def fast_adapt(batch, learner, loss, adaptation_steps, i, mode):
batch['train'][0] = batch['train'][0].squeeze()
batch['test'][0] = batch['test'][0].squeeze()
batch['train'][1] = batch['train'][1].squeeze()
batch['test'][1] = batch['test'][1].squeeze()
inference_array = []
target_array = []
#data, labels = batch
#data, labels = data.to(device), labels.to(device)
# Separate data into adaptation/evalutation sets
#adaptation_indices = np.zeros(data.size(0), dtype=bool)
#adaptation_indices[np.arange(shots*ways) * 2] = True
#evaluation_indices = torch.from_numpy(~adaptation_indices)
#adaptation_indices = torch.from_numpy(adaptation_indices)
#adaptation_data, adaptation_labels = data[adaptation_indices], labels[adaptation_indices]
#evaluation_data, evaluation_labels = data[evaluation_indices], labels[evaluation_indices]
adaptation_data, adaptation_labels = batch['train']
evaluation_data, evaluation_labels = batch['test']
# Adapt the model
for step in range(adaptation_steps):
train_error = loss(learner(adaptation_data), adaptation_labels)
learner.adapt(train_error)
# Evaluate the adapted model
predictions = learner(evaluation_data)
valid_error = loss(predictions, evaluation_labels)
#valid_accuracy = accuracy(predictions, evaluation_labels)
if mode == 'test' and i % 30 == 1:
plt.rcParams["figure.figsize"] = (14, 5)
#plt.plot(peaks, np.array(inference_array)[peaks], 'xr');
plt.plot(normalize(predictions.cpu().detach().numpy()[0]))
#plt.plot(peaks2, np.array(target_array)[peaks2], 'xb');
plt.plot(normalize(evaluation_labels.cpu().detach().numpy()[0]))
plt.show()
#print(len(peaks), len(peaks2))
return valid_error
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 main(datafile = '../data_by_cookie_slim.json', outputFolder = '.', iterations = 10, epochmult = 4):
print 'Local vs Global quit analysis with MLP'
timethresh = 2
timesize = 10
filename = 'lgquit'
outputFile = '{}/{}.p'.format(outputFolder,filename)
print 'Quit locations: {}\ntime threshold: {} hours\niterations: {}\nMultiplier Samplesize Epochs: {}\noutput file: {}'.format(timesize-1,timethresh,iterations,epochmult,outputFile)
fh=open(datafile)
data=json.load(fh)
#Init values
#Properly sort data filter games
data = [[data[k][l] for l in collections.OrderedDict(sorted(data[k].items()))] for k in data]
inputdata = [[l[0] for l in k] for k in data if len(k) == timesize]
#Calculate time differences
timediff_data = [[((k[l][1] - k[l-1][1]) * 24 + (k[l][2] - k[l-1][2])) for l in range(1,len(k))] for k in data if len(k) == timesize]
#Threshold it
timediff_padthresh = [[1 if len(k) <= l or k[l] >= timethresh else 0 for l in range(timesize-1)] for k in timediff_data]
#Set up samples
samples = np.fromiter(((funcs.normalize(inputdata[i]),timediff_padthresh[i]) for i in range(len(inputdata))),
dtype=[('input', float, timesize), ('output', float, timesize-1)])
print 'Learning from {} samples...'.format(samples.size)
network = MLP(timesize,10,10,10,timesize-1)
#Calculate d' on a per element basis
def processResults(network,results):
stepf = lambda x: [0 if i < .5 else 1 for i in x]
test_data = [(t[0], t[1], stepf(t[2])) for t in results]
outnum = len(test_data[1][1])
percHits = [np.mean(k) for k in [[1 if t[2][i] == 1 else 0 for t in test_data if t[1][i] == 1] for i in range(outnum)]] # Percentage right hits per element
falseAlarm = [np.mean(k) for k in [[1 if t[2][i] == 1 else 0 for t in test_data if t[1][i] == 0] for i in range(outnum)]] # Percentage false positives per element
dPrime = funcs.dprime(percHits, falseAlarm)
out = (percHits,falseAlarm, dPrime, network.weights)
#print 'Hit % = {}, but false alarm % = {}, d\' = {}'.format(percHits,falseAlarm, dPrime)
return out
out = network.learnLoop(samples, iterations = iterations, epochs = epochmult * samples.size, processResults = processResults) #40 million epochs for full dataset.. Too many?
