def test_obs_mask_ok(Elbo, mask, num_particles): data = np.array([7., 7., 7.]) def model(): x = numpyro.sample("x", dist.Normal(0., 1.)) with numpyro.plate("plate", len(data)): y = numpyro.sample("y", dist.Normal(x, 1.), obs=data, obs_mask=mask) if not_jax_tracer(y): assert ((y == data) == mask).all() def guide(): loc = numpyro.param("loc", np.zeros(())) scale = numpyro.param("scale", np.ones(()), constraint=constraints.positive) x = numpyro.sample("x", dist.Normal(loc, scale)) with numpyro.plate("plate", len(data)): with handlers.mask(mask=np.invert(mask)): numpyro.sample("y_unobserved", dist.Normal(x, 1.)) elbo = Elbo(num_particles=num_particles) svi = SVI(model, guide, numpyro.optim.Adam(1), elbo) svi_state = svi.init(random.PRNGKey(0)) svi.update(svi_state)
def test_obs_mask_multivariate_ok(Elbo, mask, num_particles): data = np.full((4, 3), 7.0) def model(): x = numpyro.sample("x", dist.MultivariateNormal(np.zeros(3), np.eye(3))) with numpyro.plate("plate", len(data)): y = numpyro.sample("y", dist.MultivariateNormal(x, np.eye(3)), obs=data, obs_mask=mask) if not_jax_tracer(y): assert ((y == data).all(-1) == mask).all() def guide(): loc = numpyro.param("loc", np.zeros(3)) cov = numpyro.param("cov", np.eye(3), constraint=constraints.positive_definite) x = numpyro.sample("x", dist.MultivariateNormal(loc, cov)) with numpyro.plate("plate", len(data)): with handlers.mask(mask=np.invert(mask)): numpyro.sample("y_unobserved", dist.MultivariateNormal(x, np.eye(3))) elbo = Elbo(num_particles=num_particles) svi = SVI(model, guide, numpyro.optim.Adam(1), elbo) svi_state = svi.init(random.PRNGKey(0)) svi.update(svi_state)
def test_logistic_regression(auto_class, Elbo): N, dim = 3000, 3 data = random.normal(random.PRNGKey(0), (N, dim)) true_coefs = jnp.arange(1.0, dim + 1.0) logits = jnp.sum(true_coefs * data, axis=-1) labels = dist.Bernoulli(logits=logits).sample(random.PRNGKey(1)) def model(data, labels): coefs = numpyro.sample("coefs", dist.Normal(0, 1).expand([dim]).to_event()) logits = numpyro.deterministic("logits", jnp.sum(coefs * data, axis=-1)) with numpyro.plate("N", len(data)): return numpyro.sample("obs", dist.Bernoulli(logits=logits), obs=labels) adam = optim.Adam(0.01) rng_key_init = random.PRNGKey(1) guide = auto_class(model, init_loc_fn=init_strategy) svi = SVI(model, guide, adam, Elbo()) svi_state = svi.init(rng_key_init, data, labels) # smoke test if analytic KL is used if auto_class is AutoNormal and Elbo is TraceMeanField_ELBO: _, mean_field_loss = svi.update(svi_state, data, labels) svi.loss = Trace_ELBO() _, elbo_loss = svi.update(svi_state, data, labels) svi.loss = TraceMeanField_ELBO() assert abs(mean_field_loss - elbo_loss) > 0.5 def body_fn(i, val): svi_state, loss = svi.update(val, data, labels) return svi_state svi_state = fori_loop(0, 2000, body_fn, svi_state) params = svi.get_params(svi_state) if auto_class not in (AutoDAIS, AutoIAFNormal, AutoBNAFNormal): median = guide.median(params) assert_allclose(median["coefs"], true_coefs, rtol=0.1) # test .quantile method if auto_class is not AutoDelta: median = guide.quantiles(params, [0.2, 0.5]) assert_allclose(median["coefs"][1], true_coefs, rtol=0.1) # test .sample_posterior method posterior_samples = guide.sample_posterior(random.PRNGKey(1), params, sample_shape=(1000, )) expected_coefs = jnp.array([0.97, 2.05, 3.18]) assert_allclose(jnp.mean(posterior_samples["coefs"], 0), expected_coefs, rtol=0.1)
def fit_advi(model, num_iter, learning_rate=0.01, seed=0): """Automatic Differentiation Variational Inference using a Normal variational distribution with a diagonal covariance matrix. Args: model: a NumPyro's model function num_iter: number of iterations of gradient descent (Adam) learning_rate: the step size for the Adam algorithm (default: {0.01}) seed: random seed (default: {0}) Returns: a set of results of type ADVIResults """ rng_key = random.PRNGKey(seed) adam = Adam(learning_rate) # Automatically create a variational distribution (aka "guide" in Pyro's terminology) guide = AutoDiagonalNormal(model) svi = SVI(model, guide, adam, AutoContinuousELBO()) svi_state = svi.init(rng_key) # Run optimization last_state, losses = lax.scan(lambda state, i: svi.update(state), svi_state, np.zeros(num_iter)) results = ADVIResults(svi=svi, guide=guide, state=last_state, losses=losses) return results
