def test_block_full(self):
model_trace = poutine.trace(poutine.block(self.model)).get_trace()
guide_trace = poutine.trace(poutine.block(self.guide)).get_trace()
for name in model_trace.nodes.keys():
assert model_trace.nodes[name]["type"] in ("args", "return")
for name in guide_trace.nodes.keys():
assert guide_trace.nodes[name]["type"] in ("args", "return")
def test_block_full_hide_expose(self):
try:
poutine.block(self.model,
hide=self.partial_sample_sites.keys(),
expose=self.partial_sample_sites.keys())()
assert False
except AssertionError:
assert True
def test_block_full_expose(self):
model_trace = poutine.trace(poutine.block(self.model,
expose=self.model_sites)).get_trace()
guide_trace = poutine.trace(poutine.block(self.guide,
expose=self.guide_sites)).get_trace()
for name in self.model_sites:
assert name in model_trace
for name in self.guide_sites:
assert name in guide_trace
def test_guide_list(auto_class):
def model():
pyro.sample("x", dist.Normal(0., 1.))
pyro.sample("y", dist.MultivariateNormal(torch.zeros(5), torch.eye(5, 5)))
guide = AutoGuideList(model)
guide.add(auto_class(poutine.block(model, expose=["x"]), prefix="auto_x"))
guide.add(auto_class(poutine.block(model, expose=["y"]), prefix="auto_y"))
guide()
def test_block_partial_expose(self):
model_trace = poutine.trace(
poutine.block(self.model, expose=self.partial_sample_sites.keys())).get_trace()
guide_trace = poutine.trace(
poutine.block(self.guide, expose=self.partial_sample_sites.keys())).get_trace()
for name in self.full_sample_sites.keys():
if name in self.partial_sample_sites:
assert name in model_trace
assert name in guide_trace
else:
assert name not in model_trace
assert name not in guide_trace
def _setup_prototype(self, *args, **kwargs):
# run the model so we can inspect its structure
model = config_enumerate(self.model, default="parallel")
self.prototype_trace = poutine.block(poutine.trace(model).get_trace)(*args, **kwargs)
self.prototype_trace = prune_subsample_sites(self.prototype_trace)
if self.master is not None:
self.master()._check_prototype(self.prototype_trace)
self._discrete_sites = []
self._cond_indep_stacks = {}
self._iaranges = {}
for name, site in self.prototype_trace.nodes.items():
if site["type"] != "sample" or site["is_observed"]:
continue
if site["infer"].get("enumerate") != "parallel":
raise NotImplementedError('Expected sample site "{}" to be discrete and '
'configured for parallel enumeration'.format(name))
# collect discrete sample sites
fn = site["fn"]
Dist = type(fn)
if Dist in (dist.Bernoulli, dist.Categorical, dist.OneHotCategorical):
params = [("probs", fn.probs.detach().clone(), fn.arg_constraints["probs"])]
else:
raise NotImplementedError("{} is not supported".format(Dist.__name__))
self._discrete_sites.append((site, Dist, params))
# collect independence contexts
self._cond_indep_stacks[name] = site["cond_indep_stack"]
for frame in site["cond_indep_stack"]:
if frame.vectorized:
self._iaranges[frame.name] = frame
else:
raise NotImplementedError("AutoDiscreteParallel does not support pyro.irange")
def _gen_weighted_samples(self, *args, **kwargs):
for tr, log_w in poutine.block(self.trace_dist._traces)(*args, **kwargs):
if self.sites == "_RETURN":
val = tr.nodes["_RETURN"]["value"]
else:
val = {name: tr.nodes[name]["value"]
for name in self.sites}
yield (val, log_w)
def test_trace_data(self):
tr1 = poutine.trace(
poutine.block(self.model, expose_types=["sample"])).get_trace()
tr2 = poutine.trace(
poutine.condition(self.model, data=tr1)).get_trace()
assert tr2.nodes["latent2"]["type"] == "sample" and \
tr2.nodes["latent2"]["is_observed"]
assert tr2.nodes["latent2"]["value"] is tr1.nodes["latent2"]["value"]
def test_block_tutorial_case(self):
model_trace = poutine.trace(self.model).get_trace()
guide_trace = poutine.trace(
poutine.block(self.guide, hide_types=["observe"])).get_trace()
assert "latent1" in model_trace
assert "latent1" in guide_trace
assert "obs" in model_trace
assert "obs" not in guide_trace
def __call__(self, *args, **kwargs):
# if first time
if self.compiled is None:
# param capture
with poutine.block():
with poutine.trace(param_only=True) as first_param_capture:
self.fn(*args, **kwargs)
self._param_names = list(set(first_param_capture.trace.nodes.keys()))
weakself = weakref.ref(self)
@torch.jit.compile(**self._jit_options)
def compiled(unconstrained_params, *args):
self = weakself()
constrained_params = {}
for name, unconstrained_param in zip(self._param_names, unconstrained_params):
constrained_param = pyro.param(name) # assume param has been initialized
assert constrained_param.unconstrained() is unconstrained_param
constrained_params[name] = constrained_param
return poutine.replay(
self.fn, params=constrained_params)(*args, **kwargs)
self.compiled = compiled
param_list = [pyro.param(name).unconstrained()
for name in self._param_names]
with poutine.block(hide=self._param_names):
with poutine.trace(param_only=True) as param_capture:
ret = self.compiled(param_list, *args, **kwargs)
new_params = filter(lambda name: name not in self._param_names,
param_capture.trace.nodes.keys())
for name in new_params:
# enforce uniqueness
if name not in self._param_names:
self._param_names.append(name)
return ret
def __call__(self, *args, **kwargs):
traces, logits = [], []
for tr, logit in poutine.block(self._traces)(*args, **kwargs):
traces.append(tr)
logits.append(logit)
logits = torch.stack(logits).squeeze()
logits -= util.log_sum_exp(logits)
if not isinstance(logits, torch.autograd.Variable):
logits = Variable(logits)
ix = dist.categorical(logits=logits, one_hot=False)
return traces[ix.data[0]]
def test_discrete_parallel(continuous_class):
K = 2
data = torch.tensor([0., 1., 10., 11., 12.])
def model(data):
weights = pyro.sample('weights', dist.Dirichlet(0.5 * torch.ones(K)))
locs = pyro.sample('locs', dist.Normal(0, 10).expand_by([K]).independent(1))
scale = pyro.sample('scale', dist.LogNormal(0, 1))
with pyro.iarange('data', len(data)):
weights = weights.expand(torch.Size((len(data),)) + weights.shape)
assignment = pyro.sample('assignment', dist.Categorical(weights))
pyro.sample('obs', dist.Normal(locs[assignment], scale), obs=data)
guide = AutoGuideList(model)
guide.add(continuous_class(poutine.block(model, hide=["assignment"])))
guide.add(AutoDiscreteParallel(poutine.block(model, expose=["assignment"])))
elbo = TraceEnum_ELBO(max_iarange_nesting=1)
loss = elbo.loss_and_grads(model, guide, data)
assert np.isfinite(loss), loss
def run(self, *args, **kwargs):
"""
Calls `self._traces` to populate execution traces from a stochastic
Pyro model.
:param args: optional args taken by `self._traces`.
:param kwargs: optional keywords args taken by `self._traces`.
"""
self._init()
for tr, logit in poutine.block(self._traces)(*args, **kwargs):
self.exec_traces.append(tr)
self.log_weights.append(logit)
self._categorical = Categorical(logits=torch.tensor(self.log_weights))
return self
def __init__(self, model, guide=None, num_samples=None):
"""
Constructor. default to num_samples = 10, guide = model
"""
super(Importance, self).__init__()
if num_samples is None:
num_samples = 10
logger.warn("num_samples not provided, defaulting to {}".format(num_samples))
if guide is None:
# propose from the prior by making a guide from the model by hiding observes
guide = poutine.block(model, hide_types=["observe"])
self.num_samples = num_samples
self.model = model
self.guide = guide
def _setup_prototype(self, *args, **kwargs):
# run the model so we can inspect its structure
self.prototype_trace = poutine.block(poutine.trace(self.model).get_trace)(*args, **kwargs)
self.prototype_trace = prune_subsample_sites(self.prototype_trace)
if self.master is not None:
self.master()._check_prototype(self.prototype_trace)
self._iaranges = {}
for name, site in self.prototype_trace.nodes.items():
if site["type"] != "sample" or site["is_observed"]:
continue
for frame in site["cond_indep_stack"]:
if frame.vectorized:
self._iaranges[frame.name] = frame
else:
raise NotImplementedError("AutoGuideList does not support pyro.irange")
def run(self, *args, **kwargs):
"""
Calls `self._traces` to populate execution traces from a stochastic
Pyro model.
