def test_foreign_latents_infer(seed):
# Same as test.inference_quality.micro.test_latents.testHMMSP1
builtins = builtin.builtInSPs()
ripl = get_ripl(seed=seed)
ripl.bind_foreign_sp("test_lazy_hmm", builtins["make_lazy_hmm"])
ripl.assume(
"f", """
(test_lazy_hmm
(simplex 0.5 0.5)
(matrix (array (array 0.7 0.3)
(array 0.3 0.7)))
(matrix (array (array 0.9 0.2)
(array 0.1 0.8))))
""")
ripl.observe("(f 1)", "integer<0>")
ripl.observe("(f 2)", "integer<0>")
ripl.observe("(f 3)", "integer<1>")
ripl.observe("(f 4)", "integer<0>")
ripl.observe("(f 5)", "integer<0>")
ripl.predict("(f 6)", label="pid")
ripl.predict("(f 7)")
ripl.predict("(f 8)")
predictions = collectSamples(ripl, "pid")
ans = [(0, 0.6528), (1, 0.3472)]
return reportKnownDiscrete(ans, predictions)
def test_foreign_aaa_uc():
builtins = builtin.builtInSPs()
ripl = get_ripl()
ripl.bind_foreign_sp("test_sym_dir_cat", builtins["make_uc_sym_dir_cat"])
ripl.assume("f", "(test_sym_dir_cat 1 1)")
assert ripl.sample("f")["counts"] == [0]
ripl.observe("(f)", "integer<0>")
assert ripl.sample("f")["counts"] == [1]
ripl.infer(defaultInfer())
assert ripl.sample("f")["counts"] == [1]
def test_foreign_latents():
builtins = builtin.builtInSPs()
ripl = get_ripl()
ripl.bind_foreign_sp("test_lazy_hmm", builtins["make_lazy_hmm"])
ripl.assume("f", "(test_lazy_hmm (simplex 1) (id_matrix 1) (id_matrix 1))")
assert ripl.sample("f")[0] == []
ripl.observe("(f 1)", "integer<0>")
assert ripl.sample("f")[0] == [np.matrix([[1]]), np.matrix([[1]])]
ripl.infer(defaultInfer())
assert ripl.sample("f")[0] == [np.matrix([[1]]), np.matrix([[1]])]
def __init__(self, seed):
self.results = {}
self.env = env.VentureEnvironment()
for name, val in builtin.builtInValues().iteritems():
self.bindPrimitiveName(name, val)
for name, sp in builtin.builtInSPs().iteritems():
self.bindPrimitiveSP(name, sp)
self.sealEnvironment() # New frame so users can shadow globals
assert seed is not None
rng = random.Random(seed)
self.np_rng = npr.RandomState(rng.randint(1, 2**31 - 1))
self.py_rng = random.Random(rng.randint(1, 2**31 - 1))
def test_foreign_aaa_infer(seed):
# Same as test.inference_quality.micro.test_misc_aaa.testMakeBetaBernoulli1
builtins = builtin.builtInSPs()
ripl = get_ripl(seed=seed)
ripl.bind_foreign_sp("test_beta_bernoulli",
builtins["make_uc_beta_bernoulli"])
ripl.assume("a", "(normal 10.0 1.0)")
ripl.assume("f", "(test_beta_bernoulli a a)")
ripl.predict("(f)", label="pid")
for _ in range(20):
ripl.observe("(f)", "true")
predictions = collectSamples(ripl, "pid")
ans = [(False, .25), (True, .75)]
return reportKnownDiscrete(ans, predictions)
def __init__(self, seed, trace=None):
assert trace is None or isinstance(trace, puma.Trace)
self.py_rng = random.Random(None)
self.np_rng = npr.RandomState(None)
if trace is None:
self.trace = puma.Trace()
self.set_seed(seed)
for name, sp in builtInSPs().iteritems():
if self.trace.boundInGlobalEnv(name):
# Already there
pass
else:
# Use the Python SP as a fallback to not having a fast one
self.bindPrimitiveSP(name, WarningSP(name, sp))
else:
assert isinstance(trace, puma.Trace)
self.trace = trace
py_state, np_state = seed
self.py_rng.setstate(py_state)
self.np_rng.set_state(np_state)
def check_foreign_sp(mode):
v = get_ripl()
builtins = builtin.builtInSPs()
resample = '[INFER (resample{0} 1)]'.format(mode)
v.execute_instruction(resample)
v.bind_foreign_sp('test_binomial', builtins['binomial'])
v.bind_foreign_sp('test_sym_dir_cat', builtins['make_sym_dir_cat'])
test_binomial_result = 1
test_sym_dir_result = {
'alpha': 1.0,
'counts': [0],
'n': 1,
'type': 'sym_dir_cat'
}
eq_(v.sample('(test_binomial 1 1)'), test_binomial_result)
eq_(v.sample('(test_sym_dir_cat 1 1)'), test_sym_dir_result)
engine = v.sivm.core_sivm.engine
dumped = engine.model.retrieve_dump(0)
restored = engine.model.restore_trace(dumped)
engine.model.create_trace_pool([restored])
# Make sure that the restored trace still has the foreign SP's
eq_(v.sample('(test_binomial 1 1)'), test_binomial_result)
eq_(v.sample('(test_sym_dir_cat 1 1)'), test_sym_dir_result)
def testForeignInfSPs():
r = get_ripl(persistent_inference_trace=True)
r.bind_foreign_inference_sp("my_mul", builtin.builtInSPs()["mul"])
r.infer("(resimulation_mh default one (+ (my_mul 2 2) 1))")
