def test_orderbias():
"""Test orderbias methods"""
cfg = treedict.TreeDict()
cfg.s_areas = (((0.0, 10.0), ), ((10.0, 20.0), ), ((20.0, 30.0), ))
se = StaticExplorer((1, ), cfg=cfg)
dataset = []
for i in xrange(6):
goal = (5.0, )
effect = (float(i), )
order = effect
se.add_effect(effect, goal=goal)
dataset.append((order, effect))
for i in xrange(6):
goal = (15.0, )
effect = (10.0 + float(i) / 2, )
order = effect
se.add_effect(effect, goal=goal)
dataset.append((order, effect))
for i in xrange(6):
goal = (25.0, )
effect = (25.0, )
order = effect
se.add_effect(effect, goal=goal)
dataset.append((order, effect))
ob = bias.IMBias(se, dataset)
#print ", ".join(str(int(x[0])) for x in ob.order_bias)
return True
def config_test(key):
cfg = treedict.TreeDict()
cfg.exp.expname = key[1]
cfg.exp.key = key
cfg.exp.stage = 'primary' if key[0] == names.kPrimaryTest else 'secondary'
cfg.test.res = 20
cfg.test.freq = 100
cfg.test.max = 10001
seed = random.randint(0, sys.maxint)
cfg.hardware.seed.test = seed
datakey = (names.kPrimaryData if key[0] == names.kPrimaryTest else
names.kSecondaryData, ) + key[1:]
cfg.hardware.datafile = names.key2jobname(datakey)
datacfg = datafile.load_config(cfg.hardware.datafile)
cfg.test.fixed_dim = None
if datacfg.sim.sensors == 'toy':
cfg.test.fixed_dim = [None, None, 1.0]
cfg.hardware.testfile = names.key2jobname(key)
cfg.hardware.configfile = names.key2jobname(key) + '.cfg'
jobgraph.add_job(cfg.hardware.testfile, cfg, [cfg.hardware.datafile])
return cfg
def __init__(self):
self.s_feats = range(10)
self.s_bounds = tuple((i+0.0, i+1.0) for i in range(10))
self.m_feats = range(-1, 0)
self.m_bounds = ((0.0, 1.0),)
self.cfg = treedict.TreeDict()
self.cfg.verbose = False
def test_gridbias():
"""Test basic methods of GridBias"""
cfg = treedict.TreeDict()
cfg.s_bounds = ((0.0, 1.0), (0.0, 1.0))
cfg.s_res = (10, 10)
ge = grid.GridExplorer((0, 1), cfg=cfg)
dataset = []
for _ in xrange(1000):
effect = (random.random(), random.random())
goal = (random.random(), random.random())
prediction = (random.random(), random.random())
ge.add_effect(effect, goal=goal, prediction=prediction)
order = (random.random(), ) * 6
dataset.append((order, effect))
ob = bias.IMBias(ge, dataset)
gb = bias.GridBias(ob, (0, 1), cfg=cfg)
gb.setup(10)
for _ in xrange(1000):
effect = (random.random(), random.random())
goal = (random.random(), random.random())
prediction = (random.random(), random.random())
ge.add_effect(effect, goal=goal, prediction=prediction)
ge.next_goal()
return True
def test_belongs():
"""Test if filtered values are correctly recognized"""
cfg = treedict.TreeDict()
cfg.effect.s_bounds = ((-1.0, 1.0),)
cfg.effect.s_res = (10,)
goalexplorer = effect.GridExplorer((1.0,), cfg = cfg)
goalexplorer = effect.EffectFilter(goalexplorer, (((0.0, 0.0),),))
return goalexplorer._in_filtered_values((0.0,)) and not goalexplorer._in_filtered_values((1.0,))
def test1():
"""Basic instanciation of BoundedRandomExplorer"""
cfg = treedict.TreeDict()
cfg.effect.s_bounds = ((-1, 1), (0, 10))
bre = BoundedRandomExplorer((0, 1), cfg=cfg)
for i in range(10):
g = bre.next_goal()
bre.add_effect(g, g)
return True
def test1():
"""Basic instanciation of StaticExplorer"""
cfg = treedict.TreeDict()
cfg.s_areas = (((-1, 1), (0, 10)),)
se = StaticExplorer((0, 1), cfg = cfg)
for i in range(10):
g = se.next_goal()
se.add_effect(g, g)
return True
def compile_primary(expname):
datafile.update()
prim_names = datafile.prim_test_available()
