def __init__ (self, tag, nmix = 4):
self.tag = tag
self.mcontext = DMM(nmix)
class TagContextModel (object):
def __init__ (self, tag, nmix = 4):
self.tag = tag
self.mcontext = DMM(nmix)
'''
train the context model
@input tmsn: SMNs of the songs (multiple SMNs per song) positively associated with the tag in the ground truth
@input kpreproc: to pre-process the data using the kurtosis analysis
'''
def train (self, tsmn, kpreproc = True):
print 'learning context model for tag: %s' % self.tag
print ' --- using %d mixtures' % self.mcontext.nmix
print ' --- using %d training SMNs' % tsmn.shape[0]
if kpreproc:
tsmn = self.__kpreproc (tsmn)
print ' --- kurtosis-based pre-processed data'
if self.mcontext.training (tsmn, False):
print ' --- model learned correctly'
return
'''
predict tag weight for a set of semantic multinomials
@input smn: SMNs of the input song (multiple SMNs per song by windowing the song)
@output: return geometric mean of the predictions
'''
def predict (self, smn):
return exp(self.loglikelihood(smn))
'''
prediction in log base
@output: prediciton in log-version
'''
def loglikelihood (self, smn):
p = self.mcontext.estimate (smn)
return log(p).mean()
# save the model
def save (self, filename):
try:
cPickle.dump(self, open(filename, 'wb'))
return True
except Exception as e:
print e
return False
''' private functions '''
# pre-process training data using kurtosis
# NOTE: can "remove max(3, ..)" when the vocabulary is larger (add it just for small vocabularies, to retain some peaks)
def __kpreproc (self, smn):
k = kurtosis (smn, axis=1, fisher=False)
meank = k.mean()
sind = argsort (smn, axis = 1)[:,::-1]
for i in xrange(smn.shape[0]):
if k[i] > meank:
npeak = max (3, ceil(0.05 * smn.shape[1]))
else:
npeak = max (3, ceil(0.1 * smn.shape[1]))
smn[i][sind[i][npeak:]] = min(smn[i])
smn[i] /= smn[i].sum()
return smn