def run_inference(model, inputs, method=None): if method is None: # NUTS num_samples = 5000 logger.info('NUTS sampling') kernel = NUTS(model) mcmc = MCMC(kernel, num_warmup=300, num_samples=num_samples) rng_key = random.PRNGKey(0) mcmc.run(rng_key, **inputs, extra_fields=('potential_energy', )) logger.info(r'MCMC summary for: {}'.format(model.__name__)) mcmc.print_summary(exclude_deterministic=False) samples = mcmc.get_samples() else: #SVI logger.info('Guide generation...') rng_key = random.PRNGKey(0) guide = AutoDiagonalNormal(model=model) logger.info('Optimizer generation...') optim = Adam(0.05) logger.info('SVI generation...') svi = SVI(model, guide, optim, AutoContinuousELBO(), **inputs) init_state = svi.init(rng_key) logger.info('Scan...') state, loss = lax.scan(lambda x, i: svi.update(x), init_state, np.zeros(2000)) params = svi.get_params(state) samples = guide.sample_posterior(random.PRNGKey(1), params, (1000, )) logger.info(r'SVI summary for: {}'.format(model.__name__)) numpyro.diagnostics.print_summary(samples, prob=0.90, group_by_chain=False) return samples
def test_logistic_regression(auto_class, Elbo): N, dim = 3000, 3 data = random.normal(random.PRNGKey(0), (N, dim)) true_coefs = jnp.arange(1., dim + 1.) logits = jnp.sum(true_coefs * data, axis=-1) labels = dist.Bernoulli(logits=logits).sample(random.PRNGKey(1)) def model(data, labels): coefs = numpyro.sample('coefs', dist.Normal(jnp.zeros(dim), jnp.ones(dim))) logits = jnp.sum(coefs * data, axis=-1) return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels) adam = optim.Adam(0.01) rng_key_init = random.PRNGKey(1) guide = auto_class(model, init_loc_fn=init_strategy) svi = SVI(model, guide, adam, Elbo()) svi_state = svi.init(rng_key_init, data, labels) # smoke test if analytic KL is used if auto_class is AutoNormal and Elbo is TraceMeanField_ELBO: _, mean_field_loss = svi.update(svi_state, data, labels) svi.loss = Trace_ELBO() _, elbo_loss = svi.update(svi_state, data, labels) svi.loss = TraceMeanField_ELBO() assert abs(mean_field_loss - elbo_loss) > 0.5 def body_fn(i, val): svi_state, loss = svi.update(val, data, labels) return svi_state svi_state = fori_loop(0, 2000, body_fn, svi_state) params = svi.get_params(svi_state) if auto_class not in (AutoIAFNormal, AutoBNAFNormal): median = guide.median(params) assert_allclose(median['coefs'], true_coefs, rtol=0.1) # test .quantile method median = guide.quantiles(params, [0.2, 0.5]) assert_allclose(median['coefs'][1], true_coefs, rtol=0.1) # test .sample_posterior method posterior_samples = guide.sample_posterior(random.PRNGKey(1), params, sample_shape=(1000, )) assert_allclose(jnp.mean(posterior_samples['coefs'], 0), true_coefs, rtol=0.1)
def test_collapse_beta_bernoulli(): data = 0. def model(): c = numpyro.sample("c", dist.Gamma(1, 1)) with handlers.collapse(): probs = numpyro.sample("probs", dist.Beta(c, 2)) numpyro.sample("obs", dist.Bernoulli(probs), obs=data) def guide(): a = numpyro.param("a", 1., constraint=constraints.positive) b = numpyro.param("b", 1., constraint=constraints.positive) numpyro.sample("c", dist.Gamma(a, b)) svi = SVI(model, guide, numpyro.optim.Adam(1), Trace_ELBO()) svi_state = svi.init(random.PRNGKey(0)) svi.update(svi_state)
def test_collapse_beta_binomial_plate(): data = np.array([0., 1., 5., 5.]) def model(): c = numpyro.sample("c", dist.Gamma(1, 1)) with handlers.collapse(): probs = numpyro.sample("probs", dist.Beta(c, 2)) with numpyro.plate("plate", len(data)): numpyro.sample("obs", dist.Binomial(10, probs), obs=data) def guide(): a = numpyro.param("a", 1., constraint=constraints.positive) b = numpyro.param("b", 1., constraint=constraints.positive) numpyro.sample("c", dist.Gamma(a, b)) svi = SVI(model, guide, numpyro.optim.Adam(1), Trace_ELBO()) svi_state = svi.init(random.PRNGKey(0)) svi.update(svi_state)
