def extractMachineData():
datafiles = fileutils.getFilelist(
"D:/googleClusterData/clusterdata-2011-1/task_usage")
machineUsage = {}
startAt = 0
for machine in machines:
machineUsage[machine] = []
for datafile in datafiles[startAt:]:
print datafile
for row in fileutils.getCsvRows(datafile):
curMachine = row[4]
if curMachine in machines:
machineUsage[curMachine].append(row)
for machine in machineUsage.keys():
if startAt == 0:
fileutils.writeCSV("d:/data/perMachine5/" + machine + ".csv",
machineUsage[machine])
else:
fileutils.writeCSV("d:/data/perMachine5/" + machine + ".csv",
machineUsage[machine],
mode='ab')
startAt += 1
machineUsage.clear()
for machine in machines:
machineUsage[machine] = []
def extractMachineData():
datafiles = fileutils.getFilelist("D:/googleClusterData/clusterdata-2011-1/task_usage")
machineUsage = {}
startAt = 0;
for machine in machines:
machineUsage[machine] = []
for datafile in datafiles[startAt:]:
print datafile
for row in fileutils.getCsvRows(datafile):
curMachine = row[4]
if curMachine in machines:
machineUsage[curMachine].append(row)
for machine in machineUsage.keys():
if startAt == 0:
fileutils.writeCSV("d:/data/perMachine5/"+machine+".csv", machineUsage[machine])
else:
fileutils.writeCSV("d:/data/perMachine5/"+machine+".csv", machineUsage[machine], mode='ab')
startAt += 1
machineUsage.clear()
for machine in machines:
machineUsage[machine] = []
trainer = BackpropTrainer(n,
trainds,
learningrate=eta,
weightdecay=lmda,
momentum=0.1,
shuffle=False)
trainer.trainEpochs(epochs)
pred = np.nan_to_num(n.activateOnDataset(validds))
validerr = eval.calc_RMSE(validds['target'], pred)
varscore = explained_variance_score(validds['target'], pred)
return validerr, varscore, n
if __name__ == '__main__':
files = fileutils.getFilelist("../data/cpuRate")
# target_files = ['cpu_1095481','cpu_1303745','cpu_1335782','cpu_1338948','cpu_1442486585','cpu_155313295','cpu_1664088958','cpu_317488701','cpu_317499484','cpu_3858945898','cpu_4304743890','cpu_4820238819','cpu_5796442','cpu_660404','cpu_711355','cpu_717319','cpu_904514','cpu_905062','cpu_907812']
for machine in files[98:100]:
# for machine in target_files[16:19]:
machine = machine.strip('.csv').split('/')[-1]
# machine = 'cpu_1095481'
print(machine)
data = np.genfromtxt("../data/cpuRate/" + machine + ".csv",
skip_header=1,
delimiter=',',
usecols=(1))
miniters = 100
data = data/np.max(data)
minimum = np.percentile(data,minpercentile)
N = len(data)
result = []
print filename, "started..."
y = data[0:input_window].tolist()
model = HW_model.HW_model(y, minimum, 'additive')
for strIndex in range(0,N-input_window - predic_window, step):
if strIndex == 0:
model.fit()
else:
y = data[strIndex:strIndex+input_window].tolist()
model.update(y)
y_pred = model.predict(fc=predic_window)
result.append(y_pred)
f = filename.split('/')[-1]
fileutils.writeCSV("D:/Wikipage data/pageviews_hw/"+f, np.atleast_2d(result))
print filename, "complete!"
