/
estCRFParamsNonparam.py
executable file
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/
estCRFParamsNonparam.py
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#CRF and SemiCRF parameter estimation
import numpy as np
import scipy as sp
import scipy.io
import scipy.optimize
import sys
sys.path.append("lib")
import HistoneUtilities
import myutilities as myutil
import gzip
import os
from copy import deepcopy
import itertools
import time
import math
import random
import string
import operator
import sklearn
import sklearn.linear_model
import cPickle as cpickle
#sys.path.append("Tests")
#from TestSEDFMest import TestSEDFMest
def testCrfParamEst(tprobs,nprobs,blen,node2count,inters,sorteddoms,allnodes):
"""tests crf param estimate
Args:
tprobs,nprobs,blen:
node2cout,inter,sorteddoms,allnodes:
"""
poskeys = tprobs.keys()
random.shuffle(poskeys)
for s,e in poskeys[0:50000]:
sprob = np.array([0.0] * (2*blen))
sprob[0:blen] = node2count[s] + node2count[e]
for nind in xrange(s+1,e):
sprob[blen:] += node2count[nind]
assert sum(sprob-tprobs[(s,e)]) < 0.1
negkeys = nprobs.keys()
random.shuffle(negkeys)
for s,e in negkeys[0:50000]:
sprob = np.array([0.0] * blen)
for nind in xrange(s,e+1):
sprob += node2count[nind]
assert sum(sprob-nprobs[(s,e)]) < 0.1
for start,end in tprobs.keys():
assert end >= start+1
for start,end in nprobs.keys():
assert end >= start
allblocks = inters + sorteddoms
for b1,b2 in itertools.combinations(allblocks,2):
assert len(set(range(b1[0],b1[1]+1)).intersection(set(range(b2[0],b2[1]+1)))) == 0
seennodes = [item for start,end in allblocks for item in xrange(start,end+1)]
assert len(seennodes) == len(set(seennodes)) and len(seennodes) == len(allnodes)
return True
def estCRFParamsNonparam(marklist,domlist,sortmarkers,varcount,nodecounts,params):
"""estimates CRF parameters for nonparam
Args:
marklist,domlist,sortmarkers:
varcount,nodecounts,params:
Returns:
infodict,lincoefs:
"""
basecount = params["basecount"]
getLog = lambda tval: NEGINF if tval < 1.0e-320 else math.log(tval)
infodict,loginfodict,lincoefs = {}, {}, np.zeros((varcount,),dtype=np.float64)
blen,blen2 = varcount/3, 2*varcount/3
for mind,markinfo in enumerate(marklist):
sorteddoms = sorted(domlist[mind])
nodecount = nodecounts[mind]
mark2pos2count = HistoneUtilities.processMarkData(markinfo)
prenode2count = [None]+[np.array(HistoneUtilities.getCountVec(mark2pos2count,tpos,sortmarkers,params["width"],1),dtype=np.float) for tpos in xrange(1,nodecount+1)]
node2count = [None]
for tnode in xrange(1,nodecount+1):
putvec = []
for mval in prenode2count[tnode]:
usevec = [(1-mval)**(basecount-1)]
for bind in xrange(1,basecount):
usevec.append(usevec[bind-1]*(basecount-bind)*mval/(bind*(1-mval)))
putvec.extend(usevec)
node2count.append(np.array(putvec))
infodict[mind] = {"coef": {spos: np.array(node2count[spos]) for spos in xrange(1,nodecount+1)}}
loginfodict[mind] = {"coef": {spos: np.array([getLog(node2count[spos][varind]) for varind in xrange(blen)]) for spos in xrange(1,nodecount+1)}}
allnodes = range(1,nodecount+1)
inters = HistoneUtilities.getEmptyClusters(sorteddoms,allnodes)
for start,end in sorteddoms:
sprob = np.array([0.0]* blen2)
sprob[0:blen] = node2count[start] + node2count[end]
for tpos in xrange(start+1,end):
sprob[blen:] += node2count[tpos]
lincoefs[0:blen2] -= sprob
for start,end in inters:
sprob = np.array(node2count[start])