def svi(model, guide, num_steps, lr, rng_key, X, Y): """ Helper function for doing SVI inference. """ svi = SVI(model, guide, optim.Adam(lr), ELBO(num_particles=1), X=X, Y=Y) svi_state = svi.init(rng_key) print('Optimizing...') state, loss = lax.scan(lambda x, i: svi.update(x), svi_state, np.zeros(num_steps)) return loss, svi.get_params(state)
def test_improper(): y = random.normal(random.PRNGKey(0), (100,)) def model(y): lambda1 = numpyro.sample('lambda1', dist.ImproperUniform(dist.constraints.real, (), ())) lambda2 = numpyro.sample('lambda2', dist.ImproperUniform(dist.constraints.real, (), ())) sigma = numpyro.sample('sigma', dist.ImproperUniform(dist.constraints.positive, (), ())) mu = numpyro.deterministic('mu', lambda1 + lambda2) numpyro.sample('y', dist.Normal(mu, sigma), obs=y) guide = AutoDiagonalNormal(model) svi = SVI(model, guide, optim.Adam(0.003), Trace_ELBO(), y=y) svi_state = svi.init(random.PRNGKey(2)) lax.scan(lambda state, i: svi.update(state), svi_state, jnp.zeros(10000))
def test_module(): x = random.normal(random.PRNGKey(0), (100, 10)) y = random.normal(random.PRNGKey(1), (100,)) def model(x, y): nn = numpyro.module("nn", Dense(1), (10,)) mu = nn(x).squeeze(-1) sigma = numpyro.sample("sigma", dist.HalfNormal(1)) numpyro.sample("y", dist.Normal(mu, sigma), obs=y) guide = AutoDiagonalNormal(model) svi = SVI(model, guide, optim.Adam(0.003), Trace_ELBO(), x=x, y=y) svi_state = svi.init(random.PRNGKey(2)) lax.scan(lambda state, i: svi.update(state), svi_state, jnp.zeros(1000))
def test_collapse_beta_binomial(): total_count = 10 data = 3. def model1(): c1 = numpyro.param("c1", 0.5, constraint=dist.constraints.positive) c0 = numpyro.param("c0", 1.5, constraint=dist.constraints.positive) with handlers.collapse(): probs = numpyro.sample("probs", dist.Beta(c1, c0)) numpyro.sample("obs", dist.Binomial(total_count, probs), obs=data) def model2(): c1 = numpyro.param("c1", 0.5, constraint=dist.constraints.positive) c0 = numpyro.param("c0", 1.5, constraint=dist.constraints.positive) numpyro.sample("obs", dist.BetaBinomial(c1, c0, total_count), obs=data) trace1 = handlers.trace(model1).get_trace() trace2 = handlers.trace(model2).get_trace() assert "probs" in trace1 assert "obs" not in trace1 assert "probs" not in trace2 assert "obs" in trace2 svi1 = SVI(model1, lambda: None, numpyro.optim.Adam(1), Trace_ELBO()) svi2 = SVI(model2, lambda: None, numpyro.optim.Adam(1), Trace_ELBO()) svi_state1 = svi1.init(random.PRNGKey(0)) svi_state2 = svi2.init(random.PRNGKey(0)) params1 = svi1.get_params(svi_state1) params2 = svi2.get_params(svi_state2) assert_allclose(params1["c1"], params2["c1"]) assert_allclose(params1["c0"], params2["c0"]) params1 = svi1.get_params(svi1.update(svi_state1)[0]) params2 = svi2.get_params(svi2.update(svi_state2)[0]) assert_allclose(params1["c1"], params2["c1"]) assert_allclose(params1["c0"], params2["c0"])
def test_laplace_approximation_warning(): def model(x, y): a = numpyro.sample("a", dist.Normal(0, 10)) b = numpyro.sample("b", dist.Normal(0, 10), sample_shape=(3,)) mu = a + b[0] * x + b[1] * x ** 2 + b[2] * x ** 3 numpyro.sample("y", dist.Normal(mu, 0.001), obs=y) x = random.normal(random.PRNGKey(0), (3,)) y = 1 + 2 * x + 3 * x ** 2 + 4 * x ** 3 guide = AutoLaplaceApproximation(model) svi = SVI(model, guide, optim.Adam(0.1), Trace_ELBO(), x=x, y=y) init_state = svi.init(random.PRNGKey(0)) svi_state = fori_loop(0, 10000, lambda i, val: svi.update(val)[0], init_state) params = svi.get_params(svi_state) with pytest.warns(UserWarning, match="Hessian of log posterior"): guide.sample_posterior(random.PRNGKey(1), params)
def run_svi_inference(model, guide, rng_key, X, Y, optimizer, n_epochs=1_000): # initialize svi svi = SVI(model, guide, optimizer, loss=Trace_ELBO()) # initialize state init_state = svi.init(rng_key, X, Y.squeeze()) # Run optimizer for 1000 iteratons. state, losses = jax.lax.scan( lambda state, i: svi.update(state, X, Y.squeeze()), init_state, n_epochs ) # Extract surrogate posterior. params = svi.get_params(state) return params
