def main():
assert (len(argv) == 3), "need the sampleID and number of trials, and nothing else"
sampleId = argv[1]
numTrials = int(argv[2])
random_seed("acorn")
explainFailure = False
path = "kmer_histograms"
# ask the curve fitter what the default paramters are
fitter = EnrichedHapDipFitter(path+"/"+sampleId+".mixed.kmer_dist")
paramNames = fitter.paramNames
defaultParams = fitter.default_params()
if (defaultParams == None):
print "(failed to get default params)"
if (explainFailure):
print "... return code ..."
print hdFitter.retCode
print "... stdout ..."
print hdFitter.stdout
print "... stderr ..."
print hdFitter.stderr
assert (False)
defaultParams = params_to_float(defaultParams)
# read the "good" parameters (usually produced by explore3_hap_dip)
fitFilename = path+"/"+sampleId+".mixed.fit"
f = file(fitFilename,"rt")
goodParams = params_from_text([line for line in f])
f.close()
for name in defaultParams:
assert (name in goodParams), \
"parameter \"%s\" missing from %s" % (name,fitFilename)
for name in goodParams:
assert (name in defaultParams), \
"extra parameter \"%s\" in %s" % (name,fitFilename)
goodParams = params_to_float(goodParams)
print params_to_text(paramNames,goodParams,defaultParams,
prefix="good:",prefix2="dflt:")
# run the convergence trials
convergenceCount = 0
for trialNumber in xrange(numTrials):
print "=== trial %d of %d ===" \
% (1+trialNumber,numTrials)
# choose initial params as a random point in hypercube between "good"
# and "bad"
initParams = dict(goodParams)
norm2Init = 0.0
for (paramIx,name) in enumerate(paramNames):
step = unit_random()
initParams[name] += step*(defaultParams[name]-goodParams[name])
norm2Init += step*step
normInit = sqrt(norm2Init) / len(paramNames)
fitter.set_params(initParams)
fitParams = fitter.fit()
if (fitParams == None):
print params_to_text(paramNames,initParams,prefix="init-[%d]:" % trialNumber)
print "normInit: %.8f" % normInit
print "(failure or non-convergence)"
if (explainFailure):
print "... return code ..."
print fitter.retCode
print "... stdout ..."
print fitter.stdout
print "... stderr ..."
print fitter.stderr
continue
print params_to_text(paramNames,initParams,fitParams,
prefix="init+[%d]:" % trialNumber,
prefix2="cvrg[%d]:" % trialNumber)
fitParams = params_to_float(fitParams)
dGood = vector_distance(fitParams,goodParams)
print "normInit: %.8f" % normInit
print "dGood: %.8f" % dGood
convergenceCount += 1
print "%d of %d trials converged" % (convergenceCount,numTrials)
def main():
assert (len(argv) == 2), "need the sampleID and nothing else"
sampleId = argv[1]
saveConvergence = True
explainFailure = True
path = "kmer_histograms"
# clear the convergence file, in case we have a failure (we don't want
# previous results to leak through)
if (saveConvergence):
f = file(path+"/"+sampleId+".mixed.fit","wt")
f.close()
# perform haploid fit to the haploid component
hFitter = HaploidFitter(path+"/"+sampleId+".haploid_from_mixed.kmer_dist")
hParamNames = hFitter.paramNames
hFitParams = hFitter.fit()
if (hFitParams == None):
print "(haploid: failure or non-convergence)"
if (explainFailure):
print "... return code ..."
print hFitter.retCode
print "... stdout ..."
print hFitter.stdout
print "... stderr ..."
print hFitter.stderr
else:
print params_to_text(hParamNames,hFitParams,prefix="cvrg.haploid:")
# perform diploid fit to the diploid component
dFitter = DiploidFitter(path+"/"+sampleId+".diploid_from_mixed.kmer_dist")
dParamNames = dFitter.paramNames
dFitParams = dFitter.fit()
if (dFitParams == None):
print "(diploid: failure or non-convergence)"
if (explainFailure):
print "... return code ..."
print dFitter.retCode
print "... stdout ..."
print dFitter.stdout
print "... stderr ..."
print dFitter.stderr
else:
print params_to_text(dParamNames,dFitParams,prefix="cvrg.diploid:")
# create an initial vector for the enrichment model, combining elements
# from the component fits with the usual defaults
hdFitter = EnrichedHapDipFitter(path+"/"+sampleId+".mixed.kmer_dist")
hdParamNames = hdFitter.paramNames
hdDefaultParams = hdFitter.default_params()
if (hdDefaultParams == None):
print "(hap-dip: failed to get default params)"
if (explainFailure):
print "... return code ..."
print hdFitter.retCode
print "... stdout ..."
print hdFitter.stdout
print "... stderr ..."
