def _test_serialize_program(v, label, action):
engine = v.sivm.core_sivm.engine
if action == 'serialize':
trace1 = engine.getDistinguishedTrace()
serialized = trace1.dump()
trace2 = engine.model.restore_trace(serialized)
assert isinstance(serialized, tuple)
assert len(serialized) == 3
assert isinstance(serialized[0], list)
assert all(isinstance(x, dict) for x in serialized[0])
assert isinstance(serialized[1],
dict) # Mapping directive ids to directives
for (key, val) in serialized[1].iteritems():
assert isinstance(key, int)
assert isinstance(val, list)
assert isinstance(serialized[2], set) # Names of bound foreign sps
for elem in serialized[2]:
assert isinstance(elem, basestring)
assert isinstance(trace2, type(trace1))
assert isinstance(trace2.trace, type(trace1.trace))
elif action == 'copy':
trace1 = engine.getDistinguishedTrace()
trace2 = engine.model.copy_trace(trace1)
assert isinstance(trace2, type(trace1))
assert isinstance(trace2.trace, type(trace1.trace))
elif action == 'convert_puma':
try:
from venture.puma import trace
except ImportError:
raise SkipTest("Puma backend does not appear to be installed")
trace1 = engine.getDistinguishedTrace()
engine.to_puma()
trace2 = engine.getDistinguishedTrace()
assert 'venture.puma' in trace2.trace.__module__
elif action == 'convert_lite':
trace1 = engine.getDistinguishedTrace()
engine.to_lite()
trace2 = engine.getDistinguishedTrace()
assert 'venture.lite' in trace2.trace.__module__
else:
assert False
infer = "(resimulation_mh default one %s)" % default_num_transitions_per_sample(
)
engine.model.create_trace_pool([trace2])
r2 = collectStateSequence(v, label, infer=infer)
engine.model.create_trace_pool([trace1])
r1 = collectStateSequence(v, label, infer=infer)
return reportSameDiscrete(r1, r2)
def testEnumerateCoupledChoices2(seed):
# A second illustration of Issue #462 (second manifestation).
#
# If enumeration computes the set of candidate values before
# detaching, as an independent product, it will invent combinations
# that are actually distinct representations of semantically the
# same option. Thus, even though all possibilities will (as in this
# variant) be considered, some will be overweighted.
#
# Specifically, if the initial state is three calls to the same CRP
# with distinct values (arranged by the force statements),
# enumeration will invent 4^3 different combinations of tables to
# try; whereas there are only 5 that differ up to renumbering of the
# tables: (1, 1, 1), (1, 1, 2), (1, 2, 1), (1, 2, 2), and (1, 2, 3).
# They cannot, therefore, be overrepresented evenly, and this leads
# to the wrong posterior.
raise SkipTest(
"Fails due to https://github.com/probcomp/Venturecxx/issues/462")
r = get_ripl(seed=seed)
r.assume("crp", "(make_crp 1)")
r.assume("result1", "(crp)")
r.assume("result2", "(crp)")
r.assume("result3", "(crp)")
r.predict(
"(and (not (eq result1 result2))"
"(and (not (eq result2 result3))"
"(not (eq result1 result3))))",
label="pid")
ans = collectSamples(r,
"pid",
infer="reset_to_prior",
num_samples=default_num_samples(4))
gibbs_from_different = """(do
(force result1 atom<1>)
(force result2 atom<2>)
(force result3 atom<3>)
(gibbs default all 1))"""
# One step of Gibbs from any initial condition should move to the
# posterior (which in this case equals the prior).
predicts = collectSamples(r,
"pid",
infer=gibbs_from_different,
num_samples=default_num_samples(4))
return reportSameDiscrete(ans, predicts)
def testEnumerateCoupledChoices1(seed):
