def train_svm(self, dataset, kernel='linear', C=1.0, gamma=0.0):
"""
Train classifiers for class
"""
print '%d trains class %s'%(mpi.comm_rank, self.cls)
t = time.time()
pos = dataset.get_pos_samples_for_class(self.cls)
neg = dataset.get_neg_samples_for_class(self.cls)
pos_gist = self.gist_table[pos, :]
neg_gist = self.gist_table[neg, :]
x = np.concatenate((pos_gist, neg_gist))
y = [1]*pos.shape[0] + [-1]*neg.shape[0]
print '%d compute svm for %s'%(mpi.comm_rank, self.cls)
svm_filename = config.get_gist_svm_filename(self.cls, dataset)
print svm_filename
self.svm = train_svm(x, y, kernel, C, gamma)
print '\ttook', time.time()-t,'sec'
print 'the score on train-data is %f'%self.svm.score(x,y)
table_t = self.svm.predict_proba(x)
y2 = np.array(y)
y2 = (y2+1)/2 # switch to 0/1
ap,_,_ = Evaluation.compute_cls_pr(table_t[:,1], y2)
print 'ap on train: %f'%ap
cPickle.dump(self.svm, open(svm_filename, 'w'))
return ap
def train_svm(self, dataset, kernel='linear', C=1.0, gamma=0.0):
"""
Train classifiers for class
"""
print '%d trains class %s' % (mpi.comm_rank, self.cls)
t = time.time()
pos = dataset.get_pos_samples_for_class(self.cls)
neg = dataset.get_neg_samples_for_class(self.cls)
pos_gist = self.gist_table[pos, :]
neg_gist = self.gist_table[neg, :]
x = np.concatenate((pos_gist, neg_gist))
y = [1] * pos.shape[0] + [-1] * neg.shape[0]
print '%d compute svm for %s' % (mpi.comm_rank, self.cls)
svm_filename = config.get_gist_svm_filename(self.cls, dataset)
print svm_filename
self.svm = train_svm(x, y, kernel, C, gamma)
print '\ttook', time.time() - t, 'sec'
print 'the score on train-data is %f' % self.svm.score(x, y)
table_t = self.svm.predict_proba(x)
y2 = np.array(y)
y2 = (y2 + 1) / 2 # switch to 0/1
ap, _, _ = Evaluation.compute_cls_pr(table_t[:, 1], y2)
print 'ap on train: %f' % ap
cPickle.dump(self.svm, open(svm_filename, 'w'))
return ap
def load_svm(self):
filename = config.get_gist_svm_filename(self.cls,self.train_d)
if os.path.exists(filename):
print 'load svm %s'%filename
try:
svm = cPickle.loads(open(filename).read())
self.svm = svm
except:
print 'could not load svm. Probably we are in crossvalidation and there is much going on'
svm = None
else:
print 'gist svm for',self.cls,'does not exist at %s'%filename
svm = None
return svm
def load_svm(self):
filename = config.get_gist_svm_filename(self.cls, self.train_d)
if os.path.exists(filename):
print 'load svm %s' % filename
try:
svm = cPickle.loads(open(filename).read())
self.svm = svm
except:
print 'could not load svm. Probably we are in crossvalidation and there is much going on'
svm = None
else:
print 'gist svm for', self.cls, 'does not exist at %s' % filename
svm = None
return svm
