def structure_discrete_hmsvm_bmrm (m_data_dict=data_dict): from modshogun import RealMatrixFeatures, SequenceLabels, HMSVMModel, Sequence, TwoStateModel from modshogun import StructuredAccuracy, SMT_TWO_STATE try: from modshogun import DualLibQPBMSOSVM except ImportError: print("DualLibQPBMSOSVM not available") exit(0) labels_array = m_data_dict['label'][0] idxs = numpy.nonzero(labels_array == -1) labels_array[idxs] = 0 labels = SequenceLabels(labels_array, 250, 500, 2) features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500) num_obs = 4 # given by the data file used model = HMSVMModel(features, labels, SMT_TWO_STATE, num_obs) sosvm = DualLibQPBMSOSVM(model, labels, 5000.0) sosvm.train() #print sosvm.get_w() predicted = sosvm.apply(features) evaluator = StructuredAccuracy() acc = evaluator.evaluate(predicted, labels)
def structure_discrete_hmsvm_bmrm(m_data_dict=data_dict): from modshogun import RealMatrixFeatures, SequenceLabels, HMSVMModel, Sequence, TwoStateModel from modshogun import StructuredAccuracy, SMT_TWO_STATE try: from modshogun import DualLibQPBMSOSVM except ImportError: print("DualLibQPBMSOSVM not available") exit(0) labels_array = m_data_dict['label'][0] idxs = numpy.nonzero(labels_array == -1) labels_array[idxs] = 0 labels = SequenceLabels(labels_array, 250, 500, 2) features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500) num_obs = 4 # given by the data file used model = HMSVMModel(features, labels, SMT_TWO_STATE, num_obs) sosvm = DualLibQPBMSOSVM(model, labels, 5000.0) sosvm.train() #print sosvm.get_w() predicted = sosvm.apply(features) evaluator = StructuredAccuracy() acc = evaluator.evaluate(predicted, labels)
def structure_factor_graph_model(tr_samples=samples, tr_labels=labels, w=w_all, ftype=ftype_all): from modshogun import SOSVMHelper, LabelsFactory from modshogun import FactorGraphModel, MAPInference, TREE_MAX_PROD from modshogun import DualLibQPBMSOSVM, StochasticSOSVM # create model model = FactorGraphModel(tr_samples, tr_labels, TREE_MAX_PROD, False) w_truth = [w[0].copy(), w[1].copy(), w[2].copy()] w[0] = np.zeros(8) w[1] = np.zeros(4) w[2] = np.zeros(2) ftype[0].set_w(w[0]) ftype[1].set_w(w[1]) ftype[2].set_w(w[2]) model.add_factor_type(ftype[0]) model.add_factor_type(ftype[1]) model.add_factor_type(ftype[2]) # --- training with BMRM --- bmrm = DualLibQPBMSOSVM(model, tr_labels, 0.01) #bmrm.set_verbose(True) bmrm.train() #print 'learned weights:' #print bmrm.get_w() #print 'ground truth weights:' #print w_truth # evaluation lbs_bmrm = LabelsFactory.to_structured(bmrm.apply()) acc_loss = 0.0 ave_loss = 0.0 for i in xrange(num_samples): y_pred = lbs_bmrm.get_label(i) y_truth = tr_labels.get_label(i) acc_loss = acc_loss + model.delta_loss(y_truth, y_pred) ave_loss = acc_loss / num_samples #print('BMRM: Average training error is %.4f' % ave_loss) # show primal objs and dual objs #hbm = bmrm.get_helper() #print hbm.get_primal_values() #print hbm.get_eff_passes() #print hbm.get_train_errors() # --- training with SGD --- sgd = StochasticSOSVM(model, tr_labels) #sgd.set_verbose(True) sgd.set_lambda(0.01) sgd.train()
def structure_plif_hmsvm_bmrm (num_examples, example_length, num_features, num_noise_features): from modshogun import RealMatrixFeatures, TwoStateModel, DualLibQPBMSOSVM, StructuredAccuracy model = TwoStateModel.simulate_data(num_examples, example_length, num_features, num_noise_features) sosvm = DualLibQPBMSOSVM(model, model.get_labels(), 5000.0) sosvm.train() #print sosvm.get_w() predicted = sosvm.apply(model.get_features()) evaluator = StructuredAccuracy() acc = evaluator.evaluate(predicted, model.get_labels())
