From: Paul Brossier Date: Fri, 17 Feb 2006 16:07:36 +0000 (+0000) Subject: update to new bench onset X-Git-Tag: bzr2git~774 X-Git-Url: http://git.tremily.us/?a=commitdiff_plain;h=e968939e0135dcc1d09d611d875dc07b0605e862;p=aubio.git update to new bench onset update to new bench onset --- diff --git a/python/test/bench/onset/bench-onset b/python/test/bench/onset/bench-onset index d8ede4e8..9b3ee436 100755 --- a/python/test/bench/onset/bench-onset +++ b/python/test/bench/onset/bench-onset @@ -3,60 +3,150 @@ from aubio.bench.node import * from aubio.tasks import * + + + +def mmean(l): + return sum(l)/float(len(l)) + +def stdev(l): + smean = 0 + lmean = mmean(l) + for i in l: + smean += (i-lmean)**2 + smean *= 1. / len(l) + return smean**.5 + class benchonset(bench): + + valuenames = ['orig','missed','Tm','expc','bad','Td'] + valuelists = ['l','labs'] + printnames = [ 'mode', 'thres', 'dist', 'prec', 'recl', 'Ttrue', 'Tfp', 'Tfn', 'Tm', 'Td', + 'aTtrue', 'aTfp', 'aTfn', 'aTm', 'aTd', 'mean', 'smean', 'amean', 'samean'] + + formats = {'mode': "%12s" , + 'thres': "%5.4s", + 'dist': "%5.4s", + 'prec': "%5.4s", + 'recl': "%5.4s", + + 'Ttrue': "%5.4s", + 'Tfp': "%5.4s", + 'Tfn': "%5.4s", + 'Tm': "%5.4s", + 'Td': "%5.4s", + + 'aTtrue':"%5.4s", + 'aTfp': "%5.4s", + 'aTfn': "%5.4s", + 'aTm': "%5.4s", + 'aTd': "%5.4s", + + 'mean': "%5.40s", + 'smean': "%5.40s", + 'amean': "%5.40s", + 'samean': "%5.40s"} - def dir_eval(self): - self.P = 100*float(self.expc-self.missed-self.merged)/(self.expc-self.missed-self.merged + self.bad+self.doubled) - self.R = 100*float(self.expc-self.missed-self.merged)/(self.expc-self.missed-self.merged + self.missed+self.merged) - if self.R < 0: self.R = 0 - self.F = 2* self.P*self.R / (self.P+self.R) - - self.values = [self.params.onsetmode, - "%2.3f" % self.params.threshold, - self.orig, - self.expc, - self.missed, - self.merged, - self.bad, - self.doubled, - (self.orig-self.missed-self.merged), - "%2.3f" % (100*float(self.orig-self.missed-self.merged)/(self.orig)), - "%2.3f" % (100*float(self.bad+self.doubled)/(self.orig)), - "%2.3f" % (100*float(self.orig-self.missed)/(self.orig)), - "%2.3f" % (100*float(self.bad)/(self.orig)), - "%2.3f" % self.P, - "%2.3f" % self.R, - "%2.3f" % self.F ] + def file_gettruth(self,input): + from os.path import isfile + ftrulist = [] + # search for match as filetask.input,".txt" + ftru = '.'.join(input.split('.')[:-1]) + ftru = '.'.join((ftru,'txt')) + if isfile(ftru): + ftrulist.append(ftru) + else: + # search for matches for filetask.input in the list of results + for i in range(len(self.reslist)): + check = '.'.join(self.reslist[i].split('.')[:-1]) + check = '_'.join(check.split('_')[:-1]) + if check == '.'.join(input.split('.')[:-1]): + ftrulist.append(self.reslist[i]) + return ftrulist def file_exec(self,input,output): filetask = self.task(input,params=self.params) computed_data = filetask.compute_all() - results = filetask.eval(computed_data) - self.orig += filetask.orig - self.missed += filetask.missed - self.merged += filetask.merged - self.expc += filetask.expc - self.bad += filetask.bad - self.doubled += filetask.doubled + ftrulist = self.file_gettruth(filetask.input) + for i in ftrulist: + #print i + filetask.eval(computed_data,i,mode='rocloc',vmode='') + for i in self.valuenames: + self.v[i] += filetask.v[i] + for i in filetask.v['l']: + self.v['l'].append(i) + for i in filetask.v['labs']: + self.v['labs'].append(i) + + def dir_exec(self): + """ run file_exec on every input file """ + self.l , self.labs = [], [] + self.v = {} + for i in self.valuenames: + self.v[i] = 0. + for i in self.valuelists: + self.v[i] = [] + self.v['thres'] = self.params.threshold + act_on_files(self.file_exec,self.sndlist,self.reslist, \ + suffix='',filter=sndfile_filter) + def dir_eval(self): + totaltrue = self.v['expc']-self.v['bad']-self.v['Td'] + totalfp = self.v['bad']+self.v['Td'] + totalfn = self.v['missed']+self.v['Tm'] + self.P = 100*float(totaltrue)/max(totaltrue + totalfp,1) + self.R = 100*float(totaltrue)/max(totaltrue + totalfn,1) + if self.R < 0: self.R = 0 + self.F = 2.