From 331d2bf04acb0f3d5259272261bbc9ef2f460d11 Mon Sep 17 00:00:00 2001 From: slamitza Date: Sun, 10 Dec 2023 22:31:55 +0100 Subject: [PATCH 01/24] SMC2 --- .../python/experimental/mcmc/BUILD | 8 +- .../experimental/mcmc/particle_filter.py | 403 ++++++- .../experimental/mcmc/particle_filter_test.py | 322 +++-- .../mcmc/sequential_monte_carlo_kernel.py | 11 +- .../sequential_monte_carlo_kernel_test.py | 16 +- tfp_nightly.egg-info/PKG-INFO | 244 ++++ tfp_nightly.egg-info/SOURCES.txt | 1068 +++++++++++++++++ tfp_nightly.egg-info/dependency_links.txt | 1 + tfp_nightly.egg-info/not-zip-safe | 1 + tfp_nightly.egg-info/requires.txt | 14 + tfp_nightly.egg-info/top_level.txt | 1 + 11 files changed, 1955 insertions(+), 134 deletions(-) create mode 100644 tfp_nightly.egg-info/PKG-INFO create mode 100644 tfp_nightly.egg-info/SOURCES.txt create mode 100644 tfp_nightly.egg-info/dependency_links.txt create mode 100644 tfp_nightly.egg-info/not-zip-safe create mode 100644 tfp_nightly.egg-info/requires.txt create mode 100644 tfp_nightly.egg-info/top_level.txt diff --git a/tensorflow_probability/python/experimental/mcmc/BUILD b/tensorflow_probability/python/experimental/mcmc/BUILD index 7fb140497b..aa2843bfb9 100644 --- a/tensorflow_probability/python/experimental/mcmc/BUILD +++ b/tensorflow_probability/python/experimental/mcmc/BUILD @@ -18,8 +18,6 @@ # //tensorflow_probability/python/internal/auto_batching # internally. -# Placeholder: py_library -# Placeholder: py_test load( "//tensorflow_probability/python:build_defs.bzl", "multi_substrate_py_library", @@ -548,6 +546,9 @@ multi_substrate_py_library( "//tensorflow_probability/python/internal:prefer_static", "//tensorflow_probability/python/internal:tensor_util", "//tensorflow_probability/python/internal:tensorshape_util", + "//tensorflow_probability/python/distributions:batch_reshape", + "//tensorflow_probability/python/distributions:batch_broadcast", + "//tensorflow_probability/python/distributions:independent" ], ) @@ -574,6 +575,8 @@ multi_substrate_py_test( "//tensorflow_probability/python/distributions:sample", "//tensorflow_probability/python/distributions:transformed_distribution", "//tensorflow_probability/python/distributions:uniform", + "//tensorflow_probability/python/distributions:categorical", + "//tensorflow_probability/python/distributions:hidden_markov_model", "//tensorflow_probability/python/internal:test_util", "//tensorflow_probability/python/math:gradient", # "//third_party/tensorflow/compiler/jit:xla_cpu_jit", # DisableOnExport @@ -652,6 +655,7 @@ multi_substrate_py_test( "//tensorflow_probability/python/distributions:mvn_diag", "//tensorflow_probability/python/distributions:normal", "//tensorflow_probability/python/distributions:sample", + "//tensorflow_probability/python/experimental/mcmc:sequential_monte_carlo_kernel", "//tensorflow_probability/python/distributions:uniform", "//tensorflow_probability/python/distributions/internal:statistical_testing", "//tensorflow_probability/python/internal:test_util", diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index 1bcbc870f4..b920b0db85 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -25,6 +25,11 @@ from tensorflow_probability.python.internal import prefer_static as ps from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.mcmc.internal import util as mcmc_util +from tensorflow_probability.python.distributions import batch_reshape +from tensorflow_probability.python.distributions import batch_broadcast +from tensorflow_probability.python.distributions import normal +from tensorflow_probability.python.distributions import uniform + __all__ = [ 'infer_trajectories', @@ -44,6 +49,39 @@ def _default_trace_fn(state, kernel_results): kernel_results.incremental_log_marginal_likelihood) +def _default_kernel(parameters): + mean, variance = tf.nn.moments(parameters, axes=[0]) + proposal_distribution = normal.Normal(loc=tf.fill(parameters.shape, mean), scale=tf.sqrt(variance)) + return proposal_distribution + + +def _default_extra_fn(step, + state, + seed + ): + return state.extra + + +def where_fn(accept, a, b, num_outer_particles, num_inner_particles): + is_scalar = tf.rank(a) == tf.constant(0) + is_nan = tf.math.is_nan(tf.cast(a, tf.float32)) + is_all_nan = tf.reduce_all(is_nan) + if is_scalar and is_all_nan: + return a + elif a.shape == 2 and b.shape == 2: + # extra + return a + elif a.shape == num_outer_particles and b.shape == num_outer_particles: + return mcmc_util.choose(accept, a, b) + elif a.shape == [num_outer_particles, num_inner_particles] and \ + b.shape == [num_outer_particles, num_inner_particles]: + return mcmc_util.choose(accept, a, b) + elif a.shape == () and b.shape == (): + return a + else: + raise ValueError("Unexpected tensor shapes") + + particle_filter_arg_str = """\ Each latent state is a `Tensor` or nested structure of `Tensor`s, as defined by the `initial_state_prior`. @@ -435,6 +473,344 @@ def seeded_one_step(seed_state_results, _): return traced_results +def smc_squared( + inner_observations, + initial_parameter_prior, + num_outer_particles, + inner_initial_state_prior, + inner_transition_fn, + inner_observation_fn, + num_inner_particles, + outer_trace_fn=_default_trace_fn, + outer_rejuvenation_criterion_fn=None, + outer_resample_criterion_fn=None, + outer_resample_fn=weighted_resampling.resample_systematic, + inner_resample_criterion_fn=smc_kernel.ess_below_threshold, + inner_resample_fn=weighted_resampling.resample_systematic, + extra_fn=_default_extra_fn, + parameter_proposal_kernel=_default_kernel, + inner_proposal_fn=None, + inner_initial_state_proposal=None, + outer_trace_criterion_fn=_always_trace, + parallel_iterations=1, + num_transitions_per_observation=1, + static_trace_allocation_size=None, + initial_parameter_proposal=None, + unbiased_gradients=True, + seed=None, +): + init_seed, loop_seed, step_seed = samplers.split_seed(seed, n=3, salt='smc_squared') + + num_observation_steps = ps.size0(tf.nest.flatten(inner_observations)[0]) + + # TODO: The following two lines compensates for having the first empty step in smc2 + num_timesteps = (1 + num_transitions_per_observation * + (num_observation_steps - 1)) + 1 + last_obs_expanded = tf.expand_dims(inner_observations[-1], axis=0) + inner_observations = tf.concat([inner_observations, last_obs_expanded], axis=0) + + if outer_rejuvenation_criterion_fn is None: + outer_rejuvenation_criterion_fn = lambda *_: tf.constant(False) + + if outer_resample_criterion_fn is None: + outer_resample_criterion_fn = lambda *_: tf.constant(False) + + # If trace criterion is `None`, we'll return only the final results. + never_trace = lambda *_: False + if outer_trace_criterion_fn is None: + static_trace_allocation_size = 0 + outer_trace_criterion_fn = never_trace + + if initial_parameter_proposal is None: + initial_state = initial_parameter_prior.sample(num_outer_particles, + seed=seed) + initial_log_weights = ps.zeros_like( + initial_parameter_prior.log_prob(initial_state)) + else: + initial_state = initial_parameter_proposal.sample(num_outer_particles, + seed=seed) + initial_log_weights = ( + initial_parameter_prior.log_prob(initial_state) - + initial_parameter_proposal.log_prob(initial_state) + ) + + # Normalize the initial weights. If we used a proposal, the weights are + # normalized in expectation, but actually normalizing them reduces variance. + initial_log_weights = tf.nn.log_softmax(initial_log_weights, axis=0) + + inner_weighted_particles = _particle_filter_initial_weighted_particles( + observations=inner_observations, + observation_fn=inner_observation_fn(initial_state), + initial_state_prior=inner_initial_state_prior(0, initial_state), + initial_state_proposal=(inner_initial_state_proposal(0, initial_state) + if inner_initial_state_proposal is not None else None), + num_particles=num_inner_particles, + particles_dim=1, + seed=seed + ) + + init_state = smc_kernel.WeightedParticles(*inner_weighted_particles) + + batch_zeros = tf.zeros(ps.shape(initial_state)) + + initial_filter_results = smc_kernel.SequentialMonteCarloResults( + steps=0, + parent_indices=smc_kernel._dummy_indices_like(init_state.log_weights), + incremental_log_marginal_likelihood=batch_zeros, + accumulated_log_marginal_likelihood=batch_zeros, + seed=samplers.zeros_seed()) + + initial_state = smc_kernel.WeightedParticles( + particles=(initial_state, + inner_weighted_particles, + initial_filter_results.parent_indices, + initial_filter_results.incremental_log_marginal_likelihood, + initial_filter_results.accumulated_log_marginal_likelihood), + log_weights=initial_log_weights, + extra=(tf.constant(0), + initial_filter_results.seed) + ) + + outer_propose_and_update_log_weights_fn = ( + _outer_particle_filter_propose_and_update_log_weights_fn( + outer_rejuvenation_criterion_fn=outer_rejuvenation_criterion_fn, + inner_observations=inner_observations, + inner_transition_fn=inner_transition_fn, + inner_proposal_fn=inner_proposal_fn, + inner_observation_fn=inner_observation_fn, + inner_resample_fn=inner_resample_fn, + inner_resample_criterion_fn=inner_resample_criterion_fn, + parameter_proposal_kernel=parameter_proposal_kernel, + initial_parameter_prior=initial_parameter_prior, + num_transitions_per_observation=num_transitions_per_observation, + unbiased_gradients=unbiased_gradients, + inner_initial_state_prior=inner_initial_state_prior, + inner_initial_state_proposal=inner_initial_state_proposal, + num_inner_particles=num_inner_particles, + num_outer_particles=num_outer_particles, + extra_fn=extra_fn + ) + ) + + traced_results = sequential_monte_carlo( + initial_weighted_particles=initial_state, + propose_and_update_log_weights_fn=outer_propose_and_update_log_weights_fn, + resample_fn=outer_resample_fn, + resample_criterion_fn=outer_resample_criterion_fn, + trace_criterion_fn=outer_trace_criterion_fn, + static_trace_allocation_size=static_trace_allocation_size, + parallel_iterations=parallel_iterations, + unbiased_gradients=unbiased_gradients, + num_steps=num_timesteps, + particles_dim=0, + trace_fn=outer_trace_fn, + seed=loop_seed + ) + + return traced_results + + +def _outer_particle_filter_propose_and_update_log_weights_fn( + inner_observations, + inner_transition_fn, + inner_proposal_fn, + inner_observation_fn, + initial_parameter_prior, + inner_initial_state_prior, + inner_initial_state_proposal, + num_transitions_per_observation, + inner_resample_fn, + inner_resample_criterion_fn, + outer_rejuvenation_criterion_fn, + unbiased_gradients, + parameter_proposal_kernel, + num_inner_particles, + num_outer_particles, + extra_fn +): + """Build a function specifying a particle filter update step.""" + def _outer_propose_and_update_log_weights_fn(step, state, seed=None): + outside_parameters = state.particles[0] + inner_weighted_particles, log_weights = state.particles[1], state.log_weights + + filter_results = smc_kernel.SequentialMonteCarloResults( + steps=step, + parent_indices=state.particles[2], + incremental_log_marginal_likelihood=state.particles[3], + accumulated_log_marginal_likelihood=state.particles[4], + seed=state.extra[1]) + + inner_propose_and_update_log_weights_fn = ( + _particle_filter_propose_and_update_log_weights_fn( + observations=inner_observations, + transition_fn=inner_transition_fn(outside_parameters), + proposal_fn=(inner_proposal_fn(outside_parameters) + if inner_proposal_fn is not None else None), + observation_fn=inner_observation_fn(outside_parameters), + particles_dim=1, + num_transitions_per_observation=num_transitions_per_observation, + extra_fn=extra_fn + ) + ) + + kernel = smc_kernel.SequentialMonteCarlo( + propose_and_update_log_weights_fn=inner_propose_and_update_log_weights_fn, + resample_fn=inner_resample_fn, + resample_criterion_fn=inner_resample_criterion_fn, + particles_dim=1, + unbiased_gradients=unbiased_gradients + ) + + inner_weighted_particles, filter_results = kernel.one_step(inner_weighted_particles, + filter_results, + seed=seed) + + updated_log_weights = log_weights + filter_results.incremental_log_marginal_likelihood + + do_rejuvenation = outer_rejuvenation_criterion_fn(step, state) + + def rejuvenate_particles(outside_parameters, updated_log_weights, inner_weighted_particles, filter_results): + proposed_parameters = parameter_proposal_kernel(outside_parameters).sample(seed=seed) + + rej_params_log_weights = ps.zeros_like( + initial_parameter_prior.log_prob(proposed_parameters) + ) + rej_params_log_weights = tf.nn.log_softmax(rej_params_log_weights, axis=0) + + rej_inner_weighted_particles = _particle_filter_initial_weighted_particles( + observations=inner_observations, + observation_fn=inner_observation_fn(proposed_parameters), + initial_state_prior=inner_initial_state_prior(0, proposed_parameters), + initial_state_proposal=(inner_initial_state_proposal(0, proposed_parameters) + if inner_initial_state_proposal is not None else None), + num_particles=num_inner_particles, + particles_dim=1, + seed=seed) + + batch_zeros = tf.zeros(ps.shape(log_weights)) + + rej_filter_results = smc_kernel.SequentialMonteCarloResults( + steps=tf.constant(0, dtype=tf.int32), + parent_indices=smc_kernel._dummy_indices_like( + rej_inner_weighted_particles.log_weights + ), + incremental_log_marginal_likelihood=batch_zeros, + accumulated_log_marginal_likelihood=batch_zeros, + seed=samplers.zeros_seed()) + + rej_inner_particles_weights = rej_inner_weighted_particles.log_weights + + rej_inner_propose_and_update_log_weights_fn = ( + _particle_filter_propose_and_update_log_weights_fn( + observations=inner_observations, + transition_fn=inner_transition_fn(proposed_parameters), + proposal_fn=(inner_proposal_fn(proposed_parameters) + if inner_proposal_fn is not None else None), + observation_fn=inner_observation_fn(proposed_parameters), + extra_fn=extra_fn, + particles_dim=1, + num_transitions_per_observation=num_transitions_per_observation) + ) + + rej_kernel = smc_kernel.SequentialMonteCarlo( + propose_and_update_log_weights_fn=rej_inner_propose_and_update_log_weights_fn, + resample_fn=inner_resample_fn, + resample_criterion_fn=inner_resample_criterion_fn, + particles_dim=1, + unbiased_gradients=unbiased_gradients) + + def condition(i, + rej_inner_weighted_particles, + rej_filter_results, + rej_parameters_weights, + rej_params_log_weights): + return tf.less_equal(i, step) + + def body(i, + rej_inner_weighted_particles, + rej_filter_results, + rej_parameters_weights, + rej_params_log_weights): + + rej_inner_weighted_particles, rej_filter_results = rej_kernel.one_step( + rej_inner_weighted_particles, rej_filter_results, seed=seed + ) + + rej_parameters_weights += rej_inner_weighted_particles.log_weights + + rej_params_log_weights = rej_params_log_weights + rej_filter_results.incremental_log_marginal_likelihood + return i + 1, rej_inner_weighted_particles, rej_filter_results, rej_parameters_weights, rej_params_log_weights + + i, rej_inner_weighted_particles, rej_filter_results, rej_inner_particles_weights, rej_params_log_weights = tf.while_loop( + condition, + body, + loop_vars=[0, + rej_inner_weighted_particles, + rej_filter_results, + rej_inner_particles_weights, + rej_params_log_weights + ] + ) + + log_a = rej_filter_results.accumulated_log_marginal_likelihood - \ + filter_results.accumulated_log_marginal_likelihood + \ + parameter_proposal_kernel(proposed_parameters).log_prob(outside_parameters) - \ + parameter_proposal_kernel(outside_parameters).log_prob(proposed_parameters) + + acceptance_probs = tf.minimum(1., tf.exp(log_a)) + + random_numbers = uniform.Uniform(0., 1.).sample(num_outer_particles, seed=seed) + + # Determine if the proposed particle should be accepted or reject + accept = random_numbers > acceptance_probs + + # Update the chosen particles and filter restults based on the acceptance step + outside_parameters = tf.where(accept, outside_parameters, proposed_parameters) + updated_log_weights = tf.where(accept, updated_log_weights, rej_params_log_weights) + + inner_weighted_particles_particles = mcmc_util.choose( + accept, + inner_weighted_particles.particles, + rej_inner_weighted_particles.particles + ) + inner_weighted_particles_log_weights = mcmc_util.choose( + accept, + inner_weighted_particles.log_weights, + rej_inner_weighted_particles.log_weights + ) + + inner_weighted_particles = smc_kernel.WeightedParticles( + particles=inner_weighted_particles_particles, + log_weights=inner_weighted_particles_log_weights, + extra=inner_weighted_particles.extra + ) + + filter_results = tf.nest.map_structure( + lambda a, b: where_fn(accept, a, b, num_outer_particles, num_inner_particles), + filter_results, + rej_filter_results + ) + + return outside_parameters, updated_log_weights, inner_weighted_particles, filter_results + + outside_parameters, updated_log_weights, inner_weighted_particles, filter_results = tf.cond( + do_rejuvenation, + lambda: (rejuvenate_particles(outside_parameters, updated_log_weights, inner_weighted_particles, filter_results)), + lambda: (outside_parameters, updated_log_weights, inner_weighted_particles, filter_results) + ) + + return smc_kernel.WeightedParticles( + particles=(outside_parameters, + inner_weighted_particles, + filter_results.parent_indices, + filter_results.incremental_log_marginal_likelihood, + filter_results.accumulated_log_marginal_likelihood), + log_weights=updated_log_weights, + extra=(step, + filter_results.seed)) + return _outer_propose_and_update_log_weights_fn + + @docstring_util.expand_docstring( particle_filter_arg_str=particle_filter_arg_str.format(scibor_ref_idx=1)) def particle_filter(observations, @@ -442,6 +818,7 @@ def particle_filter(observations, transition_fn, observation_fn, num_particles, + extra_fn=_default_extra_fn, initial_state_proposal=None, proposal_fn=None, resample_fn=weighted_resampling.resample_systematic, @@ -526,7 +903,9 @@ def particle_filter(observations, particles_dim=particles_dim, proposal_fn=proposal_fn, observation_fn=observation_fn, - num_transitions_per_observation=num_transitions_per_observation)) + num_transitions_per_observation=num_transitions_per_observation, + extra_fn=extra_fn + )) return sequential_monte_carlo( initial_weighted_particles=initial_weighted_particles, @@ -549,6 +928,7 @@ def _particle_filter_initial_weighted_particles(observations, initial_state_proposal, num_particles, particles_dim=0, + extra=np.nan, seed=None): """Initialize a set of weighted particles including the first observation.""" # Propose an initial