def main(file_path=None, logspace=False, plot_list=None):
if file_path is None:
path_list = glob("./sirf/output/csv/*.csv.gz")
file_path = sorted(path_list)[-1] # get most recent file
with util.openz(file_path) as lf:
data = pandas.read_csv(lf)
time_stamp = file_path[file_path.rfind("/") + 1 :]
time_stamp = ".".join(time_stamp.split(".")[0:2])
gb = data.groupby(["method", "samples"], axis=0)
d_group_samples = dict(izip(gb.groups.keys(), [len(x) for x in gb.groups.values()]))
mean_data = gb.mean()
std_data = gb.std()
md = mean_data.reset_index()
sd = std_data.reset_index()
methods = md.method.unique()
losses = md.filter(regex="test-\S+|true-\S+|sample-reward").columns.unique()
keys = ["alpha", "gamma", "k", "lambda", "size", "regularization"] # min imporovement, beta, patience, maxiter?
assert set(keys).issubset(set(data.columns))
vals = [data.xs(0)[x] for x in keys] # get first row values for keys
d_params = dict(izip(keys, vals)) # for output strings later
# print 'columns in data: ', md.columns.unique()
if plot_list is None:
plot_list = methods.tolist()
# remove extra losses
# print plot_list
# plot_list.remove('covariance')
# plot_list.remove('prediction')
# plot_list.remove('value_prediction')
# plot_list.remove('prediction-layered')
# plot_list.remove('value_prediction-layered')
plot_all_losses(md, sd, losses, methods, d_group_samples, d_params, plot_list, logspace=logspace)
fig_path = out_string(
"./sirf/output/plots/",
"%s.n_samples_all_losses%s" % (time_stamp, "" if not logspace else "_logspace"),
d_params,
".pdf",
)
plt.savefig(fig_path)
plt.clf()
plot_important_losses(md, sd, losses, methods, d_group_samples, d_params)
fig_path = out_string("./sirf/output/plots/", "%s.n_samples_performance" % time_stamp, d_params, ".pdf")
plt.savefig(fig_path)
def main(workers = 0,
k = 36,
encoding = 'tile',
env_size = 9,
n_runs = 1,
n_reward_samples = 2500,
n_reward_runs = 25,
lam = 0.,
gam = 0.995,
beta = 0.995,
alpha = 1.,
eta = 0.5,
eps = 1e-5,
patience = 16,
max_iter = 8,
l1theta = None,
l1code = None,
l2code = None,
n_samples = None,
nonlin = None,
nonzero = None,
training_methods = None,
min_imp = 0.0001,
min_delta = 1e-6,
fldir = '/scratch/cluster/ccor/feature-learning/',
req_rew = False,
record_runs = False,
):
if n_samples:
n_samples = map(int, n_samples.split(','))
