def score(self, data, *options, **args):
if 'targetClass' in args:
targetClass = args['targetClass']
else:
targetClass = None
if 'otherClass' in args:
otherClass = args['otherClass']
else:
otherClass = None
if (targetClass is not None
and otherClass is not None) or (self.scoreName
in self.multiClass):
return self.scoreFunc(data, targetClass, otherClass, **args)
elif data.labels.numClasses == 2:
return self._score(data, **args)
elif self.mode == "oneAgainstRest":
if targetClass is not None:
labels = labels.oneAgainstRest(data.labels, targetClass)
return self._score(data, 1, 0, Y=labels.Y)
else:
raise ValueError, 'need to specify a target class'
elif self.mode == 'oneAgainstOne':
return self.oneAgainstOne(data, targetClass, **args)
def allOneAgainstRest(classifier,
data,
resultsFile,
numFolds=5,
minClassSize=8):
import myio
labels = data.labels
results = {}
if os.path.exists(resultsFile):
results = assess.loadResults(resultsFile)
for label in labels.classLabels:
if label in results: continue
if (minClassSize is not None
and labels.classSize[labels.classDict[label]] < minClassSize):
continue
myio.log('class: ' + label + '\n')
data = oneAgainstRest(data, label)
try:
results[label] = classifier.stratifiedCV(data, numFolds)
except:
results[label] = None
assess.saveResultObjects(results, resultsFile)
data.attachLabels(labels)
def selectFeatures(self, data, **args) :
labels = data.labels
features = []
for k in range(data.labels.numClasses) :
data2 = labels.oneAgainstRest(data, k)
features2 = self.featureSelector.selectFeatures(data2)
features = misc.union(features, features2)
data.attachLabels(labels)
return features
def selectFeatures(self, data, **args):
labels = data.labels
features = []
for k in range(data.labels.numClasses):
data2 = labels.oneAgainstRest(data, k)
features2 = self.featureSelector.selectFeatures(data2)
features = misc.union(features, features2)
data.attachLabels(labels)
return features
def train(self, data, **args) :
'''train k classifiers'''
Classifier.train(self, data, **args)
numClasses = self.labels.numClasses
if numClasses <= 2:
raise ValueError, 'Not a multi class problem'
self.classifiers = [self.classifier.__class__(self.classifier)
for i in range(numClasses)]
for i in range(numClasses) :
# make a copy of the data; this is done in case the classifier modifies the data
datai = data.__class__(data, deepcopy = self.classifier.deepcopy)
datai = oneAgainstRest(datai, data.labels.classLabels[i])
self.classifiers[i].train(datai)
self.log.trainingTime = self.getTrainingTime()
def load(self, fileName, data) :
"""load a trained classifier from a file. Also provide the data on which
the classifier was trained. It assumes the underlying binary classifier is
an SVM"""
from PyML import svm
Classifier.train(self, data)
file_handle = open(fileName)
numClasses = self.labels.numClasses
self.classifiers = [self.classifier.__class__(self.classifier)
for i in range(numClasses)]
for i in range(numClasses) :
datai = data.__class__(data, deepcopy = self.classifier.deepcopy)
datai = oneAgainstRest(datai, data.labels.classLabels[i])
self.classifiers[i] = svm.loadSVM(file_handle, datai)
def train(self, data, **args):
'''train k classifiers'''
