def doEntropyWeights(ee,e,ciss,link_func=maxSim,proms=None): precs=[] recalls=[] weights=getEntropyWeights(e,ciss) cis_keys=ciss.keys() sims=dict() for x in cis_keys: sims[x]=dict() for y in cis_keys: sims[x][y]=dmSim(ciss[x].seq,ciss[y].seq\ ,w1=weights[x],w2=weights[y]\ ,useLength=True) ss=flatenDict(netAlignInfer(link_func,e,sims)) for thresh in np.arange(0,1,0.1): (p,r)=prEval(ee,ss,thresh) precs.append(p) recalls.append(r) return (precs,recalls)
dmSimsL=getPickle(pp3) ''' # Repeats of experiments N=5 ratio=0.3 x=[] y=[] x2=[] y2=[] x3=[] y3=[] ee=flatenDictList(edges) for exp_i in range(N): e=mdf.global_remove(edges,0.3) scores1=netAlignInfer(maxSim,e,nwSims) scores2=netAlignInfer(maxSim,e,nwSimsL) scores3=netAlignInfer(maxSim,e,dmSimsL) ss1=flatenDict(scores1) ss2=flatenDict(scores2) ss3=flatenDict(scores3) for thresh in np.arange(0,1,0.1): result=prEval(ee,ss1,thresh) result2=prEval(ee,ss2,thresh) result3=prEval(ee,ss3,thresh) x.append(result[0]) y.append(result[1]) x2.append(result2[0]) y2.append(result2[1]) x3.append(result3[0])