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jointSynth.py
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jointSynth.py
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import sys
import numpy
import numpy as np
import colorsys
from socket import gethostname
import time
import argparse
import os
import colorsys
import copy
import SigmoidModel
parser = argparse.ArgumentParser(description='Launches voxel-wise/point-wise DPM on ADNI'
'using cortical thickness maps derived from MRI')
parser.add_argument('--agg', dest='agg', type=int, default=0,
help='agg=1 => plot figures without using Xwindows, for use on cluster where the plots cannot be displayed '
' agg=0 => plot with Xwindows (for use on personal machine)')
parser.add_argument('--runIndex', dest='runIndex', type=int,
default=1, help='index of run instance/process .. for cross-validation')
parser.add_argument('--nrProc', dest='nrProc', type=int,
default=1, help='# of processes')
parser.add_argument('--modelToRun', dest='modelToRun', type=int,
help='index of model to run')
parser.add_argument('--cluster', action="store_true",
help='need to include this flag if runnin on cluster')
parser.add_argument('--nrRows', dest='nrRows', type=int,
help='nr of subfigure rows to plot at every iteration')
parser.add_argument('--nrCols', dest='nrCols', type=int,
help='nr of subfigure columns to plot at every iteration')
parser.add_argument('--penalty', dest='penalty', type=float,
help='penalty value for non-monotonic trajectories. between 0 (no effect) and 10 (strong effect). ')
parser.add_argument('--regData', action="store_true", default=False,
help=' add this flag to regenerate the data')
parser.add_argument('--runPartStd', dest='runPartStd', default='RR',
help=' choose whether to (R) run or (L) load from the checkpoints: '
'either LL, RR, LR or RL. ')
# parser.add_argument('--disModelObj', dest='disModelObj',
# help=' either SigmoidModel or ')
parser.add_argument('--expName', dest="expName",
help='synth1 or synth2')
args = parser.parse_args()
if args.agg:
# print(matplotlib.__version__)
import matplotlib
matplotlib.use('Agg')
# print(asds)
import genSynthData
import ParHierModel
import Plotter
from auxFunc import *
import evaluationFramework
from matplotlib import pyplot as pl
plotTrajParams = {}
plotTrajParams['SubfigTrajWinSize'] = (1200,600)
plotTrajParams['nrRows'] = args.nrRows
plotTrajParams['nrCols'] = args.nrCols
plotTrajParams['diagColors'] = {CTL:'g', MCI:'y', AD:'r',
CTL2:'g', PCA:'y', AD2:'r'}
plotTrajParams['diagScatterMarkers'] = {CTL:'o', MCI:'o', AD:'o',
CTL2:'x', PCA:'x', AD2:'x'}
plotTrajParams['legendCols'] = 4
plotTrajParams['diagLabels'] = {CTL:'CTL', AD:'AD', PCA:'PCA', CTL2:'CTL2'}
# plotTrajParams['ylimitsRandPoints'] = (-3,2)
# plotTrajParams['blenderPath'] = blenderPath
plotTrajParams['isSynth'] = True
plotTrajParams['padTightLayout'] = 1
if args.agg:
plotTrajParams['agg'] = True
else:
plotTrajParams['agg'] = False
hostName = gethostname()
if hostName == 'razvan-Inspiron-5547':
height = 350
else: #if hostName == 'razvan-Precision-T1700':
height = 450
def main():
nrSubjLong = 100
nrTimepts = 4
lowerAgeLim = 60
upperAgeLim = 80
shiftsLowerLim = -13
shiftsUpperLim = 10
outFolder = 'resfiles/synth/'
expName = args.expName
fileName = '%s.npz' % expName
regenerateData = args.regData
params = {}
nrFuncUnits = 2
nrBiomkInFuncUnits = 3
nrDis = 2
nrBiomk = nrBiomkInFuncUnits * nrFuncUnits
mapBiomkToFuncUnits = np.array(list(range(nrFuncUnits)) * nrBiomkInFuncUnits)
# should give smth like [0,1,2,3,0,1,2,3,0,1,2,3]
print('mapBiomkToFuncUnits', mapBiomkToFuncUnits)
biomkInFuncUnit = [0 for u in range(nrFuncUnits+1)]
for u in range(nrFuncUnits):
biomkInFuncUnit[u] = np.where(mapBiomkToFuncUnits == u)[0]
biomkInFuncUnit[nrFuncUnits] = np.array([]) # need to leave this as empty list
plotTrajParams['biomkInFuncUnit'] = biomkInFuncUnit
plotTrajParams['labels'] = ['biomarker %d' % n for n in range(nrBiomk)]
plotTrajParams['nrRowsFuncUnit'] = 3
plotTrajParams['nrColsFuncUnit'] = 4
plotTrajParams['colorsTrajBiomkB'] = [colorsys.hsv_to_rgb(hue, 1, 1) for hue in
np.linspace(0, 1, num=nrBiomk, endpoint=False)]
plotTrajParams['colorsTrajUnitsU'] = [colorsys.hsv_to_rgb(hue, 1, 1) for hue in
np.linspace(0, 1, num=nrFuncUnits, endpoint=False)]
# plotTrajParams['yNormMode'] = 'zScoreTraj'
# plotTrajParams['yNormMode'] = 'zScoreEarlyStageTraj'
plotTrajParams['yNormMode'] = 'unscaled'