Classifier.train(self, data, **args)
numClasses = self.labels.numClasses
if numClasses <= 2:
raise ValueError, 'Not a multi class problem'
self.classifiers = [
self.classifier.__class__(self.classifier)
for i in range(numClasses)
]
for i in range(numClasses):
# make a copy of the data; this is done in case the classifier modifies the data
datai = data.__class__(data, deepcopy=self.classifier.deepcopy)
datai = oneAgainstRest(datai, data.labels.classLabels[i])
self.classifiers[i].train(datai)
self.log.trainingTime = self.getTrainingTime()
def load(self, fileName, data):
"""load a trained classifier from a file. Also provide the data on which
the classifier was trained. It assumes the underlying binary classifier is
an SVM"""
from PyML import svm
Classifier.train(self, data)
file_handle = open(fileName)
numClasses = self.labels.numClasses
self.classifiers = [
self.classifier.__class__(self.classifier)
for i in range(numClasses)
]
for i in range(numClasses):
datai = data.__class__(data, deepcopy=self.classifier.deepcopy)
datai = oneAgainstRest(datai, data.labels.classLabels[i])
self.classifiers[i] = svm.loadSVM(file_handle, datai)
def allOneAgainstRest(classifier, data, resultsFile, numFolds = 5, minClassSize = 8) :
import myio
labels = data.labels
results = {}
if os.path.exists(resultsFile) :
results = assess.loadResults(resultsFile)
for label in labels.classLabels :
if label in results : continue
if (minClassSize is not None and
labels.classSize[labels.classDict[label]] < minClassSize) : continue
myio.log('class: ' + label + '\n')
data = oneAgainstRest(data, label)
try :
results[label] = classifier.stratifiedCV(data, numFolds)
except :
results[label] = None
assess.saveResultObjects(results, resultsFile)
data.attachLabels(labels)
def score(self, data, *options, **args) :
if 'targetClass' in args :
targetClass = args['targetClass']
else :
targetClass = None
if 'otherClass' in args :
otherClass = args['otherClass']
else :
otherClass = None
if (targetClass is not None and otherClass is not None) or (
self.scoreName in self.multiClass) :
return self.scoreFunc(data, targetClass, otherClass, **args)
elif data.labels.numClasses == 2 :
return self._score(data, **args)
elif self.mode == "oneAgainstRest" :
if targetClass is not None :
labels = labels.oneAgainstRest(data.labels, targetClass)
return self._score(data, 1, 0, Y=labels.Y)
else :
raise ValueError, 'need to specify a target class'
elif self.mode == 'oneAgainstOne' :
return self.oneAgainstOne(data, targetClass, **args)
def test(component="svm", **args):
container = "SparseDataSet"
if "container" in args:
container = args["container"]
try:
DataSet = getattr(vectorDatasets, container)
except:
raise ValueError, "wrong container " + container
results = {}
comp = "general"
if component == "all" or component == comp:
s = svm.SVM()
results = {}
d = DataSet(heartdatafile, labelsColumn=0)
s.train(d)
s.test(d)
s = svm.SVM()
s.stratifiedCV(d)
print "starting aggregate****************"
d2 = Aggregate([d, d])
print "end aggregate"
r = s.stratifiedCV(d2)
d.attachKernel("polynomial")
s.cv(d)
d.attachKernel("linear")
s = svm.SVM()
s.train(d)
s.train(d, saveSpace=False)
s.save("tmp")
loaded = svm.loadSVM("tmp", datasetClass=DataSet)