:param args: optional args taken by `self._traces`.
:param kwargs: optional keywords args taken by `self._traces`.
"""
self._reset()
with poutine.block():
for i, vals in enumerate(self._traces(*args, **kwargs)):
if len(vals) == 2:
chain_id = 0
tr, logit = vals
else:
tr, logit, chain_id = vals
assert chain_id < self.num_chains
self.exec_traces.append(tr)
self.log_weights.append(logit)
self.chain_ids.append(chain_id)
self._idx_by_chain[chain_id].append(i)
self._categorical = Categorical(logits=torch.tensor(self.log_weights))
return self
def predict(self, x, trg_y):
"""Predict labels.
Args:
x (torch.utils.data.DataLoader):
trg_y (np.array): array for storing the predicted labels
Returns:
float: loss
"""
# resize `self._test_wbench` if necessary
n_instances = x[0].shape[0]
self._resize_wbench(n_instances)
self._test_wbench *= 0
# print("self._test_wbench:", repr(self._test_wbench))
with poutine.block():
with torch.no_grad():
for wbench_i in self._test_wbench:
wbench_i[:n_instances] = self.guide(*x)
mean = np.mean(self._test_wbench, axis=0)
trg_y[:] = np.argmax(mean[:n_instances], axis=-1)
return trg_y
def main(_argv):
transition_alphas = torch.tensor([[10., 90.],
[90., 10.]])
emission_alphas = torch.tensor([[[30., 20., 5.]],
[[5., 10., 100.]]])
lengths = torch.randint(10, 30, (10000,))
trace = poutine.trace(model).get_trace(transition_alphas, emission_alphas, lengths)
obs_sequences = [site['value'] for name, site in
trace.nodes.items() if name.startswith("element_")]
obs_sequences = torch.stack(obs_sequences, dim=-2)
guide = AutoDelta(poutine.block(model, hide_fn=lambda site: site['name'].startswith('state')),
init_loc_fn=init_to_sample)
svi = SVI(model, guide, Adam(dict(lr=0.1)), JitTraceEnum_ELBO())
total = 1000
with tqdm.trange(total) as t:
for i in t:
loss = svi.step(0.5 * torch.ones((2, 2), dtype=torch.float),
0.3 * torch.ones((2, 1, 3), dtype=torch.float),
lengths, obs_sequences)
t.set_description_str(f"SVI ({i}/{total}): {loss}")
median = guide.median()
print("Transition probs: ", median['transition_probs'].detach().numpy())
print("Emission probs: ", median['emission_probs'].squeeze().detach().numpy())
def posterior_entropy(y_dist, design):
# Important that y_dist is sampled *within* the function
y = pyro.sample("conditioning_y", y_dist)
y_dict = {
label: y[i, ...]
for i, label in enumerate(observation_labels)
}
conditioned_model = pyro.condition(model, data=y_dict)
# Here just using SVI to run the MAP optimization
guide.train()
SVI(conditioned_model,
guide=guide,
loss=loss,
optim=optim,
num_steps=num_steps,
num_samples=1).run(design)
# Recover the entropy
with poutine.block():
final_loss = loss(conditioned_model, guide, design)
guide.finalize(final_loss, target_labels)
entropy = mean_field_entropy(guide, [design],
whitelist=target_labels)
return entropy
def relbo(model, guide, *args, **kwargs):
approximation = kwargs.pop('approximation', None)
# Run the guide with the arguments passed to SVI.step() and trace the execution,
# i.e. record all the calls to Pyro primitives like sample() and param().
guide_trace = trace(guide).get_trace(*args, **kwargs)
# Now run the model with the same arguments and trace the execution. Because
# model is being run with replay, whenever we encounter a sample site in the
# model, instead of sampling from the corresponding distribution in the model,
# we instead reuse the corresponding sample from the guide. In probabilistic
# terms, this means our loss is constructed as an expectation w.r.t. the joint
# distribution defined by the guide.
model_trace = trace(replay(model, guide_trace)).get_trace(*args, **kwargs)
approximation_trace = trace(
replay(block(approximation, expose=["obs"]),
guide_trace)).get_trace(*args, **kwargs)
# We will accumulate the various terms of the ELBO in `elbo`.
elbo = 0.
# Loop over all the sample sites in the model and add the corresponding
# log p(z) term to the ELBO. Note that this will also include any observed
# data, i.e. sample sites with the keyword `obs=...`.
elbo = elbo + model_trace.log_prob_sum()
# Loop over all the sample sites in the guide and add the corresponding
# -log q(z) term to the ELBO.
elbo = elbo - guide_trace.log_prob_sum()
elbo = elbo - approximation_trace.log_prob_sum()
# Return (-elbo) since by convention we do gradient descent on a loss and
# the ELBO is a lower bound that needs to be maximized.
if elbo < 10e-8 and PRINT_TRACES:
print('Guide trace')
print(guide_trace.log_prob_sum())
print('Model trace')
print(model_trace.log_prob_sum())
print('Approximation trace')
print(approximation_trace.log_prob_sum())
return -elbo
def pol_att(self, x, y, t, e):
num_samples = self.num_samples
if not torch._C._get_tracing_state():
assert x.dim() == 2 and x.size(-1) == self.feature_dim
dataloader = [x]
print("Evaluating {} minibatches".format(len(dataloader)))
result_pol = []
result_eatt = []
for x in dataloader:
# x = self.whiten(x)
with pyro.plate("num_particles", num_samples, dim=-2):
with poutine.trace() as tr, poutine.block(hide=["y", "t"]):
self.guide(x)
with poutine.do(data=dict(t=torch.zeros(()))):
y0 = poutine.replay(self.model.y_mean, tr.trace)(x)
with poutine.do(data=dict(t=torch.ones(()))):
y1 = poutine.replay(self.model.y_mean, tr.trace)(x)
ite = (y1 - y0).mean(0)
ite[t > 0] = -ite[t > 0]
eatt = torch.abs(torch.mean(ite[(t + e) > 1]))
pols = []
for s in range(num_samples):
pols.append(policy_val(ypred1=y1[s], ypred0=y0[s], y=y, t=t))
pol = torch.stack(pols).mean(0)
if not torch._C._get_tracing_state():
print("batch eATT = {:0.6g}".format(eatt))
print("batch RPOL = {:0.6g}".format(pol))
result_pol.append(pol)
result_eatt.append(eatt)
return torch.stack(result_pol), torch.stack(result_eatt)
def _guess_max_plate_nesting(self, model, guide, *args, **kwargs):
"""
Guesses max_plate_nesting by running the (model,guide) pair once
without enumeration. This optimistically assumes static model
structure.
