Python LibLinear.set_features Examples

Example #1

File: classifier_liblinear_modular.py Project: polyactis/test

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()

Example #2

File: features_director_dot_modular.py Project: AlexBinder/shogun

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()

Example #3

File: features_director_dot_modular.py Project: ratschlab/shogun

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()

Example #4

File: classifier_liblinear_modular.py Project: joseph-chan/rqpersonalsvn

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()

Example #5

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()

Example #6

File: features_director_dot_modular.py Project: changguanghua/shogun

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()