r = loaded.test(d)
d.attachKernel("gaussian", gamma=0.01)
s.train(d, saveSpace=False)
s.save("tmp")
loaded = svm.loadSVM("tmp", datasetClass=DataSet, labelsColumn=1)
r = loaded.test(d)
os.remove("tmp")
d = DataSet(numpy.random.randn(100, 10))
d = DataSet([[1, 2], [2, 3]])
d = SequenceData(["asa", "ben", "hur"])
comp = "svm"
if component == "all" or component == comp:
d = DataSet(heartdatafile, labelsColumn=0)
results[comp] = []
d.attachKernel("polynomial")
s = svm.SVM()
results[comp].append(s.cv(d, saveSpace=True))
d.attachKernel("linear")
results[comp].append(s.cv(d))
comp = "kernelData"
if component == "all" or component == comp:
d = DataSet(heartdatafile, labelsColumn=0)
results[comp] = []
kdata = KernelData("heart.kernel", gistFormat=True)
kdata.attachLabels(d.labels)
s = svm.SVM()
results[comp].append(s.cv(kdata))
kdata.attachKernel("gaussian", gamma=0.1)
results[comp].append(s.cv(kdata))
comp = "normalization"
if component == "all" or component == comp:
results[comp] = []
data = DataSet(heartdatafile, labelsColumn=0)
data.attachKernel("polynomial", degree=4, normalization="dices")
s = svm.SVM()
results[comp].append(s.cv(data))
comp = "svr"
if component == "all" or component == comp:
d = DataSet(heartdatafile, labelsColumn=0, numericLabels=True)
results[comp] = []
s = svm.SVR()
# results[comp].append(
# s.cv(d, saveSpace = True))
# results[comp].append(
# s.trainTest(d, range(150), range(151, 250)))
results[comp].append(s.cv(d))
comp = "save"
if component == "all" or component == comp:
results[comp] = []
s = svm.SVM()
data = DataSet(heartdatafile, labelsColumn=0)
import tempfile
tmpfile = tempfile.mktemp()
r = s.cv(data)
r.save(tmpfile)
r = assess.loadResults(tmpfile)
results["save"].append(r)
r = s.nCV(data)
r.save(tmpfile)
results["save"].append(assess.loadResults(tmpfile))
r = {}
for i in range(10):
r[i] = s.cv(data)
assess.saveResultObjects(r, tmpfile)
r = assess.loadResults(tmpfile)
comp = "classifiers"
if component == "all" or component == comp:
d = DataSet(heartdatafile, labelsColumn=0)
results[comp] = []
cl = knn.KNN()
results[comp].append(cl.stratifiedCV(d))
print "testing ridge regression"
ridge = ridgeRegression.RidgeRegression()
results[comp].append(ridge.cv(d))
comp = "platt"
if component == "all" or component == "platt":
results[comp] = []
d = DataSet(heartdatafile, labelsColumn=0)
p = platt.Platt2(svm.SVM())
results[comp].append(p.stratifiedCV(d))
comp = "multi"
if component == "all" or component == comp:
results[comp] = []
d = DataSet(irisdatafile, labelsColumn=-1)
mc = multi.OneAgainstOne(svm.SVM())
results[comp].append(mc.cv(d))
d = DataSet(irisdatafile, labelsColumn=-1)
mc = multi.OneAgainstRest(svm.SVM())
results[comp].append(mc.cv(d))
mc = multi.OneAgainstRest(svm.SVM())
d.attachKernel("poly")
results[comp].append(mc.cv(d))
d.attachKernel("linear")
mc = multi.OneAgainstRest(svm.SVM())
# kdata = datafunc.KernelData('iris.linear.kernel',
# labelsFile = 'irisY.csv', labelsColumn = 0, gistFormat = True)
# results[comp].append(mc.cv(kdata))
comp = "featsel"
if component == "all" or component == comp:
results[comp] = []
s = svm.SVM()
d = DataSet(yeastdatafile, labelsColumn=0)
d2 = labels.oneAgainstRest(d, "2")