# if False, plot estimated traj. in separate plot from true traj.
plotTrajParams['allTrajOverlap'] = True
params['unitNames'] = ['Unit%d' % f for f in range(nrFuncUnits)]
params['runIndex'] = args.runIndex
params['nrProc'] = args.nrProc
params['cluster'] = args.cluster
params['plotTrajParams'] = plotTrajParams
params['penalty'] = args.penalty
params['penaltyUnits'] = 20
params['penaltyDis'] = 1
params['nrFuncUnits'] = nrFuncUnits
params['nrFuncUnitsImgOnly'] = nrFuncUnits
params['biomkInFuncUnit'] = biomkInFuncUnit
params['nrBiomkDisModel'] = nrFuncUnits
params['nrExtraBiomk'] = 0
params['nrGlobIterUnit'] = 10 # these parameters are specific for the Joint Model of Disease (JMD)
params['iterParamsUnit'] = 50
params['nrGlobIterDis'] = 10
params['iterParamsDis'] = 50
# # params['unitModelObjList'] = MarcoModel.GP_progression_model
# params['unitModelObjList'] = SigmoidModel.SigmoidModel
# params['disModelObj'] = SigmoidModel.SigmoidModel
# by default we have no priors
params['priors'] = None
####### set priors for specific models #########
# params['priors'] = dict(prior_length_scale_mean_ratio=0.33, # mean_length_scale = (self.maxX-self.minX)/3
# prior_length_scale_std=1e-4, prior_sigma_mean=2,prior_sigma_std = 1e-3,
# prior_eps_mean = 1, prior_eps_std = 1e-2)
# params['priors'] = dict(prior_length_scale_mean_ratio=0.9, # mean_length_scale = (self.maxX-self.minX)/3
# prior_length_scale_std=1e-4, prior_sigma_mean=3, prior_sigma_std=1e-3,
# prior_eps_mean=0.1, prior_eps_std=1e-6)
params['priorsUnitModelsMarcoModel'] = [dict(prior_length_scale_mean_ratio=0.05, # mean_length_scale = (self.maxX-self.minX)/3
prior_length_scale_std=1e-6, prior_sigma_mean=0.5, prior_sigma_std=1e-3,
prior_eps_mean=0.1, prior_eps_std=1e-6) for u in range(nrFuncUnits)]
transitionTimePriorMean = 1 # in DPS 0-1 space, prior mean
transitionTimePriorMin = 0.1
transitionTimePriorMax = 10
bPriorShape, bPriorRate = getGammShapeRateFromTranTime(
transitionTimePriorMean, transitionTimePriorMin, transitionTimePriorMax)
params['priorsDisModels'] = [dict(meanA=1, stdA=1e-5, meanD=0, stdD=1e-5,
shapeB=bPriorShape, rateB=bPriorRate, timeShiftStd=15)
for d in range(nrDis)]
params['priorsUnitModels'] = [None for d in range(nrDis)]
##### disease agnostic parameters ###########
# params of individual biomarkers
thetas = np.zeros((nrBiomk, 4), float)
thetas[:, 0] = 1
thetas[:, 3] = 0
for f in range(nrFuncUnits):
thetas[mapBiomkToFuncUnits == f, 2] = np.linspace(0.2, 0.9, num=nrBiomkInFuncUnits, endpoint=True)
# set first funtional unit to have traj with lower slopes
thetas[mapBiomkToFuncUnits == 0, 1] = 5
thetas[mapBiomkToFuncUnits == 1, 1] = 10
# thetas[mapBiomkToFuncUnits == 2, 1] = 7
if args.expName == 'synth1':
sigmaB = 0.05 * np.ones(nrBiomk)
elif args.expName == 'synth2':
sigmaB = 0.01 * np.ones(nrBiomk)
else:
raise ValueError('expName should be synth1 or synth2')