state. @@ -574,6 +954,14 @@ def _particle_filter_initial_weighted_particles(observations, axis=particles_dim) # Return particles weighted by the initial observation. + if extra is np.nan: + if len(ps.shape(initial_log_weights)) == 1: + # initial extra for particle filter + extra = tf.constant(0) + else: + # initial extra for inner particles of smc_squared + extra = tf.constant(0, shape=ps.shape(initial_log_weights)) + return smc_kernel.WeightedParticles( particles=initial_state, log_weights=initial_log_weights + _compute_observation_log_weights( @@ -581,7 +969,8 @@ def _particle_filter_initial_weighted_particles(observations, particles=initial_state, observations=observations, observation_fn=observation_fn, - particles_dim=particles_dim)) + particles_dim=particles_dim), + extra=extra) def _particle_filter_propose_and_update_log_weights_fn( @@ -589,6 +978,7 @@ def _particle_filter_propose_and_update_log_weights_fn( transition_fn, proposal_fn, observation_fn, + extra_fn, num_transitions_per_observation=1, particles_dim=0): """Build a function specifying a particle filter update step.""" @@ -619,13 +1009,18 @@ def propose_and_update_log_weights_fn(step, state, seed=None): else: proposed_particles = transition_dist.sample(seed=seed) + updated_extra = extra_fn(step, + state, + seed) + with tf.control_dependencies(assertions): return smc_kernel.WeightedParticles( particles=proposed_particles, log_weights=log_weights + _compute_observation_log_weights( step + 1, proposed_particles, observations, observation_fn, num_transitions_per_observation=num_transitions_per_observation, - particles_dim=particles_dim)) + particles_dim=particles_dim), + extra=updated_extra) return propose_and_update_log_weights_fn @@ -670,6 +1065,8 @@ def _compute_observation_log_weights(step, observation = tf.nest.map_structure( lambda x, step=step: tf.gather(x, observation_idx), observations) + if particles_dim == 1: + observation = tf.expand_dims(observation, axis=0) observation = tf.nest.map_structure( lambda x: tf.expand_dims(x, axis=particles_dim), observation) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 6508eb6231..e190c76bda 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -21,6 +21,7 @@ from tensorflow_probability.python.bijectors import shift from tensorflow_probability.python.distributions import bernoulli from tensorflow_probability.python.distributions import deterministic +from tensorflow_probability.python.distributions import independent from tensorflow_probability.python.distributions import joint_distribution_auto_batched as jdab from tensorflow_probability.python.distributions import joint_distribution_named as jdn from tensorflow_probability.python.distributions import linear_gaussian_ssm as lgssm @@ -177,128 +178,6 @@ def observation_fn(_, state): self.assertAllEqual(incremental_log_marginal_likelihoods.shape, [num_timesteps] + batch_shape) - def test_batch_of_filters_particles_dim_1(self): - - batch_shape = [3, 2] - num_particles = 1000 - num_timesteps = 40 - - # Batch of priors on object 1D positions and velocities. - initial_state_prior = jdn.JointDistributionNamed({ - 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), - 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) - }) - - def transition_fn(_, previous_state): - return jdn.JointDistributionNamed({ - 'position': - normal.Normal( - loc=previous_state['position'] + previous_state['velocity'], - scale=0.1), - 'velocity': - normal.Normal(loc=previous_state['velocity'], scale=0.01) - }) - - def observation_fn(_, state): - return normal.Normal(loc=state['position'], scale=0.1) - - # Batch of synthetic observations, . - true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) - true_velocities = 0.1 * np.random.randn( - *batch_shape).astype(self.dtype) - observed_positions = ( - true_velocities * - np.arange(num_timesteps).astype( - self.dtype)[..., tf.newaxis, tf.newaxis] + - true_initial_positions) - - (particles, log_weights, parent_indices, - incremental_log_marginal_likelihoods) = self.evaluate( - particle_filter.particle_filter( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - seed=test_util.test_seed(), - particles_dim=1)) - - self.assertAllEqual(particles['position'].shape, - [num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]]) - self.assertAllEqual(particles['velocity'].shape, - [num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]]) - self.assertAllEqual(parent_indices.shape, - [num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]]) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - - self.assertAllClose( - self.evaluate( - tf.reduce_sum(tf.exp(log_weights) * - particles['position'], axis=2)), - observed_positions, - atol=0.3) - - velocity_means = tf.reduce_sum(tf.exp(log_weights) * - particles['velocity'], axis=2) - - self.assertAllClose( - self.evaluate(tf.reduce_mean(velocity_means, axis=0)), - true_velocities, atol=0.05) - - # Uncertainty in velocity should decrease over time. - velocity_stddev = self.evaluate( - tf.math.reduce_std(particles['velocity'], axis=2)) - self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) - - trajectories = self.evaluate( - particle_filter.reconstruct_trajectories(particles, - parent_indices, - particles_dim=1)) - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['velocity'].shape) - - # Verify that `infer_trajectories` also works on batches. - trajectories, incremental_log_marginal_likelihoods = self.evaluate( - particle_filter.infer_trajectories( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - particles_dim=1, - seed=test_util.test_seed())) - - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['velocity'].shape) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - def test_reconstruct_trajectories_toy_example(self): particles = tf.convert_to_tensor([[1, 2, 3], [4, 5, 6,], [7, 8, 9]]) # 1 -- 4 -- 7 @@ -734,6 +613,205 @@ def marginal_log_likelihood(level_scale, noise_scale): self.assertAllNotNone(grads) self.assertAllAssertsNested(self.assertNotAllZero, grads) + def test_smc_squared_rejuvenation_parameters(self): + def particle_dynamics(params, _, previous_state): + reshaped_params = tf.reshape(params, [params.shape[0]] + [1] * (previous_state.shape.rank - 1)) + broadcasted_params = tf.broadcast_to(reshaped_params, previous_state.shape) + return normal.Normal(previous_state + broadcasted_params + 1, 0.1) + + def rejuvenation_criterion(step, state): + # Rejuvenation every 2 steps + cond = tf.logical_and( + tf.equal(tf.math.mod(step, tf.constant(2)), tf.constant(0)), + tf.not_equal(state.extra[0], tf.constant(0)) + ) + return tf.cond(cond, lambda: tf.constant(True), lambda: tf.constant(False)) + + inner_observations = tf.range(30, dtype=tf.float32) + + num_outer_particles = 3 + num_inner_particles = 7 + + loc = tf.broadcast_to([0., 0.], [num_outer_particles, 2]) + scale_diag = tf.broadcast_to([0.05, 0.05], [num_outer_particles, 2]) + + params, inner_pt = self.evaluate(particle_filter.smc_squared( + inner_observations=inner_observations, + inner_initial_state_prior=lambda _, params: mvn_diag.MultivariateNormalDiag( + loc=loc, scale_diag=scale_diag + ), + initial_parameter_prior=normal.Normal(3., 1.), + num_outer_particles=num_outer_particles, + num_inner_particles=num_inner_particles, + outer_rejuvenation_criterion_fn=rejuvenation_criterion, + inner_transition_fn=lambda params: ( + lambda _, state: independent.Independent(particle_dynamics(params, _, state), 1)), + inner_observation_fn=lambda params: ( + lambda _, state: independent.Independent(normal.Normal(state, 2.), 1)), + outer_trace_fn=lambda s, r: ( + s.particles[0], + s.particles[1] + ), + parameter_proposal_kernel=lambda params: normal.Normal(params, 3), + seed=test_util.test_seed() + ) + ) + + abs_params = tf.abs(params) + differences = abs_params[1:] - abs_params[:-1] + mask_parameters = tf.reduce_all(tf.less_equal(differences, 0), axis=0) + + self.assertAllTrue(mask_parameters) + + def test_smc_squared_can_step_dynamics_faster_than_observations(self): + initial_state_prior = jdn.JointDistributionNamed({ + 'position': deterministic.Deterministic([1.]), + 'velocity': deterministic.Deterministic([0.]) + }) + + # Use 100 steps between observations to integrate a simple harmonic + # oscillator. + dt = 0.01 + def simple_harmonic_motion_transition_fn(_, state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=state['position'] + dt * state['velocity'], + scale=dt * 0.01), + 'velocity': + normal.Normal( + loc=state['velocity'] - dt * state['position'], + scale=dt * 0.01) + }) + + def observe_position(_, state): + return normal.Normal(loc=state['position'], scale=0.01) + + particles, lps = self.evaluate(particle_filter.smc_squared( + inner_observations=tf.convert_to_tensor( + [tf.math.cos(0.), tf.math.cos(1.)]), + inner_initial_state_prior=lambda _, params: initial_state_prior, + initial_parameter_prior=deterministic.Deterministic(0.), + num_outer_particles=1, + inner_transition_fn=lambda params: simple_harmonic_motion_transition_fn, + inner_observation_fn=lambda params: observe_position, + num_inner_particles=1024, + outer_trace_fn=lambda s, r: ( + s.particles[1].particles, + s.particles[3] + ), + num_transitions_per_observation=100, + seed=test_util.test_seed()) + ) + + self.assertAllEqual(ps.shape(particles['position']), tf.constant([102, 1, 1024])) + + self.assertAllClose(tf.transpose(np.mean(particles['position'], axis=-1)), + tf.reshape(tf.math.cos(dt * np.arange(102)), [1, -1]), + atol=0.04) + + self.assertAllEqual(ps.shape(lps), [102, 1]) + self.assertGreater(lps[1][0], 1.) + self.assertGreater(lps[-1][0], 3.) + + def test_smc_squared_custom_outer_trace_fn(self): + def trace_fn(state, _): + # Traces the mean and stddev of the particle population at each step. + weights = tf.exp(state[0][1].log_weights[0]) + mean = tf.reduce_sum(weights * state[0][1].particles[0], axis=0) + variance = tf.reduce_sum( + weights * (state[0][1].particles[0] - mean[tf.newaxis, ...]) ** 2) + return {'mean': mean, + 'stddev': tf.sqrt(variance), + # In real usage we would likely not track the particles and + # weights. We keep them here just so we can double-check the + # stats, below. + 'particles': state[0][1].particles[0], + 'weights': weights} + + results = self.evaluate(particle_filter.smc_squared( + inner_observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + inner_initial_state_prior=lambda _, params: normal.Normal([0.], 1.), + initial_parameter_prior=deterministic.Deterministic(0.), + inner_transition_fn=lambda params: (lambda _, state: normal.Normal(state, 1.)), + inner_observation_fn=lambda params: (lambda _, state: normal.Normal(state, 1.)), + num_inner_particles=1024, + num_outer_particles=1, + outer_trace_fn=trace_fn, + seed=test_util.test_seed()) + ) + + # Verify that posterior means are increasing. + self.assertAllGreater(results['mean'][1:] - results['mean'][:-1], 0.) + + # Check that our traced means and scales match values computed + # by averaging over particles after the fact. + all_means = self.evaluate(tf.reduce_sum( + results['weights'] * results['particles'], axis=1)) + all_variances = self.evaluate( + tf.reduce_sum( + results['weights'] * + (results['particles'] - all_means[..., tf.newaxis])**2, + axis=1)) + self.assertAllClose(results['mean'], all_means) + self.assertAllClose(results['stddev'], np.sqrt(all_variances)) + + def test_smc_squared_indices_to_trace(self): + num_outer_particles = 7 + num_inner_particles = 13 + + def rejuvenation_criterion(step, state): + # Rejuvenation every 3 steps + cond = tf.logical_and( + tf.equal(tf.math.mod(step, tf.constant(3)), tf.constant(0)), + tf.not_equal(state.extra[0], tf.constant(0)) + ) + return tf.cond(cond, lambda: tf.constant(True), lambda: tf.constant(False)) + + (parameters, weight_parameters, inner_particles, inner_log_weights, lp) = self.evaluate( + particle_filter.smc_squared( + inner_observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_parameter_prior=deterministic.Deterministic(0.), + inner_initial_state_prior=lambda _, params: normal.Normal([0.] * num_outer_particles, 1.), + inner_transition_fn=lambda params: (lambda _, state: normal.Normal(state, 10.)), + inner_observation_fn=lambda params: (lambda _, state: normal.Normal(state, 