def structure_factor_graph_model(tr_samples = samples, tr_labels = labels, w = w_all, ftype = ftype_all): from modshogun import SOSVMHelper, LabelsFactory from modshogun import FactorGraphModel, MAPInference, TREE_MAX_PROD from modshogun import DualLibQPBMSOSVM, StochasticSOSVM # create model model = FactorGraphModel(tr_samples, tr_labels, TREE_MAX_PROD, False) w_truth = [w[0].copy(), w[1].copy(), w[2].copy()] w[0] = np.zeros(8) w[1] = np.zeros(4) w[2] = np.zeros(2) ftype[0].set_w(w[0]) ftype[1].set_w(w[1]) ftype[2].set_w(w[2]) model.add_factor_type(ftype[0]) model.add_factor_type(ftype[1]) model.add_factor_type(ftype[2]) # --- training with BMRM --- bmrm = DualLibQPBMSOSVM(model, tr_labels, 0.01) #bmrm.set_verbose(True) bmrm.train() #print 'learned weights:' #print bmrm.get_w() #print 'ground truth weights:' #print w_truth # evaluation eva_bmrm = bmrm.apply() lbs_bmrm = LabelsFactory.to_structured(eva_bmrm) acc_loss = 0.0 ave_loss = 0.0 for i in xrange(num_samples): y_pred = lbs_bmrm.get_label(i) y_truth = tr_labels.get_label(i) acc_loss = acc_loss + model.delta_loss(y_truth, y_pred) ave_loss = acc_loss / num_samples #print('BMRM: Average training error is %.4f' % ave_loss) # show primal objs and dual objs #hbm = bmrm.get_helper() #print hbm.get_primal_values() #print hbm.get_eff_passes() #print hbm.get_train_errors() # --- training with SGD --- sgd = StochasticSOSVM(model, tr_labels) #sgd.set_verbose(True) sgd.set_lambda(0.01) sgd.train()
def structure_plif_hmsvm_bmrm (num_examples, example_length, num_features, num_noise_features): from modshogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy try: from modshogun import DualLibQPBMSOSVM except ImportError: print("DualLibQPBMSOSVM not available") exit(0) model = TwoStateModel.simulate_data(num_examples, example_length, num_features, num_noise_features) sosvm = DualLibQPBMSOSVM(model, model.get_labels(), 5000.0) sosvm.set_store_train_info(False) sosvm.train() #print sosvm.get_w() predicted = sosvm.apply(model.get_features()) evaluator = StructuredAccuracy() acc = evaluator.evaluate(predicted, model.get_labels())
# Dimension of the data dim = 2 X, y = gen_data() cnt = 250 X2, y2 = fill_data(cnt, np.min(X), np.max(X)) labels = MulticlassSOLabels(y) features = RealFeatures(X.T) model = MulticlassModel(features, labels) lambda_ = 1e1 sosvm = DualLibQPBMSOSVM(model, labels, lambda_) sosvm.set_cleanAfter(10) # number of iterations that cutting plane has to be inactive for to be removed sosvm.set_cleanICP(True) # enables inactive cutting plane removal feature sosvm.set_TolRel(0.001) # set relative tolerance sosvm.set_verbose(True) # enables verbosity of the solver sosvm.set_cp_models(16) # set number of cutting plane models sosvm.set_solver(BMRM) # select training algorithm # sosvm.set_solver(PPBMRM) # sosvm.set_solver(P3BMRM) sosvm.train() res = sosvm.get_result() Fps = np.array(res.get_hist_Fp_vector()) Fds = np.array(res.get_hist_Fp_vector())
def structure_multiclass_bmrm(fm_train_real=traindat, label_train_multiclass=label_traindat): from modshogun import RealFeatures from modshogun import SOSVMHelper from modshogun import BMRM, PPBMRM, P3BMRM from modshogun import MulticlassModel, MulticlassSOLabels, DualLibQPBMSOSVM, RealNumber labels = MulticlassSOLabels(label_train_multiclass) features = RealFeatures(fm_train_real.T) model = MulticlassModel(features, labels) sosvm = DualLibQPBMSOSVM(model, labels, 1.0) # BMRM sosvm.set_solver(BMRM) sosvm.set_verbose(True) sosvm.train() out = sosvm.apply() count = 0 for i in xrange(out.get_num_labels()): yi_pred = RealNumber.obtain_from_generic(out.get_label(i)) if yi_pred.value == label_train_multiclass[i]: count = count + 1 #print("BMRM: Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() )) #hp = sosvm.get_helper() #print hp.get_primal_values() #print hp.get_train_errors() # PPBMRM w = np.zeros(model.get_dim()) sosvm.set_w(w) sosvm.set_solver(PPBMRM) sosvm.set_verbose(True) sosvm.train() out = sosvm.apply() count = 0 for i in xrange(out.get_num_labels()): yi_pred = RealNumber.obtain_from_generic(out.get_label(i)) if yi_pred.value == label_train_multiclass[i]: count = count + 1 #print("PPBMRM: Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() )) # P3BMRM w = np.zeros(model.get_dim()) sosvm.set_w(w) sosvm.set_solver(P3BMRM) sosvm.set_verbose(True) sosvm.train() out = sosvm.apply() count = 0 for i in xrange(out.get_num_labels()): yi_pred = RealNumber.obtain_from_generic(out.get_label(i)) if yi_pred.value == label_train_multiclass[i]: count = count + 1
