def modelLinear(outName, testset, trainset=None, wqData=None):
master = loadMaster(outName)
dataName = master['dataName']
if wqData is None:
wqData = waterQuality.DataModelWQ(dataName)
if trainset is None:
trainset = master['trainName']
infoTrain = wqData.info.iloc[wqData.subset[trainset]].reset_index()
infoTest = wqData.info.iloc[wqData.subset[testset]].reset_index()
# linear reg data
statTup = loadStat(outName)
varTup = (master['varX'], master['varXC'], master['varY'], master['varYC'])
dataTup1 = wqData.transIn(subset=trainset, varTup=varTup, statTup=statTup)
dataTup2 = wqData.transIn(subset=testset, varTup=varTup, statTup=statTup)
dataTup1 = trainTS.dealNaN(dataTup1, master['optNaN'])
dataTup2 = trainTS.dealNaN(dataTup2, master['optNaN'])
varYC = varTup[3]
statYC = statTup[3]
x1 = dataTup1[0][-1, :, :]
yc1 = dataTup1[3]
x2 = dataTup2[0][-1, :, :]
# point test l2 - linear
nc = len(varYC)
matP1 = np.full([len(infoTrain), nc], np.nan)
matP2 = np.full([len(infoTest), nc], np.nan)
siteNoLst = infoTest['siteNo'].unique().tolist()
for siteNo in siteNoLst:
ind1 = infoTrain[infoTrain['siteNo'] == siteNo].index
ind2 = infoTest[infoTest['siteNo'] == siteNo].index
xT1 = x1[ind1, :]
ycT1 = yc1[ind1, :]
for ic in range(nc):
[xx, yy], iv = utils.rmNan([xT1, ycT1[:, ic]])
if len(iv) > 0:
modelYC = LinearRegression().fit(xx, yy)
matP1[ind1, ic] = modelYC.predict(xT1)
if len(ind2) > 0:
xT2 = x2[ind2, :]
matP1[ind2, ic] = modelYC.predict(xT2)
matO1 = wqData.transOut(matP1, statYC, varYC)
matO2 = wqData.transOut(matP2, statYC, varYC)
return matO1, matO2
def trainModelTS(outName):
outFolder = nameFolder(outName)
dictP = loadMaster(outName)
# load data
wqData = waterQuality.DataModelWQ(dictP['dataName'])
varTup = (dictP['varX'], dictP['varXC'], dictP['varY'], dictP['varYC'])
dataTup, statTup = wqData.transIn(subset=dictP['trainName'], varTup=varTup)
dataTup = trainTS.dealNaN(dataTup, dictP['optNaN'])
wrapStat(outName, statTup)
# train model
[nx, nxc, ny, nyc, nt, ns] = trainTS.getSize(dataTup)
if dictP['modelName'] == 'CudnnLSTM':
model = rnn.CudnnLstmModel(nx=nx + nxc,
ny=ny + nyc,
hiddenSize=dictP['hiddenSize'])
lossFun = crit.RmseLoss()
if torch.cuda.is_available():
lossFun = lossFun.cuda()
model = model.cuda()
optim = torch.optim.Adadelta(model.parameters())
lossLst = list()
nEp = dictP['nEpoch']
sEp = dictP['saveEpoch']
logFile = os.path.join(outFolder, 'log')
if os.path.exists(logFile):
os.remove(logFile)
for k in range(0, nEp, sEp):
model, optim, lossEp = trainTS.trainModel(dataTup,
model,
lossFun,
optim,
batchSize=dictP['batchSize'],
nEp=sEp,
cEp=k,
logFile=logFile)
# save model
saveModel(outName, k + sEp, model, optim=optim)
lossLst = lossLst + lossEp
lossFile = os.path.join(outFolder, 'loss.csv')
pd.DataFrame(lossLst).to_csv(lossFile, index=False, header=False)
def testModel(outName, testset, wqData=None, ep=None, reTest=False):
# load master
master = loadMaster(outName)
if ep is None:
ep = master['nEpoch']
outFolder = nameFolder(outName)
testFileName = 'testP-{}-Ep{}.npz'.format(testset, ep)