prim_cfgs = [[] for _ in range(10)]
for filename in prim_names:
cat, file_expname, primid, rep = names.name2key(filename)
if file_expname == expname:
prim_cfgs[primid].append(filename)
prim_cfgs = [sorted(filenames) for filenames in prim_cfgs]
testset, results = perf(prim_cfgs)
cfg = treedict.TreeDict()
cfg.hardware.resultsfile = names.r_pname(names.kPrimaryResults, expname)
exp.save_results(cfg, time.time(), testset, results)
def test_qsub_options():
"""Test if qsub option are correctly handled"""
check = True
cfg = treedict.TreeDict()
cfg.qsub.resources.mem = '16gb'
node = missing.DependencyNode('bla', cfg)
check *= node.qsub_options() == ' -l mem=16gb '
cfg.qsub.resources.walltime = '5:00:00'
node = missing.DependencyNode('bla', cfg)
check *= node.qsub_options() == ' -l mem=16gb,walltime=5:00:00 '
return check
def test1():
"""Basic instanciation of Guide"""
cfg = treedict.TreeDict()
g = guide.Guide((-1,), (1,), ((-10.0, 10.0),), cfg)
def f(x):
return (2*x[0]**1.5,)
for _ in range(20):
action = g.next_action()
if action[0] == 'goal':
break
return True
def load_config(filename, configdir=paths.configdir):
if filename[-4:] != '.cfg':
filename = filename + '.cfg'
filepath = os.path.join(configdir, filename)
filepath = os.path.expanduser(filepath)
with open(filepath, 'r') as f:
s = f.read()
d = {}
for line in s.split('\n'):
key, value = line.split('=')
key = key.strip()
value = eval(value.strip(), {}, {})
d[key] = value
return treedict.TreeDict().fromdict(d)
def compile_secondary(primname, source, expname):
datafile.update()
sec_names = datafile.sec_test_available()
sec_cfgs = [[] for _ in range(10)]
for filename in sec_names:
cat, file_primname, primid, file_secname, secid, rep = names.name2key(
filename)
if file_primname == primname and file_secname == expname and primid == source:
sec_cfgs[secid].append(filename)
sec_cfgs = [sorted(filenames) for filenames in sec_cfgs]
testset, sec_results = perf(sec_cfgs)
cfg = treedict.TreeDict()
cfg.hardware.resultsfile = names.r_sname(names.kSecondaryResults, primname,
source, expname)
exp.save_results(cfg, time.time(), testset, sec_results)
def test2():
"""Basic instanciation of Guide with a GridExplorer"""
cfg = treedict.TreeDict()
cfg.effect.s_bounds = ((-64, 64),)
cfg.effect.s_res = (25,)
goalexplorer = effect.GridExplorer((1, ), cfg)
g = guide.Guide((-1,), (1,), ((-10.0, 10.0),), cfg, goalexplorer = goalexplorer)
def f(x):
return (2*x[0]**1.5,)
for _ in range(20):
action = g.next_action()
if action[0] == 'goal':
break
return True
def test1():
"""Check basic cell behavior"""
cfg = treedict.TreeDict()
dcell = cell.DualCell(((-100.0, 100.0), ), None, None, cfg, w=[1.0])
goal = (10.0, )
for i in xrange(40):
effect = (10.0 + random.uniform(-(10 - i), 10 - i), )
prediction = (10.0 + random.uniform(-(10 - i), 10 - i), )
dcell.add(effect, goal=goal, prediction=prediction)
dcell.gcell.cp_all
dcell.ecell.pei_all
dcell.interest_all()
check = len(dcell.gcell) == 40 and len(dcell.ecell) == 40
return check
def test1():
"""Test basic method of competence"""
check = True
cfg = treedict.TreeDict()
cfg.cmpce.min_d = 0.01
cfg.cmpce.function = 'ident'
cmpce.competence([1.0], [0.5], cfg)
cfg.cmpce.exp_alpha = 3.0
cfg.cmpce.function = 'exp'
cmpce.competence([1.0], [0.5], cfg)
cfg.cmpce.log_beta = 0.1
cfg.cmpce.function = 'log'
cmpce.competence([1.0], [0.5], cfg)
check *= cmpce.competence([0.01], [0.005], cfg) == 0.0
return check
def test1():
"""Test basic methods of gridexplorer"""
cfg = treedict.TreeDict()