0.1)), + num_inner_particles=num_inner_particles, + num_outer_particles=num_outer_particles, + outer_rejuvenation_criterion_fn=rejuvenation_criterion, + outer_trace_fn=lambda s, r: ( # pylint: disable=g-long-lambda + s.particles[0], + s.log_weights, + s.particles[1].particles, + s.particles[1].log_weights, + r.accumulated_log_marginal_likelihood), + seed=test_util.test_seed()) + ) + + # TODO: smc_squared at the moment starts his run with an empty step + self.assertAllEqual(ps.shape(parameters), [6, 7]) + self.assertAllEqual(ps.shape(weight_parameters), [6, 7]) + self.assertAllEqual(ps.shape(inner_particles), [6, 7, 13]) + self.assertAllEqual(ps.shape(inner_log_weights), [6, 7, 13]) + self.assertAllEqual(ps.shape(lp), [6]) + + def test_extra(self): + def step_hundred(step, state, seed): + return step * 2 + + results = self.evaluate( + particle_filter.particle_filter( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_state_prior=normal.Normal(0., 1.), + transition_fn=lambda _, state: normal.Normal(state, 1.), + observation_fn=lambda _, state: normal.Normal(state, 1.), + num_particles=1024, + extra_fn=step_hundred, + trace_fn=lambda s, r: s.extra, + seed=test_util.test_seed()) + ) + + self.assertAllEqual(results, [0, 0, 2, 4, 6]) + # TODO(b/186068104): add tests with dynamic shapes. class ParticleFilterTestFloat32(_ParticleFilterTest): diff --git a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel.py b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel.py index 73cb0f8414..300418c87d 100644 --- a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel.py +++ b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel.py @@ -34,7 +34,7 @@ # SequentialMonteCarlo `state` structure. class WeightedParticles(collections.namedtuple( - 'WeightedParticles', ['particles', 'log_weights'])): + 'WeightedParticles', ['particles', 'log_weights', 'extra'])): """Particles with corresponding log weights. This structure serves as the `state` for the `SequentialMonteCarlo` transition @@ -50,6 +50,10 @@ class WeightedParticles(collections.namedtuple( `exp(reduce_logsumexp(log_weights, axis=0)) == 1.`. These must be used in conjunction with `particles` to compute expectations under the target distribution. + extra: a (structure of) Tensor(s) each of shape + `concat([[b1, ..., bN], event_shape])`, where `event_shape` + may differ across component `Tensor`s. This represents global state of the + sampling process that is not associated with individual particles. In some contexts, particles may be stacked across multiple inference steps, in which case all `Tensor` shapes will be prefixed by an additional dimension @@ -292,7 +296,7 @@ def one_step(self, state, kernel_results, seed=None): - tf.gather(normalized_log_weights, 0, axis=self.particles_dim)) do_resample = self.resample_criterion_fn( - state, particles_dim=self.particles_dim) + state, self.particles_dim) # Some batch elements may require resampling and others not, so # we first do the resampling for all elements, then select whether to # use the resampled values for each batch element according to @@ -326,7 +330,8 @@ def one_step(self, state, kernel_results, seed=None): normalized_log_weights)) return (WeightedParticles(particles=resampled_particles, - log_weights=log_weights), + log_weights=log_weights, + extra=state.extra), SequentialMonteCarloResults( steps=kernel_results.steps + 1, parent_indices=resample_indices, diff --git a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py index 2a9302a420..2e29f6c4dd 100644 --- a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py +++ b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py @@ -42,7 +42,9 @@ def propose_and_update_log_weights_fn(_, weighted_particles, seed=None): return WeightedParticles( particles=proposed_particles, log_weights=weighted_particles.log_weights + - normal.Normal(loc=-2.6, scale=0.1).log_prob(proposed_particles)) + normal.Normal(loc=-2.6, scale=0.1).log_prob(proposed_particles), + extra=tf.constant(np.nan) + ) num_particles = 16 initial_state = self.evaluate( @@ -50,7 +52,9 @@ def propose_and_update_log_weights_fn(_, weighted_particles, seed=None): particles=tf.random.normal([num_particles], seed=test_util.test_seed()), log_weights=tf.fill([num_particles], - -tf.math.log(float(num_particles))))) + -tf.math.log(float(num_particles))), + extra=tf.constant(np.nan) + )) # Run a couple of steps. seeds = samplers.split_seed( @@ -96,7 +100,9 @@ def testMarginalLikelihoodGradientIsDefined(self): WeightedParticles( particles=samplers.normal([num_particles], seed=seeds[0]), log_weights=tf.fill([num_particles], - -tf.math.log(float(num_particles))))) + -tf.math.log(float(num_particles))), + extra=tf.constant(np.nan) + )) def propose_and_update_log_weights_fn(_, weighted_particles, @@ -110,7 +116,9 @@ def propose_and_update_log_weights_fn(_, particles=proposed_particles, log_weights=(weighted_particles.log_weights + transition_dist.log_prob(proposed_particles) - - proposal_dist.log_prob(proposed_particles))) + proposal_dist.log_prob(proposed_particles)), + extra=tf.constant(np.nan) + ) def marginal_logprob(transition_scale): kernel = SequentialMonteCarlo( diff --git a/tfp_nightly.egg-info/PKG-INFO b/tfp_nightly.egg-info/PKG-INFO new file mode 100644 index 0000000000..96ea3cdd82 --- /dev/null +++ b/tfp_nightly.egg-info/PKG-INFO @@ -0,0 +1,244 @@ +Metadata-Version: 2.1 +Name: tfp-nightly +Version: 0.24.0.dev0 +Summary: Probabilistic modeling and statistical inference in TensorFlow +Home-page: http://github.com/tensorflow/probability +Author: Google LLC +Author-email: no-reply@google.com +License: Apache 2.0 +Keywords: tensorflow probability statistics bayesian machine learning +Platform: UNKNOWN +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Requires-Python: >=3.9 +Description-Content-Type: text/markdown +Provides-Extra: jax +Provides-Extra: tfds +License-File: LICENSE + +# TensorFlow Probability + +TensorFlow Probability is a library for probabilistic reasoning and statistical +analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow +Probability provides integration of probabilistic methods with deep networks, +gradient-based inference via automatic differentiation, and scalability to +large datasets and models via hardware acceleration (e.g., GPUs) and distributed +computation. + +__TFP also works as "Tensor-friendly Probability" in pure JAX!