testFile = os.path.join(outFolder, testFileName)
if os.path.exists(testFile) and reTest is False:
print('load saved test result')
npz = np.load(testFile, allow_pickle=True)
yP = npz['yP']
ycP = npz['ycP']
else:
statTup = loadStat(outName)
model = loadModel(outName, ep=ep)
# load test data
if wqData is None:
wqData = waterQuality.DataModelWQ(master['dataName'])
varTup = (master['varX'], master['varXC'], master['varY'],
master['varYC'])
testDataLst = wqData.transIn(subset=testset,
statTup=statTup,
varTup=varTup)
sizeLst = trainTS.getSize(testDataLst)
testDataLst = trainTS.dealNaN(testDataLst, master['optNaN'])
x = testDataLst[0]
xc = testDataLst[1]
ny = sizeLst[2]
# test model - point by point
yOut, ycOut = trainTS.testModel(model, x, xc, ny)
yP = wqData.transOut(yOut, statTup[2], master['varY'])
ycP = wqData.transOut(ycOut, statTup[3], master['varYC'])
np.savez(testFile, yP=yP, ycP=ycP)
return yP, ycP
def testModelSeq(outName,
siteNoLst,
wqData=None,
ep=None,
returnOut=False,
retest=False,
sd=np.datetime64('1979-01-01'),
ed=np.datetime64('2019-12-31')):
# run sequence test for all sites, default to be from first date to last date
if type(siteNoLst) is not list:
siteNoLst = [siteNoLst]
master = loadMaster(outName)
if master['crit'] == 'SigmaLoss':
doSigma = True
else:
doSigma = False
if ep is None:
ep = master['nEpoch']
outDir = nameFolder(outName)
sdS = pd.to_datetime(sd).strftime('%Y%m%d')
edS = pd.to_datetime(ed).strftime('%Y%m%d')
saveDir = os.path.join(outDir, 'seq-{}-{}-ep{}'.format(sdS, edS, ep))
if not os.path.exists(saveDir):
os.mkdir(saveDir)
siteSaveLst = os.listdir(saveDir)
if retest is True:
sitePredLst = siteNoLst
else:
sitePredLst = [
siteNo for siteNo in siteNoLst if siteNo not in siteSaveLst
]
if len(sitePredLst) != 0:
if wqData is None:
wqData = waterQuality.DataModelWQ(master['dataName'])
(varX, varXC, varY, varYC) = (master['varX'], master['varXC'],
master['varY'], master['varYC'])
(statX, statXC, statY, statYC) = loadStat(outName)
model = loadModel(outName, ep=ep)
tabG = gageII.readData(varLst=varXC, siteNoLst=siteNoLst)
tabG = gageII.updateCode(tabG)
for siteNo in sitePredLst:
if 'DRAIN_SQKM' in varXC:
area = tabG[tabG.index == siteNo]['DRAIN_SQKM'].values[0]
else:
area = None
# test model
print('testing {} from {} to {}'.format(siteNo, sdS, edS))
freq = wqData.freq
dfX = waterQuality.readSiteTS(siteNo,
varX,
freq=freq,
area=area,
sd=sd,
ed=ed)
# dfX = waterQuality.readSiteX(
# siteNo, varX, sd=sd, ed=ed, area=area, nFill=5)
xA = np.expand_dims(dfX.values, axis=1)
xcA = np.expand_dims(tabG.loc[siteNo].values.astype(np.float),
axis=0)
mtdX = waterQuality.extractVarMtd(varX)
x = transform.transInAll(xA, mtdX, statLst=statX)
mtdXC = waterQuality.extractVarMtd(varXC)
xc = transform.transInAll(xcA, mtdXC, statLst=statXC)
[x, xc] = trainTS.dealNaN([x, xc], master['optNaN'][:2])
yOut = trainTS.testModel(model, x, xc)
# transfer out
nt = len(dfX)
ny = len(varY) if varY is not None else 0
nyc = len(varYC) if varYC is not None else 0
if doSigma:
