def build_model(args):
feature_fn, margin_fn, num_features, actions = select_feature_fn(args)
print 'Building model into {}.'.format(args.modelfile)
if args.model == 'multi':
mm_model = MultiSlackMaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
elif args.model == 'bellman':
mm_model = BellmanMaxMarginModel(actions, args.C, args.D, args.F, 1, num_features, feature_fn, margin_fn) # changed
else:
mm_model = MaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
if not args.goal_constraints and args.model == 'bellman':
demofile = h5py.File(args.demofile, 'r')
ignore_keys = [k for k in demofile if demofile[k]['knot'][()]]
demofile.close()
else:
ignore_keys = None
mm_model.load_constraints_from_file(args.constraintfile, ignore_keys)
mm_model.save_model(args.modelfile)
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)
def build_constraints(args):
#test_features(args, "sc")
test_features(args, "rope_dist")
feature_fn, margin_fn, num_features, actions = select_feature_fn(args)
print 'Building constraints into {}.'.format(args.constraintfile)
if args.model == 'multi':
mm_model = MultiSlackMaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
elif args.model == 'bellman':
mm_model = BellmanMaxMarginModel(actions, args.C, args.D, args.F, .9, num_features, feature_fn, margin_fn)
else:
mm_model = MaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
if args.model == 'bellman':
add_bellman_constraints_from_demo(mm_model,
args.demofile,
args.start, args.end,
outfile=args.constraintfile,
verbose=True)
else:
add_constraints_from_demo(mm_model,
args.demofile,
args.start, args.end,
outfile=args.constraintfile,
verbose=True)
def bellman_test_features(args):
if args.model != 'bellman':
raise Exception, 'wrong model for this'
(feature_fn, margin_fn, num_features, actions) = select_feature_fn(args)
demofile = h5py.File(args.demofile, 'r')
# get a random set of trajectories
trajectories = []
traj = []
for uid in range(len(demofile)):
key = str(uid)
group = demofile[key]
state = [key,group['cloud_xyz'][:]] # these are already downsampled
action = group['action'][()] if not group['knot'][()] else 'done'
if group['pred'][()] == key:
if traj:
trajectories.append(traj)
traj = []
traj.append([state, action])
if traj:
trajectories.append(traj)
random.shuffle(trajectories)
constraint_trajs = trajectories[:args.num_constraints]
# put them into a Bellman model
mm_model = BellmanMaxMarginModel(action, 500, 1000, 10, .9, num_features, feature_fn, margin_fn)
for i, t in enumerate(constraint_trajs):
mm_model.add_trajectory(t, str(i), True)
weights, w0 = mm_model.optimize_model()
# evaluate value fn performance
num_evals = len(trajectories) if args.num_evals < 0 else args.num_constraints + args.num_evals
trajectories = trajectories[args.num_constraints:num_evals]
values = np.zeros((len(trajectories), 6))
num_decreases = 0
for (i, t) in enumerate(trajectories):
for j, (s, a) in enumerate(t):
values[i, j] = np.dot(mm_model.weights, mm_model.feature_fn(s, a))
for k in range(j):
if values[i, k] > values[i, k+1]:
num_decreases += 1
print "DECREASE", values[i, k], values[i, k+1]
sys.stdout.write('num decreases:\t{} computed values for trajectory {}\r'.format(num_decreases, i))
sys.stdout.flush()
sys.stdout.write('\n')
print "num decreases:\t", num_decreases
ipy.embed()