results[comp].append(s.stratifiedCV(d2))
# feature selection using RFE
m = composite.FeatureSelect(s, featsel.RFE())
results[comp].append(m.stratifiedCV(d2, 3))
fs = featsel.FeatureScore("golub")
f = featsel.Filter(fs, sigma=2)
f = featsel.Filter(fs, numFeatures=20)
m = composite.FeatureSelect(s, f)
results[comp].append(m.stratifiedCV(d2, 3))
# same thing but with a Chain:
c = composite.Chain([f, s])
# r = c.stratifiedCV (d2)
comp = "modelSelection"
if component == "all" or component == comp:
results[comp] = []
s = svm.SVM()
d = DataSet(heartdatafile, labelsColumn=0)
p = modelSelection.ParamGrid(svm.SVM(ker.Polynomial()), "C", [0.1, 1, 10, 100], "kernel.degree", [2, 3, 4])
p = modelSelection.ParamGrid(svm.SVM(ker.Gaussian()), "C", [0.1, 1, 10, 100], "kernel.gamma", [0.01, 0.1, 1])
# p = modelSelection.Param(svm.SVM(), 'C', [0.1, 1, 10, 100])
m = modelSelection.ModelSelector(p, measure="roc", foldsToPerform=2)
m = modelSelection.ModelSelector(p)
# m = modelSelection.SVMselect()
results[comp].append(m.cv(d))
return results
def test(component='svm', **args):
if 'container' in args:
container = args['container']
else:
container = 'SparseDataSet'
try:
DataSet = getattr(vectorDatasets, container)
except:
raise ValueError, 'wrong container ' + container
s = svm.SVM()
results = {}
comp = 'general'
if component == 'all' or component == comp:
d = DataSet(heartdatafile, labelsColumn=0)
s.train(d)
s.test(d)
s = svm.SVM()
s.stratifiedCV(d)
d2 = Aggregate([d, d])
#r = s.stratifiedCV(d2)
#r = s.loo(d)
d.attachKernel('polynomial')
s.cv(d)
d.attachKernel('linear')
s = svm.SVM()
s.train(d)
d = DataSet(numpy.random.randn(100, 10))
d = DataSet([[1, 2], [2, 3]])
#d = SequenceData(['asa', 'ben', 'hur'])
comp = 'svm'
if component == 'all' or component == comp:
d = DataSet(heartdatafile, labelsColumn=0)
results[comp] = []
d.attachKernel('polynomial')
results[comp].append(s.cv(d, saveSpace=True))
d.attachKernel('linear')
results[comp].append(s.cv(d))
results[comp].append(
SVM(optimizer='liblinear', loss='l1', C=1).stratifiedCV(d, seed=1))
results[comp].append(
SVM(optimizer='liblinear', loss='l2', C=1).stratifiedCV(d, seed=1))
comp = 'kernelData'
if component == 'all' or component == comp:
d = DataSet(heartdatafile, labelsColumn=0)
results[comp] = []
kdata = KernelData('heart.kernel', gistFormat=True)
kdata.attachLabels(d.labels)
s = svm.SVM()
results[comp].append(s.cv(kdata))
kdata.attachKernel('gaussian', gamma=0.1)
results[comp].append(s.cv(kdata))
comp = 'normalization'
if component == 'all' or component == comp:
results[comp] = []
data = DataSet(heartdatafile, labelsColumn=0)
data.attachKernel('polynomial', degree=4, normalization='dices')
s = svm.SVM()
results[comp].append(s.cv(data))
comp = 'svr'
if component == 'all' or component == comp:
d = DataSet(heartdatafile, labelsColumn=0, numericLabels=True)
results[comp] = []
s = svm.SVR()
#results[comp].append(
# s.cv(d, saveSpace = True))
#results[comp].append(
# s.trainTest(d, range(150), range(151, 250)))
results[comp].append(s.cv(d))
comp = 'save'
if component == 'all' or component == comp:
results[comp] = []
s = svm.SVM()
data = DataSet(heartdatafile, labelsColumn=0)
import tempfile
tmpfile = tempfile.mktemp()
r = s.cv(data)