"""
# Ignore validation to allow model-enumerated sites absent from the guide.
with poutine.block():
guide_trace = poutine.trace(guide).get_trace(*args, **kwargs)
model_trace = poutine.trace(
poutine.replay(model, trace=guide_trace)).get_trace(*args, **kwargs)
guide_trace = prune_subsample_sites(guide_trace)
model_trace = prune_subsample_sites(model_trace)
sites = [site
for trace in (model_trace, guide_trace)
for site in trace.nodes.values()
if site["type"] == "sample"]
# Validate shapes now, since shape constraints will be weaker once
# max_plate_nesting is changed from float('inf') to some finite value.
# Here we know the traces are not enumerated, but later we'll need to
# allow broadcasting of dims to the left of max_plate_nesting.
if is_validation_enabled():
guide_trace.compute_log_prob()
model_trace.compute_log_prob()
for site in sites:
check_site_shape(site, max_plate_nesting=float('inf'))
dims = [frame.dim
for site in sites
for frame in site["cond_indep_stack"]
if frame.vectorized]
self.max_plate_nesting = -min(dims) if dims else 0
if self.vectorize_particles and self.num_particles > 1:
self.max_plate_nesting += 1
logging.info('Guessed max_plate_nesting = {}'.format(self.max_plate_nesting))
def belief_policy_model_guide(belief,
t,
discount=1.0,
discount_factor=0.95,
max_depth=10):
# prior weights is uniform
weights = pyro.param("action_weights",
torch.ones(len(actions)),
constraint=dist.constraints.simplex)
state = states[pyro.sample("s%d" % t, belief)]
history = "" # we can start from empty history
# This is just to generate the other variables
with poutine.block(hide=["a%d" % t]):
# Need to hide 'at' generated by the policy_model;
# we don't care about it; because that's what we
# are inferring -- we are calling pyro.sample("a%d" % t ..) next.
history_policy_model(state,
history,
t,
discount=discount,
discount_factor=discount_factor,
max_depth=max_depth)
# We eventually generate actions based on the weights
action = pyro.sample("a%d" % t, dist.Categorical(weights))
def align_samples(samples, model, particle_dim):
"""
Unsqueeze stacked samples such that their particle dim all aligns.
This traces ``model`` to determine the ``event_dim`` of each site.
"""
assert particle_dim < 0
sample = {name: value[0] for name, value in samples.items()}
with poutine.block(), poutine.trace() as tr, poutine.condition(
data=sample):
model()
samples = samples.copy()
for name, value in samples.items():
event_dim = tr.trace.nodes[name]["fn"].event_dim
pad = event_dim - particle_dim - value.dim()
if pad < 0:
raise ValueError(
"Cannot align samples, try moving particle_dim left")
if pad > 0:
shape = value.shape[:1] + (1, ) * pad + value.shape[1:]
samples[name] = value.reshape(shape)
return samples
def initialize(seed, model, data):
global global_guide, svi
pyro.set_rng_seed(seed)
pyro.clear_param_store()
exposed_params = []
# set the parameters inferred through the guide based on the kind of data
if 'gr' in mtype:
if dtype == 'norm':
exposed_params = ['weights', 'concentration']
elif dtype == 'raw':
exposed_params = ['weights', 'alpha', 'beta']
elif 'dim' in mtype:
if dtype == 'norm':
exposed_params = [
'topic_weights', 'topic_concentration', 'participant_topics'
]
elif dtype == 'raw':
exposed_params = [
'topic_weights', 'topic_a', 'topic_b', 'participant_topics'
]
global_guide = AutoDelta(poutine.block(model, expose=exposed_params))
svi = SVI(model, global_guide, optim, loss=elbo)
return svi.loss(model, global_guide, data)
def main(args):
pyro.set_rng_seed(0)
pyro.clear_param_store()
pyro.enable_validation(__debug__)
# load data
if args.dataset == "dipper":
capture_history_file = os.path.dirname(
os.path.abspath(__file__)) + '/dipper_capture_history.csv'
elif args.dataset == "vole":
capture_history_file = os.path.dirname(
os.path.abspath(__file__)) + '/meadow_voles_capture_history.csv'
else:
raise ValueError("Available datasets are \'dipper\' and \'vole\'.")
capture_history = torch.tensor(
np.genfromtxt(capture_history_file, delimiter=',')).float()[:, 1:]
N, T = capture_history.shape
print(
"Loaded {} capture history for {} individuals collected over {} time periods."
.format(args.dataset, N, T))
if args.dataset == "dipper" and args.model in ["4", "5"]:
sex_file = os.path.dirname(
os.path.abspath(__file__)) + '/dipper_sex.csv'
sex = torch.tensor(np.genfromtxt(sex_file, delimiter=',')).float()[:,
1]
print("Loaded dipper sex data.")
elif args.dataset == "vole" and args.model in ["4", "5"]:
raise ValueError(
"Cannot run model_{} on meadow voles data, since we lack sex " +
"information for these animals.".format(args.model))
else:
sex = None
model = models[args.model]
# we use poutine.block to only expose the continuous latent variables
# in the models to AutoDiagonalNormal (all of which begin with 'phi'
# or 'rho')
def expose_fn(msg):
return msg["name"][0:3] in ['phi', 'rho']
# we use a mean field diagonal normal variational distributions (i.e. guide)
# for the continuous latent variables.
guide = AutoDiagonalNormal(poutine.block(model, expose_fn=expose_fn))
# since we enumerate the discrete random variables,
# we need to use TraceEnum_ELBO or TraceTMC_ELBO.
optim = Adam({'lr': args.learning_rate})
if args.tmc:
elbo = TraceTMC_ELBO(max_plate_nesting=1)
tmc_model = poutine.infer_config(model, lambda msg: {
"num_samples": args.tmc_num_samples,
"expand": False
} if msg["infer"].get("enumerate", None) == "parallel" else {}
) # noqa: E501
svi = SVI(tmc_model, guide, optim, elbo)
else:
elbo = TraceEnum_ELBO(max_plate_nesting=1,
num_particles=20,
vectorize_particles=True)
svi = SVI(model, guide, optim, elbo)
losses = []
print(
"Beginning training of model_{} with Stochastic Variational Inference."
.format(args.model))
for step in range(args.num_steps):
loss = svi.step(capture_history, sex)
losses.append(loss)
if step % 20 == 0 and step > 0 or step == args.num_steps - 1:
print("[iteration %03d] loss: %.3f" %
(step, np.mean(losses[-20:])))
# evaluate final trained model
elbo_eval = TraceEnum_ELBO(max_plate_nesting=1,
num_particles=2000,
vectorize_particles=True)
svi_eval = SVI(model, guide, optim, elbo_eval)
print("Final loss: %.4f" % svi_eval.evaluate_loss(capture_history, sex))
def auto_guide_list_x(model):
guide = AutoGuideList(model)
guide.add(AutoDelta(poutine.block(model, expose=["x"])))
guide.add(AutoDiagonalNormal(poutine.block(model, hide=["x"])))
return guide
# for x in batch:
# ret.append(inner(observed[x], start, 0, 0, prefix=str(x))[0])
# return ret
return inner(observed, start, 0, 0)
if __name__ == '__main__':
observed = ["the dog bit the man".split()]
nonterminals = ["S", "NP", "VP"]
preterminals = ["DT", "NN", "VB"]
model = partial(model,
start="S",
nonterminals=nonterminals,
productions=[("NP", "VP"), ("DT", "NN"), ("VB", "NN")],
preterminals=preterminals,
terminals=["the", "dog", "man", "bit"])
expose_params = ["binary_%s" % nonterm for nonterm in nonterminals] \
+ ["unary_%s" % preterm for preterm in preterminals]
guide = AutoContinuous(poutine.block(model, expose=expose_params))
elbo = Trace_ELBO()
optim = Adam({"lr": 0.001})
svi = SVI(model, guide, optim, elbo)
for _ in range(30):
loss = svi.step(observed[0])
print(loss)
def _create_autoguide(
self,
model,
amortised,
encoder_kwargs,
data_transform,
encoder_mode,
init_loc_fn=init_to_mean(fallback=init_to_feasible),
n_cat_list: list = [],
encoder_instance=None,
guide_class=AutoNormal,
guide_kwargs: Optional[dict] = None,
):
if guide_kwargs is None:
guide_kwargs = dict()
if not amortised:
if getattr(model, "discrete_variables", None) is not None:
model = poutine.block(model, hide=model.discrete_variables)
if issubclass(guide_class, poutine.messenger.Messenger):
