shithub: aubio

ref: 83c67348be306b34e1783ba44972675c9e33df30
dir: /python/test/bench/onset/bench-onset/

#! /usr/bin/python

from aubio.tasks import *

from benchonset import mmean, stdev, benchonset

class mybenchonset(benchonset):

	def run_bench(self,modes=['dual'],thresholds=[0.5]):
		from os.path import dirname,basename
		self.thresholds = thresholds
		self.pretty_titles()
		d,e,f = [],[],[]
		for mode in modes:
			self.vlist = []
			self.params.onsetmode = mode
			for threshold in self.thresholds:
				self.params.threshold = threshold
				self.dir_exec()
				self.dir_eval()
				self.pretty_print()
				#print self.v
				self.vlist.append(self.v)
			self.plotroc(d)
			self.plotfmeas(e)
			self.plotpr(f)
			#print vlist
		#self.plotplotroc(d)
		#self.plotplotfmeas(e)
		#self.plotplotpr(f)
		outplot = basename(self.datadir)
		for ext in ("png","svg","ps"):
			self.plotplotroc(d,outplot=outplot,extension=ext)
			self.plotplotfmeas(e,outplot=outplot,extension=ext)
			self.plotplotpr(f,outplot=outplot,extension=ext)


	def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]):
		""" simple dichotomia like algorithm to optimise threshold """
		self.modes = modes
		self.pretty_titles()
		for mode in self.modes:
			steps = 11 
			lesst = thresholds[0] 
			topt = thresholds[1]
			self.params.onsetmode = mode

			self.params.threshold = topt 
			self.dir_exec()
			self.dir_eval()
			self.pretty_print()
			topF = self.F 

			self.params.threshold = lesst 
			self.dir_exec()
			self.dir_eval()
			self.pretty_print()
			lessF = self.F 

			for i in range(steps):
				self.params.localmin = True
				self.params.delay = 1. 
				self.dir_exec()
				self.dir_eval()
				self.params.threshold = ( lesst + topt ) * .5 
				self.dir_exec()
				self.dir_eval()
				self.pretty_print()
				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.

	def auto_learn2(self,modes=['dual'],thresholds=[0.00001,1.0]):
		""" simple dichotomia like algorithm to optimise threshold """
		self.modes = modes
		self.pretty_titles([])
		for mode in self.modes:
			steps = 10 
			step = 0.4
			self.params.onsetmode = mode
			self.params.threshold = thresholds[0] 
			cur = 0

			for i in range(steps):
				self.dir_exec()
				self.dir_eval()
				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', 'hfc', 'specdiff', 'kl', 'mkl', 'dual']
	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]
	#modes = [ 'hfc' ]
	#thresholds = [0.1, 1.5]

	#datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
	respath = '/var/tmp/DB-testings'

	benchonset = mybenchonset(datapath,respath,checkres=True,checkanno=True)
	benchonset.params = taskparams()
	benchonset.task = taskonset
	benchonset.valuesdict = {}

	try:
		#benchonset.auto_learn2(modes=modes)
		benchonset.run_bench(modes=modes,thresholds=thresholds)
	except KeyboardInterrupt:
		sys.exit(1)