#results = network.learn(samples, epochs = samples.size * 400)
pickle.dump(out,open(outputFile, 'wb'))
#print out
#results = network.test(samples)
dprimes = pickle.load(open(outputFile,'rb'))
#set nan to 0
#Set NaN to 0 (tends to be Inf - Inf, so 0 makes sense). -Inf to 0 as well,
#usually is due to small but negligible difference in hit rate and false alarm rate,
#that cause false alarm rate to become exactly 0 and hit rate to be slightly above it.
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(timesize-1)])
print 'Std : {}'.format([np.std([k[i] for k in dprimes]) for i in xrange(timesize-1)])
print 'Max : {}'.format([np.max([k[i] for k in dprimes]) for i in xrange(timesize-1)])
print
print
def main(datafile = '../data_by_cookie_slim.json', outputFolder = '.', iterations = 10, epochmult = 4):
filename = 'runPerceptronReal'
outputFile = '{}/{}.p'.format(outputFolder,filename)
#plt.close('all')
#Load data
print 'Perceptron quit after 20 from 10 games with Perceptron'
data = funcs.loadData(datafile)
#Generate artificial training data
#mu = np.mean([k[:10] for k in data if len(k) >= 10],axis=0)
#signalf = lambda x: 0 if x[2] < 18000 else 1
#training_data = funcs.generateSamples(500000, signalf)
#Set up perceptron
s = 10 # games
eta = 0.2 # learning rate
dPrimes = [0]*iterations #
endweights = []
out = []
training_data = [(np.array(k[:s]),0 if len(k) < 2*s else 1) for k in data if len(k) >= s]
n = len(training_data) * epochmult
print 'iterations: {}\nMultiplier Samplesize Epochs: {}\noutput file: {}\n'.format(iterations,epochmult,outputFile)
print 'Overall plays over 20 plays: {}'.format(np.mean([t[1] for t in training_data]))
print 'Learning from {} samples...'.format(len(training_data))
for i in xrange(iterations):
#print 'Preparing training data'
w = 2 * np.random.rand(s) - 1
stepf = lambda x: 0 if x < 0 else 1
#print 'Training perceptron - n = {} and s = {}'.format(n,s)
for j in xrange(n):
x, expected = choice(training_data)
result = np.dot(w,x)
error = expected - stepf(result)
w += eta * error * funcs.normalize(x)
#print 'Training completed'
#print 'Testing performance'
#test_data consists of rows (x, expected, result) or (x, signal, response)
#print w
test_data = [(t[0], t[1], stepf(np.dot(w,t[0]))) for t in training_data]
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)
#if percHits> .65:
endweights.append(funcs.normalize(w))
dPrimes[i] = dPrime
#print 'Hit % = {}, but false alarm % = {}, d\' = {}'.format(percHits,falseAlarm, dPrime)
out.append((percHits, falseAlarm, dPrime, w))
#print w # print the weights
pickle.dump(out,open(outputFile, 'wb'))
#print out
#results = network.test(samples)
dprimes = pickle.load(open(outputFile,'rb'))
#set nan to 0
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
loss = criterion(outputs, target)
else:
loss_0 = criterion(outputs[:][0], target[:][0])
loss_1 = criterion(outputs[:][1], target[:][1])
loss_2 = criterion(outputs[:][2], target[:][2])
loss = loss_0 + loss_2 + loss_1
if ~torch.isfinite(loss):
continue
running_loss += loss.item()
tepoch.set_postfix(loss=running_loss /
(params["train_batch_size"] /
params["test_batch_size"]))
if model_params["name"] in ["PhysNet", "PhysNet_LSTM"]:
inference_array.extend(
normalize(outputs.cpu().numpy()[0]))
target_array.extend(
normalize(target.cpu().numpy()[0]))
else:
inference_array.extend(outputs[:][0].cpu().numpy())
target_array.extend(target[:][0].cpu().numpy())
if tepoch.n == 0 and __TIME__:
save_time = time.time()
# postprocessing
if model_params["name"] in ["DeepPhys"]:
inference_array = detrend(np.cumsum(inference_array), 100)
target_array = detrend(np.cumsum(target_array), 100)
if __TIME__ and epoch == 0:
log_info_time(