for tpos in xrange(start+1,end+1):
sprob += node2count[tpos]
lincoefs[blen2:] -= sprob
return infodict,loginfodict,lincoefs
def estLogBetasCRF(paramx,marklist,infodict,nodecounts):
"""estimates betas log for crf: backward
Args:
paramx,marklist,infodict,nodecounts:
Returns:
logalphadict: alphadict for markers
"""
logbetadict,poslen = {}, len(paramx)/3
for maind,markinfo in enumerate(marklist):
nodecount = nodecounts[maind]
logbetas = np.full((4*(nodecount+1),), NEGINF, dtype=np.float64) #domstart,inside,domend,empty
logbetas[4*nodecount+2:] = 0.0
for nind in xrange(nodecount,0,-1):
boundval,insideval,emptyval = [np.dot(paramx[tind*poslen:(tind+1)*poslen],infodict[maind]["coef"][nind]) for tind in xrange(3)]
tval1,tval2 = logbetas[4*nind+1] + insideval, logbetas[4*nind+2] + boundval
logbetas[4*(nind-1)] = HistoneUtilities.logSumExp([tval1,tval2])
tval1,tval2 = logbetas[4*nind+1] + insideval, logbetas[4*nind+2] + boundval
logbetas[4*(nind-1)+1] = HistoneUtilities.logSumExp([tval1,tval2])
tval1,tval2 = logbetas[4*nind] + boundval, logbetas[4*nind+3] + emptyval
logbetas[4*(nind-1)+2] = HistoneUtilities.logSumExp([tval1,tval2])
tval1,tval2 = logbetas[4*nind] + boundval, logbetas[4*nind+3] + emptyval
logbetas[4*(nind-1)+3] = HistoneUtilities.logSumExp([tval1,tval2])
logbetadict[maind] = np.array(logbetas)
return logbetadict
def estLogAlphasCRF(paramx,marklist,infodict,nodecounts):
"""estimates alphas log for crf
Args:
paramx,marklist,infodict,nodecounts,maxdomlen:
Returns:
logalphadict: alphadict for markers
"""
logalphadict,poslen = {}, len(paramx)/3
for maind,markinfo in enumerate(marklist):
nodecount = nodecounts[maind]
logalphas = np.full((4*(nodecount+1),), NEGINF, dtype=np.float64) #domstart,inside,domend,empty
logalphas[3] = 0.0
for nind in xrange(1,nodecount+1):
boundval,insideval,emptyval = [np.dot(paramx[tind*poslen:(tind+1)*poslen],infodict[maind]["coef"][nind]) for tind in xrange(3)]
tval1,tval2 = logalphas[4*nind-1] + boundval, logalphas[4*nind-2] + boundval
logalphas[4*nind] = HistoneUtilities.logSumExp([tval1,tval2])
tval1,tval2 = logalphas[4*nind-4] + insideval, logalphas[4*nind-3] + insideval
logalphas[4*nind+1] = HistoneUtilities.logSumExp([tval1,tval2])
tval1,tval2 = logalphas[4*nind-4] + boundval, logalphas[4*nind-3] + boundval
logalphas[4*nind+2] = HistoneUtilities.logSumExp([tval1,tval2])
tval1,tval2 = logalphas[4*nind-1] + emptyval, logalphas[4*nind-2] + emptyval
logalphas[4*nind+3] = HistoneUtilities.logSumExp([tval1,tval2])
logalphadict[maind] = np.array(logalphas)
return logalphadict
def estLogEtasCRF(paramx,logalphadict,marklist,infodict,loginfodict,nodecounts):
"""estimates etas log for crf
Args:
paramx,logalphadict,marklist:
infodict,loginfodict,nodecounts:
Returns:
logetadict:
"""
logetadict,poslen,poslen2,varcount = {}, len(paramx)/3, 2*len(paramx)/3, len(paramx)
for maind,markinfo in enumerate(marklist):
nodecount = nodecounts[maind]
logalphas = logalphadict[maind]
logetas = np.full([len(paramx), 4*(nodecount+1)], NEGINF, dtype=np.float64) #domstart,inside,domend,empty
for nind in xrange(1,nodecount+1):
boundval,insideval,emptyval = [np.dot(paramx[tind*poslen:(tind+1)*poslen],infodict[maind]["coef"][nind]) for tind in xrange(3)]
for varind in xrange(varcount):
tval1,tval3 = logetas[varind,4*nind-1]+boundval, logetas[varind,4*nind-2]+boundval
