/
explore_hap_dip.py
executable file
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/
explore_hap_dip.py
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#!/usr/bin/env python
"""
Explore setting of initial parameters for hap-dip model, by seeing how far any
of the parameters can vary from the "right" answer before we lose convergence.
"""
from sys import argv,stderr
from math import sqrt
from kmervature import EnrichedHapDipFitter,params_to_text
def main():
assert (len(argv) == 1), "give me no arguments"
path = "kmer_histograms"
sampleId = "mixedB"
fitter = EnrichedHapDipFitter(path+"/"+sampleId+".mixed.kmer_dist")
paramNames = fitter.paramNames
defaultParams = {"zp.copy.y" : 3.000,
"zp.copy.hom" : 3.000,
"zp.copy.het" : 3.000,
"p.e" : 0.942,
"shape.e" : 3.000,
"scale.e" : 1.000,
"p.y" : 0.900,
"u.y" : 64.000,
"sd.y" : 14.826,
"shape.y" : 0.000,
"p.hom" : 0.800,
"u.hom" : 5.120,
"sd.hom" : 1.186,
"var.het" : 1.407}
goodParams = {"zp.copy.y" : 2.042,
"zp.copy.hom" : 3.157,
"zp.copy.het" : 17.795,
"p.e" : 0.935,
"shape.e" : 0.096,
"scale.e" : 0.465,
"p.y" : 0.621,
"u.y" : 68.084,
"sd.y" : 8.626,
"shape.y" : 0.057,
"p.hom" : 0.853,
"u.hom" : 11.101,
"sd.hom" : 3.600,
"var.het" : 10.916}
numSteps = 10
for (paramIx,name) in enumerate(paramNames):
if (paramIx != 0): print
for step in xrange(1,numSteps+1):
print "=== param %d of %s (\"%s\") step %d of %s ===" \
% (1+paramIx,len(paramNames),name,step,numSteps)
initParams = dict(goodParams)
initParams[name] += step*(defaultParams[name]-goodParams[name])/numSteps
fitter.set_params(initParams)
fitParams = fitter.fit()
if (fitParams == None):
print params_to_text(paramNames,initParams,prefix="init:")
print "(failure or non-convergence)"
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:",prefix2="cvrg:")
fitParams = params_to_float(fitParams)
distance = vector_distance(fitParams,goodParams)
print "dGood: %.8f" % distance
def params_to_float(params):
return {name:float(params[name]) for name in params}
def vector_distance(vector1,vector2):
if ([name for name in vector1 if (name not in vector2)] != []): raise valueError
if ([name for name in vector2 if (name not in vector1)] != []): raise valueError
return sqrt(sum([((vector1[name]-vector2[name])**2) for name in vector1]))
if __name__ == "__main__": main()