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)