tval2 = loginfodict[maind]["coef"][nind][varind] + logalphas[4*nind-1] + boundval if varind < poslen else NEGINF
tval4 = loginfodict[maind]["coef"][nind][varind] + logalphas[4*nind-2] + boundval if varind < poslen else NEGINF
logetas[varind,4*nind] = HistoneUtilities.logSumExp([tval1,tval2,tval3,tval4])
tval1,tval3 = logetas[varind,4*nind-4]+insideval, logetas[varind,4*nind-3]+insideval
tval2 = loginfodict[maind]["coef"][nind][varind-poslen] + logalphas[4*nind-4] + insideval if varind >= poslen and varind < poslen2 else NEGINF
tval4 = loginfodict[maind]["coef"][nind][varind-poslen] + logalphas[4*nind-3] + insideval if varind >= poslen and varind < poslen2 else NEGINF
logetas[varind,4*nind+1] = HistoneUtilities.logSumExp([tval1,tval2,tval3,tval4])
tval1,tval3 = logetas[varind,4*nind-4]+boundval, logetas[varind,4*nind-3]+boundval
tval2 = loginfodict[maind]["coef"][nind][varind] + logalphas[4*nind-4] + boundval if varind < poslen else NEGINF
tval4 = loginfodict[maind]["coef"][nind][varind] + logalphas[4*nind-3] + boundval if varind < poslen else NEGINF
logetas[varind,4*nind+2] = HistoneUtilities.logSumExp([tval1,tval2,tval3,tval4])
tval1,tval3 = logetas[varind,4*nind-1]+emptyval, logetas[varind,4*nind-2]+emptyval
tval2 = loginfodict[maind]["coef"][nind][varind-poslen2] + logalphas[4*nind-1] + emptyval if varind >= poslen2 else NEGINF
tval4 = loginfodict[maind]["coef"][nind][varind-poslen2] + logalphas[4*nind-2] + emptyval if varind >= poslen2 else NEGINF
logetas[varind,4*nind+3] = HistoneUtilities.logSumExp([tval1,tval2,tval3,tval4])
logetadict[maind] = np.array(logetas)
return logetadict
def estJacobianCoefsCRF(logalphadict,logbetadict,nodecounts,markcount,infodict,loginfodict,paramx):
"""estimates jacobian coefs for crf
Args:
logalphadict,logbetadict:
nodecounts,markcount:
infodict,loginfodict,paramx:
Returns:
jacveclist:
"""
def estfMdict(markind,varcount,curnodecount):
"""estimates f*M dictionary
"""
logfMdict = {node: {} for node in xrange(1,curnodecount+1)}
for node in xrange(1,curnodecount+1):
boundval,insideval,emptyval = [np.dot(paramx[poslen*tind:poslen*(tind+1)],infodict[markind]["coef"][node]) for tind in xrange(3)]
logfMdict[node][(0,1)] = [(poslen+ind,item) for ind,item in enumerate(list(loginfodict[markind]["coef"][node] + insideval))] # start to inside
logfMdict[node][(0,2)] = [(ind,item) for ind,item in enumerate(list(loginfodict[markind]["coef"][node] + boundval))] # start to end
logfMdict[node][(1,1)] = [(poslen+ind,item) for ind,item in enumerate(list(loginfodict[markind]["coef"][node] + insideval))] # inside to inside
logfMdict[node][(1,2)] = [(ind,item) for ind,item in enumerate(list(loginfodict[markind]["coef"][node] + boundval))] # inside to empty
logfMdict[node][(2,0)] = [(ind,item) for ind,item in enumerate(list(loginfodict[markind]["coef"][node] + boundval))] # end to start
logfMdict[node][(2,3)] = [(poslen2+ind,item) for ind,item in enumerate(list(loginfodict[markind]["coef"][node] + emptyval))] # end to empty
logfMdict[node][(3,0)] = [(ind,item) for ind,item in enumerate(list(loginfodict[markind]["coef"][node] + boundval))] # empt to start
logfMdict[node][(3,3)] = [(poslen2+ind,item) for ind,item in enumerate(list(loginfodict[markind]["coef"][node] + emptyval))] # emp to empty
return logfMdict
varcount = len(paramx)
useindices = [(0,1),(0,2),(1,1),(1,2),(2,0),(2,3),(3,0),(3,3)]
poslen,poslen2 = varcount/3, 2*varcount/3
