train, feats_train, False) kernel.io.set_loglevel(MSG_DEBUG) # init kernel labels = RegressionLabels(train_lt) # two svr models: epsilon and nu print "Ready to train!" svr_epsilon = LibSVR(svm_c, SVRPARAM, kernel, labels, LIBSVR_EPSILON_SVR) svr_epsilon.io.set_loglevel(MSG_DEBUG) svr_epsilon.train() # predictions print "Making predictions!" out1_epsilon = svr_epsilon.apply(feats_train).get_labels() kernel.init(feats_train, feats_test) out2_epsilon = svr_epsilon.apply(feats_test).get_labels() return out1_epsilon, out2_epsilon, kernel
def runShogunSVRWDKernel(train_xt, train_lt, test_xt, svm_c=1): """ serialize svr with string kernels """ ################################################## # set up svr feats_train = StringCharFeatures(train_xt, PROTEIN) feats_test = StringCharFeatures(test_xt, PROTEIN) kernel = WeightedDegreePositionStringKernel(feats_train, feats_train, DEGREE) kernel.io.set_loglevel(MSG_DEBUG) kernel.set_shifts(NUMSHIFTS*ones(len(train_xt[0]), dtype=int32)) kernel.set_position_weights(ones(len(train_xt[0]), dtype=float64)) # init kernel labels = RegressionLabels(train_lt) # two svr models: epsilon and nu print "Ready to train!" svr_epsilon=LibSVR(svm_c, SVRPARAM, kernel, labels, LIBSVR_EPSILON_SVR) svr_epsilon.io.set_loglevel(MSG_DEBUG) svr_epsilon.train() #svr_nu=LibSVR(svm_c, SVRPARAM, kernel, labels, LIBSVR_NU_SVR) #svr_nu.train() # predictions print "Making predictions!" out1_epsilon=svr_epsilon.apply(feats_train).get_labels() kernel.init(feats_train, feats_test) out2_epsilon=svr_epsilon.apply(feats_test).get_labels() #out1_nu=svr_epsilon.apply(feats_train).get_labels() #out2_nu=svr_epsilon.apply(feats_test).get_labels() #return out1_epsilon,out2_epsilon,out1_nu,out2_nu ,kernel return out1_epsilon,out2_epsilon,kernel