def runTrain(masterDict, *, screen="test", cudaID):
if type(masterDict) is str:
mFile = masterDict
masterDict = master.readMasterFile(mFile)
else:
mFile = master.writeMasterFile(masterDict)
codePath = os.path.realpath(__file__)
if screen is None:
cmd = "CUDA_VISIBLE_DEVICES={} python {} -F {} -M {}".format(
cudaID, codePath, "train", mFile)
else:
cmd = "CUDA_VISIBLE_DEVICES={} screen -dmS {} python {} -F {} -M {}".format(
cudaID, screen, codePath, "train", mFile)
# if screen is None:
# #add some debugs Dapeng
# parser = argparse.ArgumentParser()
# parser.add_argument('-F', dest='func', type=str, default='train')
# parser.add_argument('-M', dest='mFile', type=str, default=mFile)
# args = parser.parse_args()
# if args.func == 'train':
# mDict = master.readMasterFile(args.mFile)
# master.train(mDict)
# # out = mDict['out']
# # email.sendEmail(subject='Training Done', text=out)
print(cmd)
os.system(cmd)
def runTrain(masterDict, *, screen='test', cudaID):
if type(masterDict) is str:
mFile = masterDict
masterDict = master.readMasterFile(mFile)
else:
mFile = master.writeMasterFile(masterDict)
codePath = os.path.realpath(__file__)
if screen is None:
cmd = 'CUDA_VISIBLE_DEVICES={} python {} -F {} -M {}'.format(
cudaID, codePath, 'train', mFile)
else:
cmd = 'CUDA_VISIBLE_DEVICES={} screen -dmS {} python {} -F {} -M {}'.format(
cudaID, screen, codePath, 'train', mFile)
print(cmd)
os.system(cmd)
else:
cmd = "CUDA_VISIBLE_DEVICES={} screen -dmS {} python {} -F {} -M {}".format(
cudaID, screen, codePath, "train", mFile)
# if screen is None:
# #add some debugs Dapeng
# parser = argparse.ArgumentParser()
# parser.add_argument('-F', dest='func', type=str, default='train')
# parser.add_argument('-M', dest='mFile', type=str, default=mFile)
# args = parser.parse_args()
# if args.func == 'train':
# mDict = master.readMasterFile(args.mFile)
# master.train(mDict)
# # out = mDict['out']
# # email.sendEmail(subject='Training Done', text=out)
print(cmd)
os.system(cmd)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-F", dest="func", type=str)
parser.add_argument("-M", dest="mFile", type=str)
args = parser.parse_args()
if args.func == "train":
mDict = master.readMasterFile(args.mFile)
master.train(mDict)
# out = mDict['out']
# email.sendEmail(subject='Training Done', text=out)
FDCLS = gageid[testInd][sampleInLst[iT]][nearID].tolist()
FDCList.append(FDCLS)
outLst = [
os.path.join(save_path, str(tempseed), testregdic, x)
for x in caseLst
]
# all the directories to test in this list
icount = 0
imig = 0
for out in outLst:
# testing sequence: LSTM, LSTM with FDC, LSTM with fractional FDC migration
if interfaceOpt == 1:
# load testing data
mDict = master.readMasterFile(out)
optData = mDict["data"]
df = camels.DataframeCamels(subset=testBasin, tRange=tRange)
x = df.getDataTs(varLst=optData["varT"],
doNorm=False,
rmNan=False)
obs = df.getDataObs(doNorm=False, rmNan=False, basinnorm=False)
c = df.getDataConst(varLst=optData["varC"],
doNorm=False,
rmNan=False)
# do normalization and remove nan
# load the saved statDict
statFile = os.path.join(out, "statDict.json")
with open(statFile, "r") as fp:
statDict = json.load(fp)
# masterDict = master.wrapMaster(out, optData, optModel, optLoss, optTrain)
# master.runTrain(masterDict, cudaID=cid % 3, screen='test-DA' + str(nDay))
# cid = cid + 1
# test original model
caseLst = ['All-90-95']
nDayLst = [1, 3, 7]
for nDay in nDayLst:
caseLst.append('All-90-95-DA' + str(nDay))
outLst = [os.path.join(pathCamels['Out'], save_path, x) for x in caseLst]
# subset = 'All'
# subset = pubid
tRange = [19950101, 20000101]
predLst = list()
for out in outLst:
tempdict = master.readMasterFile(out)
trainid = np.array(tempdict['data']['subset'])
subset = trainid.tolist()
df, pred, obs = master.test(out,
tRange=tRange,
subset=subset,
basinnorm=True,
epoch=200)
# pred=np.maximum(pred,0)
predLst.append(pred)
# plot box
statDictLst = [stat.statError(x.squeeze(), obs.squeeze()) for x in predLst]
# keyLst = list(statDictLst[0].keys())
keyLst = ['Bias', 'RMSE', 'NSE']
dataBox = list()