jacveclist = []
for markind in xrange(markcount):
logfMdict = estfMdict(markind,varcount,nodecounts[markind])
curlist = {varind:[] for varind in xrange(varcount)}
albetasum = {tnode:[logalphadict[markind][4*(tnode-1)+ind1]+logbetadict[markind][4*tnode+ind2] for ind1,ind2 in useindices] for tnode in xrange(1,nodecounts[markind]+1)}
for tnode in xrange(1,nodecounts[markind]+1):
for etind,(ind1,ind2) in enumerate(useindices):
for varind,varitem in logfMdict[tnode][(ind1,ind2)]:
curlist[varind].append(varitem+albetasum[tnode][etind])
curarr = [HistoneUtilities.logSumExp(curlist[varind]) for varind in xrange(varcount)]
jacveclist.append(np.array(curarr))
return jacveclist
logalphadict = None
logbetadict = None
normlist = None
def trainCRF(infodict,loginfodict,lincoefs,regcoefs,marklist,domlist,sortmarkers,varcount,nodecounts,params,muvec,initx=None):
"""trains crf
Args:
infodict,loginfodict,lincoefs,regcoefs:
marklist,domlist,sortmarkers:
varcount,nodecounts,params:
muvec: group lasso mus
initx: initial solution
Returns:
xvec,objval:
"""
initx = np.zeros((varcount,),dtype=np.float64) if initx == None else initx
markcount = len(sortmarkers)
def jacloglikeCRF(paramx):
"""jacobian of log likelihood
Args:
paramx:
"""
jacvec = np.array(lincoefs)
jacveclist = estJacobianCoefsCRF(logalphadict,logbetadict,nodecounts,len(marklist),infodict,loginfodict,paramx)
if TESTMODE:
print "here"
logetadict = estLogEtasCRF(paramx,logalphadict,marklist,infodict,loginfodict,nodecounts)
testjacvec= jacvec + np.array([sum([math.exp(HistoneUtilities.logSumExp(logetadict[lind][ind,-2:])-normlist[lind]) for lind in xrange(len(marklist))]) for ind in xrange(varcount)])
jacvec += np.array([sum([math.exp(jacveclist[markind][varind]-normlist[markind]) for markind in xrange(len(marklist))]) for varind in xrange(varcount)])
jacvec += (2.0*params["grlambda"]) * paramx
#jacvec += (2.0*params["lambda"]) * np.array([item for uind in xrange(3) for mind in xrange(markcount) for item in list(np.dot(regcoefs,paramx[(markcount*uind+mind)*params["width"]*params["basecount"]:(markcount*uind+mind+1)*params["width"]*params["basecount"]]))])
#jacvec += 2.0*params["lambda"]*np.array([paramx[(markcount*uind+mind)*params["width"]*params["basecount"]:(markcount*uind+mind+1)*params["width"]*params["basecount"]]/float(muvec[uind,mind]) for uind in xrange(3) for mind in xrange(markcount)]).flatten()
if TESTMODE:
assert logalphadict != None and logbetadict != None and normlist != None
# print "iter info: ",np.linalg.norm(jacvec-tjacvec)
# assert np.linalg.norm(jacvec-tjacvec) < 0.001
return jacvec
def loglikeCRF(paramx):
"""log likelihood
Args:
paramx:
Returns:
objval:
"""
global logalphadict,logbetadict,normlist
tobjval = np.dot(paramx,lincoefs)
logalphadict = estLogAlphasCRF(paramx,marklist,infodict,nodecounts)
logbetadict = estLogBetasCRF(paramx,marklist,infodict,nodecounts)
normlist = [HistoneUtilities.logSumExp([logalphadict[tind][-2],logalphadict[tind][-1]]) for tind in xrange(len(marklist))]
tobjval += sum(normlist)
tobjval += params["grlambda"]*(np.linalg.norm(paramx)**2)
#sideval = 0.0
#for uind in xrange(3):
# for mind in xrange(markcount):
# usex = paramx[(markcount*uind+mind)*params["width"]*params["basecount"]:(markcount*uind+mind+1)*params["width"]*params["basecount"]]
# sideval += (np.linalg.norm(usex)**2)/muvec[uind,mind]
# sideval += np.dot(np.dot(usex,regcoefs),usex)
#tobjval += params["lambda"]*sideval
