def test_counterfactual_query(intervene, observe, flip):
# x -> y -> z -> w
sites = ["x", "y", "z", "w"]
observations = {"x": 1., "y": None, "z": 1., "w": 1.}
interventions = {"x": None, "y": 0., "z": 2., "w": 1.}
def model():
with handlers.seed(rng_seed=0):
x = numpyro.sample("x", dist.Normal(0, 1))
y = numpyro.sample("y", dist.Normal(x, 1))
z = numpyro.sample("z", dist.Normal(y, 1))
w = numpyro.sample("w", dist.Normal(z, 1))
return dict(x=x, y=y, z=z, w=w)
if not flip:
if intervene:
model = handlers.do(model, data=interventions)
if observe:
model = handlers.condition(model, data=observations)
elif flip and intervene and observe:
model = handlers.do(handlers.condition(model, data=observations),
data=interventions)
with handlers.trace() as tr:
actual_values = model()
for name in sites:
# case 1: purely observational query like handlers.condition
if not intervene and observe:
if observations[name] is not None:
assert tr[name]['is_observed']
assert_allclose(observations[name], actual_values[name])
assert_allclose(observations[name], tr[name]['value'])
if interventions[name] != observations[name]:
if interventions[name] is not None:
assert_raises(AssertionError, assert_allclose,
interventions[name], actual_values[name])
# case 2: purely interventional query like old handlers.do
elif intervene and not observe:
assert not tr[name]['is_observed']
if interventions[name] is not None:
assert_allclose(interventions[name], actual_values[name])
if observations[name] is not None:
assert_raises(AssertionError, assert_allclose,
observations[name], tr[name]['value'])
if interventions[name] is not None:
assert_raises(AssertionError, assert_allclose,
interventions[name], tr[name]['value'])
# case 3: counterfactual query mixing intervention and observation
elif intervene and observe:
if observations[name] is not None:
assert tr[name]['is_observed']
assert_allclose(observations[name], tr[name]['value'])
if interventions[name] is not None:
assert_allclose(interventions[name], actual_values[name])
if interventions[name] != observations[name]:
if interventions[name] is not None:
assert_raises(AssertionError, assert_allclose,
interventions[name], tr[name]['value'])
def test_condition():
def model():
x = numpyro.sample("x", dist.Delta(0.0))
y = numpyro.sample("y", dist.Normal(0.0, 1.0))
return x + y
model = handlers.condition(handlers.seed(model, random.PRNGKey(1)), {"y": 2.0})
model_trace = handlers.trace(model).get_trace()
assert model_trace["y"]["value"] == 2.0
assert model_trace["y"]["is_observed"]
assert handlers.condition(model, {"y": 3.0})() == 3.0
def test_condition():
def model():
x = numpyro.sample('x', dist.Delta(0.))
y = numpyro.sample('y', dist.Normal(0., 1.))
return x + y
model = handlers.condition(handlers.seed(model, random.PRNGKey(1)),
{'y': 2.})
model_trace = handlers.trace(model).get_trace()
assert model_trace['y']['value'] == 2.
assert model_trace['y']['is_observed']
assert handlers.condition(model, {'y': 3.})() == 3.
def test_condition():
def model():
x = numpyro.sample('x', dist.Delta(0.))
y = numpyro.sample('y', dist.Normal(0., 1.))
return x + y
model = handlers.condition(handlers.seed(model, random.PRNGKey(1)),
{'y': 2.})
model_trace = handlers.trace(model).get_trace()
assert model_trace['y']['value'] == 2.
assert model_trace['y']['is_observed']