if __name__ == '__main__':
aggregatedRmse = None
pool = ThreadPool(4)
files = fileutils.getFilelist("D:/Wikipage data/pageviews")
# performsSlidingWindowForecast(files[1])
pool.map(performsSlidingWindowForecast, files)
pool.close()
pool.join()
def main():
global METHOD
global TYPE
global OUTPUT
global INPUT
# Launch main menu
# main_menu()
print "Comparing Forecasting methods:\n"
print "Please complete the following:"
print "Enter the type of data used in the evaluation:"
TYPE = raw_input(" >> ")
print "The base INPUT directory is:", INPUT+TYPE
print "and OUTPUT directory is:", OUTPUT+TYPE
print "Would you like to change it? y or n"
ch = raw_input(" >> ").lower()
if ch == 'y':
print "Enter the base path for the INPUT directory (without the type):"
INPUT = raw_input(" >> ")
if not os.path.isdir(INPUT):
print "Error: Please try again; INPUT directory:"
INPUT = raw_input(" >> ")
print "And enter the base path for evaluation OUTPUT directory (without the type):"
OUTPUT = raw_input(" >> ")
if not os.path.isdir(OUTPUT):
print "Error: Please try again; evaluation OUTPUT directory:"
OUTPUT = raw_input(" >> ")
#########
print "Please choose a method to evaluate:"
print "1. Holt-Winters"
print "2. Auto-regression"
print "3. 1st Markov chain"
print "4. 2nd Markov chain"
print "5. PRESS"
print "6. Agile"
print "7. FFNN Model"
print "8. RNN Model"
print "9. Entwine Model"
print "10. Moving Average"
print "11. Average combo4 Model"
print "12. FFNN combo4 Model"
print "13. Weighted Average model"
print "0. Quit"
choice = raw_input(" >> ")
ch = choice.lower();
if ch == '':
menu_actions['main_menu']()
elif ch == '0':
exit()
else:
METHOD = methods_dict[ch]
pool = ThreadPool(4)
files = fileutils.getFilelist(INPUT+TYPE)
params = []
if METHOD =='fnn':
if TYPE.startswith("memory"):
hyperpath = "../data/"+TYPE.replace("memory", "cpu")+"_networks/hyperparams.csv"
else:
hyperpath = "../data/"+TYPE+"_networks/hyperparams.csv"
hyperparms = np.genfromtxt(hyperpath, delimiter=',', dtype=None)
for curRow in hyperparms:
if TYPE.startswith("memory"):
params.append([INPUT+TYPE+'/'+curRow[0].replace("cpu", "memory").strip("'")+".csv", METHOD, TYPE, OUTPUT, INPUT, curRow[3], curRow[4]])
else:
params.append([INPUT+TYPE+'/'+curRow[0].strip("'")+".csv", METHOD, TYPE, OUTPUT, INPUT, curRow[3], curRow[4]])
elif METHOD =='rnn':
if TYPE.startswith("memory"):
hyperpath = "../data/"+TYPE.replace("memory", "cpu")+"_rnn_networks/hyperparams.csv"
else:
hyperpath = "../data/"+TYPE+"_rnn_networks/hyperparams.csv"
hyperparms = np.genfromtxt(hyperpath, delimiter=',', dtype=None)
for curRow in hyperparms:
if TYPE.startswith("memory"):
params.append([INPUT+TYPE+'/'+curRow[0].replace("cpu", "memory").strip("'")+".csv", METHOD, TYPE, OUTPUT, INPUT, curRow[3], curRow[4]])
else:
params.append([INPUT+TYPE+'/'+curRow[0].strip("'")+".csv", METHOD, TYPE, OUTPUT, INPUT, curRow[3], curRow[4]])
elif METHOD == 'entwine':
hyperpath = "../data/entwine_networks/hyperparams.csv"
hyperparms = np.genfromtxt(hyperpath, delimiter=',', dtype=None)
for curRow in hyperparms:
params.append([INPUT+TYPE+'/'+curRow[0].strip("'")+".csv", METHOD, TYPE, OUTPUT, INPUT, curRow[3], curRow[4]])
else:
for f in files:
params.append([f, METHOD, TYPE, OUTPUT, INPUT])
if METHOD == 'avg4' or METHOD == 'combo4' or METHOD == 'wa':
# ensembleModel(params[0])
pool.map(ensembleModel,params)
pool.close()
pool.join()
else:
# print "skip"
# performsSlidingWindowForecast(params[0])
pool.map(performsSlidingWindowForecast, params)
pool.close()
pool.join()
pool = ThreadPool(4)
results = pool.map(performEvaluations, params)
pool.close()
pool.join()
fileutils.writeCSV(OUTPUT+"results/"+TYPE+"_"+METHOD+".csv", results)
print METHOD+" "+ TYPE + " complete"
exit()
result = []
max = np.max(data)
print filename, "started..."
for strIndex in range(0,N-input_window - predic_window, step):
if strIndex == 0:
y = data[:input_window]
model = Press_model.Press_model(y, maximum=max)
model.fit()
else:
# y = data[strIndex:strIndex+input_window,1]
y = data[input_window + strIndex - step:input_window + strIndex]
model.update(y)
y_pred = model.predict(predic_window)
y_pred[y_pred<0] = minimum
result.append(y_pred)
res = np.zeros((len(result),predic_window))
for i in range(len(result)):
res[i,:len(result[i])] = result[i]
f = filename.split('/')[-1]
fileutils.writeCSV("d:/data/"+TYPE+"_press/"+f, np.atleast_2d(res))
print filename, "complete!"
if __name__ == '__main__':
aggregatedRmse = None
pool = ThreadPool(4)
files = fileutils.getFilelist("D:/data/"+TYPE)
pool.map(performsSlidingWindowForecast, files)
pool.close()
pool.join()
data = np.nan_to_num(np.genfromtxt(filename, delimiter=',', skip_header=1)[:,1]).ravel()
minimum = np.percentile(data,minpercentile)
N = len(data)
result = []
max = np.max(data)
print filename, "started..."