beta_ratio = beta/gam
# append reward to basis when using perfect info?
if training_methods is None:
training_methods = [
#(['covariance', 'prediction', 'value_prediction', 'bellman'],[['theta-all'],['theta-all'],['theta-all'],['theta-all','w']]),
#(['prediction', 'value_prediction', 'bellman'],[['theta-all'],['theta-all'],['theta-all','w']]),
#(['value_prediction'],[['theta-all']]),
#(['value_prediction', 'bellman'],[['theta-all'],['theta-all','w']]),
#(['prediction'],[['theta-all']]),
#(['prediction', 'bellman'], [['theta-all'],['theta-all','w']]),
#(['covariance'], [['theta-all']]),
(['prediction', 'bellman'], [['theta-all'],['theta-all','w']]),
(['bellman', 'prediction', 'bellman'], [['theta-all','w'], ['theta-all'],['theta-all','w']]),
(['full_covariance', 'bellman'], [['theta-all'],['theta-all','w']]),
(['covariance', 'bellman'], [['theta-all'],['theta-all','w']]),
(['full_laplacian'], [['theta-all', 'w']]),
(['laplacian'], [['theta-all', 'w']]),
(['bellman'], [['theta-all', 'w']]), # baseline
]
losses = ['sample-reward', 'test-lsbellman', 'test-bellman', 'test-reward', 'test-model', 'test-fullmodel', # test-training
'true-policy', 'true-policy-uniform', 'true-bellman', 'true-lsbellman', 'true-reward', 'true-model', 'true-fullmodel', 'true-lsq'] \
if n_samples else \
['sample-reward', 'true-policy-uniform', 'true-policy', 'true-bellman', 'true-lsbellman', 'true-reward', 'true-model', 'true-fullmodel', 'true-lsq']
logger.info('building environment of size %i' % env_size)
mdp = grid_world.MDP(walls_on = True, size = env_size)
env = mdp.env
n_states = env_size**2
m = Model(mdp.R, mdp.P, gam = gam)
# create raw data encoder (constant appended in encoder by default)
if encoding is 'tabular':
encoder = TabularFeatures(env_size, append_const = True)
elif encoding is 'tile':
encoder = TileFeatures(env_size, append_const = True)
elif encoding is 'factored':
raise NotImplementedError
def sample(n):
logger.info('sampling from a grid world')
# currently defaults to on-policy sampling
n_extra = calc_discount_horizon(lam, gam, eps) - 1 # mdp returns n+1 states and n rewards
kw = dict(n_samples = n + n_extra, encoder = encoder, req_rew = req_rew)
R, X, _ = mdp.sample_encoding(**kw)
if req_rew:
logger.info('reward required')
assert sum(R.todense()) > 0
logger.info('reward sum: %.2f' % sum(R.todense()))
R_val, X_val, _ = mdp.sample_encoding(**kw)
R_test, X_test, _ = mdp.sample_encoding(**kw)
#losses = ['test-bellman', 'test-reward', 'test-model',
#'true-bellman', 'true-reward', 'true-model', 'true-lsq'] # test-training
weighting = 'policy'
return (X, X_val, X_test), (R, R_val, R_test), weighting
def full_info():
logger.info('using perfect information')
# gen stacked matrices of I, P, P^2, ...
R = numpy.array([])
S = sp.eye(n_states, n_states)
P = sp.eye(n_states, n_states)
for i in xrange(calc_discount_horizon(lam, gam, eps)): # decay epsilon
R = numpy.append(R, P * m.R)
P = m.P * P
S = sp.vstack((S, P))
X = encoder.encode(S)
R = sp.csr_matrix(R[:,None])
X_val = X_test = X
R_val = R_test = R
#losses = ['true-bellman', 'true-reward', 'true-model']
weighting = 'uniform'
return (X, X_val, X_test), (R, R_val, R_test), weighting
reg = None
if l1theta is not None:
reg = ('l1theta', l1theta)
if l1code is not None:
reg = ('l1code', l1code)
if l2code is not None:
reg = ('l2code', l2code)
run_param_keys = ['k','method','encoding','samples', 'reward_samples', 'reward_runs', 'size','weighting',
'lambda','gamma','alpha', 'eta', 'regularization','nonlinear']