# messenger guides don't need create_plates function
_guide = guide_class(
model,
init_loc_fn=init_loc_fn,
**guide_kwargs,
)
else:
_guide = guide_class(
model,
init_loc_fn=init_loc_fn,
**guide_kwargs,
create_plates=self.model.create_plates,
)
else:
encoder_kwargs = encoder_kwargs if isinstance(
encoder_kwargs, dict) else dict()
n_hidden = encoder_kwargs[
"n_hidden"] if "n_hidden" in encoder_kwargs.keys() else 200
if isinstance(data_transform, np.ndarray):
# add extra info about gene clusters as input to NN
self.register_buffer(
"gene_clusters",
torch.tensor(data_transform.astype("float32")))
n_in = model.n_vars + data_transform.shape[1]
data_transform = self._data_transform_clusters()
elif data_transform == "log1p":
# use simple log1p transform
data_transform = torch.log1p
n_in = self.model.n_vars
elif (isinstance(data_transform, dict)
and "var_std" in list(data_transform.keys())
and "var_mean" in list(data_transform.keys())):
# use data transform by scaling
n_in = model.n_vars
self.register_buffer(
"var_mean",
torch.tensor(
data_transform["var_mean"].astype("float32").reshape(
(1, n_in))),
)
self.register_buffer(
"var_std",
torch.tensor(
data_transform["var_std"].astype("float32").reshape(
(1, n_in))),
)
data_transform = self._data_transform_scale()
else:
# use custom data transform
data_transform = data_transform
n_in = model.n_vars
amortised_vars = model.list_obs_plate_vars()
if len(amortised_vars["input"]) >= 2:
encoder_kwargs["n_cat_list"] = n_cat_list
amortised_vars["input_transform"][0] = data_transform
if getattr(model, "discrete_variables", None) is not None:
model = poutine.block(model, hide=model.discrete_variables)
_guide = AutoAmortisedHierarchicalNormalMessenger(
model,
amortised_plate_sites=amortised_vars,
n_in=n_in,
n_hidden=n_hidden,
encoder_kwargs=encoder_kwargs,
encoder_mode=encoder_mode,
encoder_instance=encoder_instance,
**guide_kwargs,
)
return _guide
len(mix_params),
device=x.device))
beta_scale = pyro.param(
'beta_resp_scale',
torch.tril(
1. * torch.eye(len(mix_params), len(mix_params), device=x.device)),
constraint=constraints.lower_cholesky)
pyro.sample(
"beta_resp",
dist.MultivariateNormal(beta_loc, scale_tril=beta_scale).to_event(1))
# In[11]:
guide = AutoGuideList(model)
guide.add(AutoDiagonalNormal(poutine.block(model, expose=['theta',
'L_omega'])))
guide.add(my_local_guide) # automatically wrapped in an AutoCallable
# # Run variational inference
# In[12]:
# prepare data for running inference
train_x = torch.tensor(alt_attributes, dtype=torch.float)
train_x = train_x.cuda()
train_y = torch.tensor(true_choices, dtype=torch.int)
train_y = train_y.cuda()
alt_av_cuda = torch.from_numpy(alt_availability)
alt_av_cuda = alt_av_cuda.cuda()
alt_av_mat = alt_availability.copy()
alt_av_mat[np.where(alt_av_mat == 0)] = -1e9
def trainVI(
data,
hidden_dim,
learning_rate=1e-03,
n_iters=500,
trace_embeddings_interval=20,
writer=None,
moved_points={},
sigma_fix=1e-3,
):
"""Train (Deep) PPCA model with VI.
Using tensorboardX SummaryWriter to log to tensorboard.
Logging the internal result by setting "trace_embeddings_interval"
to the expected interval (50, 100, ...).
Set it to value larger than "n_iters" to disable interval logging.
"""
pyro.enable_validation(True)
pyro.set_rng_seed(0)
pyro.clear_param_store()
model = partial(
ppca_model,
hidden_dim=hidden_dim,
z_dim=2,
moved_points=moved_points,
sigma_fix=sigma_fix,
)
guide = AutoGuideList(model)
guide.add(AutoDiagonalNormal(model=poutine.block(model, expose=["sigma"])))
guide.add(AutoDiagonalNormal(model=poutine.block(model, expose=["Z"]), prefix="qZ"))
optim = Adam({"lr": learning_rate})
svi = SVI(model, guide, optim, loss=Trace_ELBO())
fig_title = f"lr={learning_rate}/hidden-dim={hidden_dim}"
metric = DRMetric(X=data, Y=None)
data = torch.tensor(data, dtype=torch.float)
for n_iter in tqdm(range(n_iters)):
loss = svi.step(data)
if writer and n_iter % 10 == 0:
writer.add_scalar("train_vi/loss", loss, n_iter)
if writer and n_iter % trace_embeddings_interval == 0:
z2d_loc = pyro.param("qZ_loc").reshape(-1, 2).data.numpy()
auc_rnx = metric.update(Y=z2d_loc).auc_rnx()
writer.add_scalar("metrics/auc_rnx", auc_rnx, n_iter)
fig = get_fig_plot_z2d(z2d_loc, fig_title + f", auc_rnx={auc_rnx:.3f}")
writer.add_figure("train_vi/z2d", fig, n_iter)
# show named rvs
print("List params and their size: ")
for p_name, p_val in pyro.get_param_store().items():
print(p_name, p_val.shape)
z2d_loc = pyro.param("qZ_loc").reshape(-1, 2).data.numpy()
z2d_scale = pyro.param("qZ_scale").reshape(-1, 2).data.numpy()
if writer:
fig = get_fig_plot_z2d(z2d_loc, fig_title)
writer.add_figure("train_vi/z2d", fig, n_iters)
return z2d_loc, z2d_scale
def main(args):
if args.cuda:
torch.set_default_tensor_type("torch.cuda.FloatTensor")
logging.info("Loading data")
data = poly.load_data(poly.JSB_CHORALES)
logging.info("-" * 40)
model = models[args.model]
logging.info("Training {} on {} sequences".format(
model.__name__, len(data["train"]["sequences"])))
sequences = data["train"]["sequences"]
lengths = data["train"]["sequence_lengths"]
# find all the notes that are present at least once in the training set
present_notes = (sequences == 1).sum(0).sum(0) > 0
# remove notes that are never played (we remove 37/88 notes)
sequences = sequences[..., present_notes]
if args.truncate:
lengths = lengths.clamp(max=args.truncate)
sequences = sequences[:, :args.truncate]
num_observations = float(lengths.sum())
pyro.set_rng_seed(args.seed)
pyro.clear_param_store()
# We'll train using MAP Baum-Welch, i.e. MAP estimation while marginalizing
# out the hidden state x. This is accomplished via an automatic guide that
# learns point estimates of all of our conditional probability tables,
# named probs_*.
guide = AutoDelta(
poutine.block(model,
expose_fn=lambda msg: msg["name"].startswith("probs_")))
# To help debug our tensor shapes, let's print the shape of each site's
# distribution, value, and log_prob tensor. Note this information is
# automatically printed on most errors inside SVI.
if args.print_shapes:
first_available_dim = -2 if model is model_0 else -3
guide_trace = poutine.trace(guide).get_trace(
sequences, lengths, args=args, batch_size=args.batch_size)
model_trace = poutine.trace(
poutine.replay(poutine.enum(model, first_available_dim),
guide_trace)).get_trace(sequences,
lengths,
args=args,
batch_size=args.batch_size)
logging.info(model_trace.format_shapes())
# Enumeration requires a TraceEnum elbo and declaring the max_plate_nesting.
# All of our models have two plates: "data" and "tones".
optim = Adam({"lr": args.learning_rate})
if args.tmc:
if args.jit:
raise NotImplementedError(
"jit support not yet added for TraceTMC_ELBO")
elbo = TraceTMC_ELBO(max_plate_nesting=1 if model is model_0 else 2)
tmc_model = poutine.infer_config(
model,
lambda msg: {
"num_samples": args.tmc_num_samples,
"expand": False
} if msg["infer"].get("enumerate", None) == "parallel" else {},
) # noqa: E501
svi = SVI(tmc_model, guide, optim, elbo)
else:
Elbo = JitTraceEnum_ELBO if args.jit else TraceEnum_ELBO
elbo = Elbo(
max_plate_nesting=1 if model is model_0 else 2,
strict_enumeration_warning=(model is not model_7),
jit_options={"time_compilation": args.time_compilation},
)
svi = SVI(model, guide, optim, elbo)
# We'll train on small minibatches.
logging.info("Step\tLoss")
for step in range(args.num_steps):
loss = svi.step(sequences,
lengths,
args=args,
batch_size=args.batch_size)
logging.info("{: >5d}\t{}".format(step, loss / num_observations))
if args.jit and args.time_compilation:
logging.debug("time to compile: {} s.".format(
elbo._differentiable_loss.compile_time))