print hdFitter.stderr
else:
print params_to_text(hdParamNames,hdDefaultParams,prefix="dflt.hapdip:")
assert (hFitParams != None) and (dFitParams != None) and (hdDefaultParams != None), \
"(no point in trying to fit the hap-dip model)"
hdInitParams = {}
hdInitParams["zp.copy.y" ] = hFitParams["zp.copy"]
hdInitParams["zp.copy.hom"] = dFitParams["zp.copy"]
hdInitParams["zp.copy.het"] = dFitParams["zp.copy.het"]
hdInitParams["p.e" ] = hFitParams["p.e"]
hdInitParams["shape.e" ] = hFitParams["shape.e"]
hdInitParams["scale.e" ] = hFitParams["scale.e"]
hdInitParams["p.y" ] = hdDefaultParams["p.y"]
hdInitParams["u.y" ] = hFitParams["u.v"]
hdInitParams["sd.y" ] = hFitParams["sd.v"]
hdInitParams["shape.y" ] = hFitParams["shape.v"]
hdInitParams["p.hom" ] = 1 - float(dFitParams["p.d"])
hdInitParams["u.hom" ] = dFitParams["u.v"]
hdInitParams["sd.hom" ] = dFitParams["sd.v"]
hdInitParams["var.het" ] = dFitParams["var.w"]
# perform hap-dip fit to the mixed components
hdFitParams = hdFitter.fit(hdInitParams)
if (hdFitParams == None):
print "(hap-dip: failure or non-convergence)"
print params_to_text(hdParamNames,hdInitParams,prefix="init.hapdip:")
if (explainFailure):
print "... return code ..."
print hdFitter.retCode
print "... stdout ..."
print hdFitter.stdout
print "... stderr ..."
print hdFitter.stderr
else:
print params_to_text(hdParamNames,hdInitParams,hdFitParams,
prefix="init.hapdip:",prefix2="cvrg.hapdip:")
# write the convergence file
if (saveConvergence):
f = file(path+"/"+sampleId+".mixed.fit","wt")
print >>f, params_to_text(hdParamNames,hdFitParams)
f.close()
def main():
assert (len(argv) == 2), "need the sampleID and nothing else"
sampleId = argv[1]
explainFailure = True
path = "kmer_histograms"
print sampleId
# perform haploid fit to the sample (ignoring thge diploid component)
hFitter = HaploidFitter(path+"/"+sampleId+".mixed.kmer_dist")
hParamNames = hFitter.paramNames
hFitParams = hFitter.fit()
if (hFitParams == None):
print >>stderr, "haploid: failure or non-convergence"
print "(haploid: failure or non-convergence)"
if (explainFailure):
print "... return code ..."
print hFitter.retCode
print "... stdout ..."
print hFitter.stdout
print "... stderr ..."
print hFitter.stderr
else:
print params_to_text(hParamNames,hFitParams,prefix="cvrg.haploid:")
# ask for default values for the hap-hap enrichment model
hhFitter = EnrichedHapHapFitter(path+"/"+sampleId+".mixed.kmer_dist")
hhParamNames = hhFitter.paramNames
hhDefaultParams = hhFitter.default_params()
if (hhDefaultParams == None):
print >>stderr, "hap-hap: failed to get default params"
print "(hap-hap: failed to get default params)"
if (explainFailure):
print "... return code ..."
print hhFitter.retCode
print "... stdout ..."
print hhFitter.stdout
print "... stderr ..."
print hhFitter.stderr
else:
print params_to_text(hhParamNames,hhDefaultParams,prefix="dflt.haphap:")
assert (hFitParams != None) and (hhDefaultParams != None), \
"(no point in trying to fit the hap-hap model)"
# create an initial vector for the enrichment model, borrowing some
# elements from the haploid model fit
hhInitParams = dict(hhDefaultParams)
hhInitParams["zp.copy.y"] = hFitParams["zp.copy"]
hhInitParams["p.e" ] = hFitParams["p.e"]
hhInitParams["shape.e" ] = hFitParams["shape.e"]
hhInitParams["scale.e" ] = hFitParams["scale.e"]
hhInitParams["u.y" ] = hFitParams["u.v"]
hhInitParams["sd.y" ] = hFitParams["sd.v"]
hhInitParams["shape.y" ] = hFitParams["shape.v"]
pAuto = 1 - float(hhInitParams["p.y"])
hhInitParams["u.auto" ] = pAuto * float(hhInitParams["u.y"])
hhInitParams["sd.auto" ] = sdHom = pAuto * float(hhInitParams["sd.y"])
# perform hap-hap fit to the mixed components
hhFitParams = hhFitter.fit(hhInitParams)
if (hhFitParams == None):
print >>stderr, "hap-hap: failure or non-convergence"
print "(hap-hap: failure or non-convergence)"
print params_to_text(hhParamNames,hhInitParams,prefix="smart.haphap:")
if (explainFailure):
print "... return code ..."
print hhFitter.retCode
print "... stdout ..."
print hhFitter.stdout
print "... stderr ..."