__: +`from tensorflow_probability.substrates import jax as tfp` -- +Learn more [here](https://www.tensorflow.org/probability/examples/TensorFlow_Probability_on_JAX). + +Our probabilistic machine learning tools are structured as follows. + +__Layer 0: TensorFlow.__ Numerical operations. In particular, the LinearOperator +class enables matrix-free implementations that can exploit special structure +(diagonal, low-rank, etc.) for efficient computation. It is built and maintained +by the TensorFlow Probability team and is now part of +[`tf.linalg`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/ops/linalg) +in core TF. + +__Layer 1: Statistical Building Blocks__ + +* Distributions ([`tfp.distributions`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/distributions)): + A large collection of probability + distributions and related statistics with batch and + [broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) + semantics. See the + [Distributions Tutorial](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb). +* Bijectors ([`tfp.bijectors`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/bijectors)): + Reversible and composable transformations of random variables. Bijectors + provide a rich class of transformed distributions, from classical examples + like the + [log-normal distribution](https://en.wikipedia.org/wiki/Log-normal_distribution) + to sophisticated deep learning models such as + [masked autoregressive flows](https://arxiv.org/abs/1705.07057). + +__Layer 2: Model Building__ + +* Joint Distributions (e.g., [`tfp.distributions.JointDistributionSequential`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/distributions/joint_distribution_sequential.py)): + Joint distributions over one or more possibly-interdependent distributions. + For an introduction to modeling with TFP's `JointDistribution`s, check out + [this colab](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Modeling_with_JointDistribution.ipynb) +* Probabilistic Layers ([`tfp.layers`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/layers)): + Neural network layers with uncertainty over the functions they represent, + extending TensorFlow Layers. + +__Layer 3: Probabilistic Inference__ + +* Markov chain Monte Carlo ([`tfp.mcmc`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/mcmc)): + Algorithms for approximating integrals via sampling. Includes + [Hamiltonian Monte Carlo](https://en.wikipedia.org/wiki/Hamiltonian_Monte_Carlo), + random-walk Metropolis-Hastings, and the ability to build custom transition + kernels. +* Variational Inference ([`tfp.vi`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/vi)): + Algorithms for approximating integrals via optimization. +* Optimizers ([`tfp.optimizer`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/optimizer)): + Stochastic optimization methods, extending TensorFlow Optimizers. Includes + [Stochastic Gradient Langevin Dynamics](http://www.icml-2011.org/papers/398_icmlpaper.pdf). +* Monte Carlo ([`tfp.monte_carlo`](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/python/monte_carlo)): + Tools for computing Monte Carlo expectations. + +TensorFlow Probability is under active development. Interfaces may change at any +time. + +## Examples + +See [`tensorflow_probability/examples/`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/) +for end-to-end examples. It includes tutorial notebooks such as: + +* [Linear Mixed Effects Models](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.ipynb). + A hierarchical linear model for sharing statistical strength across examples. +* [Eight Schools](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Eight_Schools.ipynb). + A hierarchical normal model for exchangeable treatment effects. +* [Hierarchical Linear Models](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/HLM_TFP_R_Stan.ipynb). + Hierarchical linear models compared among TensorFlow Probability, R, and Stan. +* [Bayesian Gaussian Mixture Models](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Bayesian_Gaussian_Mixture_Model.ipynb). + Clustering with a probabilistic generative model. +* [Probabilistic Principal Components Analysis](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_PCA.ipynb). + Dimensionality reduction with latent variables. +* [Gaussian Copulas](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Gaussian_Copula.ipynb). + Probability distributions for capturing dependence across random variables. +* [TensorFlow Distributions: A Gentle Introduction](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb). + Introduction to TensorFlow Distributions. +* [Understanding TensorFlow Distributions Shapes](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Understanding_TensorFlow_Distributions_Shapes.ipynb). + How to distinguish between samples, batches, and events for arbitrarily shaped + probabilistic computations. +* [TensorFlow Probability Case Study: Covariance Estimation](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Probability_Case_Study_Covariance_Estimation.ipynb). + A user's case study in applying TensorFlow Probability to estimate covariances. + +It also includes example scripts such as: + + Representation learning with a latent code and variational inference. +* [Vector-Quantized Autoencoder](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/vq_vae.py). + Discrete representation learning with vector quantization. +* [Disentangled Sequential Variational Autoencoder](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/disentangled_vae.py) + Disentangled representation learning over sequences with variational inference. +* [Bayesian Neural Networks](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/bayesian_neural_network.py). + Neural networks with uncertainty over their weights. +* [Bayesian Logistic Regression](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/logistic_regression.py). + Bayesian inference for binary