# We evaluate on the entire training dataset,
# excluding the prior term so our results are comparable across models.
train_loss = elbo.loss(model,
guide,
sequences,
lengths,
args,
include_prior=False)
logging.info("training loss = {}".format(train_loss / num_observations))
# Finally we evaluate on the test dataset.
logging.info("-" * 40)
logging.info("Evaluating on {} test sequences".format(
len(data["test"]["sequences"])))
sequences = data["test"]["sequences"][..., present_notes]
lengths = data["test"]["sequence_lengths"]
if args.truncate:
lengths = lengths.clamp(max=args.truncate)
num_observations = float(lengths.sum())
# note that since we removed unseen notes above (to make the problem a bit easier and for
# numerical stability) this test loss may not be directly comparable to numbers
# reported on this dataset elsewhere.
test_loss = elbo.loss(model,
guide,
sequences,
lengths,
args=args,
include_prior=False)
logging.info("test loss = {}".format(test_loss / num_observations))
# We expect models with higher capacity to perform better,
# but eventually overfit to the training set.
capacity = sum(
value.reshape(-1).size(0) for value in pyro.get_param_store().values())
logging.info("{} capacity = {} parameters".format(model.__name__,
capacity))
def fit_advi_iterative(self,
n=3,
method='advi',
n_type='restart',
n_iter=None,
learning_rate=None,
progressbar=True,
num_workers=2,
train_proportion=None,
stratify_cv=None,
l2_weight=False,
sample_scaling_weight=0.5,
checkpoints=None,
checkpoint_dir='./checkpoints',
tracking=False):
r""" Train posterior using ADVI method.
(maximising likehood of the data and minimising KL-divergence of posterior to prior)
:param n: number of independent initialisations
:param method: to allow for potential use of SVGD or MCMC (currently only ADVI implemented).
:param n_type: type of repeated initialisation:
'restart' to pick different initial value,
'cv' for molecular cross-validation - splits counts into n datasets,
for now, only n=2 is implemented
'bootstrap' for fitting the model to multiple downsampled datasets.
Run `mod.bootstrap_data()` to generate variants of data
:param n_iter: number of iterations, supersedes self.n_iter
:param train_proportion: if not None, which proportion of cells to use for training and which for validation.
:param checkpoints: int, list of int's or None, number of checkpoints to save while model training or list of
iterations to save checkpoints on
:param checkpoint_dir: str, directory to save checkpoints in
:param tracking: bool, track all latent variables during training - if True makes training 2 times slower
:return: None
"""
# initialise parameter store
self.svi = {}
self.hist = {}
self.guide_i = {}
self.samples = {}
self.node_samples = {}
if tracking:
self.logp_hist = {}
if n_iter is None:
n_iter = self.n_iter
if type(checkpoints) is int:
if n_iter < checkpoints:
checkpoints = n_iter
checkpoints = np.linspace(0, n_iter, checkpoints + 1,
dtype=int)[1:]
self.checkpoints = list(checkpoints)
else:
self.checkpoints = checkpoints
self.checkpoint_dir = checkpoint_dir
self.n_type = n_type
self.l2_weight = l2_weight
self.sample_scaling_weight = sample_scaling_weight
self.train_proportion = train_proportion
if stratify_cv is not None:
self.stratify_cv = stratify_cv
if train_proportion is not None:
self.validation_hist = {}
self.training_hist = {}
if tracking:
self.logp_hist_val = {}
self.logp_hist_train = {}
if learning_rate is None:
learning_rate = self.learning_rate
if np.isin(n_type, ['bootstrap']):
if self.X_data_sample is None:
self.bootstrap_data(n=n)
elif np.isin(n_type, ['cv']):
self.generate_cv_data() # cv data added to self.X_data_sample
init_names = ['init_' + str(i + 1) for i in np.arange(n)]
for i, name in enumerate(init_names):
################### Initialise parameters & optimiser ###################
# initialise Variational distribution = guide
if method is 'advi':
self.guide_i[name] = AutoGuideList(self.model)
normal_guide_block = poutine.block(
self.model,
expose_all=True,
hide_all=False,
hide=self.point_estim +
flatten_iterable(self.custom_guides.keys()))
self.guide_i[name].append(
AutoNormal(normal_guide_block, init_loc_fn=init_to_mean))
self.guide_i[name].append(
AutoDelta(
poutine.block(self.model,
hide_all=True,
expose=self.point_estim)))
for k, v in self.custom_guides.items():
self.guide_i[name].append(v)
elif method is 'custom':
self.guide_i[name] = self.guide
# initialise SVI inference method
self.svi[name] = SVI(
self.model,
self.guide_i[name],
optim.ClippedAdam({
'lr': learning_rate,
# limit the gradient step from becoming too large
'clip_norm': self.total_grad_norm_constraint
}),
loss=JitTrace_ELBO())
pyro.clear_param_store()
self.set_initial_values()
# record ELBO Loss history here
self.hist[name] = []
if tracking:
self.logp_hist[name] = defaultdict(list)
if train_proportion is not None:
self.validation_hist[name] = []
if tracking:
self.logp_hist_val[name] = defaultdict(list)
################### Select data for this iteration ###################
if np.isin(n_type, ['cv', 'bootstrap']):
X_data = self.X_data_sample[i].astype(self.data_type)
else:
X_data = self.X_data.astype(self.data_type)
################### Training / validation split ###################
# split into training and validation
if train_proportion is not None:
idx = np.arange(len(X_data))
train_idx, val_idx = train_test_split(
idx,
train_size=train_proportion,
shuffle=True,
stratify=self.stratify_cv)
extra_data_val = {
k: torch.FloatTensor(v[val_idx]).to(self.device)
for k, v in self.extra_data.items()
}
extra_data_train = {
k: torch.FloatTensor(v[train_idx])
for k, v in self.extra_data.items()
}
x_data_val = torch.FloatTensor(X_data[val_idx]).to(self.device)
x_data = torch.FloatTensor(X_data[train_idx])
else:
# just convert data to CPU tensors
x_data = torch.FloatTensor(X_data)
extra_data_train = {
k: torch.FloatTensor(v)
for k, v in self.extra_data.items()
}
################### Move data to cuda - FULL data ###################
# if not minibatch do this:
if self.minibatch_size is None:
# move tensors to CUDA
x_data = x_data.to(self.device)
for k in extra_data_train.keys():
extra_data_train[k] = extra_data_train[k].to(self.device)
# extra_data_train = {k: v.to(self.device) for k, v in extra_data_train.items()}
################### MINIBATCH data ###################
else:
# create minibatches
dataset = MiniBatchDataset(x_data,
extra_data_train,
return_idx=True)
loader = DataLoader(dataset,
batch_size=self.minibatch_size,
num_workers=0) # TODO num_workers
################### Training the model ###################
# start training in epochs
epochs_iterator = tqdm(range(n_iter))
for epoch in epochs_iterator:
if self.minibatch_size is None:
################### Training FULL data ###################
iter_loss = self.step_train(name, x_data, extra_data_train)
self.hist[name].append(iter_loss)
# save data for posterior sampling
self.x_data = x_data
self.extra_data_train = extra_data_train
if tracking:
guide_tr, model_tr = self.step_trace(
name, x_data, extra_data_train)
self.logp_hist[name]['guide'].append(
guide_tr.log_prob_sum().item())
self.logp_hist[name]['model'].append(
model_tr.log_prob_sum().item())
for k, v in model_tr.nodes.items():
if "log_prob_sum" in v:
self.logp_hist[name][k].append(
v["log_prob_sum"].item())
else:
################### Training MINIBATCH data ###################
aver_loss = []
if tracking:
aver_logp_guide = []
aver_logp_model = []
aver_logp = defaultdict(list)
for batch in loader:
x_data_batch, extra_data_batch = batch
x_data_batch = x_data_batch.to(self.device)
