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)
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)