print "current obj: ",tobjval
if TESTMODE:
for item in normlist:
assert item != 0.0
assert tobjval >= 0.0
return tobjval
xvec,objval,d = scipy.optimize.fmin_l_bfgs_b(loglikeCRF, initx, fprime=jacloglikeCRF,maxiter=params['itercount'])
#xvec,objval,d = scipy.optimize.fmin_l_bfgs_b(loglikeCRF, initx, approx_grad=1, disp=None, maxiter=params['itercount'],epsilon=1e-08)
print ",".join([str(item) for item in xvec])
print "objval: ",objval
return xvec,objval
def loglikeEst(paramx,lincoefs,marklist,infodict,nodecounts,regcoefs,sortmarkers,params,tmuvec):
"""negative log likelihood obj + penalty
Args:
paramx,lincoefs,marklist,infodict,nodecounts:
regcoefs,sortmarkers,params,tmuvec:
Returns:
tobjval:
"""
tobjval = np.dot(paramx,lincoefs)
hlogalphadict = estLogAlphasCRF(paramx,marklist,infodict,nodecounts)
hlogbetadict = estLogBetasCRF(paramx,marklist,infodict,nodecounts)
hnormlist = [HistoneUtilities.logSumExp([hlogalphadict[tind][-2],hlogalphadict[tind][-1]]) for tind in xrange(len(marklist))]
markcount = len(sortmarkers)
tobjval += sum(hnormlist)
tobjval += params["grlambda"]*(np.linalg.norm(paramx)**2)
#sideval = 0.0
#for uind in xrange(3):
# for mind in xrange(markcount):
# usex = paramx[(markcount*uind+mind)*params["width"]*params["basecount"]:(markcount*uind+mind+1)*params["width"]*params["basecount"]]
# sideval += (np.linalg.norm(usex)**2)/tmuvec[uind,mind]
# sideval += np.dot(np.dot(usex,regcoefs),usex)
#tobjval += params["lambda"]*sideval
return tobjval
def iterativeRunner(marklist,domlist,sortmarkers,varcount,nodecounts,params):
"""iterative runner
Args:
marklist,domlist:
sortmarkers:
varcount,nodecounts:
params:
Returns:
"""
muvec = np.zeros((3,len(sortmarkers)),dtype=np.float)
for tind in xrange(3):
muvec[tind,:] = 1.0/len(sortmarkers)
regcoefs = estBernRegCoefs(params["basecount"]-1)
assert testestReg(params['basecount'])
solx = np.zeros((varcount,),dtype=np.float64)
infodict,loginfodict,lincoefs = estCRFParamsNonparam(marklist,domlist,sortmarkers,varcount,nodecounts,params)
solobjval = loglikeEst(solx,lincoefs,marklist,infodict,nodecounts,regcoefs,sortmarkers,params,muvec)
ind = 0
while True:
print ind,solobjval
cursolx,curobjval = trainCRF(infodict,loginfodict,lincoefs,regcoefs,marklist,domlist,sortmarkers,varcount,nodecounts,params,muvec,initx=solx)
#if ind == 0 and curobjval > solobjval:
# break
ind += 1
#if ind == 1:
# solx = np.array(cursolx)
# solobjval = curobjval
# break
curobjval -= estPenaltyNonparam(cursolx,muvec,len(sortmarkers),params["basecount"],params["width"],params["lambda"])
assert curobjval >= 0.0
muvec = estMuVecNonparam(cursolx,sortmarkers,varcount,params["basecount"],params["width"])
curobjval += estPenaltyNonparam(cursolx,muvec,len(sortmarkers),params["basecount"],params["width"],params["lambda"])
testtobjval = loglikeEst(cursolx,lincoefs,marklist,infodict,nodecounts,regcoefs,sortmarkers,params,muvec)
assert abs(testtobjval - curobjval) < 0.1
if curobjval >= solobjval - 0.01:
break
solobjval,solx = curobjval, np.array(cursolx)
return solx,solobjval,muvec
def estPenaltyNonparam(tx,tmuvec,markcount,compcount,width,curlam):
"""estimates penalty part of objective
Args:
tx,tmuvec,markcount:
compcount,width,curlam: smoothing parameter
Returns:
"""
sideval = 0.0
for uind in xrange(3):
for mind in xrange(markcount):