extra_data_batch = {
k: v.to(self.device)
for k, v in extra_data_batch.items()
}
loss = self.step_train(name, x_data_batch,
extra_data_batch)
if tracking:
guide_tr, model_tr = self.step_trace(
name, x_data_batch, extra_data_batch)
aver_logp_guide.append(
guide_tr.log_prob_sum().item())
aver_logp_model.append(
model_tr.log_prob_sum().item())
for k, v in model_tr.nodes.items():
if "log_prob_sum" in v:
aver_logp[k].append(
v["log_prob_sum"].item())
aver_loss.append(loss)
iter_loss = np.sum(aver_loss)
# save data for posterior sampling
self.x_data = x_data_batch
self.extra_data_train = extra_data_batch
self.hist[name].append(iter_loss)
if tracking:
iter_logp_guide = np.sum(aver_logp_guide)
iter_logp_model = np.sum(aver_logp_model)
self.logp_hist[name]['guide'].append(iter_logp_guide)
self.logp_hist[name]['model'].append(iter_logp_model)
for k, v in aver_logp.items():
self.logp_hist[name][k].append(np.sum(v))
if self.checkpoints is not None:
if (epoch + 1) in self.checkpoints:
self.save_checkpoint(epoch + 1, prefix=name)
################### Evaluating cross-validation loss ###################
if train_proportion is not None:
iter_loss_val = self.step_eval_loss(
name, x_data_val, extra_data_val)
if tracking:
guide_tr, model_tr = self.step_trace(
name, x_data_val, extra_data_val)
self.logp_hist_val[name]['guide'].append(
guide_tr.log_prob_sum().item())
self.logp_hist_val[name]['model'].append(
model_tr.log_prob_sum().item())
for k, v in model_tr.nodes.items():
if "log_prob_sum" in v:
self.logp_hist_val[name][k].append(
v["log_prob_sum"].item())
self.validation_hist[name].append(iter_loss_val)
epochs_iterator.set_description(f'ELBO Loss: ' + '{:.4e}'.format(iter_loss) \
+ ': Val loss: ' + '{:.4e}'.format(iter_loss_val))
else:
epochs_iterator.set_description('ELBO Loss: ' +
'{:.4e}'.format(iter_loss))
if epoch % 20 == 0:
torch.cuda.empty_cache()
if train_proportion is not None:
# rescale loss
self.validation_hist[name] = [
i / (1 - train_proportion)
for i in self.validation_hist[name]
]
self.hist[name] = [
i / train_proportion for i in self.hist[name]
]
# reassing the main loss to be displayed
self.training_hist[name] = self.hist[name]
self.hist[name] = self.validation_hist[name]
if tracking:
for k, v in self.logp_hist[name].items():
self.logp_hist[name][k] = [
i / train_proportion
for i in self.logp_hist[name][k]
]
self.logp_hist_val[name][k] = [
i / (1 - train_proportion)
for i in self.logp_hist_val[name][k]
]
self.logp_hist_train[name] = self.logp_hist[name]
self.logp_hist[name] = self.logp_hist_val[name]
if self.verbose:
print(plt.plot(np.log10(self.hist[name][0:])))
def fit_advi_iterative_simple(
self,
n: int = 3,
method='advi',
n_type='restart',
n_iter=None,
learning_rate=None,
progressbar=True,
):
r""" Find posterior using ADVI (deprecated)
(maximising likehood of the data and minimising KL-divergence of posterior to prior)
:param n: number of independent initialisations
:param method: which approximation of the posterior (guide) to use?.
* ``'advi'`` - Univariate normal approximation (pyro.infer.autoguide.AutoDiagonalNormal)
* ``'custom'`` - Custom guide using conjugate posteriors
:return: self.svi dictionary with svi pyro objects for each n, and sefl.elbo dictionary storing training history.
"""
# Pass data to pyro / pytorch
self.x_data = torch.tensor(self.X_data.astype(
self.data_type)) # .double()
# initialise parameter store
self.svi = {}
self.hist = {}
self.guide_i = {}
self.samples = {}
self.node_samples = {}
self.n_type = n_type
if n_iter is None:
n_iter = self.n_iter
if learning_rate is None:
learning_rate = self.learning_rate
if np.isin(n_type, ['bootstrap']):
if self.X_data_sample is None:
self.bootstrap_data(n=n)
elif np.isin(n_type, ['cv']):
self.generate_cv_data() # cv data added to self.X_data_sample
init_names = ['init_' + str(i + 1) for i in np.arange(n)]
for i, name in enumerate(init_names):
# initialise Variational distributiion = guide
if method is 'advi':
self.guide_i[name] = AutoGuideList(self.model)
self.guide_i[name].append(
AutoNormal(poutine.block(self.model,
expose_all=True,
hide_all=False,
hide=self.point_estim),
init_loc_fn=init_to_mean))
self.guide_i[name].append(
AutoDelta(
poutine.block(self.model,
hide_all=True,
expose=self.point_estim)))
elif method is 'custom':
self.guide_i[name] = self.guide
# initialise SVI inference method
self.svi[name] = SVI(
self.model,
self.guide_i[name],
optim.ClippedAdam({
'lr': learning_rate,
# limit the gradient step from becoming too large
'clip_norm': self.total_grad_norm_constraint
}),
loss=JitTrace_ELBO())
pyro.clear_param_store()
# record ELBO Loss history here
self.hist[name] = []
# pick dataset depending on the training mode and move to GPU
if np.isin(n_type, ['cv', 'bootstrap']):
self.x_data = torch.tensor(self.X_data_sample[i].astype(
self.data_type))
else:
self.x_data = torch.tensor(self.X_data.astype(self.data_type))
if self.use_cuda:
# move tensors and modules to CUDA
self.x_data = self.x_data.cuda()
# train for n_iter
it_iterator = tqdm(range(n_iter))
for it in it_iterator:
hist = self.svi[name].step(self.x_data)
it_iterator.set_description('ELBO Loss: ' +
str(np.round(hist, 3)))
self.hist[name].append(hist)
# if it % 50 == 0 & self.verbose:
# logging.info("Elbo loss: {}".format(hist))
if it % 500 == 0:
torch.cuda.empty_cache()
def step(self, state):
with poutine.block(hide_types=["observe"]):
super().step(state.copy())
def _quantized_model(self):
"""
Quantized vectorized model used for parallel-scan enumerated inference.
This method is called only outside particle_plate.
"""
C = len(self.compartments)
T = self.duration
Q = self.num_quant_bins
R_shape = getattr(self.population, "shape", ()) # Region shape.
# Sample global parameters and auxiliary variables.
params = self.global_model()
auxiliary, non_compartmental = self._sample_auxiliary()
# Manually enumerate.
curr, logp = quantize_enumerate(auxiliary, min=0, max=self.population,
num_quant_bins=self.num_quant_bins)
curr = OrderedDict(zip(self.compartments, curr.unbind(0)))
logp = OrderedDict(zip(self.compartments, logp.unbind(0)))
curr.update(non_compartmental)
# Truncate final value from the right then pad initial value onto the left.
init = self.initialize(params)
prev = {}
for name, value in init.items():
if name in self.compartments:
if isinstance(value, torch.Tensor):
value = value[..., None] # Because curr is enumerated on the right.
prev[name] = cat2(value, curr[name][:-1],
dim=-3 if self.is_regional else -2)
else: # non-compartmental
prev[name] = cat2(init[name], curr[name][:-1], dim=-curr[name].dim())
# Reshape to support broadcasting, similar to EnumMessenger.
def enum_reshape(tensor, position):
assert tensor.size(-1) == Q
assert tensor.dim() <= self.max_plate_nesting + 2
tensor = tensor.permute(tensor.dim() - 1, *range(tensor.dim() - 1))
shape = [Q] + [1] * (position + self.max_plate_nesting - (tensor.dim() - 2))
shape.extend(tensor.shape[1:])
return tensor.reshape(shape)
for e, name in enumerate(self.compartments):
curr[name] = enum_reshape(curr[name], e)
logp[name] = enum_reshape(logp[name], e)
prev[name] = enum_reshape(prev[name], e + C)
# Enable approximate inference by using aux as a non-enumerated proxy
# for enumerated compartment values.
for name in self.approximate:
aux = auxiliary[self.compartments.index(name)]
curr[name + "_approx"] = aux
prev[name + "_approx"] = cat2(init[name], aux[:-1],
dim=-2 if self.is_regional else -1)
# Record transition factors.
with poutine.block(), poutine.trace() as tr:
with self.time_plate:
t = slice(0, T, 1) # Used to slice data tensors.