for strIndex in range(0,N-input_window - predic_window, step):
if strIndex == 0:
y = data[:input_window]
model = Markov_model.Markov_model(y, maximum=max, order=order_)
model.fit()
else:
# y = data[strIndex:strIndex+input_window,1]
y = data[input_window + strIndex - step:input_window + strIndex]
model.update(y)
y_pred = model.predict(predic_window)
y_pred[y_pred<0] = minimum
result.append(y_pred.ravel())
f = filename.split('/')[-1]
fileutils.writeCSV("d:/data/cpu_markov"+str(order_)+"/"+f, np.atleast_2d(result))
print filename, "complete!"
if __name__ == '__main__':
aggregatedRmse = None
pool = ThreadPool(4)
files = fileutils.getFilelist("D:/data/cpu")
performsSlidingWindowForecast(files[0])
# pool.map(performsSlidingWindowForecast, files)
# pool.close()
# pool.join()
if (time >= strTime and time < endTime):
row_f = np.float_(row[1])
aggregatedData[x, 1] += row_f
else:
strTime = endTime
endTime += 300e6
x += 1
aggregatedData[x, 0] = strTime
while x < numberOfRows:
strTime = endTime
endTime += 300e6
aggregatedData[x, 0] = strTime
x += 1
fileutils.writeCSV(outputDir + '/' + resource + '_' + fileCsv,
aggregatedData,
header=('Time', resource.capitalize()))
if __name__ == '__main__':
# ids = np.genfromtxt('d:/data/machineIDs.csv',dtype=str)
# machines = machines4[100:200]
# extractMachineData()
for f in fileutils.getFilelist("D:/data/perMachine5"):
print f
readAndAggregate(f, "d:/data/cpu5", 'cpu')
readAndAggregate(f, "d:/data/memory5", 'memory')
n.addConnection(FullConnection(n['in'], n['hidden'], name = 'in_to_hidden'))
n.addConnection(FullConnection(n['hidden'], n['out'], name = 'hidden_to_out'))
n.addRecurrentConnection(FullConnection(n['hidden'], n['context']))
rnet = n
rnet.sortModules()
trainer = BackpropTrainer(n, trainds, learningrate=eta, weightdecay=lmda, momentum=0.1, shuffle=False)
trainer.trainEpochs(epochs)
pred = np.nan_to_num(n.activateOnDataset(validds))
validerr = eval.calc_RMSE(validds['target'], pred)
varscore = explained_variance_score(validds['target'], pred)
return validerr, varscore, n
if __name__ == '__main__':
files = fileutils.getFilelist("D:/Wikipage data/network")
# target_files = ['cpu_1095481','cpu_1303745','cpu_1335782','cpu_1338948','cpu_1442486585','cpu_155313295','cpu_1664088958','cpu_317488701','cpu_317499484','cpu_3858945898','cpu_4304743890','cpu_4820238819','cpu_5796442','cpu_660404','cpu_711355','cpu_717319','cpu_904514','cpu_905062','cpu_907812']
for machine in files[30:]:
# for machine in target_files[16:19]:
machine = machine.replace('.csv','').split('/')[-1]
# machine = 'cpu_1095481'
print(machine)
# data = np.genfromtxt("d:/data/cpu5/"+machine+".csv",skip_header=1, delimiter=',',usecols=(1))
data = np.nan_to_num(np.genfromtxt("D:/Wikipage data/network/"+machine)).ravel()
data = data/np.max(data)
miniters=100
maxiters=1000
data = np.nan_to_num(np.genfromtxt(filename, delimiter=',', skip_header=1)[:,1]).ravel()
minimum = np.percentile(data,minpercentile)
N = len(data)
result = []
print filename, "started..."
for strIndex in range(0,N-input_window - predic_window, step):
if strIndex == 0:
y = data[:input_window]
model = AR_model.AR_model(y, order=30)
model.fit()
else:
y = data[strIndex:strIndex+input_window]
model.update(y)
y_pred = model.predict(predic_window)
y_pred[y_pred[:,0]<0,0] = minimum
result.append(y_pred[:,0])
f = filename.split('/')[-1]
fileutils.writeCSV("d:/data/"+TYPE+"_ar/"+f, np.atleast_2d(result))
print filename, "complete!"
if __name__ == '__main__':
aggregatedRmse = None
pool = ThreadPool(4)
files = fileutils.getFilelist("D:/data/"+TYPE)
# print files
# performsSlidingWindowForecast(files[0])
pool.map(performsSlidingWindowForecast, files)
pool.close()
pool.join()
result = []
print filename, "started..."