def yield_jobs():
for i,n in enumerate(n_samples or [n_states]):
logger.info('creating job with %i samples/states' % n)
# build bellman operator matrices
logger.info('making mixing matrices')
Mphi, Mrew = BellmanBasis.get_mixing_matrices(n, lam, gam,
sampled = bool(n_samples), eps = eps)
for r in xrange(n_runs):
n_features = encoder.n_features
# initialize parameters
theta_init = numpy.random.standard_normal((n_features, k))
theta_init /= numpy.sqrt((theta_init * theta_init).sum(axis=0))
w_init = numpy.random.standard_normal((k+1,1))
w_init = w_init / numpy.linalg.norm(w_init)
# sample or gather full info data
X_data, R_data, weighting = sample(n) if n_samples else full_info()
bb_params = [n_features, [k], beta_ratio]
bb_dict = dict( alpha = alpha, reg_tuple = reg, nonlin = nonlin,
nonzero = nonzero, thetas = [theta_init])
for j, tm in enumerate(training_methods):
loss_list, wrt_list = tm
assert len(loss_list) == len(wrt_list)
run_param_values = [k, tm, encoder, n,
n_reward_samples, n_reward_runs,
env_size, weighting,
lam, gam, alpha, eta,
reg[0]+str(reg[1]) if reg else 'None',
nonlin if nonlin else 'None']
d_run_params = dict(izip(run_param_keys, run_param_values))
yield (train_basis,[d_run_params, bb_params, bb_dict,
env, m, losses, # environment, model and loss list
X_data, R_data, Mphi, Mrew, # training data
max_iter, patience, min_imp, min_delta, # optimization params
fldir, record_runs]) # recording params
# create output file path
date_str = time.strftime('%y%m%d.%X').replace(':','')
out_dir = fldir + 'sirf/output/csv/'
root = '%s.%s_results' % (
date_str,
'n_samples' if n_samples else 'full_info')
d_experiment_params = dict(izip(['k','encoding','size',
'lambda','gamma','alpha','regularization','nl'],
[k, encoder, env_size, lam, gam, alpha,
reg[0]+str(reg[1]) if reg else 'None',
nonlin if nonlin else 'None']))
save_path = out_string(out_dir, root, d_experiment_params, '.csv.gz')
logger.info('saving results to %s' % save_path)
# get column title list ordered params | losses using dummy dicts
d_param = dict(izip(run_param_keys, numpy.zeros(len(run_param_keys))))
d_loss = dict(izip(losses, numpy.zeros(len(run_param_keys))))
col_keys_array,_ = reorder_columns(d_param, d_loss)
with openz(save_path, "wb") as out_file:
writer = csv.writer(out_file)
writer.writerow(col_keys_array)
for (_, out) in condor.do(yield_jobs(), workers):
keys, vals = out
assert (keys == col_keys_array).all() # todo catch
writer.writerow(vals)
def experiment(workers = 80, n_runs = 9, k = 16, env_size = 15, gam = 0.998, lam = 0., eps = 1e-5,
partition = None, patience = 8, max_iter = 8, weighting = 'uniform', reward_init = False,
nonlin = 1e-8, n_samples = None, beta_ratio = 1.,
training_methods = None):
if training_methods is None:
# note: for each loss string, you need a corresponding wrt list
if reward_init:
training_methods = [
(['prediction'],[['theta-model']]),
(['prediction', 'layered'], [['theta-model'],['theta-model','w']]),
(['covariance'],[['theta-model']]), # with reward, without fine-tuning
(['covariance', 'layered'], [['theta-model'],['theta-model','w']]), # theta-model here for 2nd wrt?
(['layered'], [['theta-all','w']])] # baseline
else:
training_methods = [(['prediction'],[['theta-all']]),
(['prediction', 'layered'], [['theta-all'],['theta-all','w']]),
(['covariance'],[['theta-all']]), # with reward, without fine-tuning
(['covariance', 'layered'], [['theta-all'],['theta-all','w']]), # theta-model here for 2nd wrt?