# Raise ValueError when site is already observed.
with pytest.raises(ValueError):
handlers.condition(model, {'y': 3.})()
def fit(self, df, iter=500, seed=42, **kwargs):
teams = sorted(list(set(df["home_team"]) | set(df["away_team"])))
home_team = df["home_team"].values
away_team = df["away_team"].values
home_goals = df["home_goals"].values
away_goals = df["away_goals"].values
gameweek = ((df["date"] - df["date"].min()).dt.days // 7).values
self.team_to_index = {team: i for i, team in enumerate(teams)}
self.index_to_team = {
value: key
for key, value in self.team_to_index.items()
}
self.n_teams = len(teams)
self.min_date = df["date"].min()
conditioned_model = condition(self.model,
param_map={
"home_goals": home_goals,
"away_goals": away_goals
})
nuts_kernel = NUTS(conditioned_model)
mcmc = MCMC(nuts_kernel,
num_warmup=iter // 2,
num_samples=iter,
**kwargs)
rng_key = random.PRNGKey(seed)
mcmc.run(rng_key, home_team, away_team, gameweek)
self.samples = mcmc.get_samples()
mcmc.print_summary()
return self
def predict(
rng_key: np.ndarray, post_samples: np.ndarray, model: Callable, *args: Any, **kwargs: Any
) -> np.ndarray:
model = handlers.seed(handlers.condition(model, post_samples), rng_key)
model_trace = handlers.trace(model).get_trace(*args, **kwargs)
return model_trace["obs"]["value"]
def body_fn(wrapped_carry, x):
i, rng_key, carry = wrapped_carry
rng_key, subkey = random.split(rng_key) if rng_key is not None else (
None, None)
with handlers.block():
# we need to tell unconstrained messenger in potential energy computation
# that only the item at time `i` is needed when transforming
fn = handlers.infer_config(
f, config_fn=lambda msg: {"_scan_current_index": i})
seeded_fn = handlers.seed(fn, subkey) if subkey is not None else fn
for subs_type, subs_map in substitute_stack:
subs_fn = partial(_subs_wrapper, subs_map, i, length)
if subs_type == "condition":
seeded_fn = handlers.condition(seeded_fn,
condition_fn=subs_fn)
elif subs_type == "substitute":
seeded_fn = handlers.substitute(seeded_fn,
substitute_fn=subs_fn)
with handlers.trace() as trace:
carry, y = seeded_fn(carry, x)
return (i + 1, rng_key, carry), (PytreeTrace(trace), y)
def body_fn(wrapped_carry, x, prefix=None):
i, rng_key, carry = wrapped_carry
init = True if (not_jax_tracer(i) and i == 0) else False
rng_key, subkey = random.split(rng_key) if rng_key is not None else (None, None)
seeded_fn = handlers.seed(f, subkey) if subkey is not None else f
for subs_type, subs_map in substitute_stack:
subs_fn = partial(_subs_wrapper, subs_map, i, length)
if subs_type == 'condition':
seeded_fn = handlers.condition(seeded_fn, condition_fn=subs_fn)
elif subs_type == 'substitute':
seeded_fn = handlers.substitute(seeded_fn, substitute_fn=subs_fn)
if init:
with handlers.scope(prefix="_init"):
new_carry, y = seeded_fn(carry, x)
trace = {}
else:
with handlers.block(), packed_trace() as trace, promote_shapes(), enum(), markov():
# Like scan_wrapper, we collect the trace of scan's transition function
# `seeded_fn` here. To put time dimension to the correct position, we need to
# promote shapes to make `fn` and `value`
# at each site have the same batch dims (e.g. if `fn.batch_shape = (2, 3)`,
# and value's batch_shape is (3,), then we promote shape of
# value so that its batch shape is (1, 3)).
new_carry, y = config_enumerate(seeded_fn)(carry, x)
# store shape of new_carry at a global variable
nonlocal carry_shape_at_t1
carry_shape_at_t1 = [jnp.shape(x) for x in tree_flatten(new_carry)[0]]
# make new_carry have the same shape as carry
# FIXME: is this rigorous?
new_carry = tree_multimap(lambda a, b: jnp.reshape(a, jnp.shape(b)),
new_carry, carry)
return (i + jnp.array(1), rng_key, new_carry), (PytreeTrace(trace), y)
def test_no_split_deterministic():
def model():
x = numpyro.sample('x', dist.Normal(0., 1.))
y = numpyro.sample('y', dist.Normal(0., 1.))
return x + y
model = handlers.condition(model, {'x': 1., 'y': 2.})
assert model() == 3.
def test_no_split_deterministic():
def model():
x = numpyro.sample("x", dist.Normal(0.0, 1.0))
y = numpyro.sample("y", dist.Normal(0.0, 1.0))
return x + y
model = handlers.condition(model, {"x": 1.0, "y": 2.0})
assert model() == 3.0
def log_likelihood(
rng_key: np.ndarray, params: np.ndarray, model: Callable, *args: Any, **kwargs: Any
) -> np.ndarray:
model = handlers.condition(model, params)
model_trace = handlers.trace(model).get_trace(*args, **kwargs)
obs_node = model_trace["obs"]
return obs_node["fn"].log_prob(obs_node["value"])
def single_prediction(rng, samples):
model_trace = trace(seed(condition(model, samples),
rng)).get_trace(*args, **kwargs)
sites = model_trace.keys() - samples.keys(
) if return_sites is None else return_sites
return {
name: site['value']
for name, site in model_trace.items() if name in sites
}
def body_fn(wrapped_carry, x, prefix=None):
i, rng_key, carry = wrapped_carry
init = True if (not_jax_tracer(i)
and i in range(unroll_steps)) else False
rng_key, subkey = random.split(rng_key) if rng_key is not None else (
None, None)
# we need to tell unconstrained messenger in potential energy computation
# that only the item at time `i` is needed when transforming
fn = handlers.infer_config(
f, config_fn=lambda msg: {"_scan_current_index": i})
seeded_fn = handlers.seed(fn, subkey) if subkey is not None else fn
for subs_type, subs_map in substitute_stack:
subs_fn = partial(_subs_wrapper, subs_map, i, length)
if subs_type == "condition":
seeded_fn = handlers.condition(seeded_fn, condition_fn=subs_fn)
elif subs_type == "substitute":
seeded_fn = handlers.substitute(seeded_fn,
substitute_fn=subs_fn)
if init:
# handler the name to match the pattern of sakkar_bilmes product
with handlers.scope(prefix="_PREV_" * (unroll_steps - i),
divider=""):
new_carry, y = config_enumerate(seeded_fn)(carry, x)
trace = {}
else:
# Like scan_wrapper, we collect the trace of scan's transition function
# `seeded_fn` here. To put time dimension to the correct position, we need to
# promote shapes to make `fn` and `value`
# at each site have the same batch dims (e.g. if `fn.batch_shape = (2, 3)`,
# and value's batch_shape is (3,), then we promote shape of
# value so that its batch shape is (1, 3)).
# Here we will promote `fn` shape first. `value` shape will be promoted after scanned.
# We don't promote `value` shape here because we need to store carry shape
# at this step. If we reshape the `value` here, output carry might get wrong shape.
with _promote_fn_shapes(), packed_trace() as trace:
new_carry, y = config_enumerate(seeded_fn)(carry, x)
# store shape of new_carry at a global variable
if len(carry_shapes) < (history + 1):
carry_shapes.append(
[jnp.shape(x) for x in tree_flatten(new_carry)[0]])
# make new_carry have the same shape as carry
# FIXME: is this rigorous?
new_carry = tree_multimap(
lambda a, b: jnp.reshape(a, jnp.shape(b)), new_carry, carry)
return (i + 1, rng_key, new_carry), (PytreeTrace(trace), y)
def body_fn(wrapped_carry, x):
i, rng_key, carry = wrapped_carry
rng_key, subkey = random.split(rng_key) if rng_key is not None else (None, None)
with handlers.block():
seeded_fn = handlers.seed(f, subkey) if subkey is not None else f
for subs_type, subs_map in substitute_stack:
subs_fn = partial(_subs_wrapper, subs_map, i, length)
if subs_type == 'condition':
seeded_fn = handlers.condition(seeded_fn, condition_fn=subs_fn)
elif subs_type == 'substitute':
seeded_fn = handlers.substitute(seeded_fn, substitute_fn=subs_fn)
with handlers.trace() as trace:
carry, y = seeded_fn(carry, x)
return (i + 1, rng_key, carry), (PytreeTrace(trace), y)
def single_prediction(val):
rng_key, samples = val
if infer_discrete:
from numpyro.contrib.funsor import config_enumerate
from numpyro.contrib.funsor.discrete import _sample_posterior
model_trace = prototype_trace
temperature = 1
pred_samples = _sample_posterior(
config_enumerate(condition(model, samples)),
first_available_dim,
temperature,
rng_key,
*model_args,
**model_kwargs,
)
else:
model_trace = trace(
seed(substitute(masked_model, samples),
rng_key)).get_trace(*model_args, **model_kwargs)
pred_samples = {
name: site["value"]
for name, site in model_trace.items()
}
if return_sites is not None:
if return_sites == "":
sites = {
k
for k, site in model_trace.items()
if site["type"] != "plate"
}
else:
sites = return_sites
else:
sites = {
k
for k, site in model_trace.items()
if (site["type"] == "sample" and k not in samples) or (
site["type"] == "deterministic")
}
return {
name: value
for name, value in pred_samples.items() if name in sites
}
def wrapper(wrapped_operand):
rng_key, operand = wrapped_operand
with handlers.block():
seeded_fn = handlers.seed(fn,
rng_key) if rng_key is not None else fn
for subs_type, subs_map in substitute_stack:
subs_fn = partial(_subs_wrapper, subs_map)
if subs_type == "condition":
seeded_fn = handlers.condition(seeded_fn,
condition_fn=subs_fn)
elif subs_type == "substitute":
seeded_fn = handlers.substitute(seeded_fn,
substitute_fn=subs_fn)
with handlers.trace() as trace:
value = seeded_fn(operand)
return value, PytreeTrace(trace)
def test_mcmc_model_side_enumeration(model, temperature):
mcmc = infer.MCMC(infer.NUTS(model), 0, 1)
mcmc.run(random.PRNGKey(0))
mcmc_data = {
k: v[0] for k, v in mcmc.get_samples().items() if k in ["loc", "scale"]
}
# MAP estimate discretes, conditioned on posterior sampled continous latents.
model = handlers.seed(model, rng_seed=1)
actual_trace = handlers.trace(
infer_discrete(
# TODO support replayed sites in infer_discrete.
# handlers.replay(config_enumerate(model), mcmc_trace),
handlers.condition(config_enumerate(model), mcmc_data),
temperature=temperature,
rng_key=random.PRNGKey(1),
)
).get_trace()
# Check site names and shapes.
expected_trace = handlers.trace(model).get_trace()
assert set(actual_trace) == set(expected_trace)
def transformed_model_fn(*args, **kwargs):
mapped_args, mapped_kwargs, fixed_obs = obs_to_model_args_fn(*args, **kwargs)
return condition(model, data=fixed_obs)(*mapped_args, **mapped_kwargs)
def _wrap_model(model, *args, **kwargs):
gibbs_values = kwargs.pop("_gibbs_sites", {})
with condition(data=gibbs_values), substitute(data=gibbs_values):
return model(*args, **kwargs)
def fn(*args, **kwargs):
gibbs_values = kwargs.pop("_gibbs_sites", {})
with condition(data=gibbs_values), substitute(data=gibbs_values):
model(*args, **kwargs)