# A test case for the first problem identified in Issue #462.
#
# If enumaration collects the candidate value sets all at once at
# the beginning of the enumeration run, and if the set of options
# for a later choice depends on the choice taken at an earlier one
# (e.g., for the made SP of make_crp), trouble will ensue because we
# need to compute a dependent rather than independent product.
#
# This example suffices to bring the problem into relief. If a CRP
# has only one extant table at the time enumeration is invoked,
# (arranged by the force calls), each node will consider that table
# and one new table as the only options. Enumeration will therefore
# consider 8 options, in none of which will all three nodes be
# assigned to distinct tables.
raise SkipTest(
"Fails due to https://github.com/probcomp/Venturecxx/issues/462")
r = get_ripl(seed=seed)
r.assume("crp", "(make_crp 1)")
r.assume("result1", "(crp)")
r.assume("result2", "(crp)")
r.assume("result3", "(crp)")
r.predict(
"(and (not (eq result1 result2))"
"(and (not (eq result2 result3))"
"(not (eq result1 result3))))",
label="pid")
ans = collectSamples(r,
"pid",
infer="reset_to_prior",
num_samples=default_num_samples(4))
gibbs_from_same = """(do
(force result1 atom<1>)
(force result2 atom<1>)
(force result3 atom<1>)
(gibbs default all 1))"""
# One step of Gibbs from any initial condition should move to the
# posterior (which in this case equals the prior).
predicts = collectSamples(r,
"pid",
infer=gibbs_from_same,
num_samples=default_num_samples(4))
return reportSameDiscrete(ans, predicts)
def testEnumerateCoupledChoices3(seed):
# A third illustration of Issue #462 (second manifestation).
#
# Enumerating a single choice should not depend on the initial value
# of that choice, but due to #462 it does. The setup here is
# enumerating one of two choices from a CRP. If they are initially
# distinct, enumeration will consider three options, the latter two
# of which will be equivalent: "become the same as the other point",
# "remain the same as I was", and "become a unique snowflake". This
# will cause it to overweight the state where the choices are
# distinct by 2:1.
r = get_ripl(seed=seed)
r.assume("crp", "(make_crp 1)")
r.assume("result1", "(crp)")
r.assume("result2", "(crp)")
r.predict("(eq result1 result2)", label="pid")
gibbs_from_same = """(do
(force result1 atom<1>)
(force result2 atom<1>)
(gibbs default one 1))"""
ans = collectSamples(r,
"pid",
infer=gibbs_from_same,
num_samples=default_num_samples(6))
gibbs_from_different = """(do
(force result1 atom<1>)
(force result2 atom<2>)
(gibbs default one 1))"""
# In this case, gibbs_from_same happens to compute the exact
# posterior, which equals the prior, and is 50-50 on whether the
# atoms are the same.
predicts = collectSamples(r,
"pid",
infer=gibbs_from_different,
num_samples=default_num_samples(6))
return reportSameDiscrete(ans, predicts)
def testEnumerativeGibbsBrushRandomness(seed):
# Test that Gibbs targets the correct stationary distribution,
# even when there may be random choices downstream of variables
# being enumerated.
ripl = get_ripl(seed=seed)
ripl.assume("z", "(tag 'z 0 (flip))")
ripl.assume("x", "(if z 0 (normal 0 10))")
ripl.observe("(normal x 1)", "4")
ripl.predict("z", label="pid")
def posterior_inference_action():
# Work around the fact that Puma doesn't have rejection sampling
# by asking for a bunch of MH.
if backend_name() == 'lite':
return "(rejection default all 1)"
else:
return "(resimulation_mh default one %d)" % (
default_num_transitions_per_sample(), )
ans = collectSamples(ripl, "pid", infer=posterior_inference_action())
gibbs_inference_action = "(do %s (gibbs 'z 0 1))" % \
(posterior_inference_action(),)
predictions = collectSamples(ripl, "pid", infer=gibbs_inference_action)
return reportSameDiscrete(ans, predictions)
def checkHPYMem1(baseline, topC, botC, seed):
data = predictHPY(topC, botC, seed)
return reportSameDiscrete(baseline, data)
def testTwoSampleChi2():
data1 = range(5) * 5
data2 = sorted(data1)
assert reportSameDiscrete(data1, data2).pval == 1
