def main():
if sys.argv[1] == 'daily':
print('Using daily data...')
path_to_dataset = '../data/household_power_consumption_daily.csv'
model, y_test, predictions = run(path_to_dataset, 10, 50, 1.0)
elif sys.argv[1] == 'monthly':
print('Using monthly data...')
path_to_dataset = '../data/household_power_consumption_monthly.csv'
model, y_test, predictions = run(path_to_dataset, 30, 5, 1.0)
elif sys.argv[1] == 'hourly':
print('Using hourly data...')
path_to_dataset = '../data/household_power_consumption_hourly.csv'
model, y_test, predictions = run(path_to_dataset, 30, 50, 1.0)
else:
print('Using minute data...')
path_to_dataset = '../data/household_power_consumption.csv'
model, y_test, predictions = run(path_to_dataset)
# save for later use
model.save_weights('../output/lstm.h5', overwrite=True)
# model.load_weights('../output/lstm.h5')
graph_utils.plot('lstm', predictions, y_test)
print('RMSE: %.4f'% metrics.rmse(predictions, y_test))
print('MAPE: %.4f'% metrics.mape(predictions, y_test))
def create_graph(force):
run_cmd = cere_configure.cere_config["run_cmd"]
build_cmd = cere_configure.cere_config["build_cmd"]
clean_cmd = cere_configure.cere_config["clean_cmd"]
logger.info('Start call graph creation')
#Build again the application to be sure we give the right binary to pprof
try:
logger.debug(
subprocess.check_output(
"{0} && {1} CERE_MODE=\"original --instrument --instrument-app\""
.format(clean_cmd, build_cmd),
stderr=subprocess.STDOUT,
shell=True))
except subprocess.CalledProcessError as err:
logger.error(str(err))
logger.error(err.output)
return False
binary = which(run_cmd)
if not binary:
logger.critical(
"Cannot find the binary. Please provide binary name through cere configure --binary."
)
return False
profile_file = "{0}/app.prof".format(var.CERE_PROFILE_PATH)
if not os.path.isfile(profile_file):
logger.critical("No profiling file. Please run cere profile.")
return False
#regular expression to parse the gperf tool output
regex_list = [
r'(N.*)\s\[label\=\"(.*?)\\n([0-9]*)\s\((.*)\%\)\\rof\s(.*)\s\((.*)\%\)\\r',
r'(N.*)\s\[label\=\"(.*)\\n([0-9]*)\s\((.*)\%\)\\r',
r'(N.*)\s\-\>\s(N.*)\s\[label\=([0-9]*)\,',
r'Legend\s\[.*Total samples:\s([0-9]*).*\]'
]
cmd = subprocess.Popen("{0} -dot {1} {2}".format(var.PPROF, binary,
profile_file),
shell=True,
stdout=subprocess.PIPE)
digraph = nx.DiGraph()
digraph.graph['coverage'] = 0
digraph.graph['selector'] = None
samples, digraph = parse_gPerfTool(digraph, cmd, regex_list)
plot(digraph, "debug")
digraph = remove_cycles(digraph, samples)
if not fix_self_coverage(digraph, samples):
return False
if not delete_useless_nodes(digraph):
return False
plot(digraph)
save_graph(digraph)
logger.info('Call graph creation successful')
return True
def main():
# minute
y_test, predictions = run()
# hourly
# y_test, predictions = run(50, 1.0)
# daily
# y_test, predictions = run(50, 1.0)
graph_utils.plot('linear', predictions, y_test)
print('RMSE: %.4f'% metrics.rmse(predictions, y_test))
print('MAPE: %.4f'% metrics.mape(predictions, y_test))
def create_graph(force):
run_cmd = cere_configure.cere_config["run_cmd"]
build_cmd = cere_configure.cere_config["build_cmd"]
clean_cmd = cere_configure.cere_config["clean_cmd"]
logger.info('Start call graph creation')
#Build again the application to be sure we give the right binary to pprof
try:
logger.debug(subprocess.check_output("{0} && {1} CERE_MODE=\"original --instrument --instrument-app\"".format(clean_cmd, build_cmd), stderr=subprocess.STDOUT, shell=True))
except subprocess.CalledProcessError as err:
logger.error(str(err))
logger.error(err.output)
return False
binary = which(run_cmd)
if not binary:
logger.critical("Cannot find the binary. Please provide binary name through cere configure --binary")
return False
profile_file = "{0}/app.prof".format(var.CERE_PROFILE_PATH)
if not os.path.isfile(profile_file):
logger.critical("No profiling file. Please run cere profile")
return False
#regular expression to parse the gperf tool output
regex_list = [r'(N.*)\s\[label\=\"(.*?)\\n([0-9]*)\s\((.*)\%\)\\rof\s(.*)\s\((.*)\%\)\\r',
r'(N.*)\s\[label\=\"(.*)\\n([0-9]*)\s\((.*)\%\)\\r',
r'(N.*)\s\-\>\s(N.*)\s\[label\=([0-9]*)\,',
r'Legend\s\[.*Total samples:\s([0-9]*).*\]']
cmd = subprocess.Popen("{0} -dot {1} {2}".format(var.PPROF, binary, profile_file), shell=True, stdout=subprocess.PIPE)
digraph = nx.DiGraph()
digraph.graph['coverage'] = 0
digraph.graph['selector'] = None
samples, digraph = parse_gPerfTool(digraph, cmd, regex_list)
plot(digraph, "debug")
digraph = remove_cycles(digraph, samples)
if not fix_self_coverage(digraph, samples):
return False
if not delete_useless_nodes(digraph):
return False
plot(digraph)
save_graph(digraph)
logger.info('Call graph creation successefull')
return True
def main():
# minute
model, y_test, predictions = run()
# hourly
# model, y_test, predictions = run(30, 50, 1.0)
# daily
# model, y_test, predictions = run(100, 50, 1.0)
# save for later use
model.save_weights('../output/lstm.h5', overwrite=True)
# model.load_weights('../output/lstm.h5')
graph_utils.plot('lstm', predictions, y_test)
print('RMSE: %.4f'% metrics.rmse(predictions, y_test))
print('MAPE: %.4f'% metrics.mape(predictions, y_test))
def main():
if sys.argv[1] == 'daily':
print('Using daily data...')