# scale every biomarker with mean and std.
scalingBiomk2B = np.zeros((2, nrBiomk))
# scalingBiomk2B[:, 0] = [200, 100] # mean +/- std
# scalingBiomk2B[:, 0] = [200, 100] # mean +/- std
#
# scalingBiomk2B[:, 1] = [-20, 3] # mean +/- std
# scalingBiomk2B[:, 1] = [-20, 3] # mean +/- std
#
# scalingBiomk2B[:, 2:4] = scalingBiomk2B[:, 0:2]
# scalingBiomk2B[:, 4:6] = scalingBiomk2B[:, 0:2]
scalingBiomk2B[1,:] = 1
##### disease 1 - disease specific parameters ###########
# params of the dysfunctional trajectories
dysfuncParamsDisOne = np.zeros((nrFuncUnits, 4), float)
dysfuncParamsDisOne[:, 0] = 1 # ak
dysfuncParamsDisOne[:, 1] = [0.3, 0.2] # bk
dysfuncParamsDisOne[:, 2] = [-4, 6] # ck
dysfuncParamsDisOne[:, 3] = 0 # dk
synthModelDisOne = ParHierModel.ParHierModel(dysfuncParamsDisOne, thetas,
mapBiomkToFuncUnits, sigmoidFunc, sigmaB)
paramsDisOne = copy.deepcopy(params)
paramsDisOne = genSynthData.generateDataJMD(nrSubjLong, nrBiomk, nrTimepts,
shiftsLowerLim, shiftsUpperLim, synthModelDisOne, outFolder, fileName,
regenerateData, paramsDisOne, scalingBiomk2B, ctlDiagNr=CTL, patDiagNr=AD)
# paramsDisOne['plotTrajParams']['trueParams'] = paramsDisOne['trueParams']
replaceFigMode = True
if regenerateData:
synthPlotter = Plotter.PlotterJDM(paramsDisOne['plotTrajParams'])
fig = synthPlotter.plotTrajDataMarcoFormat(paramsDisOne['X'], paramsDisOne['Y'],
paramsDisOne['diag'], synthModelDisOne, paramsDisOne['trueParamsDis'], replaceFigMode=replaceFigMode)
fig.savefig('%s/%sDis1GenData.png' % (outFolder, expName))
##### disease 2 - disease specific parameters ###########
# params of the dysfunctional trajectories
dysfuncParamsDisTwo = copy.deepcopy(dysfuncParamsDisOne)
dysfuncParamsDisTwo[:, 1] = [0.3, 0.2] # bk
dysfuncParamsDisTwo[:, 2] = [6, -4]
synthModelDisTwo = ParHierModel.ParHierModel(dysfuncParamsDisTwo, thetas, mapBiomkToFuncUnits, sigmoidFunc, sigmaB)
paramsDisTwo = copy.deepcopy(paramsDisOne)
nrSubjLongDisTwo = 50
nrTimeptsDisTwo = 4
paramsDisTwo = genSynthData.generateDataJMD(nrSubjLongDisTwo, nrBiomk,
nrTimeptsDisTwo, shiftsLowerLim, shiftsUpperLim, synthModelDisTwo,
outFolder, fileName, regenerateData, paramsDisTwo, scalingBiomk2B,
ctlDiagNr=CTL2, patDiagNr=PCA)
# for disease two, only keep the second biomarker in each functional unit
indBiomkInDiseaseTwo = np.array(range(nrFuncUnits,(2*nrFuncUnits)))
print('indBiomkInDiseaseTwo', indBiomkInDiseaseTwo)
paramsDisTwo['Xtrue'] = paramsDisTwo['X']
paramsDisTwo['Ytrue'] = paramsDisTwo['Y']
# for disease two, change the format of the X and Y arrays, add the missing biomarkers with empty lists
XemptyListsAllBiomk = [0 for _ in range(nrBiomk)]
YemptyListsAllBiomk = [0 for _ in range(nrBiomk)]
visitIndicesDisTwoMissing = [0 for _ in range(nrBiomk)]
for b in range(nrBiomk):
XemptyListsAllBiomk[b] = [0 for _ in range(nrSubjLongDisTwo)]
YemptyListsAllBiomk[b] = [0 for _ in range(nrSubjLongDisTwo)]
visitIndicesDisTwoMissing[b] = [0 for _ in range(nrSubjLongDisTwo)]
for s in range(nrSubjLongDisTwo):
if b in indBiomkInDiseaseTwo:
XemptyListsAllBiomk[b][s] = paramsDisTwo['Xtrue'][b][s]
YemptyListsAllBiomk[b][s] = paramsDisTwo['Ytrue'][b][s]
visitIndicesDisTwoMissing[b][s] = paramsDisTwo['visitIndices'][b][s]
else:
XemptyListsAllBiomk[b][s] = np.array([])
YemptyListsAllBiomk[b][s] = np.array([])
visitIndicesDisTwoMissing[b][s] = np.array([])
paramsDisTwo['XemptyListsAllBiomk'] = XemptyListsAllBiomk
paramsDisTwo['YemptyListsAllBiomk'] = YemptyListsAllBiomk
paramsDisTwo['visitIndicesMissing'] = visitIndicesDisTwoMissing
if regenerateData:
synthPlotter = Plotter.PlotterJDM(paramsDisTwo['plotTrajParams'])