def test_autoguide(deterministic): GLOBAL["count"] = 0 guide = AutoDiagonalNormal(model) svi = SVI(model, guide, optim.Adam(0.1), Trace_ELBO(), deterministic=deterministic) svi_state = svi.init(random.PRNGKey(0)) svi_state = lax.fori_loop(0, 100, lambda i, val: svi.update(val)[0], svi_state) params = svi.get_params(svi_state) guide.sample_posterior(random.PRNGKey(1), params, sample_shape=(100, )) if deterministic: assert GLOBAL["count"] == 5 else: assert GLOBAL["count"] == 4
def test_jitted_update_fn(): data = jnp.array([1.0] * 8 + [0.0] * 2) def model(data): f = numpyro.sample("beta", dist.Beta(1.0, 1.0)) numpyro.sample("obs", dist.Bernoulli(f), obs=data) def guide(data): alpha_q = numpyro.param("alpha_q", 1.0, constraint=constraints.positive) beta_q = numpyro.param("beta_q", 1.0, constraint=constraints.positive) numpyro.sample("beta", dist.Beta(alpha_q, beta_q)) adam = optim.Adam(0.05) svi = SVI(model, guide, adam, Trace_ELBO()) svi_state = svi.init(random.PRNGKey(1), data) expected = svi.get_params(svi.update(svi_state, data)[0]) actual = svi.get_params(jit(svi.update)(svi_state, data=data)[0]) check_close(actual, expected, atol=1e-5)
def fit_advi(model, num_iter, learning_rate=0.01, seed=0): """Automatic Differentiation Variational Inference using a Normal variational distribution with a diagonal covariance matrix. """ rng_key = random.PRNGKey(seed) adam = Adam(learning_rate) # Automatically create a variational distribution (aka "guide" in Pyro's terminology) guide = AutoDiagonalNormal(model) svi = SVI(model, guide, adam, AutoContinuousELBO()) svi_state = svi.init(rng_key) # Run optimization last_state, losses = lax.scan(lambda state, i: svi.update(state), svi_state, np.zeros(num_iter)) results = ADVIResults(svi=svi, guide=guide, state=last_state, losses=losses) return results
def main(args): print("Start vanilla HMC...") nuts_kernel = NUTS(dual_moon_model) mcmc = MCMC( nuts_kernel, args.num_warmup, args.num_samples, num_chains=args.num_chains, progress_bar=False if "NUMPYRO_SPHINXBUILD" in os.environ else True) mcmc.run(random.PRNGKey(0)) mcmc.print_summary() vanilla_samples = mcmc.get_samples()['x'].copy() guide = AutoBNAFNormal( dual_moon_model, hidden_factors=[args.hidden_factor, args.hidden_factor]) svi = SVI(dual_moon_model, guide, optim.Adam(0.003), ELBO()) svi_state = svi.init(random.PRNGKey(1)) print("Start training guide...") last_state, losses = lax.scan(lambda state, i: svi.update(state), svi_state, jnp.zeros(args.num_iters)) params = svi.get_params(last_state) print("Finish training guide. Extract samples...") guide_samples = guide.sample_posterior( random.PRNGKey(2), params, sample_shape=(args.num_samples, ))['x'].copy() print("\nStart NeuTra HMC...") neutra = NeuTraReparam(guide, params) neutra_model = neutra.reparam(dual_moon_model) nuts_kernel = NUTS(neutra_model) mcmc = MCMC( nuts_kernel, args.num_warmup, args.num_samples, num_chains=args.num_chains, progress_bar=False if "NUMPYRO_SPHINXBUILD" in os.environ else True) mcmc.run(random.PRNGKey(3)) mcmc.print_summary() zs = mcmc.get_samples(group_by_chain=True)["auto_shared_latent"] print("Transform samples into unwarped space...") samples = neutra.transform_sample(zs) print_summary(samples) zs = zs.reshape(-1, 2) samples = samples['x'].reshape(-1, 2).copy() # make plots # guide samples (for plotting) guide_base_samples = dist.Normal(jnp.zeros(2), 1.).sample(random.PRNGKey(4), (1000, )) guide_trans_samples = neutra.transform_sample(guide_base_samples)['x'] x1 = jnp.linspace(-3, 3, 100) x2 = jnp.linspace(-3, 3, 100) X1, X2 = jnp.meshgrid(x1, x2) P = jnp.exp(DualMoonDistribution().log_prob(jnp.stack([X1, X2], axis=-1))) fig = plt.figure(figsize=(12, 8), constrained_layout=True) gs = GridSpec(2, 3, figure=fig) ax1 = fig.add_subplot(gs[0, 0]) ax2 = fig.add_subplot(gs[1, 0]) ax3 = fig.add_subplot(gs[0, 1]) ax4 = fig.add_subplot(gs[1, 1]) ax5 = fig.add_subplot(gs[0, 2]) ax6 = fig.add_subplot(gs[1, 2]) ax1.plot(losses[1000:]) ax1.set_title('Autoguide training loss\n(after 