self._transition_bwd(params, prev, curr, t)
tr.trace.compute_log_prob()
for name, site in tr.trace.nodes.items():
if site["type"] == "sample":
log_prob = site["log_prob"]
if log_prob.dim() <= self.max_plate_nesting: # Not enumerated.
pyro.factor("transition_" + name, site["log_prob_sum"])
continue
if self.is_regional and log_prob.shape[-1:] != R_shape:
# Poor man's tensor variable elimination.
log_prob = log_prob.expand(log_prob.shape[:-1] + R_shape) / R_shape[0]
logp[name] = site["log_prob"]
# Manually perform variable elimination.
logp = reduce(operator.add, logp.values())
logp = logp.reshape(Q ** C, Q ** C, T, -1) # prev, curr, T, batch
logp = logp.permute(3, 2, 0, 1).squeeze(0) # batch, T, prev, curr
logp = pyro.distributions.hmm._sequential_logmatmulexp(logp) # batch, prev, curr
logp = logp.reshape(-1, Q ** C * Q ** C).logsumexp(-1).sum()
warn_if_nan(logp)
pyro.factor("transition", logp)
# Apply final likelihood.
prev = {name: prev[name + "_approx"] for name in self.approximate}
curr = {name: curr[name + "_approx"] for name in self.approximate}
with _disallow_latent_variables(".finalize()"):
self.finalize(params, prev, curr)
self._clear_plates()
def guide_autodelta(self):
self.guide = AutoDelta(poutine.block(self.model,
expose=['weights',
'locs',
'scale']))
def guide_autodiagnorm(self):
self.guide = AutoDiagonalNormal(poutine.block(self.model,
expose=['weights',
'locs',
'scale']))
def guide(self,MAP = False,*args, **kwargs):
return AutoDelta(poutine.block(self.model, expose=['pi', 'norm_factor', 'cnv_probs']),
init_loc_fn=self.init_fn())
def _contstructSVI(self, optimizationStrategy, total_steps):
"""
Constructs a list of SVI functions use to train model. Takes optimizationStrategy as input, which must be in ['Full','PosteriorOnly','GuideOnly']
"""
#generate random sample just to build the param_store
test_data = self.datasetSampler.GenerateRandomTrainingSample(2)
if self.useCuda:
test_data = self._sendDataToGPU(test_data)
self.phenotypeModel.guide(*test_data)
param_store = pyro.get_param_store()
if optimizationStrategy == 'Full':
_model = self.phenotypeModel.model
_guide = self.phenotypeModel.guide
else:
if optimizationStrategy == 'DecoderOnly':
_model = self.phenotypeModel.model
_guide = poutine.block(
self.phenotypeModel.guide,
hide=[
x for x in param_store.get_all_param_names()
if ('encoder' in x)
])
else:
_model = poutine.block(
self.phenotypeModel.model,
hide=[
x for x in param_store.get_all_param_names()
if ('decoder' in x)
])
_guide = self.phenotypeModel.guide
#initialize the SVI class
AdamWOptimArgs = {'weight_decay': self.AdamW_Weight_Decay}
scheduler = OneCycleLR({
'optimizer':
AdamW,
'optim_args':
AdamWOptimArgs,
'max_lr':
self.maxLearningRate,
'total_steps':
total_steps,
'pct_start':
self.OneCycleParams['pctCycleIncrease'],
'div_factor':
self.OneCycleParams['initLRDivisionFactor'],
'final_div_factor':
self.OneCycleParams['finalLRDivisionFactor']
})
return {
'svi':
SVI(_model,
_guide,
scheduler,
loss=Trace_ELBO(num_particles=self.numParticles)),
'scheduler':
scheduler
}
def guide_multivariatenormal(self):
self.guide = AutoMultivariateNormal(poutine.block(self.model,
expose=['weights',
'locs',
'scale']))
def main(args):
if args.cuda:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
logging.info('Loading data')
data = poly.load_data(poly.JSB_CHORALES)
logging.info('-' * 40)
model = models[args.model]
logging.info('Training {} on {} sequences'.format(
model.__name__, len(data['train']['sequences'])))
sequences = data['train']['sequences']
lengths = data['train']['sequence_lengths']
# find all the notes that are present at least once in the training set
present_notes = ((sequences == 1).sum(0).sum(0) > 0)
# remove notes that are never played (we remove 37/88 notes)
sequences = sequences[..., present_notes]
if args.truncate:
lengths.clamp_(max=args.truncate)
sequences = sequences[:, :args.truncate]
num_observations = float(lengths.sum())
pyro.set_rng_seed(0)
pyro.clear_param_store()
pyro.enable_validation(True)
# We'll train using MAP Baum-Welch, i.e. MAP estimation while marginalizing
# out the hidden state x. This is accomplished via an automatic guide that
# learns point estimates of all of our conditional probability tables,
# named probs_*.
guide = AutoDelta(
poutine.block(model,
expose_fn=lambda msg: msg["name"].startswith("probs_")))
# To help debug our tensor shapes, let's print the shape of each site's
# distribution, value, and log_prob tensor. Note this information is
# automatically printed on most errors inside SVI.
if args.print_shapes:
first_available_dim = -2 if model is model_0 else -3
guide_trace = poutine.trace(guide).get_trace(
sequences, lengths, args=args, batch_size=args.batch_size)
model_trace = poutine.trace(
poutine.replay(poutine.enum(model, first_available_dim),
guide_trace)).get_trace(sequences,
lengths,
args=args,
batch_size=args.batch_size)
logging.info(model_trace.format_shapes())
# Enumeration requires a TraceEnum elbo and declaring the max_plate_nesting.
# All of our models have two plates: "data" and "tones".
Elbo = JitTraceEnum_ELBO if args.jit else TraceEnum_ELBO
elbo = Elbo(max_plate_nesting=1 if model is model_0 else 2)
optim = Adam({'lr': args.learning_rate})
svi = SVI(model, guide, optim, elbo)
# We'll train on small minibatches.
logging.info('Step\tLoss')
for step in range(args.num_steps):
loss = svi.step(sequences,
lengths,
args=args,
batch_size=args.batch_size)
logging.info('{: >5d}\t{}'.format(step, loss / num_observations))
# We evaluate on the entire training dataset,
# excluding the prior term so our results are comparable across models.
train_loss = elbo.loss(model,
guide,
sequences,
lengths,
args,
include_prior=False)
logging.info('training loss = {}'.format(train_loss / num_observations))
# Finally we evaluate on the test dataset.
logging.info('-' * 40)
logging.info('Evaluating on {} test sequences'.format(
len(data['test']['sequences'])))
sequences = data['test']['sequences'][..., present_notes]
lengths = data['test']['sequence_lengths']
if args.truncate:
lengths.clamp_(max=args.truncate)
num_observations = float(lengths.sum())
# note that since we removed unseen notes above (to make the problem a bit easier and for
# numerical stability) this test loss may not be directly comparable to numbers
# reported on this dataset elsewhere.
test_loss = elbo.loss(model,
guide,
sequences,
lengths,
args=args,
include_prior=False)
logging.info('test loss = {}'.format(test_loss / num_observations))
# We expect models with higher capacity to perform better,
# but eventually overfit to the training set.
capacity = sum(
value.reshape(-1).size(0) for value in pyro.get_param_store().values())
logging.info('{} capacity = {} parameters'.format(model.__name__,
capacity))
tr = poutine.trace(guide).get_trace(torch.tensor(2.))
