def sample_exp(expid, exp, method, n_eps, n_timesteps_per_ep):
'''
Sample from the learned model.
Args:
expid: ID of the experiment; e.g. 0, 1, 2, ...
exp: name of the experiment; e.g. 'pour', 'scoop'.
method: see run_exp.
'''
func = helper.get_func_from_exp(exp)
xx, yy, c = helper.get_xx_yy(expid, method, exp=exp)
active_learner = helper.get_learner_from_method(method, xx, yy, func)
active_learner.retrain()
# Enable gui
func.do_gui = True
# while raw_input('Continue? [y/n]') == 'y':
all_ims = []
all_actions = []
for i in range(n_eps):
print("##### %s: %d ######" % (method, i))
x = active_learner.sample(c)
import ipdb
ipdb.set_trace()
ims, actions = func(x, n_timesteps_per_ep)
# func(x)
all_ims.append(ims)
all_actions.append(actions)
return np.array(all_ims), np.array(all_actions)
def gen_data(expid, exp, n_data, save_fnm):
'''
Generate initial data for a function associated the experiment.
Args:
expid: ID of the experiment; e.g. 0, 1, 2, ...
exp: name of the experiment; e.g. 'pour', 'scoop'.
n_data: number of data points to generate.
save_fnm: a file name string where the initial data will be
saved.
'''
print('Generating data...')
func = helper.get_func_from_exp(exp)
xx, yy = helper.gen_data(func, n_data)
pickle.dump((xx, yy), open(save_fnm, 'wb'))
def run_exp(expid, exp, method, n_init_data, iters):
'''
Run the active learning experiment.
Args:
expid: ID of the experiment; e.g. 0, 1, 2, ...
exp: name of the experiment; e.g. 'pour', 'scoop'.
method: learning method, including
'nn_classification': a classification neural network
based learning algorithm that queries the input that has
the largest output.
'nn_regression': a regression neural network based
learning algorithm that queries the input that has
the largest output.
'gp_best_prob': a Gaussian process based learning algorithm
that queries the input that has the highest probability of
having a positive function value.
'gp_lse': a Gaussian process based learning algorithm called
straddle algorithm. See B. Bryan, R. C. Nichol, C. R. Genovese,
J. Schneider, C. J. Miller, and L. Wasserman, "Active learning for
identifying function threshold boundaries," in NIPS, 2006.
'random': an algorithm that query uniformly random samples.
n_data: number of data points to generate.
save_fnm: a file name string where the initial data will be
saved.
'''
dirnm = 'data/'
if not os.path.isdir(dirnm):
os.mkdir(dirnm)
init_fnm = os.path.join(dirnm, '{}_init_data_{}.pk'.format(exp, expid))
gen_data(expid, exp, n_init_data, init_fnm)
initx, inity = pickle.load(open(init_fnm, 'rb'))
func = helper.get_func_from_exp(exp)
active_learner = helper.get_learner_from_method(method, initx, inity, func)
# file name for saving the learning results
learn_fnm = os.path.join(dirnm, '{}_{}_{}.pk'.format(exp, method, expid))
# get a context
context = helper.gen_context(func)
# start running the learner
print('Start running the learning experiment...')
run_ActiveLearner(active_learner, context, learn_fnm, iters)
def sample_exp(expid, exp, method):
'''
Sample from the learned model.
Args:
expid: ID of the experiment; e.g. 0, 1, 2, ...
exp: name of the experiment; e.g. 'pour', 'scoop'.
method: see run_exp.
'''
func = helper.get_func_from_exp(exp)
xx, yy, c = helper.get_xx_yy(expid, method, exp=exp)
active_learner = helper.get_learner_from_method(method, xx, yy, func)
active_learner.retrain()
# Enable gui
func.do_gui = True
while raw_input('Continue? [y/n]') == 'y':
x = active_learner.sample(c)
func(x)