1000 steps)') ax2.contourf(X1, X2, P, cmap='OrRd') sns.kdeplot(guide_samples[:, 0], guide_samples[:, 1], n_levels=30, ax=ax2) ax2.set(xlim=[-3, 3], ylim=[-3, 3], xlabel='x0', ylabel='x1', title='Posterior using\nAutoBNAFNormal guide') sns.scatterplot(guide_base_samples[:, 0], guide_base_samples[:, 1], ax=ax3, hue=guide_trans_samples[:, 0] < 0.) ax3.set( xlim=[-3, 3], ylim=[-3, 3], xlabel='x0', ylabel='x1', title='AutoBNAFNormal base samples\n(True=left moon; False=right moon)' ) ax4.contourf(X1, X2, P, cmap='OrRd') sns.kdeplot(vanilla_samples[:, 0], vanilla_samples[:, 1], n_levels=30, ax=ax4) ax4.plot(vanilla_samples[-50:, 0], vanilla_samples[-50:, 1], 'bo-', alpha=0.5) ax4.set(xlim=[-3, 3], ylim=[-3, 3], xlabel='x0', ylabel='x1', title='Posterior using\nvanilla HMC sampler') sns.scatterplot(zs[:, 0], zs[:, 1], ax=ax5, hue=samples[:, 0] < 0., s=30, alpha=0.5, edgecolor="none") ax5.set(xlim=[-5, 5], ylim=[-5, 5], xlabel='x0', ylabel='x1', title='Samples from the\nwarped posterior - p(z)') ax6.contourf(X1, X2, P, cmap='OrRd') sns.kdeplot(samples[:, 0], samples[:, 1], n_levels=30, ax=ax6) ax6.plot(samples[-50:, 0], samples[-50:, 1], 'bo-', alpha=0.2) ax6.set(xlim=[-3, 3], ylim=[-3, 3], xlabel='x0', ylabel='x1', title='Posterior using\nNeuTra HMC sampler') plt.savefig("neutra.pdf")
class SVIHandler(Handler): """ Helper object that abstracts some of numpyros complexities. Inspired by an implementation of Florian Wilhelm. :param model: A numpyro model. :param guide: A numpyro guide. :param loss: Loss function, defaults to Trace_ELBO. :param lr: Learning rate, defaults to 0.001. :param lrd: Learning rate decay per step, defaults to 1.0 (no decay) :param rng_key: Random seed, defaults to 254. :param num_epochs: Number of epochs to train the model, defaults to 5000. :param num_samples: Number of posterior samples. :param log_func: Logging function, defaults to print. :param log_freq: Frequency of logging, defaults to 0 (no logging). :param to_numpy: Convert the posterior distribution to numpy array(s), defaults to True. """ def __init__( self, model: Model, guide: Guide, loss: Trace_ELBO = Trace_ELBO(num_particles=1), optimizer: optim.optimizers.optimizer = optim.ClippedAdam, lr: float = 0.001, lrd: float = 1.0, rng_key: int = 254, num_epochs: int = 30000, num_samples: int = 1000, log_func=_print_consumer, log_freq=1000, to_numpy: bool = True, ): self.model = model self.guide = guide self.loss = loss self.optimizer = optimizer(step_size=lambda x: lr * lrd**x) self.rng_key = random.PRNGKey(rng_key) self.svi = SVI(self.model, self.guide, self.optimizer, loss=self.loss) self.init_state = None self.log_func = log_func self.log_freq = log_freq self.num_epochs = num_epochs self.num_samples = num_samples self.loss = None self.to_numpy = to_numpy def _log(self, epoch, loss, n_digits=4): msg = f"epoch: {str(epoch).rjust(n_digits)} loss: {loss: 16.4f}" self.log_func(msg) def _fit(self, *args): def _step(state, i, *args): state = lax.cond( i % self.log_freq == 0, lambda _: host_callback.id_tap(self.log_func, (i, self.num_epochs), result=state), lambda _: state, operand=None, ) return self.svi.update(state, *args) return lax.scan( lambda state, i: _step(state, i, *args), self.init_state, jnp.arange(self.num_epochs), ) def _update_state(self, state, loss): self.state = state self.init_state = state self.loss = loss if self.loss is None else jnp.concatenate( [self.loss, loss]) def fit(self, *args, **kwargs): self.num_epochs = kwargs.pop("num_epochs", self.num_epochs) predictive_kwargs = kwargs.pop("predictive_kwargs", {}) if self.init_state is None: self.init_state = self.svi.init(self.rng_key, *args) state, loss = self._fit(*args) self._update_state(state, loss) self.params = self.svi.get_params(state) predictive = Predictive( self.model, guide=self.guide, params=self.params, num_samples=self.num_samples, **predictive_kwargs, ) self.posterior = Posterior(predictive(self.rng_key, *args), self.to_numpy) return self def predict(self, *args, **kwargs): """kwargs -> Predictive, args -> predictive""" num_samples = kwargs.pop("num_samples", self.num_samples) rng_key = kwargs.pop("rng_key", self.rng_key) predictive = Predictive( self.model, guide=self.guide, params=self.params, num_samples=num_samples, **kwargs, ) self.predictive = Posterior(predictive(rng_key, *args), self.to_numpy) def dump_params(self, file_name: str): assert self.params is not None, "'init_svi' needs to be called first" pickle.dump(self.params, open(file_name, "wb")) def load_params(self, file_name): self.params = pickle.load(open(file_name, "rb"))
