def LOOT_Eval(RFfolder):
reslist = glob.glob(os.path.join(\
RFfolder,'*.out'))
FN = {'pos': [], 'label': [], 'recname': []}
Err = {'err': [], 'pos': [], 'label': [], 'recname': []}
for fi, fname in enumerate(reslist):
with open(fname, 'r') as fin:
Results = pickle.load(fin)
fResults = ECGRF.ECGrf.resfilter(Results)
# show filtered results & raw results
#for recname , recRes in Results:
# Evaluate prediction result statistics
#
ECGstats = ECGstatistics(fResults[0:1])
pErr, pFN = ECGstats.eval(debug=False)
for kk in Err:
Err[kk].extend(pErr[kk])
for kk in FN:
FN[kk].extend(pFN[kk])
# write to log file
EvalLogfilename = os.path.join(curfolderpath, 'res.log')
ECGstatistics.dispstat0(\
pFN = FN,\
pErr = Err,\
LogFileName = EvalLogfilename,\
LogText = 'Statistics of Results in [{}]'.\
format(RFfolder)\
)
ECGstats.stat_record_analysis(pErr=Err,
pFN=FN,
LogFileName=EvalLogfilename)
def debug_show_eval_result(\
picklefilename,\
target_recname = None,\
singleRecordFile = False\
):
with open(picklefilename, 'r') as fin:
Results = pickle.load(fin)
# convert to a list
if singleRecordFile == True:
Results = [
Results,
]
for recind in xrange(0, len(Results)):
# only plot target rec
if target_recname is not None:
print 'Current FileName: {}'.format(Results[recind][0])
if Results[recind][0] != target_recname:
return
fResults = ECGRF.ECGrf.resfilter(Results)
#
# show filtered results & raw results
# Evaluate prediction result statistics
#
ECGstats = ECGstatistics(fResults[recind:recind + 1])
ECGstats.eval(debug=False)
ECGstats.dispstat0()
ECGstats.plotevalresofrec(Results[recind][0], Results)
def plot(self):
Results = [
(self.recname, self.testresult),
]
fResults = ECGRF.ECGrf.resfilter(Results)
#
# show filtered results & raw results
# Evaluate prediction result statistics
#
recind = 0
ECGstats = ECGstatistics(fResults[recind:recind + 1])
ECGstats.eval(debug=False)
ECGstats.dispstat0()
ECGstats.plotevalresofrec(Results[recind][0], Results)
def debug_show_eval_result(\
picklefilename,\
target_recname = None\
):
with open(picklefilename,'r') as fin:
Results = pickle.load(fin)
# only plot target rec
if target_recname is not None:
print Results[0][0]
if Results[0][0]!= target_recname:
return
fResults = ECGRF.ECGrf.resfilter(Results)
#
# show filtered results & raw results
# Evaluate prediction result statistics
#
ECGstats = ECGstatistics(fResults[0:1])
ECGstats.eval(debug = False)
ECGstats.dispstat0()
ECGstats.plotevalresofrec(Results[0][0],Results)
def EvalQTdbResults(resultfilelist, OutputFolder):
if resultfilelist == None or len(resultfilelist) == 0:
print "Empty result file list!"
