def blTopic():
(options, args) = parser.parse_args(sys.argv[1:]) #@UnusedVariable
dataset = options.dataset
neighbors = options.neighbors
nFold = options.nFold
nTopic = options.nTopic
beta = options.beta
dataPath = rootDir+dataset+bofDir
catmap = getCatMap(dataset)
catList = catmap.keys()
#remove if bof of 1000 words is computed for all categories
if(nTopic==1000):
dataext = bofext
else:
dataext = str(nTopic)+bofext
nCategory = len(catList)
perfMean = np.zeros(nCategory)
perfStd = np.zeros(nCategory)
for iCategory,catname in enumerate(catList):
print catname
#read the category data which will positive
fname = dataPath+catname+dataext
catpos = np.genfromtxt(fname,dtype=np.int)
catpos = catpos[:,:nTopic+1]
catpos[:,nTopic] = 1
#read the category data of remaining classes
for cats in catList:
if(cats!=catname):
firstvisit = True
if(firstvisit):
catneg = np.genfromtxt(fname,dtype=np.int)
firstvisit = False
else :
catneg = np.concatenate((catneg,np.genfromtxt(fname,dtype=np.int)),axis=0)
#sample the negative data to have equal size as the positive
nPos = catpos.shape[0]
nNeg = catneg.shape[0]
catneg = catneg[np.random.randint(0,nNeg,nPos),:]
catneg = catneg[:,:nTopic+1]
catneg[:,nTopic] = 0
#combine positive and negative data
botData = np.concatenate((catpos,catneg),axis=0)
#shuffle the rows to aid in random selection of train and test
np.random.shuffle(botData)
if(options.verbose):
print 'classifying...'
catPerfKNN = classify.knnClassify(botData, neighbors, nFold, beta, nMetrics)
perfMean[iCategory] = np.mean(catPerfKNN)
perfStd[iCategory] = np.std(catPerfKNN)
if(options.verbose):
print perfMean
print perfStd
return perfMean
def ccTopic():
(options, args) = parser.parse_args(sys.argv[1:]) #@UnusedVariable
dataset = options.dataset
nRowCluster = options.nRowCluster
nTopic = options.nTopic
ccType = options.ccType
kernelType = options.kernelType
nFold = options.nFold
nCodeword = options.nCodeword
beta = options.beta
if (options.verbose):
print dataset, nRowCluster, nTopic, ccType, kernelType, beta, nFold, nCodeword
print options
dataPath = rootDir + dataset + bofDir
catmap = getCatMap(dataset)
catList = catmap.keys()
dataext = str(nCodeword) + bofext
nCategory = len(catList)
perfMean = np.zeros(nCategory)
perfStd = np.zeros(nCategory)
for iCategory, catName in enumerate(catList):
fileName = dataPath + catName + dataext
catpos = np.genfromtxt(fileName, dtype=np.int)
if (options.verbose): print catName
catpos = catpos[:, :nCodeword + 1]
catpos[:, nCodeword] = 1
#read the category data of remaining classes
for cats in catList:
if (cats != catName):
firstvisit = True
if (firstvisit):
catneg = np.genfromtxt(fileName, dtype=np.int)
firstvisit = False
else:
catneg = np.concatenate(
(catneg, np.genfromtxt(fileName, dtype=np.int)),
axis=0)
#sample the negative data to have equal size as the positive
nPos = catpos.shape[0]
nNeg = catneg.shape[0]
catneg = catneg[np.random.randint(0, nNeg, nPos), :]
catneg = catneg[:, :nCodeword + 1]
catneg[:, nCodeword] = 0
#combine positive and negative data
bofData = np.concatenate((catpos, catneg), axis=0)
if (options.verbose):
print 'co-clustering...'
ccData = cocluster.coclust(bofData, dataset, nRowCluster, nTopic,
ccType)
ccCol = np.array([int(i) for i in ccData[1].split()])
tempCC = np.zeros((bofData.shape[0], nTopic))
for i in np.arange(bofData.shape[0]):
for j in sorted(set(ccCol)):
tempCC[i, j] = np.sum(bofData[i, ccCol == j])
botData = np.vstack((tempCC.T, bofData[:, -1])).T
if (options.verbose):
print 'classifying...'