usex = tx[(markcount*uind+mind)*width*compcount:(markcount*uind+mind+1)*width*basecount]
sideval += (np.linalg.norm(usex)**2)/tmuvec[uind,mind]
return curlam*sideval
def estMuVecNonparam(solx,sortmarkers,varcount,basecount,width):
"""estimates muvec for nonparametric case
Args:
solx,sortmarkers:
varcount,basecount,width:
Returns:
tmuvec:
"""
tmuvec = np.zeros((3,len(sortmarkers)),dtype=np.float)
blocklen = varcount/3
for tind in xrange(3):
tmuvec[tind,:] = [np.linalg.norm(solx[(tind*blocklen)+(mind*basecount*width):(tind*blocklen)+((mind+1)*basecount*width)]) for mind in xrange(len(sortmarkers))]
tmuvec[tind,:] = tmuvec[tind,:] / float(sum(tmuvec[tind,:]))
return tmuvec
def testestReg(basecount):
"""tests regularization estimation
Args:
basecount:
"""
basecount = 3
xvec = [random.uniform(0,1) for tind in xrange(basecount+1)]
regcoefs = estBernRegCoefs(basecount)
for node1 in xrange(basecount+1):
for node2 in xrange(basecount+1):
assert abs(regcoefs[node1,node2] - regcoefs[node2,node1]) < 0.00001
eigs = scipy.linalg.eigh(regcoefs)[0]
for eigval in eigs:
assert eigval >= -0.000000001
return True
def estBernRegCoefs(basecount):
"""estimates bernstein regularization coefs
Args:
basecount:
Returns:
regcoefs: array of coefs
"""
def combval(n,r):
return math.factorial(n)/(math.factorial(r)*math.factorial(n-r))
regcoefs = np.zeros((basecount+1,basecount+1),dtype=np.float64)
for i in xrange(basecount+1):
for j in xrange(basecount+1):
for q in xrange(max(0,i-basecount+2),min(2,i)+1):
for r in xrange(max(0,j-basecount+2),min(2,j)+1):
regcoefs[i,j] += math.pow(-1,q+r) * combval(2,q) * combval(2,r) * combval(basecount-2,i-q) * combval(basecount-2,j-r) * scipy.special.beta(i+j-q-r+1,2*basecount-3-i-j+q+r)
return regcoefs * (basecount*(basecount-1))**2
def getVarcount(markcount,width,basecount):
"""gets var count
Args:
markcount,width,basecount:
Returns:
varcount:
"""
varcount = 3 * basecount * width * markcount
return varcount
def checkParamsNonParam(params):
"""checks params of nonparametric case
Args:
params:
Returns:
"""
assert params['prepromodel'] in ["linear","loglinear","binary","binary0.5","colnorm","poisson0.9","poisson0.99"] and params['width']>=1 and params['itercount']>=4
return True
def normBernstein(marklist):
"""normalizes data for bernstein nonparametric
Args:
marklist:
Returns:
retmarklist:
"""
mark2max = {}
for curmarklist in marklist:
for tmark in curmarklist.keys():
curcounts = [tcount for tnode,tcount in curmarklist[tmark]]
mark2max.setdefault(tmark,0.0)
if max(curcounts) >= mark2max[tmark]:
mark2max[tmark] = max(curcounts)
for markval in mark2max.values():
assert markval <= 11.0
globmax = 11.0
retmarklist = []
for curmarklist in marklist:
putdict = {}
for tmark in curmarklist.keys():
putdict[tmark] = list([(tnode,tcount/globmax) for tnode,tcount in curmarklist[tmark]])
retmarklist.append(putdict)
return retmarklist
TESTMODE = False
NEGINF = -1.0e50
def runner(marklist,domainlist,nodecounts,outprefix,params):
"""estimates SEDFM parameters
Assumes both domains and marker indices start from 1 not 0
Args:
marklist: marker list
domainlist: list of domains(start from 1 not 0)
nodecounts: nodecounts of all domains
outprefix:
params: model order,lambda
Returns:
"""
assert checkParamsNonParam(params)
marklist = HistoneUtilities.modifyMarkerData(marklist,nodecounts,params['prepromodel'],False)
#marklist = normBernstein(marklist)