# Dimension of the data dim = 2 X, y = gen_data() cnt = 250 X2, y2 = fill_data(cnt, np.min(X), np.max(X)) labels = MulticlassSOLabels(y) features = RealFeatures(X.T) model = MulticlassModel(features, labels) lambda_ = 1e1 sosvm = DualLibQPBMSOSVM(model, labels, lambda_) sosvm.set_cleanAfter( 10 ) # number of iterations that cutting plane has to be inactive for to be removed sosvm.set_cleanICP(True) # enables inactive cutting plane removal feature sosvm.set_TolRel(0.001) # set relative tolerance sosvm.set_verbose(True) # enables verbosity of the solver sosvm.set_cp_models(16) # set number of cutting plane models sosvm.set_solver(BMRM) # select training algorithm #sosvm.set_solver(PPBMRM) #sosvm.set_solver(P3BMRM) sosvm.train() res = sosvm.get_result()
def structure_multiclass_bmrm(fm_train_real=traindat,label_train_multiclass=label_traindat): from modshogun import RealFeatures from modshogun import SOSVMHelper from modshogun import BMRM, PPBMRM, P3BMRM from modshogun import MulticlassModel, MulticlassSOLabels, DualLibQPBMSOSVM, RealNumber labels = MulticlassSOLabels(label_train_multiclass) features = RealFeatures(fm_train_real.T) model = MulticlassModel(features, labels) sosvm = DualLibQPBMSOSVM(model, labels, 1.0) # BMRM sosvm.set_solver(BMRM) sosvm.set_verbose(True) sosvm.train() out = sosvm.apply() count = 0 for i in xrange(out.get_num_labels()): yi_pred = RealNumber.obtain_from_generic(out.get_label(i)) if yi_pred.value == label_train_multiclass[i]: count = count + 1 #print("BMRM: Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() )) #hp = sosvm.get_helper() #print hp.get_primal_values() #print hp.get_train_errors() # PPBMRM w = np.zeros(model.get_dim()) sosvm.set_w(w) sosvm.set_solver(PPBMRM) sosvm.set_verbose(True) sosvm.train() out = sosvm.apply() count = 0 for i in xrange(out.get_num_labels()): yi_pred = RealNumber.obtain_from_generic(out.get_label(i)) if yi_pred.value == label_train_multiclass[i]: count = count + 1 #print("PPBMRM: Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() )) # P3BMRM w = np.zeros(model.get_dim()) sosvm.set_w(w) sosvm.set_solver(P3BMRM) sosvm.set_verbose(True) sosvm.train() out = sosvm.apply() count = 0 for i in xrange(out.get_num_labels()): yi_pred = RealNumber.obtain_from_generic(out.get_label(i)) if yi_pred.value == label_train_multiclass[i]: count = count + 1
def structure_multiclass_bmrm(fm_train_real=traindat, label_train_multiclass=label_traindat): from modshogun import MulticlassSOLabels, LabelsFactory from modshogun import RealFeatures from modshogun import SOSVMHelper try: from modshogun import BMRM, PPBMRM, P3BMRM, DualLibQPBMSOSVM except ImportError: print( "At least one of BMRM, PPBMRM, P3BMRM, DualLibQPBMSOSVM not available" ) exit(0) from modshogun import MulticlassModel, RealNumber labels = MulticlassSOLabels(label_train_multiclass) features = RealFeatures(fm_train_real.T) model = MulticlassModel(features, labels) sosvm = DualLibQPBMSOSVM(model, labels, 1.0) # BMRM sosvm.set_solver(BMRM) sosvm.set_verbose(True) sosvm.train() bmrm_out = LabelsFactory.to_multiclass_structured(sosvm.apply()) count = 0 for i in range(bmrm_out.get_num_labels()): yi_pred = RealNumber.obtain_from_generic(bmrm_out.get_label(i)) if yi_pred.value == label_train_multiclass[i]: count = count + 1 #print("BMRM: Correct classification rate: %0.2f" % ( 100.0*count/bmrm_out.get_num_labels() )) #hp = sosvm.get_helper() #print hp.get_primal_values() #print hp.get_train_errors() # PPBMRM w = np.zeros(model.get_dim()) sosvm.set_w(w) sosvm.set_solver(PPBMRM) sosvm.set_verbose(True) sosvm.train() ppbmrm_out = LabelsFactory.to_multiclass_structured(sosvm.apply()) count = 0 for i in range(ppbmrm_out.get_num_labels()): yi_pred = RealNumber.obtain_from_generic(ppbmrm_out.get_label(i)) if yi_pred.value == label_train_multiclass[i]: count = count + 1 #print("PPBMRM: Correct classification rate: %0.2f" % ( 100.0*count/ppbmrm_out.get_num_labels() )) # P3BMRM w = np.zeros(model.get_dim()) sosvm.set_w(w) sosvm.set_solver(P3BMRM) sosvm.set_verbose(True) sosvm.train() p3bmrm_out = LabelsFactory.to_multiclass_structured(sosvm.apply()) count = 0 for i in range(p3bmrm_out.get_num_labels()): yi_pred = RealNumber.obtain_from_generic(p3bmrm_out.get_label(i)) if yi_pred.value == label_train_multiclass[i]: count = count + 1 #print("P3BMRM: Correct classification rate: %0.2f" % ( 100.0*count/p3bmrm_out.get_num_labels() )) return bmrm_out, ppbmrm_out, p3bmrm_out