ref: e968939e0135dcc1d09d611d875dc07b0605e862
parent: 677b267365aa78b0ce8fa63f0939dbc41ef97ca7
author: Paul Brossier <piem@altern.org>
date: Fri Feb 17 11:07:36 EST 2006
update to new bench onset update to new bench onset
--- a/python/test/bench/onset/bench-onset
+++ b/python/test/bench/onset/bench-onset
@@ -3,48 +3,127 @@
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):
-
- 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 ]
+ 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 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:
@@ -51,12 +130,23 @@
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,
+
+ def pretty_titles(self,sep='|'):
+ for i in self.printnames:
+ print self.formats[i] % i, sep,
+
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]
@@ -66,13 +156,13 @@
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):
@@ -79,7 +169,7 @@
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 @@
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 = thresholds[0]
+ cur = 0
- self.params.threshold = curt
- self.dir_exec()
- self.dir_eval()
- self.pretty_print(self.values)
- curexp = self.expc
-
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 @@
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)