print hhFitter.stderr
else:
print params_to_text(hhParamNames,hhInitParams,hhFitParams,
prefix="smart.haphap:",prefix2="cvrg.haphap:")
assert (hhFitParams != None), \
"(no point in trying to fit the hap-dip model)"
# ask for default values for the hap-dip enrichment model
hdFitter = EnrichedHapDipFitter(path+"/"+sampleId+".mixed.kmer_dist")
hdParamNames = hdFitter.paramNames
hdDefaultParams = hdFitter.default_params()
if (hdDefaultParams == None):
print >>stderr, "hap-dip: failed to get default params"
print "(hap-dip: failed to get default params)"
if (explainFailure):
print "... return code ..."
print hdFitter.retCode
print "... stdout ..."
print hdFitter.stdout
print "... stderr ..."
print hdFitter.stderr
else:
print params_to_text(hdParamNames,hdDefaultParams,prefix="dflt.hapdip:")
assert (hdDefaultParams != None), \
"(no point in trying to fit the hap-dip model)"
# read the sample's "cheat" parameters for comparison (usually produced by
# explore3_hap_dip)
fitFilename = path+"/"+sampleId+".mixed.fit"
f = file(fitFilename,"rt")
hdCheatParams = params_from_text([line for line in f])
f.close()
for name in hdDefaultParams:
assert (name in hdCheatParams), \
"parameter \"%s\" missing from %s" % (name,fitFilename)
for name in hdCheatParams:
assert (name in hdDefaultParams), \
"extra parameter \"%s\" in %s" % (name,fitFilename)
# create an initial vector for the hap-dip enrichment model, borrowing some
# elements from the hap-hap model fit
hdInitParams = dict(hdDefaultParams)
hdInitParams["zp.copy.y"] = hhFitParams["zp.copy.y"]
hdInitParams["p.e" ] = hhFitParams["p.e"]
hdInitParams["shape.e" ] = hhFitParams["shape.e"]
hdInitParams["scale.e" ] = hhFitParams["scale.e"]
hdInitParams["p.y" ] = hhFitParams["p.y"]
hdInitParams["u.y" ] = hhFitParams["u.y"]
hdInitParams["sd.y" ] = hhFitParams["sd.y"]
hdInitParams["shape.y" ] = hhFitParams["shape.y"]
pAuto = 1 - float(hdInitParams["p.y"])
pHom = float(hdInitParams["p.hom"])
hdInitParams["u.hom" ] = pAuto * pHom * float(hdInitParams["u.y"])
hdInitParams["sd.hom" ] = sdHom = pAuto * pHom * float(hdInitParams["sd.y"])
hdInitParams["var.het" ] = sdHom * sdHom
# perform hap-dip fit to the mixed components
hdFitParams = hdFitter.fit(hdInitParams)
if (hdFitParams == None):
print >>stderr, "hap-dip: failure or non-convergence"
print "(hap-dip: failure or non-convergence)"
print params_to_text(hdParamNames,hdInitParams,hdCheatParams,
prefix="smart.hapdip:",prefix2="cheat.hapdip:")
if (explainFailure):
print "... return code ..."
print hdFitter.retCode
print "... stdout ..."
print hdFitter.stdout
print "... stderr ..."
print hdFitter.stderr
else:
print params_to_text(hdParamNames,hdInitParams,hdFitParams,
prefix="smart.hapdip:",prefix2="cvrg.hapdip:")
print params_to_text(hdParamNames,hdCheatParams,prefix="cheat.hapdip:")
# if convergence failed, try moving the initial parameters toward the
# cheat parameters in small steps until we get convergence
# $$$ a binary search would be "better"
numSteps = 100
step = 0
while (hdFitParams == None):
step += 1
if (step == numSteps): break
print >>stderr, "step %d" % step
hdStepParams = {}
for name in hdInitParams:
if (name in ["u.hom","sd.hom","var.het"]): continue
param = float(hdInitParams[name])
param += (step * (float(hdCheatParams[name]) - param)) / numSteps
hdStepParams[name] = param
pAuto = 1 - float(hdStepParams["p.y"])
pHom = float(hdStepParams["p.hom"])
hdStepParams["u.hom" ] = pAuto * pHom * float(hdStepParams["u.y"])
hdStepParams["sd.hom" ] = sdHom = pAuto * pHom * float(hdStepParams["sd.y"])
hdStepParams["var.het" ] = sdHom * sdHom
hdFitParams = hdFitter.fit(hdStepParams)
if (hdFitParams == None):
print params_to_text(hdParamNames,hdStepParams,
prefix="step[%d].hapdip:" % step)
#if (explainFailure):
# print "... return code ..."
# print hdFitter.retCode
# print "... stdout ..."
# print hdFitter.stdout
# print "... stderr ..."
# print hdFitter.stderr
else:
print params_to_text(hdParamNames,hdStepParams,hdFitParams,
prefix="step[%d].hapdip:" % step,prefix2="cvrg.hapdip:")
