def appendTimeStatistics(raw_output, _CLASSIFIER, clf, timeFit, x_test, y_test):
_timeFitLin = clf.time_fit_lin # SS_MSMS
_timeFitGauss = clf.time_fit_gauss # MM:SS
_timeFitOver = clf.time_overhead # MSMS
_timeTotal = _timeFitLin + _timeFitGauss + _timeFitOver
_score = clf.score(x_test, y_test)
_error = round((1 - _score) * 100, 2)
_timePredict = clf.time_predict ## SS_MSMS
_percentGaussTotal = round((_timeFitGauss / _timeTotal) * 100, 2)
_percentLinTotal = round((_timeFitLin / _timeTotal * 100), 2)
_percentOverTotal = round((_timeFitOver / _timeTotal * 100), 2)
# Bring Time data in readable format
_timeFit = Conversions.secondsToHourMin(_timeTotal)
_timeFitLin = Conversions.secondsToSecMilsec(_timeFitLin)
_timeFitGauss = Conversions.secondsToMinSec(_timeFitGauss)
_timeFitOver = Conversions.secondsToMilsec(_timeFitOver)
_timePredict = Conversions.secondsToSecMilsec(_timePredict)
raw_output[7].append(str(_timeTotal).replace(".", ",") + "s;")
# raw_output[8].append(_timeFitGauss + "\t(" + str(_percentGaussTotal) + "%)" + ";")
raw_output[8].append(_timeFitGauss + ";")
# raw_output[9].append(_timeFitLin + "\t(" + str(_percentLinTotal) + "%)" + ";")
raw_output[9].append(_timeFitLin + ";")
# raw_output[10].append(_timeFitOver + "\t(" + str(_percentOverTotal) + "%)" + ";")
raw_output[10].append(_timeFitOver + ";")
raw_output[11].append(_timePredict + ";")
raw_output[12].append(str(_error) + "%;")
return raw_output