r.save(tmpfile)
r = resultsObjects.loadResults(tmpfile)
results['save'].append(r)
r = s.nCV(data)
r.save(tmpfile)
results['save'].append(resultsObjects.loadResults(tmpfile))
r = {}
for i in range(10):
r[i] = s.cv(data)
resultsObjects.saveResultObjects(r, tmpfile)
r = resultsObjects.loadResults(tmpfile)
comp = 'classifiers'
if component == 'all' or component == comp:
d = DataSet(heartdatafile, labelsColumn=0)
results[comp] = []
cl = knn.KNN()
results[comp].append(cl.stratifiedCV(d))
print 'testing ridge regression'
ridge = ridgeRegression.RidgeRegression()
#results[comp].append(
# ridge.cv(d))
comp = 'platt'
if component == 'all' or component == comp:
results[comp] = []
d = DataSet(heartdatafile, labelsColumn=0)
p = platt.Platt2(s)
results[comp].append(p.stratifiedCV(d))
comp = 'save'
if component == 'all' or component == comp:
data = DataSet(heartdatafile, labelsColumn=0)
s = SVM()
s.train(data)
s.save('model.pyml')
s2 = SVM()
s2.load('model.pyml', data)
results[comp].append(s2.test(data))
d = DataSet(heartdatafile, labelsColumn=0)
kdata = KernelData('heart.kernel', gistFormat=True)
kdata.attachLabels(d.labels)
s = svm.SVM()
s.train(data)
s.save('model2.pyml')
s2 = SVM()
s2.load('model2.pyml', kdata)
results[comp].append(s2.test(kdata))
data = DataSet(irisdatafile, labelsColumn=-1)
mc = multi.OneAgainstRest(SVM())
mc.train(data)
mc.save('iris.pyml')
mc = multi.OneAgainstRest(SVM())
mc.load('iris.pyml', data)
results[comp].append(mc.test(data))
comp = 'multi'
if component == 'all' or component == comp:
results[comp] = []
d = DataSet(irisdatafile, labelsColumn=-1)
mc = multi.OneAgainstOne(svm.SVM())
results[comp].append(mc.cv(d))
d = DataSet(irisdatafile, labelsColumn=-1)
mc = multi.OneAgainstRest(svm.SVM())
results[comp].append(mc.cv(d))
mc = multi.OneAgainstRest(svm.SVM())
d.attachKernel('poly')
results[comp].append(mc.cv(d))
d.attachKernel('linear')
mc = multi.OneAgainstRest(svm.SVM())
#kdata = datafunc.KernelData('iris.linear.kernel',
# labelsFile = 'irisY.csv', labelsColumn = 0, gistFormat = True)
#results[comp].append(mc.cv(kdata))
comp = 'featsel'
if component == 'all' or component == comp:
results[comp] = []
s = svm.SVM()
d = DataSet(yeastdatafile, labelsColumn=0)
d2 = labels.oneAgainstRest(d, '2')
results[comp].append(s.stratifiedCV(d2, seed=1))
# feature selection
m = composite.FeatureSelect(s, featsel.RFE())
results[comp].append(m.stratifiedCV(d2, seed=1))
d = DataSet(yeastdatafile, labelsColumn=0)
d2 = labels.oneAgainstRest(d, '2')
fs = featsel.FeatureScore('golub')
f = featsel.Filter(fs, sigma=2)
m = composite.FeatureSelect(s, f)
results[comp].append(m.stratifiedCV(d2, seed=1))
# same thing but with a Chain:
c = composite.Chain([f, s])
#r = c.stratifiedCV (d2)
comp = 'modelSelection'
if component == 'all' or component == comp:
results[comp] = []
s = svm.SVM()
d = DataSet(heartdatafile, labelsColumn=0)
p = modelSelection.ParamGrid(svm.SVM(ker.Polynomial()), 'C',
[0.1, 1, 10, 100], 'kernel.degree',
[2, 3, 4])
p = modelSelection.ParamGrid(svm.SVM(ker.Gaussian()), 'C',
[0.1, 1, 10, 100], 'kernel.gamma',
[0.01, 0.1, 1])
#p = modelSelection.Param(svm.SVM(), 'C', [0.1, 1, 10, 100])
m = modelSelection.ModelSelector(p, measure='roc', foldsToPerform=2)
