def plotMITdbTestResult():
RFfolder = os.path.join(\
projhomepath,\
'TestResult',\
'pc',\
'r3')
TargetRecordList = [
'sel38',
'sel42',
]
# ==========================
# plot prediction result
# ==========================
reslist = glob.glob(os.path.join(\
RFfolder,'*'))
for fi, fname in enumerate(reslist):
# block *.out
if fname[-4:] == '.out' or '.json' in fname:
continue
print 'file name:', fname
currecname = os.path.split(fname)[-1]
if currecname not in TargetRecordList:
pass
pdb.set_trace()
with open(fname, 'r') as fin:
(recID, reslist) = pickle.load(fin)
# load signal
mitdb = MITdbLoader()
rawsig = mitdb.load(recID)
# plot res
resploter = ECGResultPloter(rawsig, reslist)
resploter.plot()
def plotMITdbTestResult():
RFfolder = os.path.join(\
projhomepath,\
'TestResult',\
'pc',\
'r3')
TargetRecordList = [
'sel38',
'sel42',
]
# ==========================
# plot prediction result
# ==========================
reslist = glob.glob(os.path.join(\
RFfolder,'*'))
for fi, fname in enumerate(reslist):
# block *.out
# filter file name
if fname[-4:] == '.out' or '.json' in fname:
continue
currecname = os.path.split(fname)[-1]
if currecname not in TargetRecordList:
pass
if not currecname.startswith('result'):
continue
print 'processing file', fname, '...'
with open(fname, 'r') as fin:
(recID, reslist) = pickle.load(fin)
print 'pickle file loaded.'
# load signal from MITdb
print 'loading signal data from MITdb...'
mitdb = MITdbLoader()
rawsig = mitdb.load(recID)
print 'signal loaded.'
# filter result list
resfilter = ResultFilter(reslist)
reslist = resfilter.groupresult()
# plot res
resploter = ECGResultPloter(rawsig, reslist)
dispRange = (20000, 21000)
savefolderpath = os.path.join(curfolderpath, 'tmp', 'MITdbTestResult')
# debug
#pdb.set_trace()
#resploter.plot()
#resploter.plotAndsave(os.path.join(savefolderpath,recID),plotTitle = 'ID:{},Range:{}'.format(recID,dispRange),dispRange = dispRange)
resploter.plot(plotTitle=recID)
pdb.set_trace()
def save_FNFP(self,FN,FP,OutputFolder = r'MIT_FNFP'):
# save pickle
recname = None
if len(FN['recname']) >0:
recname = FN['recname'][0]
elif len(FP['recname']) > 0:
recname = FP['recname'][0]
if recname is None:
return None
with open(os.path.join(OutputFolder,recname+'.pkl'),'w') as fout:
pickle.dump(dict(FN = FN,FP = FP),fout)
# save matlab .mat file
FNlist = []
FPlist = []
FNlist.extend(FN['pos'])
FPlist.extend(FP['pos'])
# save raw sig too
mitdb = MITdbLoader()
rawsig = mitdb.load(recname)
scipy.io.savemat(os.path.join(OutputFolder,recname+'.mat'),dict(FNlist = FNlist,FPlist = FPlist,recname = recname,MIT_rawsig = rawsig))
def plotMITdbTestResult():
RFfolder = os.path.join(\
projhomepath,\
'TestResult',\
'pc',\
'r3')
TargetRecordList = ['sel38','sel42',]
# ==========================
# plot prediction result
# ==========================
reslist = glob.glob(os.path.join(\
RFfolder,'*'))
for fi,fname in enumerate(reslist):
# block *.out
# filter file name
if fname[-4:] == '.out' or '.json' in fname:
continue
currecname = os.path.split(fname)[-1]
if currecname not in TargetRecordList:
pass
if not currecname.startswith('result'):
continue
print 'processing file name:',fname
with open(fname,'r') as fin:
(recID,reslist) = pickle.load(fin)
# load signal
mitdb = MITdbLoader()
rawsig = mitdb.load(recID)
# plot res
# group result labels
resgrper = ECGGrouper(reslist)
reslist = resgrper.group_local_result(reslist)
resploter = ECGResultPloter(rawsig,reslist)
dispRange = (20000,21000)
savefolderpath = ur'E:\ECGResults\MITdbTestResult\pic'
# debug
#resploter.plot()
resploter.plot(plotTitle = 'ID:{},Range:{}'.format(recID,dispRange),dispRange = dispRange)
def analyse(self,csvfilepath = r'tmp\MITdb_FN_FP_statistics.csv'):
# analysis false negtive status
# analysis false positive status
#RFfolder = os.path.join(\
#projhomepath,\
#'TestResult',\
#'pc',\
#'r3')
RFfolder = self.RFfolder
# write FN statistics to csv file
csv = CSVwriter(csvfilepath)
