def liblinear (): print 'LibLinear' from shogun.Features import RealFeatures, SparseRealFeatures, Labels from shogun.Classifier import LibLinear realfeat=RealFeatures(fm_train_real) feats_train=SparseRealFeatures() feats_train.obtain_from_simple(realfeat) realfeat=RealFeatures(fm_test_real) feats_test=SparseRealFeatures() feats_test.obtain_from_simple(realfeat) C=0.9 epsilon=1e-5 num_threads=1 labels=Labels(label_train_twoclass) svm=LibLinear(C, feats_train, labels) svm.set_epsilon(epsilon) svm.parallel.set_num_threads(num_threads) svm.set_bias_enabled(True) svm.train() svm.set_features(feats_test) print svm.classify().get_labels()
def features_director_dot_modular (fm_train_real, fm_test_real, label_train_twoclass, C, epsilon): from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC_DUAL from shogun.Mathematics import Math_init_random Math_init_random(17) feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=BinaryLabels(label_train_twoclass) dfeats_train=NumpyFeatures(fm_train_real) dfeats_test=NumpyFeatures(fm_test_real) dlabels=BinaryLabels(label_train_twoclass) print feats_train.get_computed_dot_feature_matrix() print dfeats_train.get_computed_dot_feature_matrix() svm=LibLinear(C, feats_train, labels) svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL) svm.set_epsilon(epsilon) svm.set_bias_enabled(True) svm.train() svm.set_features(feats_test) svm.apply().get_labels() predictions = svm.apply() dfeats_train.__disown__() dfeats_train.parallel.set_num_threads(1) dsvm=LibLinear(C, dfeats_train, dlabels) dsvm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL) dsvm.set_epsilon(epsilon) dsvm.set_bias_enabled(True) dsvm.train() dfeats_test.__disown__() dfeats_test.parallel.set_num_threads(1) dsvm.set_features(dfeats_test) dsvm.apply().get_labels() dpredictions = dsvm.apply() return predictions, svm, predictions.get_labels()
def features_director_dot_modular(fm_train_real, fm_test_real, label_train_twoclass, C, epsilon): from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC_DUAL from shogun.Mathematics import Math_init_random Math_init_random(17) feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) labels = BinaryLabels(label_train_twoclass) dfeats_train = NumpyFeatures(fm_train_real) dfeats_test = NumpyFeatures(fm_test_real) dlabels = BinaryLabels(label_train_twoclass) print feats_train.get_computed_dot_feature_matrix() print dfeats_train.get_computed_dot_feature_matrix() svm = LibLinear(C, feats_train, labels) svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL) svm.set_epsilon(epsilon) svm.set_bias_enabled(True) svm.train() svm.set_features(feats_test) svm.apply().get_labels() predictions = svm.apply() dfeats_train.__disown__() dfeats_train.parallel.set_num_threads(1) dsvm = LibLinear(C, dfeats_train, dlabels) dsvm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL) dsvm.set_epsilon(epsilon) dsvm.set_bias_enabled(True) dsvm.train() dfeats_test.__disown__() dfeats_test.parallel.set_num_threads(1) dsvm.set_features(dfeats_test) dsvm.apply().get_labels() dpredictions = dsvm.apply() return predictions, svm, predictions.get_labels()
def classifier_liblinear_modular(fm_train_real, fm_test_real, label_train_twoclass, C, epsilon): from shogun.Features import RealFeatures, SparseRealFeatures, Labels from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC_DUAL from shogun.Mathematics import Math_init_random Math_init_random(17) feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) labels = Labels(label_train_twoclass) svm = LibLinear(C, feats_train, labels) svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL) svm.set_epsilon(epsilon) svm.set_bias_enabled(True) svm.train() svm.set_features(feats_test) svm.apply().get_labels() predictions = svm.apply() return predictions, svm, predictions.get_labels()
def features_director_dot_modular (fm_train_real, fm_test_real, label_train_twoclass, C, epsilon): try: from shogun.Features import DirectorDotFeatures from shogun.Library import RealVector except ImportError: print "recompile shogun with --enable-swig-directors" return class NumpyFeatures(DirectorDotFeatures): # variables data=numpy.empty((1,1)) # constructor def __init__(self, d): DirectorDotFeatures.__init__(self) self.data = d # overloaded methods def add_to_dense_sgvec(self, alpha, vec_idx1, vec2, abs): if abs: vec2+=alpha*numpy.abs(self.data[:,vec_idx1]) else: vec2+=alpha*self.data[:,vec_idx1] def dot(self, vec_idx1, df, vec_idx2): return numpy.dot(self.data[:,vec_idx1], df.get_computed_dot_feature_vector(vec_idx2)) def dense_dot_sgvec(self, vec_idx1, vec2): return numpy.dot(self.data[:,vec_idx1], vec2[0:vec2.vlen]) def get_num_vectors(self): return self.data.shape[1] def get_dim_feature_space(self): return self.data.shape[0] # operators # def __add__(self, other): # return NumpyFeatures(self.data+other.data) # def __sub__(self, other): # return NumpyFeatures(self.data-other.data) # def __iadd__(self, other): # return NumpyFeatures(self.data+other.data) # def __isub__(self, other): # return NumpyFeatures(self.data-other.data) from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC_DUAL from shogun.Mathematics import Math_init_random Math_init_random(17) feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=BinaryLabels(label_train_twoclass) dfeats_train=NumpyFeatures(fm_train_real) dfeats_test=NumpyFeatures(fm_test_real) dlabels=BinaryLabels(label_train_twoclass) print feats_train.get_computed_dot_feature_matrix() print dfeats_train.get_computed_dot_feature_matrix() svm=LibLinear(C, feats_train, labels) svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL) svm.set_epsilon(epsilon) svm.set_bias_enabled(True) svm.train() svm.set_features(feats_test) svm.apply().get_labels() predictions = svm.apply() dfeats_train.__disown__() dfeats_train.parallel.set_num_threads(1) dsvm=LibLinear(C, dfeats_train, dlabels) dsvm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL) dsvm.set_epsilon(epsilon) dsvm.set_bias_enabled(True) dsvm.train() dfeats_test.__disown__() dfeats_test.parallel.set_num_threads(1) dsvm.set_features(dfeats_test) dsvm.apply().get_labels() dpredictions = dsvm.apply() return predictions, svm, predictions.get_labels()