for strIndex in range(0, N - input_window - predic_window, step):
if strIndex == 0:
y = data[:input_window]
model = Wavelet_model(y)
model.fit()
else:
y = data[strIndex : strIndex + input_window]
model.update(y)
y_pred = model.predict(predic_window)
y_pred[y_pred < 0] = minimum
result.append(y_pred)
f = filename.split("/")[-1]
fileutils.writeCSV("d:/data/" + TYPE + "_agile/" + f, np.atleast_2d(result))
print filename, "complete!"
if __name__ == "__main__":
aggregatedRmse = None
pool = ThreadPool(4)
root = "D:/data/" + TYPE
files = fileutils.getFilelist(root)
# performsSlidingWindowForecast(files[1])
pool.map(performsSlidingWindowForecast, files)
pool.close()
pool.join()
N = len(data)
result = []
print filename, "started..."
y = data[0:input_window].tolist()
model = HW_model.HW_model(y, minimum, 'additive')
for strIndex in range(0, N - input_window - predic_window, step):
if strIndex == 0:
model.fit()
else:
y = data[strIndex:strIndex + input_window].tolist()
model.update(y)
y_pred = model.predict(fc=predic_window)
result.append(y_pred)
f = filename.split('/')[-1]
fileutils.writeCSV("D:/Wikipage data/pageviews_hw/" + f,
np.atleast_2d(result))
print filename, "complete!"
if __name__ == '__main__':
aggregatedRmse = None
pool = ThreadPool(4)
files = fileutils.getFilelist("D:/Wikipage data/pageviews")
# performsSlidingWindowForecast(files[1])
pool.map(performsSlidingWindowForecast, files)
pool.close()
pool.join()
for row in resourcePerTask[:]:
time = np.float_(row[0])
if (time>=strTime and time<endTime):
row_f = np.float_(row[1])
aggregatedData[x,1] += row_f
else:
strTime = endTime
endTime += 300e6
x +=1
aggregatedData[x,0] = strTime
while x < numberOfRows:
strTime = endTime
endTime += 300e6
aggregatedData[x,0] = strTime
x +=1
fileutils.writeCSV(outputDir+'/' + resource + '_' +fileCsv, aggregatedData, header=('Time',resource.capitalize()))
if __name__ == '__main__':
# ids = np.genfromtxt('d:/data/machineIDs.csv',dtype=str)
# machines = machines4[100:200]
# extractMachineData()
for f in fileutils.getFilelist("D:/data/perMachine5"):
print f
readAndAggregate(f, "d:/data/cpu5", 'cpu')
readAndAggregate(f, "d:/data/memory5", 'memory')
result = []
print filename, "started..."
for strIndex in range(0, N - input_window - predic_window, step):
if strIndex == 0:
y = data[:input_window]
model = Wavelet_model(y)
model.fit()
else:
y = data[strIndex:strIndex + input_window]
model.update(y)
y_pred = model.predict(predic_window)
y_pred[y_pred < 0] = minimum
result.append(y_pred)
f = filename.split('/')[-1]
fileutils.writeCSV("d:/data/" + TYPE + "_agile/" + f,
np.atleast_2d(result))
print filename, "complete!"
if __name__ == '__main__':
aggregatedRmse = None
pool = ThreadPool(4)
root = "D:/data/" + TYPE
files = fileutils.getFilelist(root)
# performsSlidingWindowForecast(files[1])
pool.map(performsSlidingWindowForecast, files)
pool.close()
pool.join()
n.addConnection(FullConnection(n['in'], n['hidden'], name = 'in_to_hidden'))
n.addConnection(FullConnection(n['hidden'], n['out'], name = 'hidden_to_out'))
n.addRecurrentConnection(FullConnection(n['hidden'], n['context']))
rnet = n
rnet.sortModules()
trainer = BackpropTrainer(n, trainds, learningrate=eta, weightdecay=lmda, momentum=0.1, shuffle=False)
trainer.trainEpochs(epochs)
pred = np.nan_to_num(n.activateOnDataset(validds))
validerr = eval.calc_RMSE(validds['target'], pred)
varscore = explained_variance_score(validds['target'], pred)
return validerr, varscore, n
if __name__ == '__main__':
files = fileutils.getFilelist("../data/cpuRate")
# target_files = ['cpu_1095481','cpu_1303745','cpu_1335782','cpu_1338948','cpu_1442486585','cpu_155313295','cpu_1664088958','cpu_317488701','cpu_317499484','cpu_3858945898','cpu_4304743890','cpu_4820238819','cpu_5796442','cpu_660404','cpu_711355','cpu_717319','cpu_904514','cpu_905062','cpu_907812']
for machine in files[98:100]:
# for machine in target_files[16:19]:
machine = machine.strip('.csv').split('/')[-1]
# machine = 'cpu_1095481'
print(machine)
data = np.genfromtxt("../data/cpuRate/"+machine+".csv",skip_header=1, delimiter=',',usecols=(1))
miniters=100
maxiters=1000
TRAIN = 1000
VALID = 100