yP = np.full([nt, ny + nyc], np.nan)
sP = np.full([nt, ny + nyc], np.nan)
yP[:, :ny] = wqData.transOut(yOut[:, 0, :ny * 2:2], statY,
varY)
yP[:, ny:] = wqData.transOut(yOut[:, 0, ny * 2::2], statYC,
varYC)
sP[:, :ny] = wqData.transOut(
np.sqrt(np.exp(yOut[:, 0, 1:ny * 2:2])), statY, varY)
sP[:, ny:] = wqData.transOut(
np.sqrt(np.exp(yOut[:, 0, ny * 2 + 1::2])), statYC, varYC)
else:
yP = np.full([nt, ny + nyc], np.nan)
yP[:, :ny] = wqData.transOut(yOut[:, 0, :ny], statY, varY)
yP[:, ny:] = wqData.transOut(yOut[:, 0, ny:], statYC, varYC)
# save output
t = dfX.index.values.astype('datetime64[D]')
colY = [] if varY is None else varY
colYC = [] if varYC is None else varYC
dfOut = pd.DataFrame(data=yP, columns=[colY + colYC], index=t)
dfOut.index.name = 'date'
dfOut = dfOut.reset_index()
dfOut.to_csv(os.path.join(saveDir, siteNo), index=False)
if doSigma:
dfOutS = pd.DataFrame(data=sP, columns=[colY + colYC], index=t)
dfOutS.index.name = 'date'
dfOutS = dfOut.reset_index()
dfOutS.to_csv(os.path.join(saveDir, siteNo + '_sigma'),
index=False)
# load all csv
if returnOut:
dictOut = dict()
for siteNo in siteNoLst:
# print('loading {} from {} to {}'.format(siteNo, sdS, edS))
dfOut = pd.read_csv(os.path.join(saveDir, siteNo))
dictOut[siteNo] = dfOut
if doSigma:
dfOut = pd.read_csv(os.path.join(saveDir, siteNo + '_sigma'))
dictOut[siteNo + '_sigma'] = dfOut
return dictOut
def testModel(outName, testset, wqData=None, ep=None, reTest=False):
# load master
master = loadMaster(outName)
if master['crit'] == 'SigmaLoss':
doSigma = True
else:
doSigma = False
if ep is None:
ep = master['nEpoch']
outFolder = nameFolder(outName)
testFileName = 'testP-{}-Ep{}.npz'.format(testset, ep)
testFile = os.path.join(outFolder, testFileName)
if os.path.exists(testFile) and reTest is False:
print('load saved test result')
npz = np.load(testFile, allow_pickle=True)
yP = npz['yP']
ycP = npz['ycP']
if doSigma:
sP = npz['sP']
scP = npz['scP']
else:
statTup = loadStat(outName)
model = loadModel(outName, ep=ep)
# load test data
if wqData is None:
wqData = waterQuality.DataModelWQ(master['dataName'])
varTup = (master['varX'], master['varXC'], master['varY'],
master['varYC'])
testDataLst = wqData.transIn(subset=testset,
statTup=statTup,
varTup=varTup)
sizeLst = trainTS.getSize(testDataLst)
if master['optNaN'] == [2, 2, 0, 0]:
master['optNaN'] = [0, 0, 0, 0]
testDataLst = trainTS.dealNaN(testDataLst, master['optNaN'])
x = testDataLst[0]
xc = testDataLst[1]
ny = sizeLst[2]
if not doSigma:
# test model - point by point
yOut, ycOut = trainTS.testModel(model, x, xc, ny)
yP = wqData.transOut(yOut, statTup[2], master['varY'])
ycP = wqData.transOut(ycOut, statTup[3], master['varYC'])
np.savez(testFile, yP=yP, ycP=ycP)
else:
print('sigma model')
ny = ny * 2
yOut, ycOut = trainTS.testModel(model, x, xc, ny)
yP = wqData.transOut(yOut[:, :, ::2], statTup[2], master['varY'])
sP = wqData.transOut(np.sqrt(np.exp(yOut[:, :, 1::2])), statTup[2],
master['varY'])
ycP = wqData.transOut(ycOut[:, ::2], statTup[3], master['varYC'])