cfg.effect.s_bounds = ((0.0, 1.0), (0.0, 1.0))
ge = grid.GridExplorer((0, 1), cfg=cfg)
for _ in xrange(100):
effect = (random.random(), random.random())
goal = (random.random(), random.random())
prediction = (random.random(), random.random())
ge.add_effect(effect, goal=goal, prediction=prediction)
for _ in xrange(1000):
ge.next_goal()
ge.cell_list()
ge.active_cell_list()
return True
def test_config():
"""Test that configuration is not changed"""
cfg = treedict.TreeDict()
cfg.effect.s_bounds = ((-64, 64), )
cfg.effect.s_res = (25, )
goalexplorer = effect.GridExplorer((1, ), cfg)
g = guide.Guide((-1, ), (1, ), ((-10.0, 10.0), ),
cfg,
goalexplorer=goalexplorer)
#print cfg.makeReport()
cfg.freeze()
def f(x):
return (2 * x[0]**1.5, )
for _ in range(20):
action = g.next_action()
if action[0] == 'goal':
break
return True
import treedict import common cfg = treedict.TreeDict() cfg.update(common.cfg) cfg.exp.name = 'origin'
import treedict
from ..tools.splittree import SplitTree
from cell import DualCell
defaultcfg = treedict.TreeDict()
class CellTree(object):
"""Class instanciating cells around a SplitTree"""
def __init__(self, bounds, crit_size, cfg, w=None):
self.dim = len(bounds)
self.bounds = bounds
self.crit_size = crit_size
self.w = w
if self.w is None:
self.w = len(bounds) * [1.0]
self.cfg = cfg
self.cellcount = 0
self.cells = [DualCell(self.bounds, self, 0, self.cfg, w=self.w)]
# keeping track of leaves and nodes
self.leafcells = set([self.cells[0]])
self.nodecells = set()
# keeping track of node cells with effects
self.active_cells = set()
self.splittree = SplitTree(self.bounds,
self.crit_size,
def run_first(cfg):
start_time = time.time()
try:
if cfg.hardware.seed.get('primary', None) is None:
seed = random.randint(0, sys.maxint)
cfg.hardware.seed.primary = seed
random.seed(cfg.hardware.seed.primary)
bot = robots.build_robot(cfg.sim)
## Configuration ##
goalscfg = treedict.TreeDict()
goalscfg.effect.explorer = BoundedRandomExplorer
cfg.goals.update(goalscfg, overwrite = False)
cfg.goals.effect.s_feats = s_feats = bot.s_feats
cfg.goals.effect.s_bounds = s_bounds = bot.s_bounds
cfg.goals.motor.m_feats = m_feats = bot.m_feats
cfg.goals.motor.m_bounds = m_bounds = bot.m_bounds
## Instanciation of core modules ##
s_explorer = BoundedRandomExplorer(s_feats, cfg.goals)
if 'explorename' in cfg.goals.effect:
if cfg.goals.effect.explorername == 'cluster':
s_explorer = cluster.ClusterExplorer(s_feats, cfg.goals)
elif cfg.goals.effect.explorername == 'cluster_im':
s_explorer = cluster.ClusterIMExplorer(s_feats, cfg.goals)
m_explorer = MotorBabble(bot.m_feats, bot.m_bounds, cfg.goals)
guide = Guide(m_feats, s_feats, m_bounds, cfg.goals,
goalexplorer = s_explorer, motorbabble = m_explorer)
learner = Learner(m_feats, s_feats, m_bounds, fwd = 'ES-LWLR', inv = 'L-BFGS-B')
cfg.freeze()
print(cfg.makeReport())
## Runing learning ##
for i in xrange(cfg.exp.trials):
exp.learn(bot, guide, learner)
if not cfg.sim.verbose:
gfx.print_progress(i+1, cfg.exp.trials,
prefix = '{}learning... {}'.format(gfx.purple, gfx.cyan),
suffix = gfx.end, eta = 1)
print ''
exp.save_data(cfg, guide)
except Exception:
import traceback
traceback.print_exc()
finally:
bot.close()
print("ran for {}s.".format(int(time.time() - start_time)))
def config_results(key, jobdep):
cfg = treedict.TreeDict()
cfg.exp.key = key
cfg.hardware.configfile = names.key2jobname(key) + '.cfg'
jobgraph.add_job(names.key2jobname(key), cfg, dependencies=jobdep)
return cfg
