def runTrradeoffRepTime():
method='COMALE';L=50000
numExperiments = 500;
numThreads = 2;
Nu = [0.005, 0.1];
S = [0.05]
param={'numExperiments':numExperiments,'method':method, 'numThreads': numThreads,'ModelName':'TimeSeries','L':L}
if method=='COMALE': param['T']=tsutl.loadTransitions()
print Nu,S,'numThreads=',numThreads
for numReplicates in [3,5,10,15,20]:
param['numReplicates']=numReplicates
for samplingWindow in [10,30,70,100]:
param['samplingWindow']=samplingWindow
df=[]
for nu0 in Nu:
param['nu0']=nu0
for s in S:
param['s']=s
params=getParamsForExperiments(param)
if numThreads==1:
a=map(runOne,params)
else:
pool=Pool(numThreads)
a=pool.map(runOne,params)
pool.terminate()
df+=[pd.concat(a)]
print '\nMethod={}\tR={}\twin={}\tnu0={}\ts={}'.format(method, numReplicates,samplingWindow,nu0,s)
for param in params:param['s']=0;param['nu0']=0.005
df+=[pd.concat(map(runOne,params))]
df=pd.concat(df)
df.sortlevel(inplace=True)
df.dropna(axis=1,how='all',inplace=True)
df['rep']=numReplicates
df['win']=samplingWindow
df.to_pickle('{}ROC/COMALERep{}Win{}.df'.format(utl.outpath, numReplicates, samplingWindow))
plt.figure()
a = pd.Series([estt.Estimate.LD(sim.H0.astype('float'), measure=measure) for measure in measures], index=measures)
a['Rho'] = a['Rho'] ** 2
df = a.apply
hap = sim.H0.loc[sim.H0[sim.posUnderSelection].idxmax(), sim.H0.loc[sim.H0[sim.posUnderSelection].idxmax()] == 1].index
a.apply(lambda x: x.loc[sim.posUnderSelection]).T.plot(subplots=True, ax=plt.gca());
plt.figure()
for i, m in enumerate(measures):
plt.subplot(2, 2, i + 1)
df = pd.concat([a.apply(lambda x: x.loc[sim.posUnderSelection]).T, sim.H0.mean()], axis=1)
df.plot.scatter(x=0, y=m, ax=plt.gca())
df.loc[hap].plot.scatter(x=0, y=m, ax=plt.gca(), color='r')
TRANSITIONS = tsutl.loadTransitions()
TRANSITIONSEXP = tsutl.loadTransitions(path=utl.outpath + 'transition/simulation/exp/')
s = 0.025;
i = 0;
depth = 100
HMM = splt.loadHMMAllDepths()
GP = splt.loadGP().LR
FIT = pd.read_pickle(utl.outpath + 'ROC/FIT.df').iloc[:, 0]
CMH = pd.concat([pd.read_pickle(utl.outpath + 'ROC/CMH.30.df'), pd.read_pickle(utl.outpath + 'ROC/CMH.100.df')]).iloc[:,
0]
EMISSIONS = pd.read_pickle(utl.outpath + 'markov/Emissions.df')
df = pd.concat([(HMM.alt - HMM.null) * HMM.s.apply(np.sign), GP, FIT, CMH]).sort_index().loc[(depth, 0.005)].xs(1,
level='label')
r = df.groupby(level=range(3)).apply(lambda x: x.rank(ascending=False))
df = pd.concat([df, r], axis=1);