title_line = ['record name','False Negtive Number','Expert Label Number','Sensitivity','False Positive Number','True Positive Number','P+']
csv.output([title_line,])
# ==========================
# analysis FN
# ==========================
match_dist_thres = 50
nFN = 0
nExpLabel = 0
reslist = glob.glob(os.path.join(RFfolder,'*'))
N_reslist = len(reslist)
for fi,fname in enumerate(reslist):
# block *.out
# filter file name
if fname[-4:] == '.out' or '.json' in fname:
continue
currecname = os.path.split(fname)[-1]
#if currecname not in ['result_231','result_108']:
#continue
if not currecname.startswith('result'):
continue
print 'processing file:',fname
print '{} files left.'.format(N_reslist-fi-1)
with open(fname,'r') as fin:
(recID,reslist) = pickle.load(fin)
# load signal
cFN = 0
mitdb = MITdbLoader()
rawsig = mitdb.load(recID)
cExpLabel = len(mitdb.markpos)
# analyse FN
curFN,curFP,N_TP = self.getFN_FP(reslist,mitdb.markpos,recID)
# debug plot
#self.debug_FN_FP(rawsig,reslist,curFN,curFP)
self.save_FNFP(curFN,curFP)
HumanMarkFolder = r'F:\LabGit\ECG_RSWT\PaperResults\Matlab_MIT_Marker\ver1_0\MIT_keepout_Region';
curFN,curFP = self.filterFNFP_with_humanMarks(curFN,curFP,HumanMarkFolder,Not_In_Range_Mark = False);
HumanMarkFolder = r'F:\LabGit\ECG_RSWT\PaperResults\Matlab_MIT_Marker\ver1_0\MIT_keepin_Region';
curFN,curFP = self.filterFNFP_with_humanMarks(curFN,curFP,HumanMarkFolder,Not_In_Range_Mark = True);
cFN = len(curFN['pos'])
cFP = len(curFP['pos'])
# to csv
csv.output([[currecname,cFN,cExpLabel,1-float(cFN)/cExpLabel,cFP,N_TP,1-(float(cFP)/(cFP+N_TP))],])
# total stat
nFN += cFN
nExpLabel += cExpLabel
print
print 'record[{}] FN rate:{},PD rate:{}'.format(fname,float(cFN)/cExpLabel,1-float(cFN)/cExpLabel)
# =================================
# total FN
# =================================
print 'Total FN rate:{},PD rate:{}'.format(float(nFN)/nExpLabel,1.0-float(nFN)/nExpLabel)
def FN_stat(csvfilepath = r'MITdb_FalseNegtive_stat.csv'):
# analysis false negtive status
RFfolder = os.path.join(\
projhomepath,\
'TestResult',\
'pc',\
'r3')
# write FN statistics to csv file
csv = CSVwriter(csvfilepath)
title_line = ['record name','False Negtive number','Expert Label Number']
csv.output([title_line,])
TargetRecordList = ['sel38','sel42',]
# ==========================
# analysis FN
# ==========================
match_dist_thres = 50
nFN = 0
nExpLabel = 0
reslist = glob.glob(os.path.join(RFfolder,'*'))
N_reslist = len(reslist)
for fi,fname in enumerate(reslist):
# block *.out
# filter file name
if fname[-4:] == '.out' or '.json' in fname:
continue
currecname = os.path.split(fname)[-1]
if currecname not in TargetRecordList:
pass
if not currecname.startswith('result'):
continue
print 'processing file:',fname
print '{} files left.'.format(N_reslist-fi-1)
with open(fname,'r') as fin:
(recID,reslist) = pickle.load(fin)
# load signal
cFN = 0
cExpLabel = 0
mitdb = MITdbLoader()
rawsig = mitdb.load(recID)
# plot res
# group result labels
resgrper = ECGGrouper(reslist)
reslist = resgrper.group_local_result(reslist)
res_poslist,res_labellist = zip(*reslist)
#--------------------------------------
# for each expert label find its closest match,if distance of this match exceeds match_dist_thres,then it is a classification dismatch.
N_res = len(res_poslist)
cExpLabel = len(mitdb.markpos)
for expmpos in mitdb.markpos:
# find the closest match in reslist
close_ind = bisect.bisect_left(res_poslist,expmpos)
matchdist = abs(res_poslist[close_ind]-expmpos)
if close_ind+1<N_res:
matchdist = min(matchdist,abs(res_poslist[close_ind+1]-expmpos))
if matchdist>match_dist_thres:
cFN+=1
# to csv
csv.output([[currecname,cFN,cExpLabel],])
# total stat
nFN += cFN
nExpLabel += cExpLabel
print
print 'record[{}] FN rate:{},PD rate:{}'.format(fname,float(cFN)/cExpLabel,1-float(cFN)/cExpLabel)
# =================================
# total FN
# =================================
print 'Total FN rate:{},PD rate:{}'.format(float(nFN)/nExpLabel,1.0-float(nFN)/nExpLabel)