def main():
args = getArgs()
if args.seed is None:
seed = random.randrange(sys.maxsize)
args.seed = seed
print(f"generating new random seed: {seed}")
else:
print(f"setting random seed to: {args.seed}")
random.seed(args.seed)
datasetlist = args.datasets
print(f"doing experiment with {datasetlist} in that order")
k = args.kfold
results = {}
runlist = args.methods
set_keras_growth(args.gpu)
prefix = "runner"
if args.prefix is not None:
prefix = args.prefix
rootpath = prefix + datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
createdir(rootpath)
min_retain_losses = list()
min_losses = list()
writejson(f"{rootpath}/settings.json", sys.argv[1:])
if args.callbacks is not None:
callbacks = args.callbacks
else:
callbacks = list()
alphalist = [0.8]
nresults = list()
for i in range(0, args.n):
nresults.append(
runalldatasets(args,
callbacks,
datasetlist,
rootpath,
runlist,
alphalist=alphalist,
n=i,
printcvresults=args.cvsummary,
printcv=args.printcv,
doevaluation=args.doevaluation,
learning_rate=args.learning_rate))
writejson(f"{rootpath}/data.json", nresults)
resdf = pd.DataFrame(nresults)
resdf.to_csv(
f"{rootpath}/results_{args.kfold}kfold_{args.epochs}epochs_{args.onehot}onehot.csv"
)
def main(mpirank, mpisize, mpicomm):
args = getArgs()
if args.seed is None:
seed = random.randrange(sys.maxsize)
args.seed = seed
print(f"generating new random seed:{seed}")
random.seed(args.seed)
datasetlist = args.datasets
k = args.kfold
results = {}
runlist = args.methods
if "," in args.gpu:
gpus = args.gpu.split(",")
mygpu = gpus[mpirank % 2]
set_keras_growth(int(mygpu))
else:
set_keras_growth(args.gpu)
dataset_results = dict()
prefix = "runner"
if args.prefix is not None:
prefix = args.prefix
rootpath = prefix + datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
if mpirank == 0:
createdir(rootpath)
writejson(f"{rootpath}/settings.json", sys.argv[1:])
min_retain_losses = list()
min_losses = list()
datasetsdone = list()
if args.callbacks is not None:
callbacks = args.callbacks
else:
callbacks = list()
nresults = list()
alphalist = [
0.8
] # this code does not iterate over alpha, see mpi_deesweighting.py
for i in range(0, args.n):
dataset_results = runalldatasetsMPI(args,
callbacks,
datasetlist,
mpicomm,
mpirank,
rootpath,
runlist,
alphalist,
n=i,
printcvresults=args.cvsummary,
printcv=args.printcv,
doevaluation=args.doevaluation)
nresults.append(dataset_results)
if mpirank == 0:
writejson(f"{rootpath}/data.json", nresults)
resdf = pd.DataFrame(results)
resdf.to_csv(
f"{rootpath}/results_{args.kfold}kfold_{args.epochs}epochs_{args.onehot}onehot.csv"
)
def main(mpirank, mpisize, mpicomm):
args = getArgs()
if args.seed is not None:
random.seed(args.seed)
datasetlist = args.datasets
print(f"doing experiment with {datasetlist} in that order")
k = args.kfold
results = {}
runlist = args.methods
if "," in args.gpu:
gpus = args.gpu.split(",")
mygpu = gpus[mpirank % 2]
set_keras_growth(int(mygpu))
else:
set_keras_growth(args.gpu)
dataset_results = dict()
prefix = "runner"
if args.prefix is not None:
prefix = args.prefix
rootpath = prefix + datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
if mpirank == 0:
createdir(rootpath)
writejson(f"{rootpath}/settings.json", vars(args))
min_retain_losses = list()
min_losses = list()
if args.alpharange is not None:
splits = args.alpharange.split(":")
alphastart = float(splits[0])
alphastop = float(splits[1])
alpharange = np.linspace(alphastart, alphastop, args.alphacount)
else:
alpharange = np.linspace(0.000001, 1.00001, args.alphacount)
datasetsdone = list()
if args.callbacks is not None:
callbacks = args.callbacks
else:
callbacks = list()
nresults = list()
for i in range(0, args.n):
dataset_results = runalldatasetsMPI(args,
callbacks,
datasetlist,
mpicomm,
mpirank,
rootpath,
runlist,
alpharange,
n=i,
printcvresults=args.cvsummary,
printcv=args.printcv)
nresults.append(dataset_results)
if mpirank == 0:
# plotNAlphaResults(datasetlist, nresults, rootpath)
writejson(f"{rootpath}/data.json", nresults)
resdf = pd.DataFrame(results)
resdf.to_csv(
f"{rootpath}/results_{args.kfold}kfold_{args.epochs}epochs_{args.onehot}onehot.csv"
)