return None
FN = {'pos': [], 'label': [], 'recname': []}
FP = {'pos': [], 'label': [], 'recname': []}
Err = {'err': [], 'pos': [], 'label': [], 'recname': []}
#========================================
# select best round to compare with refs
#========================================
bRselector = BestRoundSelector()
#InvalidRecordList = conf['InvalidRecords']
# for each record test result
for fi, fname in enumerate(resultfilelist):
print 'json load :', fname
with open(fname, 'rU') as fin:
Results = json.load(fin)
Results = Results[0]
# skip invalid records
currecordname = Results[0]
#if currecordname in InvalidRecordList:
#continue
# ==================================
# filter result of QT
# ==================================
reslist = Results[1]
resfilter = ResultFilter(reslist)
reslist = resfilter.group_local_result(cp_del_thres=1)
reslist = resfilter.syntax_filter(reslist)
fResults = (Results[0], reslist)
fResults = [
fResults,
]
# show filtered results & raw results
#for recname , recRes in Results:
# Evaluate prediction result statistics
#
ECGstats = ECGstatistics(fResults)
pErr, pFN = ECGstats.eval(debug=False)
# get False Positive
pFP = ECGstats.pFP
# one test Error stat
print '[picle filename]:{}'.format(fname)
print '[{}] files left.'.format(len(resultfilelist) - fi)
evallabellist, evalstats = ECGstatistics.dispstat0(pFN=pFN, pErr=pErr)
# select best Round
numofFN = len(pFN['pos'])
if numofFN == 0:
ExtraInfo = 'Best Round ResultFileName[{}]\nTestSet :{}\n#False Negtive:{}\n'.format(
fname, [x[0] for x in Results], numofFN)
bRselector.input(evallabellist, evalstats, ExtraInfo=ExtraInfo)
# ==============================================
for kk in Err:
Err[kk].extend(pErr[kk])
for kk in FN:
FN[kk].extend(pFN[kk])
for kk in FP:
FP[kk].extend(pFP[kk])
#====================================
# write to log file
#EvalLogfilename = os.path.join(curfolderpath,'res.log')
output_log_filename = os.path.join(OutputFolder, 'RecordResults.log')
EvalLogfilename = output_log_filename
# display error stat for each label & save results to logfile
ECGstatistics.dispstat0(
pFN=FN,
pErr=Err,
LogFileName=EvalLogfilename,
LogText='Statistics of Results in FilePath [{}]'.format(
os.path.split(resultfilelist[0])[0]),
OutputFolder=OutputFolder)
with open(os.path.join(curfolderpath, 'Err.txt'), 'w') as fout:
pickle.dump(Err, fout)
# find best round
bRselector.dispBestRound()
bRselector.dumpBestRound(EvalLogfilename)
ECGstats.stat_record_analysis(pErr=Err,
pFN=FN,
LogFileName=EvalLogfilename)
# write csv file
outputfilename = os.path.join(OutputFolder, 'FalsePositive.csv')
ECGstats.FP2CSV(FP, Err, outputfilename)
# False Negtive
outputfilename = os.path.join(OutputFolder, 'FalseNegtive.csv')
ECGstats.FN2CSV(FN, Err, outputfilename)
def TestN_Eval(RFfolder,
output_log_filename=os.path.join(curfolderpath, 'res.log')):
# test result file list
picklereslist = glob.glob(os.path.join(RFfolder, '*.out'))
# struct Init
FN = {'pos': [], 'label': [], 'recname': []}
Err = {'err': [], 'pos': [], 'label': [], 'recname': []}
#========================================
# select best round to compare with refs
#========================================
bRselector = BestRoundSelector()
for fi, fname in enumerate(picklereslist):
with open(fname, 'rU') as fin:
Results = pickle.load(fin)
# filter result
fResults = ECGRF.ECGrf.resfilter(Results)
# show filtered results & raw results
#for recname , recRes in Results:
# Evaluate prediction result statistics
#
ECGstats = ECGstatistics(fResults)
pErr, pFN = ECGstats.eval(debug=False)
# one test Error stat
print '[picle filename]:{}'.format(fname)
print '[{}] files left.'.format(len(picklereslist) - fi)
evallabellist,evalstats = ECGstatistics.dispstat0(\
pFN = pFN,\
pErr = pErr\
)
# select best Round
numofFN = len(pFN['pos'])
if numofFN == 0:
ExtraInfo = 'Best Round ResultFileName[{}]\nTestSet :{}\n#False Negtive:{}\n'.format(
fname, [x[0] for x in Results], numofFN)
bRselector.input(evallabellist, evalstats, ExtraInfo=ExtraInfo)
for kk in Err:
Err[kk].extend(pErr[kk])
for kk in FN:
FN[kk].extend(pFN[kk])
# write to log file
#EvalLogfilename = os.path.join(curfolderpath,'res.log')
EvalLogfilename = output_log_filename
# display error stat for each label & save results to logfile
ECGstatistics.dispstat0(\
pFN = FN,\
pErr = Err,\
LogFileName = EvalLogfilename,\
LogText = 'Statistics of Results in FilePath [{}]'.format(RFfolder)\
)
with open(os.path.join(projhomepath, 'tmp', 'Err.txt'), 'w') as fout:
pickle.dump(Err, fout)
# find best round
bRselector.dispBestRound()
bRselector.dumpBestRound(EvalLogfilename)
ECGstats.stat_record_analysis(pErr=Err,
pFN=FN,
LogFileName=EvalLogfilename)
def EvalQTdbResults(resultfilelist, OutputFolder):
if resultfilelist == None or len(resultfilelist) == 0:
print "Empty result file list!"
