def _compute_pie (feats, params):
"""Compute a kernel with PluginEstimate.
@param feats kernel features
@param params dict containing various kernel parameters
"""
output=fileop.get_output(category.KERNEL, params)
lab, labels=dataop.get_labels(feats['train'].get_num_vectors())
output['classifier_labels']=lab
pie=PluginEstimate()
pie.set_labels(labels)
pie.set_features(feats['train'])
pie.train()
kfun=eval('kernel.'+params['name']+'Kernel')
kern=kfun(feats['train'], feats['train'], pie)
output['kernel_matrix_train']=kern.get_kernel_matrix()
kern.init(feats['train'], feats['test'])
pie.set_features(feats['test'])
output['kernel_matrix_test']=kern.get_kernel_matrix()
classified=pie.classify().get_labels()
output['classifier_classified']=classified
fileop.write(category.KERNEL, output)
def _compute_pie(feats, params):
"""Compute a kernel with PluginEstimate.
@param feats kernel features
@param params dict containing various kernel parameters
"""
output = fileop.get_output(category.KERNEL, params)
lab, labels = dataop.get_labels(feats["train"].get_num_vectors())
output["classifier_labels"] = lab
pie = PluginEstimate()
pie.set_labels(labels)
pie.set_features(feats["train"])
pie.train()
kfun = eval("kernel." + params["name"] + "Kernel")
kern = kfun(feats["train"], feats["train"], pie)
output["kernel_matrix_train"] = kern.get_kernel_matrix()
kern.init(feats["train"], feats["test"])
pie.set_features(feats["test"])
output["kernel_matrix_test"] = kern.get_kernel_matrix()
classified = pie.apply().get_labels()
output["classifier_classified"] = classified
fileop.write(category.KERNEL, output)
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)
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 shogun.Features import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels from shogun.Kernel import HistogramWordStringKernel from shogun.Classifier 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
def _compute_pie(feats, params):
"""Compute a kernel with PluginEstimate.
@param feats kernel features
@param params dict containing various kernel parameters
"""
output = fileop.get_output(category.KERNEL, params)
lab, labels = dataop.get_labels(feats['train'].get_num_vectors())
output['classifier_labels'] = lab
pie = PluginEstimate()
pie.set_labels(labels)
pie.set_features(feats['train'])
pie.train()
kfun = eval('kernel.' + params['name'] + 'Kernel')
kern = kfun(feats['train'], feats['train'], pie)
output['kernel_matrix_train'] = kern.get_kernel_matrix()
kern.init(feats['train'], feats['test'])
pie.set_features(feats['test'])
output['kernel_matrix_test'] = kern.get_kernel_matrix()
classified = pie.classify().get_labels()
output['classifier_classified'] = classified
fileop.write(category.KERNEL, output)
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 shogun.Features import StringCharFeatures, StringWordFeatures, DNA, Labels
from shogun.Kernel import HistogramWordStringKernel
from shogun.Classifier 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 = Labels(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.classify().get_labels()
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
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 shogun.Features import StringCharFeatures, StringWordFeatures, DNA, Labels
from shogun.Kernel import SalzbergWordStringKernel
from shogun.Classifier 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 = Labels(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
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 shogun.Features import StringCharFeatures, StringWordFeatures, DNA, Labels from shogun.Kernel import SalzbergWordStringKernel from shogun.Classifier 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=Labels(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
def plugin_estimate_salzberg (): print 'PluginEstimate w/ SalzbergWord' from shogun.Features import StringCharFeatures, StringWordFeatures, DNA, Labels from shogun.Kernel import SalzbergWordStringKernel from shogun.Classifier import PluginEstimate order=3 gap=0 reverse=False 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=Labels(label_train_dna) pie.set_labels(labels) pie.set_features(feats_train) pie.train() kernel=SalzbergWordStringKernel(feats_train, feats_test, pie, labels) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) pie.set_features(feats_test) pie.classify().get_labels() km_test=kernel.get_kernel_matrix()
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_confidences() - indata['classifier_classified']))
return util.check_accuracy(indata[prefix + 'accuracy'],
km_train=km_train,
km_test=km_test,
classified=classified)