exp_msg = r"Error while computing score_parts at site 'test_site':\s*" \
r"The value argument must be within the support"
with pytest.raises(ValueError, match=exp_msg):
tr.compute_score_parts()
def _model(a=torch.tensor(1.), b=torch.tensor(1.)):
latent = pyro.sample("latent", dist.Beta(a, b))
return pyro.sample("test_site",
dist.Bernoulli(latent),
obs=torch.tensor(1))
@pytest.mark.parametrize('wrapper', [
lambda fn: poutine.block(fn),
lambda fn: poutine.condition(fn, {'latent': 0.9}),
lambda fn: poutine.enum(fn, -1),
lambda fn: poutine.replay(fn,
poutine.trace(fn).get_trace()),
])
def test_pickling(wrapper):
wrapped = wrapper(_model)
# default protocol cannot serialize torch.Size objects (see https://github.com/pytorch/pytorch/issues/20823)
deserialized = pickle.loads(
pickle.dumps(wrapped, protocol=pickle.HIGHEST_PROTOCOL))
obs = torch.tensor(0.5)
pyro.set_rng_seed(0)
actual_trace = poutine.trace(deserialized).get_trace(obs)
pyro.set_rng_seed(0)
expected_trace = poutine.trace(wrapped).get_trace(obs)
def _sample_posterior(model, first_available_dim, temperature, *args,
**kwargs):
# For internal use by infer_discrete.
# Create an enumerated trace.
with poutine.block(), EnumerateMessenger(first_available_dim):
enum_trace = poutine.trace(model).get_trace(*args, **kwargs)
enum_trace = prune_subsample_sites(enum_trace)
enum_trace.compute_log_prob()
enum_trace.pack_tensors()
plate_to_symbol = enum_trace.plate_to_symbol
# Collect a set of query sample sites to which the backward algorithm will propagate.
log_probs = OrderedDict()
sum_dims = set()
queries = []
for node in enum_trace.nodes.values():
if node["type"] == "sample":
ordinal = frozenset(plate_to_symbol[f.name]
for f in node["cond_indep_stack"]
if f.vectorized)
log_prob = node["packed"]["log_prob"]
log_probs.setdefault(ordinal, []).append(log_prob)
sum_dims.update(log_prob._pyro_dims)
for frame in node["cond_indep_stack"]:
if frame.vectorized:
sum_dims.remove(plate_to_symbol[frame.name])
# Note we mark all sample sites with require_backward to gather
# enumerated sites and adjust cond_indep_stack of all sample sites.
if not node["is_observed"]:
queries.append(log_prob)
require_backward(log_prob)
# Run forward-backward algorithm, collecting the ordinal of each connected component.
ring = _make_ring(temperature)
log_probs = contract_tensor_tree(log_probs, sum_dims,
ring=ring) # run forward algorithm
query_to_ordinal = {}
pending = object() # a constant value for pending queries
for query in queries:
query._pyro_backward_result = pending
for ordinal, terms in log_probs.items():
for term in terms:
if hasattr(term, "_pyro_backward"):
term._pyro_backward() # run backward algorithm
# Note: this is quadratic in number of ordinals
for query in queries:
if query not in query_to_ordinal and query._pyro_backward_result is not pending:
query_to_ordinal[query] = ordinal
# Construct a collapsed trace by gathering and adjusting cond_indep_stack.
collapsed_trace = poutine.Trace()
for node in enum_trace.nodes.values():
if node["type"] == "sample" and not node["is_observed"]:
# TODO move this into a Leaf implementation somehow
new_node = {
"type": "sample",
"name": node["name"],
"is_observed": False,
"infer": node["infer"].copy(),
"cond_indep_stack": node["cond_indep_stack"],
"value": node["value"],
}
log_prob = node["packed"]["log_prob"]
if hasattr(log_prob, "_pyro_backward_result"):
# Adjust the cond_indep_stack.
ordinal = query_to_ordinal[log_prob]
new_node["cond_indep_stack"] = tuple(
f for f in node["cond_indep_stack"]
if not f.vectorized or plate_to_symbol[f.name] in ordinal)
# Gather if node depended on an enumerated value.
sample = log_prob._pyro_backward_result
if sample is not None:
new_value = packed.pack(node["value"],
node["infer"]["_dim_to_symbol"])
for index, dim in zip(jit_iter(sample),
sample._pyro_sample_dims):
if dim in new_value._pyro_dims:
index._pyro_dims = sample._pyro_dims[1:]
new_value = packed.gather(new_value, index, dim)
new_node["value"] = packed.unpack(new_value,
enum_trace.symbol_to_dim)
collapsed_trace.add_node(node["name"], **new_node)
# Replay the model against the collapsed trace.
with SamplePosteriorMessenger(trace=collapsed_trace):
return model(*args, **kwargs)
def main(args):
# setup hyperparameters for the model
hypers = {
'expected_sparsity': max(1.0, args.num_dimensions / 10),
'alpha1': 3.0,
'beta1': 1.0,
'alpha2': 3.0,
'beta2': 1.0,
'alpha3': 1.0,
'c': 1.0
}
P = args.num_dimensions
S = args.active_dimensions
Q = args.quadratic_dimensions
# generate artificial dataset
X, Y, expected_thetas, expected_quad_dims = get_data(N=args.num_data,
P=P,
S=S,
Q=Q,
sigma_obs=args.sigma)
loss_fn = Trace_ELBO().differentiable_loss
# We initialize the AutoDelta guide (for MAP estimation) with args.num_trials many
# initial parameters sampled from the vicinity of the median of the prior distribution
# and then continue optimizing with the best performing initialization.
init_losses = []
for restart in range(args.num_restarts):
pyro.clear_param_store()
pyro.set_rng_seed(restart)
guide = AutoDelta(model, init_loc_fn=init_loc_fn)
with torch.no_grad():
init_losses.append(loss_fn(model, guide, X, Y, hypers).item())
pyro.set_rng_seed(np.argmin(init_losses))
pyro.clear_param_store()
guide = AutoDelta(model, init_loc_fn=init_loc_fn)
# Instead of using pyro.infer.SVI and pyro.optim we instead construct our own PyTorch
# optimizer and take charge of gradient-based optimization ourselves.
with poutine.block(), poutine.trace(param_only=True) as param_capture:
guide(X, Y, hypers)
params = list(
[pyro.param(name).unconstrained() for name in param_capture.trace])
adam = Adam(params, lr=args.lr)
report_frequency = 50
print("Beginning MAP optimization...")
# the optimization loop
for step in range(args.num_steps):
loss = loss_fn(model, guide, X, Y, hypers) / args.num_data
loss.backward()
adam.step()
adam.zero_grad()
# we manually reduce the learning rate according to this schedule
if step in [100, 300, 700, 900]:
adam.param_groups[0]['lr'] *= 0.2
if step % report_frequency == 0 or step == args.num_steps - 1:
print("[step %04d] loss: %.5f" % (step, loss))
print("Expected singleton thetas:\n", expected_thetas.data.numpy())
# we do the final computation using double precision
median = guide.median() # == mode for MAP inference
active_dims, active_quad_dims = \
compute_posterior_stats(X.double(), Y.double(), median['msq'].double(),
median['lambda'].double(), median['eta1'].double(),
median['xisq'].double(), torch.tensor(hypers['c']).double(),
median['sigma'].double())
expected_active_dims = np.arange(S).tolist()
tp_singletons = len(set(active_dims) & set(expected_active_dims))
fp_singletons = len(set(active_dims) - set(expected_active_dims))
fn_singletons = len(set(expected_active_dims) - set(active_dims))
singleton_stats = (tp_singletons, fp_singletons, fn_singletons)
tp_quads = len(set(active_quad_dims) & set(expected_quad_dims))
fp_quads = len(set(active_quad_dims) - set(expected_quad_dims))
fn_quads = len(set(expected_quad_dims) - set(active_quad_dims))
quad_stats = (tp_quads, fp_quads, fn_quads)
# We report how well we did, i.e. did we recover the sparse set of coefficients
# that we expected for our artificial dataset?
print("[SUMMARY STATS]")
print("Singletons (true positive, false positive, false negative): " +
"(%d, %d, %d)" % singleton_stats)
print("Quadratic (true positive, false positive, false negative): " +
"(%d, %d, %d)" % quad_stats)
ax.add_artist(ell)
# Save figure
fig.savefig(figname)
if __name__ == "__main__":
pyro.enable_validation(True)
pyro.set_rng_seed(42)
# Create our model with a fixed number of components
K = 2
data = get_samples()
global_guide = AutoDelta(poutine.block(model, expose=['weights', 'locs', 'scales']))
global_guide = config_enumerate(global_guide, 'parallel')
_, svi = initialize(data)
true_colors = [0] * 100 + [1] * 100
plot(data, colors=true_colors, figname='pyro_init.png')
for i in range(151):
svi.step(data)
if i % 50 == 0:
locs = pyro.param('locs')
scales = pyro.param('scales')
weights = pyro.param('weights')
assignment_probs = pyro.param('assignment_probs')