return None
FN = {'pos': [], 'label': [], 'recname': []}
FP = {'pos': [], 'label': [], 'recname': []}
Err = {'err': [], 'pos': [], 'label': [], 'recname': []}
#========================================
# select best round to compare with refs
#========================================
bRselector = BestRoundSelector()
#InvalidRecordList = conf['InvalidRecords']
# for each record test result
for fi, fname in enumerate(resultfilelist):
print 'pickle load :', fname
with open(fname, 'rU') as fin:
Results = pickle.load(fin)
# skip invalid records
currecordname = Results[0]
#if currecordname in InvalidRecordList:
#continue
# ==================================
# filter result of QT
# ==================================
reslist = Results[1]
resfilter = ResultFilter(reslist)
reslist = resfilter.group_local_result(cp_del_thres=1)
#reslist = resfilter.syntax_filter(reslist)
fResults = (Results[0], reslist)
fResults = [
fResults,
]
# show filtered results & raw results
#for recname , recRes in Results:
# Evaluate prediction result statistics
#
ECGstats = ECGstatistics(fResults)
pErr, pFN = ECGstats.eval(debug=False)
# get False Positive
pFP = ECGstats.pFP
# one test Error stat
print '[picle filename]:{}'.format(fname)
print '[{}] files left.'.format(len(resultfilelist) - fi)
evallabellist, evalstats = ECGstatistics.dispstat0(pFN=pFN, pErr=pErr)
# select best Round
numofFN = len(pFN['pos'])
if numofFN == 0:
ExtraInfo = 'Best Round ResultFileName[{}]\nTestSet :{}\n#False Negtive:{}\n'.format(
fname, [x[0] for x in Results], numofFN)
bRselector.input(evallabellist, evalstats, ExtraInfo=ExtraInfo)
# ==============================================
for kk in Err:
Err[kk].extend(pErr[kk])
for kk in FN:
FN[kk].extend(pFN[kk])
for kk in FP:
FP[kk].extend(pFP[kk])
return (FN, FP, Err)
Err = {'err': [], 'pos': [], 'label': [], 'recname': []}
#os.mkdir(OutputFolder)
for round_i in xrange(1, 101):
RFfolder = r'F:\LabGit\ECG_RSWT\TestResult\pc\A_15\Round{}'.format(
round_i)
# =========================
# 分析训练和测试的record数目
# =========================
# 计算误差
curFN, curFP, curErr = EvalQTdbResults(getresultfilelist(RFfolder),
OutputFolder)
for key in FN.iterkeys():
FN[key].extend(curFN[key])
for key in FP.iterkeys():
FP[key].extend(curFP[key])
for key in Err.iterkeys():
Err[key].extend(curErr[key])
# write to output
ecg_statistics = ECGstatistics([])
ecg_statistics.FP2CSV(FP, Err,
os.path.join(OutputFolder, 'sel30_100Rounds_FP.csv'))
ecg_statistics.FN2CSV(FN, Err,
os.path.join(OutputFolder, 'sel30_100Rounds_FN.csv'))
EvalLogfilename = os.path.join(OutputFolder, 'Round100_plain_output.log')
ECGstatistics.dispstat0(
pFN=FN,
pErr=Err,
LogFileName=EvalLogfilename,
LogText='Statistics of Results in FilePath [pc/R15]',
OutputFolder=OutputFolder)