def run_svm (trainDataFile, trainData, testData):
s = SVM();
#param = modelSelection.Param(s, 'C', [0.0001,0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000])
#m = modelSelection.ModelSelector(param, measure='roc');
#m.train(trainData);
#m.save("trainModel.pyml");
s.train(trainData);
s.save("trainModel.pyml");
new_svm = SVM();
new_svm.load("trainModel.pyml", trainData);
r = new_svm.test(testData);
return r
def BuildModelAndTest(trainData, testData, modelFile, buildModel): if (buildModel == 'true'): print "* *** BUILDING MODEL *****" Cs = [ 10**x for x in xrange( -10, 5 ) ] param = modelSelection.Param(svm.SVM(), 'C', Cs) m = modelSelection.ModelSelector(param, measure ='roc') m.train(trainData) results = m.test(testData); #combined HuR, TTP test data m.save(modelFile); print "**** MODEL OUTPUT **** " print m.classifier.C print m.log else: s = SVM(); s.load(modelFile, trainData); results = s.test(testData) return results;
def BuildModelAndTest(trainData, testData, modelFile, buildModel):
if (buildModel == 'true'):
print "* *** BUILDING MODEL *****"
Cs = [10**x for x in xrange(-10, 5)]
param = modelSelection.Param(svm.SVM(), 'C', Cs)
m = modelSelection.ModelSelector(param, measure='roc')
m.train(trainData)
results = m.test(testData)
#combined HuR, TTP test data
m.save(modelFile)
print "**** MODEL OUTPUT **** "
print m.classifier.C
print m.log
else:
s = SVM()
s.load(modelFile, trainData)
results = s.test(testData)
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 run_svm(data, num_folds=10, C=1, seed=1):
#s.train(total_data)
r = SVM(C=C).stratifiedCV(data, numFolds=num_folds, seen=seed)
return r
def inDomainTest(trainModelFile, trainSpectrumData, testSpectrumData, resultsFile): new_svm = SVM() new_svm.load(trainModelFile, trainSpectrumData) results = new_svm.test(testSpectrumData) demo_utils.print_results(results, resultsFile)
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
