Python PluginEstimate Examples

Example #1

File: kernel.py Project: 42MachineLearning/shogun

def _evaluate_pie (indata, prefix):
	pie=PluginEstimate()
	feats=util.get_features(indata, prefix)
	labels=BinaryLabels(double(indata['classifier_labels']))
	pie.set_labels(labels)
	pie.set_features(feats['train'])
	pie.train()

	fun=eval(indata[prefix+'name']+'Kernel')
	kernel=fun(feats['train'], feats['train'], pie)
	km_train=max(abs(
		indata[prefix+'matrix_train']-kernel.get_kernel_matrix()).flat)

	kernel.init(feats['train'], feats['test'])
	pie.set_features(feats['test'])
	km_test=max(abs(
		indata[prefix+'matrix_test']-kernel.get_kernel_matrix()).flat)
	classified=max(abs(
		pie.apply().get_values()-indata['classifier_classified']))

	return util.check_accuracy(indata[prefix+'accuracy'],
		km_train=km_train, km_test=km_test, classified=classified)

Example #2

File: kernel_salzberg_word_string_modular.py Project: manantomar/test

def kernel_salzberg_word_string_modular(fm_train_dna=traindat,
                                        fm_test_dna=testdat,
                                        label_train_dna=label_traindat,
                                        order=3,
                                        gap=0,
                                        reverse=False):
    from modshogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels
    from modshogun import SalzbergWordStringKernel
    from modshogun import PluginEstimate

    charfeat = StringCharFeatures(fm_train_dna, DNA)
    feats_train = StringWordFeatures(charfeat.get_alphabet())
    feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)

    charfeat = StringCharFeatures(fm_test_dna, DNA)
    feats_test = StringWordFeatures(charfeat.get_alphabet())
    feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse)

    pie = PluginEstimate()
    labels = BinaryLabels(label_train_dna)
    pie.set_labels(labels)
    pie.set_features(feats_train)
    pie.train()

    kernel = SalzbergWordStringKernel(feats_train, feats_train, pie, labels)
    km_train = kernel.get_kernel_matrix()

    kernel.init(feats_train, feats_test)
    pie.set_features(feats_test)
    pie.apply().get_labels()
    km_test = kernel.get_kernel_matrix()
    return km_train, km_test, kernel

Example #3

File: kernel_histogram_word_string_modular.py Project: JingheZ/shogun

def kernel_histogram_word_string_modular (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,order=3,gap=0,reverse=False):

	from modshogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels
	from modshogun import HistogramWordStringKernel
	from modshogun import PluginEstimate#, MSG_DEBUG

	reverse = reverse
	charfeat=StringCharFeatures(DNA)
	#charfeat.io.set_loglevel(MSG_DEBUG)
	charfeat.set_features(fm_train_dna)
	feats_train=StringWordFeatures(charfeat.get_alphabet())
	feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse)

	charfeat=StringCharFeatures(DNA)
	charfeat.set_features(fm_test_dna)
	feats_test=StringWordFeatures(charfeat.get_alphabet())
	feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse)

	pie=PluginEstimate()
	labels=BinaryLabels(label_train_dna)
	pie.set_labels(labels)
	pie.set_features(feats_train)
	pie.train()

	kernel=HistogramWordStringKernel(feats_train, feats_train, pie)
	km_train=kernel.get_kernel_matrix()
	kernel.init(feats_train, feats_test)
	pie.set_features(feats_test)
	pie.apply().get_labels()
	km_test=kernel.get_kernel_matrix()
	return km_train,km_test,kernel

Example #4

def _evaluate_pie(indata, prefix):
    pie = PluginEstimate()
    feats = util.get_features(indata, prefix)
    labels = BinaryLabels(double(indata['classifier_labels']))
    pie.set_labels(labels)
    pie.set_features(feats['train'])
    pie.train()

    fun = eval(indata[prefix + 'name'] + 'Kernel')
    kernel = fun(feats['train'], feats['train'], pie)
    km_train = max(
        abs(indata[prefix + 'matrix_train'] - kernel.get_kernel_matrix()).flat)

    kernel.init(feats['train'], feats['test'])
    pie.set_features(feats['test'])
    km_test = max(
        abs(indata[prefix + 'matrix_test'] - kernel.get_kernel_matrix()).flat)
    classified = max(
        abs(pie.apply().get_values() - indata['classifier_classified']))

    return util.check_accuracy(indata[prefix + 'accuracy'],
                               km_train=km_train,
                               km_test=km_test,
                               classified=classified)

Example #5

File: kernel_histogram_word_string_modular.py Project: manantomar/test

def kernel_histogram_word_string_modular (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,order=3,ppseudo_count=1,npseudo_count=1):

	from modshogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels
	from modshogun import HistogramWordStringKernel, AvgDiagKernelNormalizer
	from modshogun import PluginEstimate#, MSG_DEBUG

	charfeat=StringCharFeatures(DNA)
	#charfeat.io.set_loglevel(MSG_DEBUG)
	charfeat.set_features(fm_train_dna)
	feats_train=StringWordFeatures(charfeat.get_alphabet())
	feats_train.obtain_from_char(charfeat, order-1, order, 0, False)

	charfeat=StringCharFeatures(DNA)
	charfeat.set_features(fm_test_dna)
	feats_test=StringWordFeatures(charfeat.get_alphabet())
	feats_test.obtain_from_char(charfeat, order-1, order, 0, False)

	pie=PluginEstimate(ppseudo_count,npseudo_count)
	labels=BinaryLabels(label_train_dna)
	pie.set_labels(labels)
	pie.set_features(feats_train)
	pie.train()

	kernel=HistogramWordStringKernel(feats_train, feats_train, pie)
	km_train=kernel.get_kernel_matrix()
	kernel.init(feats_train, feats_test)
	pie.set_features(feats_test)
	pie.apply().get_labels()
	km_test=kernel.get_kernel_matrix()
	return km_train,km_test,kernel