def load_svm(self, filename=None):
if not filename:
svm_file = config.get_classifier_filename(self, self.cls, self.train_dataset)
else:
svm_file = filename
if not os.path.exists(svm_file):
print ("Svm %s is not trained" % svm_file)
return None
else:
model = load_svm(svm_file)
return model
def load_svm(self, filename=None):
if not filename:
svm_file = config.get_classifier_filename(self, self.cls,
self.train_dataset)
else:
svm_file = filename
if not os.path.exists(svm_file):
print("Svm %s is not trained" % svm_file)
return None
else:
model = load_svm(svm_file)
return model
def train(self, pos, neg, kernel, C):
y = [1] * pos.shape[0] + [-1] * neg.shape[0]
x = np.concatenate((pos, neg))
model = train_svm(x, y, kernel, C)
self.svm = model
print "model.score(C=%d): %f" % (C, model.score(x, y))
table_t = svm_proba(x, model)
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 set: %f" % ap
filename = config.get_classifier_filename(self, self.cls, self.train_dataset)
self.svm = model
self.save_svm(model, filename)
return model
def train(self, pos, neg, kernel, C):
y = [1] * pos.shape[0] + [-1] * neg.shape[0]
x = np.concatenate((pos, neg))
model = train_svm(x, y, kernel, C)
self.svm = model
print 'model.score(C=%d): %f' % (C, model.score(x, y))
table_t = svm_proba(x, model)
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 set: %f' % ap
filename = config.get_classifier_filename(self, self.cls,
self.train_dataset)
self.svm = model
self.save_svm(model, filename)
return model