* self.P*self.R / max(float(self.P+self.R),1) + + N = float(len(self.reslist)) + + self.v['mode'] = self.params.onsetmode + self.v['thres'] = "%2.3f" % self.params.threshold + self.v['dist'] = "%2.3f" % self.F + self.v['prec'] = "%2.3f" % self.P + self.v['recl'] = "%2.3f" % self.R + self.v['Ttrue'] = totaltrue + self.v['Tfp'] = totalfp + self.v['Tfn'] = totalfn + self.v['aTtrue'] = totaltrue/N + self.v['aTfp'] = totalfp/N + self.v['aTfn'] = totalfn/N + self.v['aTm'] = self.v['Tm']/N + self.v['aTd'] = self.v['Td']/N + self.v['mean'] = mmean(self.v['l']) + self.v['smean'] = stdev(self.v['l']) + self.v['amean'] = mmean(self.v['labs']) + self.v['samean'] = stdev(self.v['labs']) def run_bench(self,modes=['dual'],thresholds=[0.5]): self.modes = modes self.thresholds = thresholds - self.pretty_print(self.titles) + self.pretty_titles() for mode in self.modes: self.params.onsetmode = mode for threshold in self.thresholds: self.params.threshold = threshold self.dir_exec() self.dir_eval() - self.pretty_print(self.values) + self.pretty_print() + #print self.v + + def pretty_print(self,sep='|'): + for i in self.printnames: + print self.formats[i] % self.v[i], sep, + print + + def pretty_titles(self,sep='|'): + for i in self.printnames: + print self.formats[i] % i, sep, + print def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]): """ simple dichotomia like algorithm to optimise threshold """ self.modes = modes - self.pretty_print(self.titles) + self.pretty_titles() for mode in self.modes: steps = 10 lesst = thresholds[0] @@ -66,20 +156,20 @@ class benchonset(bench): self.params.threshold = topt self.dir_exec() self.dir_eval() - self.pretty_print(self.values) + self.pretty_print() topF = self.F self.params.threshold = lesst self.dir_exec() self.dir_eval() - self.pretty_print(self.values) + self.pretty_print() lessF = self.F for i in range(steps): self.params.threshold = ( lesst + topt ) * .5 self.dir_exec() self.dir_eval() - self.pretty_print(self.values) + self.pretty_print() if self.F == 100.0 or self.F == topF: print "assuming we converged, stopping" break @@ -97,46 +187,51 @@ class benchonset(bench): if topt == lesst: lesst /= 2. - def auto_learn2(self,modes=['dual'],thresholds=[0.1,1.0]): + def auto_learn2(self,modes=['dual'],thresholds=[0.00001,1.0]): """ simple dichotomia like algorithm to optimise threshold """ self.modes = modes - self.pretty_print(self.titles) + self.pretty_titles([]) for mode in self.modes: steps = 10 - step = thresholds[1] - curt = thresholds[0] + step = 0.4 self.params.onsetmode = mode - - self.params.threshold = curt - self.dir_exec() - self.dir_eval() - self.pretty_print(self.values) - curexp = self.expc + self.params.threshold = thresholds[0] + cur = 0 for i in range(steps): - if curexp < self.orig: - #print "we found at most less onsets than annotated" - self.params.threshold -= step - step /= 2 - elif curexp > self.orig: - #print "we found more onsets than annotated" - self.params.threshold += step - step /= 2 self.dir_exec() self.dir_eval() - curexp = self.expc - self.pretty_print(self.values) - if self.orig == 100.0 or self.orig == self.expc: - print "assuming we converged, stopping" + self.pretty_print() + new = self.P + if self.R == 0.0: + #print "Found maximum, highering" + step /= 2. + self.params.threshold -= step + elif new == 100.0: + #print "Found maximum, highering" + step *= .99 + self.params.threshold += step + elif cur > new: + #print "lower" + step /= 2. + self.params.threshold -= step + elif cur < new: + #print "higher" + step *= .99 + self.params.threshold += step + else: + print "Assuming we converged" break + cur = new + if __name__ == "__main__": import sys if len(sys.argv) > 1: datapath = sys.argv[1] else: print "ERR: a path is required"; sys.exit(1) modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual'] - #modes = [ 'complex' ] - thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5] + #modes = [ 'phase' ] + thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2] #thresholds = [1.5] #datapath = "%s%s" % (DATADIR,'/onset/DB/*/') @@ -145,16 +240,11 @@ if __name__ == "__main__": benchonset = benchonset(datapath,respath,checkres=True,checkanno=True) benchonset.params = taskparams() benchonset.task = taskonset + benchonset.valuesdict = {} - benchonset.titles = [ 'mode', 'thres', 'orig', 'expc', 'missd', 'mergd', - 'bad', 'doubl', 'corrt', 'GD', 'FP', 'GD-merged', 'FP-pruned', - 'prec', 'recl', 'dist' ] - benchonset.formats = ["%12s" , "| %6s", "| %6s", "| %6s", "| %6s", "| %6s", - "| %6s", "| %6s", "| %6s", "| %8s", "| %8s", "| %8s", "| %8s", - "| %6s", "| %6s", "| %6s"] try: - benchonset.auto_learn2(modes=modes) - #benchonset.run_bench(modes=modes) + #benchonset.auto_learn2(modes=modes) + benchonset.run_bench(modes=modes,thresholds=thresholds) except KeyboardInterrupt: sys.exit(1)