def main(args): print("Start vanilla HMC...") nuts_kernel = NUTS(dual_moon_model) mcmc = MCMC(nuts_kernel, args.num_warmup, args.num_samples) mcmc.run(random.PRNGKey(0)) mcmc.print_summary() vanilla_samples = mcmc.get_samples()['x'].copy() adam = optim.Adam(0.01) # TODO: it is hard to find good hyperparameters such that IAF guide can learn this model. # We will use BNAF instead! guide = AutoIAFNormal(dual_moon_model, num_flows=2, hidden_dims=[args.num_hidden, args.num_hidden]) svi = SVI(dual_moon_model, guide, adam, AutoContinuousELBO()) svi_state = svi.init(random.PRNGKey(1)) print("Start training guide...") last_state, losses = lax.scan(lambda state, i: svi.update(state), svi_state, np.zeros(args.num_iters)) params = svi.get_params(last_state) print("Finish training guide. Extract samples...") guide_samples = guide.sample_posterior( random.PRNGKey(0), params, sample_shape=(args.num_samples, ))['x'].copy() transform = guide.get_transform(params) _, potential_fn, constrain_fn = initialize_model(random.PRNGKey(2), dual_moon_model) transformed_potential_fn = partial(transformed_potential_energy, potential_fn, transform) transformed_constrain_fn = lambda x: constrain_fn(transform(x) ) # noqa: E731 print("\nStart NeuTra HMC...") nuts_kernel = NUTS(potential_fn=transformed_potential_fn) mcmc = MCMC(nuts_kernel, args.num_warmup, args.num_samples) init_params = np.zeros(guide.latent_size) mcmc.run(random.PRNGKey(3), init_params=init_params) mcmc.print_summary() zs = mcmc.get_samples() print("Transform samples into unwarped space...") samples = vmap(transformed_constrain_fn)(zs) print_summary(tree_map(lambda x: x[None, ...], samples)) samples = samples['x'].copy() # make plots # guide samples (for plotting) guide_base_samples = dist.Normal(np.zeros(2), 1.).sample(random.PRNGKey(4), (1000, )) guide_trans_samples = vmap(transformed_constrain_fn)( guide_base_samples)['x'] x1 = np.linspace(-3, 3, 100) x2 = np.linspace(-3, 3, 100) X1, X2 = np.meshgrid(x1, x2) P = np.exp(DualMoonDistribution().log_prob(np.stack([X1, X2], axis=-1))) fig = plt.figure(figsize=(12, 16), constrained_layout=True) gs = GridSpec(3, 2, figure=fig) ax1 = fig.add_subplot(gs[0, 0]) ax2 = fig.add_subplot(gs[0, 1]) ax3 = fig.add_subplot(gs[1, 0]) ax4 = fig.add_subplot(gs[1, 1]) ax5 = fig.add_subplot(gs[2, 0]) ax6 = fig.add_subplot(gs[2, 1]) ax1.plot(np.log(losses[1000:])) ax1.set_title('Autoguide training log loss (after 1000 steps)') ax2.contourf(X1, X2, P, cmap='OrRd') sns.kdeplot(guide_samples[:, 0], guide_samples[:, 1], n_levels=30, ax=ax2) ax2.set(xlim=[-3, 3], ylim=[-3, 3], xlabel='x0', ylabel='x1', title='Posterior using AutoIAFNormal guide') sns.scatterplot(guide_base_samples[:, 0], guide_base_samples[:, 1], ax=ax3, hue=guide_trans_samples[:, 0] < 0.) ax3.set( xlim=[-3, 3], ylim=[-3, 3], xlabel='x0', ylabel='x1', title='AutoIAFNormal base samples (True=left moon; False=right moon)') ax4.contourf(X1, X2, P, cmap='OrRd') sns.kdeplot(vanilla_samples[:, 0], vanilla_samples[:, 1], n_levels=30, ax=ax4) ax4.plot(vanilla_samples[-50:, 0], vanilla_samples[-50:, 1], 'bo-', alpha=0.5) ax4.set(xlim=[-3, 3], ylim=[-3, 3], xlabel='x0', ylabel='x1', title='Posterior using vanilla HMC sampler') sns.scatterplot(zs[:, 0], zs[:, 1], ax=ax5, hue=samples[:, 0] < 0., s=30, alpha=0.5, edgecolor="none") ax5.set(xlim=[-5, 5], ylim=[-5, 5], xlabel='x0', ylabel='x1', title='Samples from the warped posterior - p(z)') ax6.contourf(X1, X2, P, cmap='OrRd') sns.kdeplot(samples[:, 0], samples[:, 1], n_levels=30, ax=ax6) ax6.plot(samples[-50:, 0], samples[-50:, 1], 'bo-', alpha=0.2) ax6.set(xlim=[-3, 3], ylim=[-3, 3], xlabel='x0', ylabel='x1', title='Posterior using NeuTra HMC sampler') plt.savefig("neutra.pdf") plt.close()