path_to_dataset = '../data/household_power_consumption_daily.csv'
y_test, predictions = run(path_to_dataset, 50, 1.0)
elif sys.argv[1] == 'monthly':
print('Using monthly data...')
path_to_dataset = '../data/household_power_consumption_monthly.csv'
y_test, predictions = run(path_to_dataset, 5, 1.0)
elif sys.argv[1] == 'hourly':
print('Using hourly data...')
path_to_dataset = '../data/household_power_consumption_hourly.csv'
y_test, predictions = run(path_to_dataset, 50, 1.0)
else:
print('Using minute data...')
path_to_dataset = '../data/household_power_consumption.csv'
y_test, predictions = run(path_to_dataset)
graph_utils.plot('linear', predictions, y_test)
print('RMSE: %.4f'% metrics.rmse(predictions, y_test))
print('MAPE: %.4f'% metrics.mape(predictions, y_test))
digraph.node[7]['_self_coverage'] = 9.
digraph.node[7]['_coverage'] = 9.
#add edges
digraph.add_edge(1, 3, weight = 30.)
digraph.add_edge(1, 4, weight = 20.)
digraph.add_edge(2, 3, weight = 35.)
digraph.add_edge(3, 4, weight = 1.)
digraph.add_edge(4, 5, weight = 1.)
digraph.add_edge(5, 3, weight = 1.)
digraph.add_edge(5, 6, weight = 4.)
digraph.add_edge(5, 7, weight = 4.)
digraph.add_edge(3, 6, weight = 22.)
digraph.add_edge(4, 7, weight = 5.)
plot(digraph, "debug")
digraph = remove_cycles(digraph, samples)
cycles = list(nx.simple_cycles(digraph))
#If there is still a cycle we have a problem
if len(cycles) != 0:
print("Cycles are still present")
plot(digraph, "final_fail")
exit(1)
if not fix_self_coverage(digraph, samples):
print("Updating coverage failed")
plot(digraph, "final_fail")
exit(1)
#If updating coverages failed to generate good coverage
if digraph.node[3]["_self_coverage"] != 50 or digraph.node[3]["_coverage"] != 85:
print("Error in removing cycle")
digraph.node[7]["_self_coverage"] = 9.0
digraph.node[7]["_coverage"] = 9.0
# add edges
digraph.add_edge(1, 3, weight=30.0)
digraph.add_edge(1, 4, weight=20.0)
digraph.add_edge(2, 3, weight=35.0)
digraph.add_edge(3, 4, weight=1.0)
digraph.add_edge(4, 5, weight=1.0)
digraph.add_edge(5, 3, weight=1.0)
digraph.add_edge(5, 6, weight=4.0)
digraph.add_edge(5, 7, weight=4.0)
digraph.add_edge(3, 6, weight=22.0)
digraph.add_edge(4, 7, weight=5.0)
plot(digraph, "debug")
digraph = remove_cycles(digraph, samples)
cycles = list(nx.simple_cycles(digraph))
# If there is still a cycle we have a problem
if len(cycles) != 0:
print("Cycles are still present")
plot(digraph, "final_fail")
exit(1)
if not fix_self_coverage(digraph, samples):
print("Updating coverage failed")
plot(digraph, "final_fail")
exit(1)
# If updating coverages failed to generate good coverage
if digraph.node[3]["_self_coverage"] != 50 or digraph.node[3]["_coverage"] != 85:
print("Error in removing cycle")