def plotPowerCLR(recompute=False):
if recompute:
mc = pd.read_pickle('{}ROC/{}'.format(utl.outpath, 'MarkovChain'))
hmm = f(pd.read_pickle('{}ROC/{}'.format(utl.outpath, 'HMM')))
a = pd.concat([mc, hmm]);
print a
a = a[a.index.get_level_values('coverage') != np.inf]
df = pd.DataFrame(a.groupby(level=range(6)).apply(lambda x: x[x >= x.quantile(Qcoverage[x.name[0]])].mean()))[0]
# df = pd.DataFrame(a.groupby(level=range(6)).apply(lambda x: x[x >= x.quantile(0.99)].mean()))
df = getPower(df, groupbyLevels=range(4))
df.to_pickle(utl.outpath + 'ROC/PowerCLR.df')
else:
df = pd.read_pickle(utl.outpath + 'ROC/PowerCLR.df')
reload(pplt)
info = pplt.getNameColorMarker(df)
info.loc[info.index.get_level_values('method') == 'HMM', 'marker'] = '--o'
info.loc[info.index.get_level_values('method') == 'MarkovChain', 'marker'] = '--s'
info.loc[info.index.get_level_values('method') == 'HMM', 'color'] = 'r'
info.loc[info.index.get_level_values('method') == 'MarkovChain', 'color'] = 'darkblue'
# info.loc[info.index.get_level_values('q')==0.99,'color']='r'
# info.loc[info.index.get_level_values('q')==1,'color']='darkblue'
fig, axes = plt.subplots(2, 3, sharey=True, sharex=True, figsize=(6, 2.5), dpi=dpi);
pplt.setStyle(lw=1);
pplt.plotOnePower(df.xs(0.005, level='nu0'), info, axes[0], legendSubplot=0, ylabel='Hard');
pplt.plotOnePower(df.xs(0.1, level='nu0'), info, axes[1], ylabel='Soft');
[pplt.annotate('({})'.format(list('ABCDEF')[j]), ax=x, fontsize=7) for j, x in enumerate(axes.reshape(-1))]
plt.gcf().subplots_adjust(bottom=0.15)
pplt.savefig('powerCLR', dpi=dpi)
plt.show()
def plotPower(recompute=False):
if recompute:
causal = lambda x: x[(x.index.get_level_values('causal') == True) | (x.index.get_level_values('label') == -1)]
FIT = pd.read_pickle(utl.outpath + 'ROC/FIT')['FIT'];
FIT[FIT.isnull()] = np.random.rand(FIT.isnull().sum())
CMH = causal(pd.read_pickle(utl.outpath + 'ROC/CMH')['CMH'].fillna(0))
GP = causal(pd.read_pickle(utl.outpath + 'ROC/GP').LR)
HMM = f(loadHMMAllDepths())
# HMM = (HMM.alt - HMM.null) ;HMM = HMM.groupby(level=range(6)).mean()
# HMM = HMM.groupby(level=range(6)).apply(lambda x: x[x >= x.quantile(0.99)].mean())
HMM = HMM.groupby(level=range(6)).apply(lambda x: x[x >= x.quantile(Qcoverage[x.name[0]])].mean())
GP = GP.groupby(level=range(6)).max()
FIT = FIT.groupby(level=range(6)).max(); # dont move this line!
CMH = CMH.groupby(level=range(6)).max();
df = getPower(pd.concat([GP, HMM, FIT, CMH]), range(4)).sort_index()
df.to_pickle(utl.outpath + 'ROC/Power.df')
else:
df = pd.read_pickle(utl.outpath + 'ROC/Power.df')
df = df[df.index.get_level_values('coverage') != np.inf]
df = fixComaleName(df)
info = fixColor(pplt.getNameColorMarker(df))
fig, axes = plt.subplots(2, 3, sharey=True, sharex=True, figsize=(6, 2.5), dpi=pplt.PLOS.dpi);
pplt.setStyle(lw=1);
reload(pplt)
pplt.plotOnePower(df.xs(0.005, level='nu0'), info, axes[0], legendSubplot=0, ylabel='Hard', panel=list('ABC'));
pplt.plotOnePower(df.xs(0.1, level='nu0'), info, axes[1], ylabel='Soft', panel=list('DEF'));
[pplt.annotate('({})'.format(list('ABCDEF')[j]), ax=x, fontsize=7) for j, x in enumerate(axes.reshape(-1))]
plt.gcf().subplots_adjust(bottom=0.15)
pplt.savefig('power', pplt.PLOS.dpi)
df.groupby(level=range(3)).mean().unstack('method').to_pickle(utl.outpath + 'ROC/avgPower.df')
csv = df.groupby(level=range(3)).mean().reset_index()
# csv.replace({'HMM': comaleName}, inplace=True)
csv.replace({np.inf: r'$\infty$'}, inplace=True)
csv.nu0.replace({0.005: 'Hard', 0.1: 'Soft'}, inplace=True)
csv.columns = [r'$\lambda$', 'Sweep', 'Method', 'Avg Power']
csv.sort_values([r'$\lambda$', 'Sweep', 'Avg Power'], ascending=False, inplace=True)
csv['Avg Power'] = csv['Avg Power'].round().astype(int)
csv = csv.set_index(['Sweep'])
i = csv[r'$\lambda$'].apply(lambda x: not isinstance(x, str))
csv.loc[i, r'$\lambda$'] = csv.loc[i, r'$\lambda$'].astype(int)
soft = csv.loc['Soft'].sort_values([r'$\lambda$', 'Avg Power'], ascending=False)
hard = csv.loc['Hard'].sort_values([r'$\lambda$', 'Avg Power'], ascending=False)
utl.DataframetolaTexTable(hard, fname=utl.paperFiguresPath + '../tables/powerHardMathods.tex')
utl.DataframetolaTexTable(soft, fname=utl.paperFiguresPath + '../tables/powerSoftMethods.tex')
plt.show()