class SVIHandler(Handler): def __init__( self, model: Model, guide: Guide, loss: Trace_ELBO = Trace_ELBO(num_particles=1), optimizer: optim.optimizers.optimizer = optim.Adam, lr: float = 0.001, rng_key: int = 254, num_epochs: int = 100000, num_samples: int = 5000, log_func=print, log_freq=0, ): self.model = model self.guide = guide self.loss = loss self.optimizer = optimizer(step_size=lr) self.rng_key = random.PRNGKey(rng_key) self.svi = SVI(self.model, self.guide, self.optimizer, loss=self.loss) self.init_state = None self.log_func = log_func self.log_freq = log_freq self.num_epochs = num_epochs self.num_samples = num_samples self.loss = None def _log(self, epoch, loss, n_digits=4): msg = f"epoch: {str(epoch).rjust(n_digits)} loss: {loss: 16.4f}" self.log_func(msg) def _fit(self, epochs, *args): return lax.scan( lambda state, i: self.svi.update(state, *args), self.init_state, jnp.arange(epochs), ) def _update_state(self, state, loss): self.state = state self.init_state = state self.loss = loss if self.loss is None else jnp.concatenate([self.loss, loss]) def fit(self, *args, **kwargs): num_epochs = kwargs.pop("num_epochs", self.num_epochs) log_freq = kwargs.pop("log_freq", self.log_freq) if self.init_state is None: self.init_state = self.svi.init(self.rng_key, *args) if log_freq <= 0: state, loss = self._fit(num_epochs, *args) self._update_state(state, loss) else: steps, rest = num_epochs // log_freq, num_epochs % log_freq for step in range(steps): state, loss = self._fit(log_freq, *args) self._log(log_freq * (step + 1), loss[-1]) self._update_state(state, loss) if rest > 0: state, loss = self._fit(rest, *args) self._update_state(state, loss) self.params = self.svi.get_params(state) predictive = Predictive( self.model, guide=self.guide, params=self.params, num_samples=self.num_samples, **kwargs, ) self.posterior = predictive(self.rng_key, *args) def get_posterior_predictive(self, *args, **kwargs): """kwargs -> Predictive, args -> predictive""" num_samples = kwargs.pop("num_samples", self.num_samples) predictive = Predictive( self.model, guide=self.guide, params=self.params, num_samples=num_samples, **kwargs, ) self.posterior_predictive = predictive(self.rng_key, *args)
class ModelHandler(object): def __init__(self, model: Model, guide: Guide, rng_key: int = 0, *, loss: ELBO = ELBO(num_particles=1), optim_builder: optim.optimizers.optimizer = optim.Adam): """Handling the model and guide for training and prediction Args: model: function holding the numpyro model guide: function holding the numpyro guide rng_key: random key as int loss: loss to optimize optim_builder: builder for an optimizer """ self.model = model self.guide = guide self.rng_key = random.PRNGKey(rng_key) # current random key self.loss = loss self.optim_builder = optim_builder self.svi = None self.svi_state = None self.optim = None self.log_func = print # overwrite e.g. logger.info(...) def reset_svi(self): """Reset the current SVI state""" self.svi = None self.svi_state = None return self def init_svi(self, X: DeviceArray, *, lr: float, **kwargs): """Initialize the SVI state Args: X: input data lr: learning rate kwargs: other keyword arguments for optimizer """ self.optim = self.optim_builder(lr, **kwargs) self.svi = SVI(self.model, self.guide, self.optim, self.loss) svi_state = self.svi.init(self.rng_key, X) if self.svi_state is None: self.svi_state = svi_state return self @property def optim_state(self) -> OptimizerState: """Current optimizer state""" assert self.svi_state is not None, "'init_svi' needs to be called first" return self.svi_state.optim_state @optim_state.setter def optim_state(self, state: OptimizerState): """Set current optimizer state""" self.svi_state = SVIState(state, self.rng_key) def dump_optim_state(self, fh: IO): """Pickle and dump optimizer state to file