def main(datafile = '../data_by_cookie_slim.json', outputFolder = '.', iterations = 10, epochmult = 4):
filename = 'runMLP'
outputFile = '{}/{}.p'.format(outputFolder,filename)
data = funcs.loadData(datafile)
#Filter away bottom 75%
data = funcs.filterByPercRank(data, 75)
print 'iterations: {}\nMultiplier Samplesize Epochs: {}\n output file: {}'.format(iterations,epochmult,outputFile)
#Get first 10 values and try to decide whether people will keep on playing past 20 games
samples = np.fromiter(((funcs.normalize(np.array(k[:10])),0 if len(k) < 20 else 1) for k in data if len(k) >= 10),
dtype=[('input', float, 10), ('output', float, 1)])
print 'Learning from {} samples...'.format(samples.size)
network = MLP(10,10,10,1)
def processResults(network,results):
stepf = lambda x: 0 if x < .5 else 1
test_data = [(t[0], t[1], stepf(t[2])) for t in results]
percHits = np.mean([1 if t[2] == 1 else 0 for t in test_data if t[1] == 1]) # Percentage right hits
falseAlarm = np.mean([1 if t[2] == 1 else 0 for t in test_data if t[1] == 0]) # Percentage false positives
dPrime = funcs.dprime(percHits, falseAlarm)
out = (percHits, falseAlarm, dPrime, network.weights)
return out
#print 'Hit % = {}, but false alarm % = {}, d\' = {}'.format(percHits,falseAlarm, dPrime)
out = network.learnLoop(samples, iterations = iterations, epochs = epochmult * samples.size, processResults = processResults) #40 million epochs for full dataset.. Too many?
pickle.dump(out,open(outputFile, 'wb'))
#print out
#results = network.test(samples)
dprimes = pickle.load(open(outputFile,'rb'))
#set nan to 0
dprimes = [[0 if np.isnan(i) or np.isinf(i) else i for i in k[2]] for k in dprimes]
print
print 'Results:'
print 'Mean d\' score for each quit opportunity: {}'.format([np.mean([k[i] for k in dprimes]) for i in xrange(1)])
print 'Std : {}'.format([np.std([k[i] for k in dprimes]) for i in xrange(1)])
print 'Max : {}'.format([np.max([k[i] for k in dprimes]) for i in xrange(1)])
print
print
def runObs(data, outfolder ='.', rankFilter = 0, preprocess = False, processX = False, processY = False):
windowSizes1 = range(5,30,5) # Sizes of attempt group 1
windowSizes2 = range(5,30,5) # Sizes of attempt group 2
if processX == False:
processX = lambda x,x_plays: np.var(x,axis=0)
if processY == False:
processY = lambda x,x_plays: np.mean(x,axis=0)
#Run preprocessing if passed along
if(preprocess != False):
data = preprocess(data)
for i1 in xrange(len(windowSizes1)):
for i2 in xrange(len(windowSizes2)):
window1 = windowSizes1[i1]
window2 = windowSizes2[i2]
first_plays = range(window1)
second_plays = range(window1,window1+window2)
total_attempts = window1 + window2
# --------------------------------------------
# look at subsample of people who played more than x times
#print "organising data"
big = [k for k in data if len(k) >= total_attempts]
# --------------------------------------------
#calc dict of maximum score for each player(=each key)
#maxscore=[max(a) for a in big]
#calc percentile ranking for each player (=each key)
#prcentiles= np.percentile(maxscore,range(100))
#construct vaiables dicts
#print "calculating summary stats"
#for each player make two lists, of plays 1-5 (first) and 6-10 (second)
#and calculate summary stats av1,var1 and av2, var2
if(rankFilter != 0):
big = funcs.filterByPercRank(big,rankFilter)
first = [k[0:window1] for k in big]
second = [k[window1:window2+window1] for k in big]
#av1=np.mean(first,axis=1)
x = processX(zip(*first),first_plays)
#x = processX(zip(*first), first_plays)
y = processY(zip(*second),second_plays) #processY(second, second_plays)
#var2 = np.var(second,axis=1)
#find percentile values
#prcentiles_x=np.percentile(x,range(100))
#prcentiles_y=np.percentile(y,range(100))
#make dict of prcentile values for each statistic for each player
#xlist=[bisect.bisect(prcentiles_x,k) for k in x]
#ylist=[bisect.bisect(prcentiles_y,k) for k in y]
#print "saving data"
pickle.dump(x, open(outfolder + '/save_a5_xlist' + str(window1) + "," + str(window2) +'.p', 'wb'))
pickle.dump(y, open(outfolder + '/save_a5_ylist' + str(window1) + "," + str(window2) +'.p', 'wb'))
#print "mean x: ",np.mean(x)
#print "mean y: ",np.mean(y)