m = modelSelection.ModelSelector(p)
#m = modelSelection.SVMselect()
results[comp].append(m.cv(d))
comp = 'preproc'
if component == 'all' or component == comp:
results[comp] = []
s = svm.SVM()
d = DataSet(yeastdatafile, labelsColumn=0)
d2 = labels.oneAgainstRest(d, '2')
results[comp].append(s.stratifiedCV(d2))
p = preproc.Standardizer()
p.train(d2)
results[comp].append(s.stratifiedCV(d2))
print p.scale
print p.translation
return results
def test (component = 'svm', **args) :
if 'container' in args :
container = args['container']
else :
container = 'SparseDataSet'
try :
DataSet = getattr(vectorDatasets, container)
except :
raise ValueError, 'wrong container ' + container
s = svm.SVM()
results = {}
comp = 'general'
if component == 'all' or component == comp :
d = DataSet (heartdatafile, labelsColumn = 0)
s.train(d)
s.test(d)
s = svm.SVM()
s.stratifiedCV(d)
d2 = Aggregate([d,d])
#r = s.stratifiedCV(d2)
#r = s.loo(d)
d.attachKernel('polynomial')
s.cv(d)
d.attachKernel('linear')
s = svm.SVM()
s.train(d)
d = DataSet(numpy.random.randn(100,10))
d = DataSet([[1,2], [2,3]])
#d = SequenceData(['asa', 'ben', 'hur'])
comp = 'svm'
if component == 'all' or component == comp :
d = DataSet (heartdatafile, labelsColumn = 0)
results[comp] = []
d.attachKernel('polynomial')
results[comp].append(
s.cv(d, saveSpace = True))
d.attachKernel('linear')
results[comp].append(
s.cv(d))
results[comp].append(SVM(optimizer = 'liblinear', loss = 'l1', C=1).stratifiedCV(d, seed = 1))
results[comp].append(SVM(optimizer = 'liblinear', loss = 'l2', C=1).stratifiedCV(d, seed = 1))
comp = 'kernelData'
if component == 'all' or component == comp :
d = DataSet (heartdatafile, labelsColumn = 0)
results[comp] = []
kdata = KernelData('heart.kernel', gistFormat = True)
kdata.attachLabels(d.labels)
s=svm.SVM()
results[comp].append(
s.cv(kdata))
kdata.attachKernel('gaussian', gamma = 0.1)
results[comp].append(
s.cv(kdata))
comp = 'normalization'
if component == 'all' or component == comp :
results[comp] = []
data = DataSet (heartdatafile, labelsColumn = 0)
data.attachKernel('polynomial', degree = 4, normalization = 'dices')
s=svm.SVM()
results[comp].append(
s.cv(data))
comp = 'svr'
if component == 'all' or component == comp :
d = DataSet (heartdatafile, labelsColumn = 0, numericLabels = True)
results[comp] = []
s = svm.SVR()
#results[comp].append(
# s.cv(d, saveSpace = True))
#results[comp].append(
# s.trainTest(d, range(150), range(151, 250)))
results[comp].append( s.cv(d) )
comp = 'save'
if component == 'all' or component == comp :
results[comp] = []
s = svm.SVM()
data = DataSet (heartdatafile, labelsColumn = 0)
import tempfile
tmpfile = tempfile.mktemp()
r = s.cv(data)
r.save(tmpfile)
r = resultsObjects.loadResults(tmpfile)
results['save'].append(r)
r = s.nCV(data)
r.save(tmpfile)
results['save'].append(resultsObjects.loadResults(tmpfile))
r = {}
for i in range(10) :
r[i] = s.cv(data)
resultsObjects.saveResultObjects(r, tmpfile)
r = resultsObjects.loadResults(tmpfile)
comp = 'classifiers'
if component == 'all' or component == comp :
d = DataSet (heartdatafile, labelsColumn = 0)
results[comp] = []
cl = knn.KNN()
results[comp].append(
cl.stratifiedCV(d))
print 'testing ridge regression'
