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 RFtest(self, testrecname):
ecgrf = ECGRF()
sel1213 = conf['sel1213']
ecgrf.training(sel1213)
Results = ecgrf.testing([
testrecname,
])
# Evaluate result
filtered_Res = ECGRF.resfilter(Results)
stats = ECGstats(filtered_Res[0:1])
Err, FN = stats.eval(debug=False)
# write to log file
EvalLogfilename = os.path.join(projhomepath, 'res.log')
stats.dispstat0(\
pFN = FN,\
pErr = Err)
# plot prediction result
stats.plotevalresofrec(Results[0][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)