def optimize_model(args):
feature_fn, margin_fn, num_features, actions = select_feature_fn(args)
print 'Found model: {}'.format(args.modelfile)
if args.model == 'multi':
mm_model = MultiSlackMaxMarginModel.read(args.modelfile, actions, num_features, feature_fn, margin_fn)
elif args.model == 'bellman':
mm_model = BellmanMaxMarginModel.read(args.modelfile, actions, num_features, feature_fn, margin_fn)
mm_model.D = args.D
mm_model.F = args.F
else:
mm_model = MaxMarginModel.read(args.modelfile, actions, num_features, feature_fn, margin_fn)
if args.save_memory:
mm_model.model.setParam('threads', 1) # Use single thread instead of maximum
# barrier method (#2) is default for QP, but uses more memory and could lead to error
mm_model.model.setParam('method', 1) # Use dual simplex method to solve model
#mm_model.model.setParam('method', 0) # Use primal simplex method to solve model
mm_model.C = args.C
mm_model.optimize_model()
mm_model.save_weights_to_file(args.weightfile)
