In [141]:

    

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

makenewdata = False

if makenewdata:
    N=50
    muac = np.random.randn(N)*15+113
    muac.sort()
    muac = muac[np.diff(muac)<7]
    N = len(muac)
    ok = ((muac - 108)+(np.random.randn(N)*15))>0
    import pickle
    pickle.dump([muac,ok],open( "example_muac.p", "wb" ))
else:
    example = pickle.load(open( "example_muac.p", "rb" ))
    muac = example[0]
    ok = example[1]
    N = len(muac)


    

In [177]:

    

if makenewdata:
    zscore = 3*((ok-0.5)*0.8 - 0.3*((muac - np.mean(muac))/np.std(muac)) + 0.3*np.random.randn(N))
    import pickle
    pickle.dump(zscore,open('example_zscore.p','wb'))
else:
    zscore = pickle.load(open( "example_zscore.p", "rb" ))


    

In [176]:

    




    

In [80]:

    

print len(muac[ok])
print len(muac[~ok])


    

    




29
19

In [202]:

    

def plotdata(muac,ok):
    plt.plot(muac[ok],np.ones(np.sum(ok)),'+g',mew=2,markersize=10)
    plt.plot(muac[~ok],np.zeros(np.sum(~ok)),'xr',mew=2,markersize=10)
    plt.ylim([-.5,1.5])
    plt.xlim([80,150])
    plt.xlabel('MUAC / mm')
    frame1 = plt.gca()
    plt.yticks([0,1], ['need\ntreatment','do not\nneed\ntreatment'])
    #frame1.axes.get_yaxis().set_visible(False)
    #frame1.axes.get_yaxis().set_ticks(['Did not recover','Recovered'])
    plt.text(115,0.5,'?',fontsize=14)
    plt.title('MUAC dataset')
    
def plotroc(muac,ok,threshes,symbol='x',skipnewfig = False):
    if not skipnewfig:
        plt.figure(num=None, figsize=(5,5), dpi=80, facecolor='w', edgecolor='k')
    falsepos = []
    trueneg = []
    for t in threshes:
        falsepos.append(np.mean(muac[ok]<t))
        trueneg.append(np.mean(muac[~ok]<t))
    plt.plot(falsepos,trueneg,symbol,lw=3,mew=3,markersize=20)
    #plt.axis('equal')
    margin = 0.01
    plt.xlim([0-margin,1+margin])
    plt.ylim([0-margin,1+margin])
    plt.plot([0,1],[0,1],'k-')
    plt.title('ROC Curve')
    plt.xlabel('Proportion of children who don\'t\nneed treatment who got it (False Positive)')
    plt.ylabel('Proportion of children who\nneed treatment who got it (True Positive)')
    #xticks = range(0,1,0.1)
    #xticks.append(len(muac[ok]))
    #plt.xticks(xticks, xticks)
    
    #yticks = range(0,len(muac[~ok]),2)
    #yticks.append(len(muac[~ok]))
    #plt.yticks(yticks, yticks)


    

In [205]:

    

def savefig(filename):
    plt.savefig(filename,bbox_inches='tight',transparent=True)
    
plotdata(muac,ok)

savefig('classify1.png')

threshes = []
plt.figure()
plotdata(muac,ok)
topthreshsh = np.max(muac[~ok])+1.5
threshes.append(topthresh)
plt.vlines([topthresh],-1,2)
plt.title('A possible threshold')
savefig('classify2.png')

#plt.figure()
plotroc(muac,ok,threshes)
savefig('roc1.png')

plt.figure()
plotdata(muac,ok)
lowthresh = np.min(muac[ok])-1.5
threshes.append(lowthresh)
plt.vlines([lowthresh],-1,2)
plt.title('Another possible threshold')
savefig('classify3.png')

plotroc(muac,ok,threshes)
savefig('roc2.png')

plt.figure()
plotdata(muac,ok)
midthresh = 4+(np.min(muac[ok])+np.max(muac[~ok]))/2 #moved it a little
threshes.append(midthresh)
plt.vlines([midthresh],-1,2)
plt.title('Somewhere in between?')
savefig('classify4.png')

plotroc(muac,ok,threshes)
savefig('roc3.png')

allthreshes = []
#for t in np.arange(np.min(muac[ok])-1,np.max(muac[~ok])+1,1.0):
for t in np.arange(0,200,1.0):
    allthreshes.append(t)

plotroc(muac,ok,threshes)
plotroc(muac,ok,allthreshes,'k-',skipnewfig=True)
savefig('roc4.png')


    

In [207]:

    

plt.title('z-score vs ')
plt.plot(zscore[ok],muac[ok],'g+',mew=2,markersize=10,label='not required')
plt.plot(zscore[~ok],muac[~ok],'xr',mew=2,markersize=10,label='need\ntreatment')
plt.legend(loc='upper right')
plt.text(0.15,115,'?',fontsize=16)
plt.savefig('both.png')
#plt.plot([-3,2.5],[150,80],'k-')


    

    
Out[207]:





<matplotlib.text.Text at 0x7f13b13fe690>






    

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