#catPerfSVM = classify.ccClassify(botData, kernelType, nFold, beta, nMetrics)
catPerfKNN = classify.knnClassify(botData, 10, nFold, beta, nMetrics)
#perfMean[iCategory,0] = np.mean(catPerfSVM)
#perfStd[iCategory,0] = np.std(catPerfSVM)
perfMean[iCategory] = np.mean(catPerfKNN)
perfStd[iCategory] = np.std(catPerfKNN)
if (options.verbose):
print perfMean
print perfStd
return [perfMean, perfStd]
def ccWord():
(options,args) = parser.parse_args(sys.argv[1:]) #@UnusedVariable
dataset = options.dataset
nRowCluster = options.nCodeword
nColCluster = options.lowerDim
ccType = options.ccType
kernelType = options.kernelType
beta = options.beta
figfmt = options.figfmt
nFold = options.nFold
desc = options.desc
nClusterSample = options.nClusterSample
if(options.verbose):
print dataset,nRowCluster,nColCluster,ccType,kernelType,beta,figfmt,nFold,nClusterSample
dataPath = rootDir+dataset+dataDir
catmap = getCatMap(dataset)
catList = catmap.keys()
dataext = '.'+desc
nCategory = len(catList)
dim = descdim.get(desc)
nSamplePerCategory = int(np.round(nClusterSample/nCategory))
if(options.verbose): print 'collating cluster data...'
clusterData = collateClusterData(dataPath,dataext,catList,nSamplePerCategory,dim)
if(options.verbose): print 'coclustering...'
ccData = cocluster.coclustWord(clusterData,dataset,nRowCluster,nColCluster,ccType)
ccRow = np.array([int(i) for i in ccData[0].split()])
ccCol = np.array([int(i) for i in ccData[1].split()])
cctemp = np.zeros((clusterData.shape[0],nColCluster))
codebook = np.zeros((nRowCluster,nColCluster))
for i in np.arange(clusterData.shape[0]):
for j in sorted(set(ccCol)):
cctemp[i,j] = np.linalg.norm(clusterData[i,ccCol==j], 2) ###############
# cctemp[i,j] = np.mean(clusterData[i,ccCol==j])
for i in sorted(set(ccRow)):
codebook[i,:] = np.mean(cctemp[ccRow==i,:],0)
if(options.verbose): print 'writing bof...'
writebof(dataset,catList,codebook,ccCol,nRowCluster,desc,ccType,nRowCluster,nColCluster)
perfMean = np.zeros(nCategory)
perfStd = np.zeros(nCategory)
for iCategory,catName in enumerate(catList):
# catboffilepath = rootDir+dataset+bofDir+catName+'_cc'+bofext
catboffilepath = rootDir+dataset+bofDir+catName+str(ccType)+str(nRowCluster)+str(nColCluster)+bofext
catpos = np.genfromtxt(catboffilepath,dtype=np.int) # catpos
catpos = catpos[:,:nColCluster+1]
catpos[:,nColCluster] = 1
for catname in catList:
if(catname!=catName):
firstvisit = True
# catboffilepath = rootDir+dataset+bofDir+catname+'_cc'+bofext
catboffilepath = rootDir+dataset+bofDir+catName+str(ccType)+str(nRowCluster)+str(nColCluster)+bofext
if(firstvisit):
catneg = np.genfromtxt(catboffilepath,dtype=np.int)
firstvisit = False
else :
catneg = np.concatenate((catneg,np.genfromtxt(catboffilepath,dtype=np.int)),axis=0)
nPos = catpos.shape[0]
nNeg = catneg.shape[0]
catneg = catneg[np.random.randint(0,nNeg,nPos),:] #catneg
catneg = catneg[:,:nColCluster+1]
catneg[:,nColCluster] = 0
#combine positive and negative data
catData = np.concatenate((catpos,catneg),axis=0)
#shuffle the rows to aid in random selection of train and test
np.random.shuffle(catData)
# catPerf = classify.ccClassify(catData, kernelType, nFold, beta, nMetrics)
catPerf = classify.knnClassify(catData, 10, nFold, beta, nMetrics)
perfMean[iCategory] = np.mean(catPerf)
perfStd[iCategory] = np.std(catPerf)
if(options.verbose):
print perfMean
print perfStd
#plotresult.ccPlot(dataset, catList, perfMean, perfStd, figfmt, 'BoW', ccType)
return [perfMean,perfStd]