handle""" pickle.dump( optim.optimizers.unpack_optimizer_state(self.optim_state[1]), fh) return self def load_optim_state(self, fh: IO): """Read and unpickle optimizer state from file handle""" state = optim.optimizers.pack_optimizer_state(pickle.load(fh)) iter0 = jnp.array(0) self.optim_state = (iter0, state) return self @property def optim_total_steps(self) -> int: """Returns the number of performed iterations in total""" return int(self.optim_state[0]) def _fit(self, X: DeviceArray, n_epochs) -> float: @jit def train_epochs(svi_state, n_epochs): def train_one_epoch(_, val): loss, svi_state = val svi_state, loss = self.svi.update(svi_state, X) return loss, svi_state return lax.fori_loop(0, n_epochs, train_one_epoch, (0., svi_state)) loss, self.svi_state = train_epochs(self.svi_state, n_epochs) return float(loss / X.shape[0]) def _log(self, n_digits, epoch, loss): msg = f"epoch: {str(epoch).rjust(n_digits)} loss: {loss: 16.4f}" self.log_func(msg) def fit(self, X: DeviceArray, *, n_epochs: int, log_freq: int = 0, lr: float, **kwargs) -> float: """Train but log with a given frequency Args: X: input data n_epochs: total number of epochs log_freq: log loss every log_freq number of eppochs lr: learning rate kwargs: parameters of `init_svi` Returns: final loss of last epoch """ self.init_svi(X, lr=lr, **kwargs) if log_freq <= 0: self._fit(X, n_epochs) else: loss = self.svi.evaluate(self.svi_state, X) / X.shape[0] curr_epoch = 0 n_digits = len(str(abs(n_epochs))) self._log(n_digits, curr_epoch, loss) for i in range(n_epochs // log_freq): curr_epoch += log_freq loss = self._fit(X, log_freq) self._log(n_digits, curr_epoch, loss) rest = n_epochs % log_freq if rest > 0: curr_epoch += rest loss = self._fit(X, rest) self._log(n_digits, curr_epoch, loss) loss = self.svi.evaluate(self.svi_state, X) / X.shape[0] self.rng_key = self.svi_state.rng_key return float(loss) @property def model_params(self) -> Optional[Dict[str, DeviceArray]]: """Gets model parameters Returns: dict of model parameters """ if self.svi is not None: return self.svi.get_params(self.svi_state) else: return None def predict(self, X: DeviceArray, **kwargs) -> DeviceArray: """Predict the parameters of a model specified by `return_sites` Args: X: input data kwargs: keyword arguments for numpro `Predictive` Returns: samples for all sample sites """ self.init_svi(X, lr=0.) # dummy initialization predictive = Predictive(self.model, guide=self.guide, params=self.model_params, **kwargs) samples = predictive(self.rng_key, X) return samples
def _train_full_data(self, x_data, obs2sample, n_epochs=20000, lr=0.002, progressbar=True, random_seed=1): idx = np.arange(x_data.shape[0]).astype("int64") # move data to default device x_data = device_put(jnp.array(x_data)) extra_data = { 'idx': device_put(jnp.array(idx)), 'obs2sample': device_put(jnp.array(obs2sample)) } # initialise SVI inference method svi = SVI( self.model.forward, self.guide, # limit the gradient step from becoming too large optim.ClippedAdam(clip_norm=jnp.array(200), **{'step_size': jnp.array(lr)}), loss=Trace_ELBO()) init_state = svi.init(random.PRNGKey(random_seed), x_data=x_data, **extra_data) self.state = init_state if not progressbar: # Training in one step epochs_iterator = tqdm(range(1)) for e in epochs_iterator: state, losses = lax.scan( lambda state_1, i: svi.update( state_1, x_data=self.x_data, **extra_data), # TODO for minibatch DataLoader goes here init_state, jnp.arange(n_epochs)) # print(state) epochs_iterator.set_description( 'ELBO Loss: ' + '{:.4e}'.format(losses[::-1][0])) self.state = state self.hist = losses else: # training using for-loop jit_step_update = jit(lambda state_1: svi.update( state_1, x_data=x_data, **extra_data)) # TODO figure out minibatch static_argnums https://github.com/pyro-ppl/numpyro/issues/869 ### very slow epochs_iterator = tqdm(range(n_epochs)) for e in epochs_iterator: self.state, loss = jit_step_update(self.state) self.hist.append(loss) epochs_iterator.set_description('ELBO Loss: ' + '{:.4e}'.format(loss)) self.state_param = svi.get_params(self.state).copy()