scP = wqData.transOut(np.sqrt(np.exp(ycOut[:, 1::2])), statTup[3],
master['varYC'])
np.savez(testFile, yP=yP, ycP=ycP, sP=sP, scP=scP)
if doSigma:
return yP, ycP, sP, scP
else:
return yP, ycP
def trainModelTS(outName):
outFolder = nameFolder(outName)
dictP = loadMaster(outName)
# load data
rmFlag = dictP['rmFlag'] if 'rmFlag' in dictP else False
wqData = waterQuality.DataModelWQ(dictP['dataName'], rmFlag)
varTup = (dictP['varX'], dictP['varXC'], dictP['varY'], dictP['varYC'])
dataTup, statTup = wqData.transIn(subset=dictP['trainName'], varTup=varTup)
dataTup = trainTS.dealNaN(dataTup, dictP['optNaN'])
wrapStat(outName, statTup)
# train model
[nx, nxc, ny, nyc, nt, ns] = trainTS.getSize(dataTup)
# define loss
if dictP['crit'] == 'RmseLoss':
lossFun = crit.RmseLoss()
elif dictP['crit'] == 'RmseLoss2D':
lossFun = crit.RmseLoss2D()
elif dictP['crit'] == 'SigmaLoss':
lossFun = crit.SigmaLoss()
ny = ny * 2
nyc = nyc * 2
else:
raise RuntimeError('loss function not specified')
# define model
if dictP['modelName'] == 'CudnnLSTM':
model = rnn.CudnnLstmModel(nx=nx + nxc,
ny=ny + nyc,
hiddenSize=dictP['hiddenSize'])
elif dictP['modelName'] == 'LstmModel':
model = rnn.LstmModel(nx=nx + nxc,
ny=ny + nyc,
hiddenSize=dictP['hiddenSize'])
elif dictP['modelName'] == 'AgeLSTM':
model = rnn.AgeLSTM2(nx=nx + nxc,
ny=ny,
nyc=nyc,
rho=365,
nh=dictP['hiddenSize'])
else:
raise RuntimeError('Model not specified')
if torch.cuda.is_available():
lossFun = lossFun.cuda()
model = model.cuda()
if dictP['optim'] == 'AdaDelta':
optim = torch.optim.Adadelta(model.parameters())
else:
raise RuntimeError('optimizor function not specified')
lossLst = list()
nEp = dictP['nEpoch']
sEp = dictP['saveEpoch']
logFile = os.path.join(outFolder, 'log')
if os.path.exists(logFile):
os.remove(logFile)
for k in range(0, nEp, sEp):
model, optim, lossEp = trainTS.trainModel(dataTup,
model,
lossFun,
optim,
batchSize=dictP['batchSize'],
nEp=sEp,
cEp=k,
logFile=logFile)
# save model
saveModel(outName, k + sEp, model, optim=optim)
lossLst = lossLst + lossEp
lossFile = os.path.join(outFolder, 'loss.csv')
pd.DataFrame(lossLst).to_csv(lossFile, index=False, header=False)
code = '00945'
label = 'plain'
trainSet = '{}-Y1'.format(code)
testSet = '{}-Y2'.format(code)
outName = '{}-{}-{}-{}'.format(dataName, code, label, trainSet)
outFolder = basins.nameFolder(outName)
dictP = basins.loadMaster(outName)
# load data
rmFlag = dictP['rmFlag'] if 'rmFlag' in dictP else False
wqData = waterQuality.DataModelWQ(dictP['dataName'], rmFlag)
varTup = (dictP['varX'], dictP['varXC'], dictP['varY'], dictP['varYC'])
dataTup, statTup = wqData.transIn(subset=dictP['trainName'], varTup=varTup)
dataTup = trainTS.dealNaN(dataTup, dictP['optNaN'])
# wrapStat(outName, statTup)
[nx, nxc, ny, nyc, nt, ns] = trainTS.getSize(dataTup)
model = basins.loadModel(outName, ep=500)
lossFun = crit.RmseLoss()
lossFun = lossFun.cuda()
model = model.cuda()
# training parts
dataLst = dataTup
sizeLst = trainTS.getSize(dataLst)
[nx, nxc, ny, nyc, nt, ns] = sizeLst
rho, nbatch = dictP['batchSize']
rho = nt