(['layered'], [['theta-all','w']])] # baseline
theano.gof.compilelock.set_lock_status(False)
theano.config.on_unused_input = 'ignore'
theano.config.warn.sum_div_dimshuffle_bug = False
if n_samples is None:
#n_samples = [100,500]
n_samples = numpy.round(numpy.linspace(50,1500,6)).astype(int)
if partition is None:
partition = {'theta-model':k-1, 'theta-reward':1}
mdp = grid_world.MDP(walls_on = True, size = env_size)
m = Model(mdp.env.R, mdp.env.P, gam = gam)
dim = env_size**2
# tracked losses
losses = ['test-bellman', 'test-reward', 'test-model', 'true-bellman', 'true-lsq']
logger.info('losses tracked: '+ str(losses))
#n_extra = bb._calc_n_steps(lam, gam, eps)
#print 'n extra sampled needed: ', n_extra
d_loss_data = {}
for key in losses:
d_loss_data[key] = numpy.zeros((len(n_samples), n_runs, len(training_methods)))
def yield_jobs():
for i,n in enumerate(n_samples):
Mphi, Mrew = BellmanBasis.get_mixing_matrices(n, lam, gam, sampled = True, eps = eps)
for r in xrange(n_runs):
# initialize features with unit norm
theta_init = numpy.random.standard_normal((dim+1, k))
if reward_init:
theta_init[:-1,-1] = m.R # XXX set last column to reward
theta_init[-1,-1] = 0
theta_init /= numpy.sqrt((theta_init * theta_init).sum(axis=0))
w_init = numpy.random.standard_normal((k+1,1))
w_init = w_init / numpy.linalg.norm(w_init)
# sample data: training, validation, and test sets
S, Sp, R, _, = mdp.sample_grid_world(n, distribution = weighting);
S = numpy.vstack((S, Sp[-1,:]))
S_val, Sp_val, R_val, _, = mdp.sample_grid_world(n, distribution = weighting)
S_val = scipy.sparse.vstack((S_val, Sp_val[-1,:]))
S_test, Sp_test, R_test, _, = mdp.sample_grid_world(n, distribution = weighting)
S_test = scipy.sparse.vstack((S_test, Sp_test[-1,:]))
bb = BellmanBasis(dim+1, k, beta_ratio, partition = partition,
theta = theta_init, w = w_init, record_loss = losses, nonlin = nonlin)
for j,tm in enumerate(training_methods):
yield (condor_job,[(i,r,j), bb, m, tm,
S, R, S_val, R_val, S_test, R_test,
Mphi, Mrew, patience, max_iter, weighting])
# aggregate the condor data
for (_, result) in condor.do(yield_jobs(), workers):
d_batch_loss, ind_tuple = result
for name in d_batch_loss.keys():
d_loss_data[name][ind_tuple] = d_batch_loss[name]
# save results!
pi_root = 'n_samples_results_rinit' if reward_init else 'n_samples_results'
out_path = os.getcwd()+'/sirf/output/pickle/%s.no_r.k=%i.l=%s.g=%s.%s.size=%i.r=%i..pickle.gz' \
% (pi_root, k, str(lam), str(gam), weighting, env_size, n_runs)
logger.info('saving results to %s' % out_path)
with util.openz(out_path, "wb") as out_file:
pickle.dump(d_loss_data, out_file, protocol = -1)
x = numpy.array(n_samples, dtype = numpy.float64) #range(len(n_samples))
f = plt.figure()
logger.info('plotting')
plot_styles = ['r-', 'b-', 'g-', 'k-', 'c-', 'm-']
for i,(key,mat) in enumerate(d_loss_data.items()):
ax = f.add_subplot(2,3,i+1) # todo generalize for arb length
for h,tm in enumerate(training_methods):
std = numpy.std(mat[:,:,h], axis=1)
mn = numpy.mean(mat[:,:,h], axis=1)
if 'test' in key:
mn = mn/x
std = std/x
ax.fill_between(x, mn-std, mn+std, facecolor='yellow', alpha=0.15)
ax.plot(x, mn, plot_styles[h], label = str(tm[0]))
plt.title(key)
#plt.axis('off')
#plt.legend(loc = 3) # lower left
pl_root = 'n_samples_rinit' if reward_init else 'n_samples'
plt.savefig(os.getcwd()+'/sirf/output/plots/%s.n=%i-%i.k=%i.l=%s.g=%s.%s.size=%i.r=%i.pdf'
% (n_samples[0], n_samples[-1], pl_root, k,
str(lam), str(gam), weighting, env_size, n_runs))
