def test_clone():
config = config0()
config2 = clone(config)
nodeset = set(dfs(config))
assert not any(n in nodeset for n in dfs(config2))
foo = recursive_set_rng_kwarg(config, scope.rng_from_seed(5))
r = rec_eval(foo)
print r
r2 = rec_eval(recursive_set_rng_kwarg(config2, scope.rng_from_seed(5)))
print r2
assert r == r2
def test_clone():
config = config0()
config2 = clone(config)
nodeset = set(dfs(config))
assert not any(n in nodeset for n in dfs(config2))
foo = recursive_set_rng_kwarg(
config,
scope.rng_from_seed(5))
r = rec_eval(foo)
print r
r2 = rec_eval(
recursive_set_rng_kwarg(
config2,
scope.rng_from_seed(5)))
print r2
assert r == r2
def __init__(self, bandit, seed=seed, cmd=None, workdir=None):
self.bandit = bandit
self.seed = seed
self.rng = np.random.RandomState(self.seed)
self.cmd = cmd
self.workdir = workdir
self.new_ids = ['dummy_id']
# -- N.B. not necessarily actually a range
self.s_new_ids = pyll.Literal(self.new_ids)
self.template_clone_memo = {}
template = pyll.clone(self.bandit.template, self.template_clone_memo)
vh = self.vh = VectorizeHelper(template, self.s_new_ids)
vh.build_idxs()
vh.build_vals()
# the keys (nid) here are strings like 'node_5'
idxs_by_nid = vh.idxs_by_id()
vals_by_nid = vh.vals_by_id()
name_by_nid = vh.name_by_id()
assert set(idxs_by_nid.keys()) == set(vals_by_nid.keys())
assert set(name_by_nid.keys()) == set(vals_by_nid.keys())
# -- replace repeat(dist(...)) with vectorized versions
t_i_v = replace_repeat_stochastic(
pyll.as_apply([
vh.vals_memo[template], idxs_by_nid, vals_by_nid]))
assert t_i_v.name == 'pos_args'
template, s_idxs_by_nid, s_vals_by_nid = t_i_v.pos_args
# -- fetch the dictionaries off the top of the cloned graph
idxs_by_nid = dict(s_idxs_by_nid.named_args)
vals_by_nid = dict(s_vals_by_nid.named_args)
# -- remove non-stochastic nodes from the idxs and vals
# because
# (a) they should be irrelevant for BanditAlgo operation,
# (b) they can be reconstructed from the template and the
# stochastic choices, and
# (c) they are often annoying when printing / saving.
for node_id, name in name_by_nid.items():
if name not in pyll.stochastic.implicit_stochastic_symbols:
del name_by_nid[node_id]
del vals_by_nid[node_id]
del idxs_by_nid[node_id]
elif name == 'one_of':
# -- one_of nodes too, because they are duplicates of randint
del name_by_nid[node_id]
del vals_by_nid[node_id]
del idxs_by_nid[node_id]
# -- make the graph runnable and SON-encodable
# N.B. operates inplace
self.s_specs_idxs_vals = recursive_set_rng_kwarg(
scope.pos_args(template, idxs_by_nid, vals_by_nid),
pyll.as_apply(self.rng))
self.vtemplate = template
self.idxs_by_nid = idxs_by_nid
self.vals_by_nid = vals_by_nid
self.name_by_nid = name_by_nid
# -- compute some document coordinate strings for the node_ids
pnames = pretty_names(bandit.template, prefix=None)
doc_coords = self.doc_coords = {}
for node, pname in pnames.items():
cnode = self.template_clone_memo[node]
if cnode.name == 'one_of':
choice_node = vh.choice_memo[cnode]
assert choice_node.name == 'randint'
doc_coords[vh.node_id[choice_node]] = pname #+ '.randint'
if cnode in vh.node_id and vh.node_id[cnode] in name_by_nid:
doc_coords[vh.node_id[cnode]] = pname
else:
#print 'DROPPING', node
pass