batchSize = [rho, nbatch]
siteNo = '07060710'
codeLst = ['00660', '00600']
# codeLst = ['00915', '00955']
nh = 256
batchSize = [365, 50]
# if not waterQuality.exist(siteNo):
# wqData = waterQuality.DataModelWQ.new(siteNo, [siteNo])
wqData = waterQuality.DataModelWQ(siteNo, rmFlag=False)
varX = wqData.varF
varXC = wqData.varG
varY = [wqData.varQ[0]]
varYC = codeLst
varTup = (varX, varXC, varY, varYC)
dataTup, statTup = wqData.transIn(varTup=varTup)
dataTup = trainTS.dealNaN(dataTup, [1, 1, 0, 0])
sizeLst = trainTS.getSize(dataTup)
[nx, nxc, ny, nyc, nt, ns] = sizeLst
tabG = gageII.readData(varLst=varXC, siteNoLst=[siteNo])
tabG = gageII.updateCode(tabG)
dfX = waterQuality.readSiteX(siteNo, varX, nFill=5)
dfY = waterQuality.readSiteY(siteNo, varY)
dfYC = waterQuality.readSiteY(siteNo, varYC)
importlib.reload(rnn)
model = rnn.AgeLSTM(nx=nx + nxc, ny=ny, nyc=nyc, nh=nh)
optim = torch.optim.Adadelta(model.parameters())
lossFun = crit.RmseMix()
if torch.cuda.is_available():
lossFun = lossFun.cuda()
varX = gridMET.varLst
varXC = gageII.lstWaterQuality
varY = usgs.varQ
varYC = None
varTup = (varX, varXC, varY, varYC)
# dataTup = wqData.extractData(varTup=varTup)
# xR, xcR, yR, ycR = dataTup
# mtdX = ['log-norm', 'norm', 'norm', 'norm', 'norm', 'norm', 'norm']
# x, statX = transform.transInAll(xR, mtdX)
dataTup, statTup = wqData.transIn(varTup=varTup)
(x, xc, y, yc) = dataTup
dataTup = trainTS.dealNaN(dataTup, [1, 1, 0, 0])
(statX, statXC, statY, statYC) = statTup
# concatenate all data
[nx, nxc, ny, nyc, nt, ns] = trainTS.getSize(dataTup)
xx = np.zeros([ns, nt, nx + nxc])
for k in range(ns):
xTemp = dataTup[0][:, k, :]
xcTemp = dataTup[1][k, :]
temp = np.concatenate([xTemp, np.tile(xcTemp, [365, 1])], axis=-1)
xx[k, :, :] = temp
xT = torch.from_numpy(xx).float().cuda()
yy = np.swapaxes(dataTup[2], 0, 1)
yT = torch.from_numpy(yy).float().cuda()
# xT = xT[0:1, :, :]
wqData = waterQuality.DataModelWQ('Silica64')
master = basins.loadMaster(outName)
dataName = master['dataName']
if wqData is None:
wqData = waterQuality.DataModelWQ(dataName)
trainset = master['trainName']
infoTrain = wqData.info.iloc[wqData.subset[trainset]].reset_index()
infoTest = wqData.info.iloc[wqData.subset[testset]].reset_index()
# linear reg data
statTup = basins.loadStat(outName)
varTup = (master['varX'], master['varXC'], master['varY'], master['varYC'])
dataTup1 = wqData.transIn(subset=trainset, varTup=varTup, statTup=statTup)
dataTup2 = wqData.transIn(subset=testset, varTup=varTup, statTup=statTup)
dataTup1 = trainTS.dealNaN(dataTup1, master['optNaN'])
dataTup2 = trainTS.dealNaN(dataTup2, master['optNaN'])
varYC = varTup[3]
statYC = statTup[3]
x1 = dataTup1[0][-1, :, :]
yc1 = dataTup1[3]
x2 = dataTup2[0][-1, :, :]
# point test l2 - linear
nc = len(varYC)
matP1 = np.full([len(infoTrain), nc], np.nan)
matP2 = np.full([len(infoTest), nc], np.nan)
siteNoLst = infoTest['siteNo'].unique().tolist()
for siteNo in siteNoLst:
ind1 = infoTrain[infoTrain['siteNo'] == siteNo].index
ind2 = infoTest[infoTest['siteNo'] == siteNo].index