sortmarkers = sorted(list(set(mark for markinfo in marklist for mark in markinfo.keys())))
print "input markers are: ",sortmarkers
markcount = len(sortmarkers)
stime = time.time()
varcount = getVarcount(markcount,params['width'],params['basecount'])
respath = "{0}_domparams.txt".format(outprefix)
objoutpath = "{0}_dommetadata.txt".format(outprefix)
solx,objval,muvec = iterativeRunner(marklist,domlist,sortmarkers,varcount,nodecounts,params)
paramdict=HistoneUtilities.sol2dict([solx[0:varcount/3],solx[varcount/3:2*varcount/3],solx[2*varcount/3:]],sortmarkers,params['width'],"crf",None,params["basecount"])
etime = time.time()
metadata = {"time":etime-stime, "logobjval": -1.0*objval}
if False:
if params['model'] in ["linear","binary"] and params["order"] == 1:
sidecoefs = [Xdom,ydom,solx]
elif params['model'] in ["linear","binary"]:
sidecoefs = [Xdom,ydom,lincoefs,logcoefs,solx,objval,muvec]
elif params['model'] == "nonparam":
sidecoefs = [lincoefs,logcoefs,solx,objval,muvec,COMPCOUNT]
assert TestSEDFMest.testDomainParamEstimateVomm(marklist,domlist,paramdict,sortmarkers,sidecoefs,nodecounts,params,varcount)
HistoneUtilities.writeDomainParamFile(respath,paramdict)
HistoneUtilities.writeMetaFile(metadata,objoutpath)
def makeParser():
"""
"""
parser = argparse.ArgumentParser(description='Parameter estimation')
parser.add_argument('-m', dest='markerpath', type=str, action='store', default='train.marklist', help='Marker File(default: train.marklist)')
parser.add_argument('-p', dest='domainpath', type=str, action='store', default='train.domainlist', help='List of domains File(default: train.domainlist)')
parser.add_argument('-o', dest='outprefix', type=str, action='store', default='freq', help='output prefix(default: freq)')
parser.add_argument('-l1', dest='lambdaval', type=float, action='store', default=0.0, help = 'smoothness parameter')
parser.add_argument('-l2', dest='grlambdaval', type=float, action='store', default=1.0, help = 'lambda for coefficient sparsity')
parser.add_argument('-w', dest='width', type=int, action='store', default=1, help='effect width(default: 1)')
parser.add_argument('-t', dest='prepromodel', type=str, action='store', default='loglinear', help='preprocess model(default: loglinear)')
parser.add_argument('-c', dest='itercount', type=int, action='store', default=1000, help='iteration count(default: 1000)')
parser.add_argument('-k', dest='basecount', type=int, action='store', default=1, help='# of base kernels(default: 1)')
parser.add_argument('-cb', dest='cb', type=float, action='store', default=1.0, help='relative weight of boundary(default: 1.0)')
parser.add_argument('-ci', dest='ci', type=float, action='store', default=1.0, help='relative weight of interior(default: 1.0)')
parser.add_argument('-ce', dest='ce', type=float, action='store', default=1.0, help='relative weight of external(default: 1.0)')
return parser
if __name__ =='__main__':
"""runs
"""
import argparse
parser = makeParser()
args = parser.parse_args(sys.argv[1:])
globals().update(vars(args))
marklist = HistoneUtilities.readMarkerFile(markerpath)
domlist,nodecounts = HistoneUtilities.readMultiDomainFile(domainpath)
params = {'lambda':lambdaval,'grlambda':grlambdaval,'itercount':itercount, "width":width, "prepromodel":prepromodel,'basecount':basecount,'ci':ci,'ce':ce,'cb':cb}
runner(marklist,domlist,nodecounts,outprefix,params)