fig = synthPlotter.plotTrajDataMarcoFormat(paramsDisTwo['Xtrue'],
paramsDisTwo['Ytrue'], paramsDisTwo['diag'],
synthModelDisTwo, paramsDisTwo['trueParamsDis'], replaceFigMode=replaceFigMode)
fig.savefig('%s/%sDis2GenDataFull.png' % (outFolder, expName))
synthPlotter = Plotter.PlotterJDM(paramsDisTwo['plotTrajParams'])
fig = synthPlotter.plotTrajDataMarcoFormat(paramsDisTwo['XemptyListsAllBiomk'],
paramsDisTwo['YemptyListsAllBiomk'], paramsDisTwo['diag'],
synthModelDisTwo, paramsDisTwo['trueParamsDis'], replaceFigMode=replaceFigMode)
fig.savefig('%s/%sDis2GenDataMissing.png' % (outFolder, expName))
############### now merge the two datasets ############
# add the biomarkers from the second dataset, same format as dataset 1
# but with missing entries
params = paramsDisOne
for b in range(nrBiomk):
params['X'][b] += paramsDisTwo['XemptyListsAllBiomk'][b]
params['Y'][b] += paramsDisTwo['YemptyListsAllBiomk'][b]
params['visitIndices'][b] += paramsDisTwo['visitIndicesMissing'][b]
# print('visitIndicesDisTwoMissing', visitIndicesDisTwoMissing)
# print(adssa)
params['RID'] = np.concatenate((params['RID'],
nrSubjLong + paramsDisTwo['RID']),axis=0) # RIDs must be different
# this is the full vector of diagnoses for all diseases
params['diag'] = np.concatenate((paramsDisOne['diag'], paramsDisTwo['diag']),axis=0)
params['plotTrajParams']['diag'] = params['diag']
params['trueParamsDis'] = [params['trueParamsDis'], paramsDisTwo['trueParamsDis']]
for f in range(nrFuncUnits):
params['trueParamsFuncUnits'][f]['subShiftsS'] = np.concatenate(
(params['trueParamsFuncUnits'][f]['subShiftsS'],
paramsDisTwo['trueParamsFuncUnits'][f]['subShiftsS']),axis=0)
# map which diagnoses belong to which disease
# first disease has CTL+AD, second disease has CTL2+PCA
params['diagsSetInDis'] = [np.array([CTL, AD]), np.array([CTL2, PCA])]
params['disLabels'] = ['Dis0', 'Dis1']
params['otherBiomkPerDisease'] = [[], []]
params['binMaskSubjForEachDisD'] = [np.in1d(params['diag'],
params['diagsSetInDis'][disNr]) for disNr in range(nrDis)]
assert params['diag'].shape[0] == len(params['X'][0])
assert np.sum(params['binMaskSubjForEachDisD'][0]) == len(params['trueParamsDis'][0]['subShiftsS'])
assert params['diag'].shape[0] == len(params['trueParamsFuncUnits'][0]['subShiftsS'])
# if np.abs(args.penalty - int(args.penalty) < 0.00001):
# expName = '%sPen%d' % (expName, args.penalty)
# else:
# expName = '%sPen%.1f' % (expName, args.penalty)
params['runPartStd'] = args.runPartStd
params['runPartMain'] = ['R', 'I', 'I'] # [mainPart, plot, stage]
params['masterProcess'] = args.runIndex == 0
expNameDisOne = '%s' % expName
modelNames, res = evaluationFramework.runModels(params, expName,
args.modelToRun, runAllExpSynth)
def runAllExpSynth(params, expName, dpmBuilder, compareTrueParamsFunc = None):
""" runs all experiments"""
res = {}
params['patientID'] = AD
params['excludeID'] = -1
params['excludeXvalidID'] = -1
params['excludeStaging'] = [-1]
params['outFolder'] = 'resfiles/synth/%s' % expName
dpmObjStd, res['std'] = evaluationFramework.runStdDPM(params,
expName, dpmBuilder, params['runPartMain'])
return res
def transferProgression(dpmObjStdDisOne, paramsDisTwo,
expNameDisTwo, dpmBuilderDisTwo, runPart):
dataIndices = np.logical_not(np.in1d(paramsDisTwo['diag'], paramsDisTwo['excludeXvalidID']))
print(np.sum(np.logical_not(dataIndices)))
print('excludeID', params['excludeXvalidID'])
print(params['diag'].shape)
dpmObj = dpmBuilder.generate(dataIndices, expNameDisTwo, paramsDisTwo)
res = None
if runPart[0] == 'R':
res = dpmObj.runStd(params['runPartStd'])
if __name__ == '__main__':
main()