ridge = ridgeRegression.RidgeRegression()
#results[comp].append(
# ridge.cv(d))
comp = 'platt'
if component == 'all' or component == comp :
results[comp] = []
d = DataSet (heartdatafile, labelsColumn = 0)
p = platt.Platt2(s)
results[comp].append(p.stratifiedCV(d))
comp = 'save'
if component == 'all' or component == comp :
data = DataSet (heartdatafile, labelsColumn = 0)
s = SVM()
s.train(data)
s.save('model.pyml')
s2 = SVM()
s2.load('model.pyml', data)
results[comp].append(s2.test(data))
d = DataSet (heartdatafile, labelsColumn = 0)
kdata = KernelData('heart.kernel', gistFormat = True)
kdata.attachLabels(d.labels)
s=svm.SVM()
s.train(data)
s.save('model2.pyml')
s2 = SVM()
s2.load('model2.pyml', kdata)
results[comp].append(s2.test(kdata))
data = DataSet(irisdatafile, labelsColumn = -1)
mc = multi.OneAgainstRest(SVM())
mc.train(data)
mc.save('iris.pyml')
mc = multi.OneAgainstRest(SVM())
mc.load('iris.pyml', data)
results[comp].append(mc.test(data))
comp = 'multi'
if component == 'all' or component == comp :
results[comp] = []
d = DataSet(irisdatafile, labelsColumn = -1)
mc = multi.OneAgainstOne (svm.SVM())
results[comp].append(
mc.cv(d))
d = DataSet(irisdatafile, labelsColumn = -1)
mc = multi.OneAgainstRest (svm.SVM())
results[comp].append(
mc.cv(d))
mc = multi.OneAgainstRest (svm.SVM())
d.attachKernel('poly')
results[comp].append(
mc.cv(d))
d.attachKernel('linear')
mc = multi.OneAgainstRest (svm.SVM())
#kdata = datafunc.KernelData('iris.linear.kernel',
# labelsFile = 'irisY.csv', labelsColumn = 0, gistFormat = True)
#results[comp].append(mc.cv(kdata))
comp = 'featsel'
if component == 'all' or component == comp :
results[comp] = []
s = svm.SVM()
d = DataSet (yeastdatafile, labelsColumn = 0)
d2 = labels.oneAgainstRest(d, '2')
results[comp].append(
s.stratifiedCV(d2, seed = 1))
# feature selection
m = composite.FeatureSelect (s, featsel.RFE())
results[comp].append(
m.stratifiedCV(d2, seed = 1))
d = DataSet (yeastdatafile, labelsColumn = 0)
d2 = labels.oneAgainstRest(d, '2')
fs = featsel.FeatureScore ('golub')
f = featsel.Filter (fs, sigma = 2)
m = composite.FeatureSelect (s, f)
results[comp].append(
m.stratifiedCV(d2, seed = 1))
# same thing but with a Chain:
c = composite.Chain ([f,s])
#r = c.stratifiedCV (d2)
comp = 'modelSelection'
if component == 'all' or component == comp :
results[comp] = []
s = svm.SVM()
d = DataSet (heartdatafile, labelsColumn = 0)
p = modelSelection.ParamGrid(svm.SVM(ker.Polynomial()), 'C', [0.1, 1, 10, 100],
'kernel.degree', [2, 3, 4])
p = modelSelection.ParamGrid(svm.SVM(ker.Gaussian()), 'C', [0.1, 1, 10, 100],
'kernel.gamma', [0.01, 0.1, 1])
#p = modelSelection.Param(svm.SVM(), 'C', [0.1, 1, 10, 100])
m = modelSelection.ModelSelector(p, measure = 'roc', foldsToPerform = 2)
m = modelSelection.ModelSelector(p)
#m = modelSelection.SVMselect()
results[comp].append(
m.cv(d))
comp = 'preproc'
if component == 'all' or component == comp :
results[comp] = []
s = svm.SVM()
d = DataSet (yeastdatafile, labelsColumn = 0)
d2 = labels.oneAgainstRest(d, '2')
results[comp].append(
s.stratifiedCV(d2))
p = preproc.Standardizer()
p.train(d2)
results[comp].append(
s.stratifiedCV(d2))
print p.scale
print p.translation
return results
