ref: 27f2c08efb7176c7f9417c1be3d17a8d3fe3fd6f
dir: /python/bench-onset/
#! /usr/bin/python from aubio.bench.node import * from aubio.tasks import * class benchonset(bench): 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_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 def run_bench(self,modes=['dual'],thresholds=[0.5]): self.modes = modes self.thresholds = thresholds self.pretty_print(self.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) 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) for mode in self.modes: steps = 10 lesst = thresholds[0] topt = thresholds[1] self.params.onsetmode = mode self.params.threshold = topt self.dir_exec() self.dir_eval() self.pretty_print(self.values) topF = self.F self.params.threshold = lesst self.dir_exec() self.dir_eval() self.pretty_print(self.values) 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) if self.F == 100.0 or self.F == topF: print "assuming we converged, stopping" break #elif abs(self.F - topF) < 0.01 : # print "done converging" # break if topF < self.F: #lessF = topF #lesst = topt topF = self.F topt = self.params.threshold elif lessF < self.F: lessF = self.F lesst = self.params.threshold if topt == lesst: lesst /= 2. 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] #thresholds = [1.5] #datapath = "%s%s" % (DATADIR,'/onset/DB/*/') respath = '/var/tmp/DB-testings' benchonset = benchonset(datapath,respath,checkres=True,checkanno=True) benchonset.params = taskparams() benchonset.task = taskonset 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_learn(modes=modes) #benchonset.run_bench(modes=modes) except KeyboardInterrupt: sys.exit(1)