def structure_hmsvm_bmrm(m_data_dict=data_dict): from shogun.Features import RealMatrixFeatures from shogun.Loss import HingeLoss from shogun.Structure import HMSVMLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE from shogun.Evaluation import StructuredAccuracy from shogun.Structure import DualLibQPBMSOSVM labels_array = m_data_dict['label'][0] idxs = numpy.nonzero(labels_array == -1) labels_array[idxs] = 0 labels = HMSVMLabels(labels_array, 250, 500, 2) features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500) loss = HingeLoss() model = HMSVMModel(features, labels, SMT_TWO_STATE, 4) sosvm = DualLibQPBMSOSVM(model, loss, labels, 5000.0) sosvm.train() print sosvm.get_w() predicted = sosvm.apply() evaluator = StructuredAccuracy() acc = evaluator.evaluate(predicted, labels) print('Accuracy = %.4f' % acc)
def structure_hmsvm_bmrm (m_data_dict=data_dict): from shogun.Features import RealMatrixFeatures from shogun.Loss import HingeLoss from shogun.Structure import SequenceLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE from shogun.Evaluation import StructuredAccuracy from shogun.Structure import DualLibQPBMSOSVM 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) loss = HingeLoss() model = HMSVMModel(features, labels, SMT_TWO_STATE, 4) sosvm = DualLibQPBMSOSVM(model, loss, labels, 5000.0) sosvm.train() #print sosvm.get_w() predicted = sosvm.apply() evaluator = StructuredAccuracy() acc = evaluator.evaluate(predicted, labels)
def structure_plif_hmsvm_bmrm(num_examples, example_length, num_features, num_noise_features): from shogun.Features import RealMatrixFeatures from shogun.Structure import TwoStateModel, DualLibQPBMSOSVM from shogun.Evaluation import 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_discrete_hmsvm_bmrm(m_data_dict=data_dict): from shogun.Features import RealMatrixFeatures from shogun.Structure import SequenceLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE from shogun.Evaluation import StructuredAccuracy from shogun.Structure import DualLibQPBMSOSVM 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)
lambda_ = 1e1 sosvm = DualLibQPBMSOSVM(model, loss, 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.hist_Fp) Fds = np.array(res.hist_Fp) wdists = np.array(res.hist_wdist) plt.figure() plt.subplot(221) plt.title('Fp and Fd history') plt.plot(xrange(res.nIter), Fps, hold=True) plt.plot(xrange(res.nIter), Fds, hold=True) plt.subplot(222) plt.title('w dist history') plt.plot(xrange(res.nIter), wdists)
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()) wdists = np.array(res.get_hist_wdist_vector()) plt.figure() plt.subplot(221) plt.title('Fp and Fd history') plt.plot(xrange(res.get_n_iters()), Fps, hold=True) plt.plot(xrange(res.get_n_iters()), Fds, hold=True) plt.subplot(222) plt.title('w dist history') plt.plot(xrange(res.get_n_iters()), wdists)