classification. + +## Installation + +For additional details on installing TensorFlow, guidance installing +prerequisites, and (optionally) setting up virtual environments, see the +[TensorFlow installation guide](https://www.tensorflow.org/install). + +### Stable Builds + +To install the latest stable version, run the following: + +```shell +# Notes: + +# - The `--upgrade` flag ensures you'll get the latest version. +# - The `--user` flag ensures the packages are installed to your user directory +# rather than the system directory. +# - TensorFlow 2 packages require a pip >= 19.0 +python -m pip install --upgrade --user pip +python -m pip install --upgrade --user tensorflow tensorflow_probability +``` + +For CPU-only usage (and a smaller install), install with `tensorflow-cpu`. + +To use a pre-2.0 version of TensorFlow, run: + +```shell +python -m pip install --upgrade --user "tensorflow<2" "tensorflow_probability<0.9" +``` + +Note: Since [TensorFlow](https://www.tensorflow.org/install) is *not* included +as a dependency of the TensorFlow Probability package (in `setup.py`), you must +explicitly install the TensorFlow package (`tensorflow` or `tensorflow-cpu`). +This allows us to maintain one package instead of separate packages for CPU and +GPU-enabled TensorFlow. See the +[TFP release notes](https://github.com/tensorflow/probability/releases) for more +details about dependencies between TensorFlow and TensorFlow Probability. + + +### Nightly Builds + +There are also nightly builds of TensorFlow Probability under the pip package +`tfp-nightly`, which depends on one of `tf-nightly` or `tf-nightly-cpu`. +Nightly builds include newer features, but may be less stable than the +versioned releases. Both stable and nightly docs are available +[here](https://www.tensorflow.org/probability/api_docs/python/tfp?version=nightly). + +```shell +python -m pip install --upgrade --user tf-nightly tfp-nightly +``` + +### Installing from Source + +You can also install from source. This requires the [Bazel]( +https://bazel.build/) build system. It is highly recommended that you install +the nightly build of TensorFlow (`tf-nightly`) before trying to build +TensorFlow Probability from source. + +```shell +# sudo apt-get install bazel git python-pip # Ubuntu; others, see above links. +python -m pip install --upgrade --user tf-nightly +git clone https://github.com/tensorflow/probability.git +cd probability +bazel build --copt=-O3 --copt=-march=native :pip_pkg +PKGDIR=$(mktemp -d) +./bazel-bin/pip_pkg $PKGDIR +python -m pip install --upgrade --user $PKGDIR/*.whl +``` + +## Community + +As part of TensorFlow, we're committed to fostering an open and welcoming +environment. + +* [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow): Ask + or answer technical questions. +* [GitHub](https://github.com/tensorflow/probability/issues): Report bugs or + make feature requests. +* [TensorFlow Blog](https://blog.tensorflow.org/): Stay up to date on content + from the TensorFlow team and best articles from the community. +* [Youtube Channel](http://youtube.com/tensorflow/): Follow TensorFlow shows. +* [tfprobability@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfprobability): + Open mailing list for discussion and questions. + +See the [TensorFlow Community](https://www.tensorflow.org/community/) page for +more details. Check out our latest publicity here: + ++ [Coffee with a Googler: Probabilistic Machine Learning in TensorFlow]( + https://www.youtube.com/watch?v=BjUkL8DFH5Q) ++ [Introducing TensorFlow Probability]( + https://medium.com/tensorflow/introducing-tensorflow-probability-dca4c304e245) + +## Contributing + +We're eager to collaborate with you! See [`CONTRIBUTING.md`](CONTRIBUTING.md) +for a guide on how to contribute. This project adheres to TensorFlow's +[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to +uphold this code. + +## References + +If you use TensorFlow Probability in a paper, please cite: + ++ _TensorFlow Distributions._ Joshua V. Dillon, Ian Langmore, Dustin Tran, +Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt +Hoffman, Rif A. Saurous. +[arXiv preprint arXiv:1711.10604, 2017](https://arxiv.org/abs/1711.10604). + +(We're aware there's a lot more to TensorFlow Probability than Distributions, but the Distributions paper lays out our vision and is a fine thing to cite for now.) + + diff --git a/tfp_nightly.egg-info/SOURCES.txt b/tfp_nightly.egg-info/SOURCES.txt new file mode 100644 index 0000000000..8645f5fa31 --- /dev/null +++ b/tfp_nightly.egg-info/SOURCES.txt @@ -0,0 +1,1068 @@ +LICENSE +README.md +setup.py +tensorflow_probability/__init__.py +tensorflow_probability/python/__init__.py +tensorflow_probability/python/version.py +tensorflow_probability/python/bijectors/__init__.py +tensorflow_probability/python/bijectors/absolute_value.py +tensorflow_probability/python/bijectors/absolute_value_test.py +tensorflow_probability/python/bijectors/ascending.py +tensorflow_probability/python/bijectors/ascending_test.py +tensorflow_probability/python/bijectors/batch_normalization.py 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a/tfp_nightly.egg-info/requires.txt b/tfp_nightly.egg-info/requires.txt new file mode 100644 index 0000000000..2a08bbd673 --- /dev/null +++ b/tfp_nightly.egg-info/requires.txt @@ -0,0 +1,14 @@ +absl-py +six>=1.10.0 +numpy>=1.13.3 +decorator +cloudpickle>=1.3 +gast>=0.3.2 +dm-tree + +[jax] +jax +jaxlib + +[tfds] +tfds-nightly diff --git a/tfp_nightly.egg-info/top_level.txt b/tfp_nightly.egg-info/top_level.txt new file mode 100644 index 0000000000..ecabf3d7f4 --- /dev/null +++ b/tfp_nightly.egg-info/top_level.txt @@ -0,0 +1 @@ +tensorflow_probability From cc799a5ddc95e2114f6586512c87d4124ed76012 Mon Sep 17 00:00:00 2001 From: slamitza Date: Sun, 10 Dec 2023 22:33:19 +0100 Subject: [PATCH 02/24] Fixes --- .../python/experimental/mcmc/BUILD | 2 + tfp_nightly.egg-info/PKG-INFO | 244 ---- tfp_nightly.egg-info/SOURCES.txt | 1068 ----------------- tfp_nightly.egg-info/dependency_links.txt | 1 - tfp_nightly.egg-info/not-zip-safe | 1 - tfp_nightly.egg-info/requires.txt | 14 - tfp_nightly.egg-info/top_level.txt | 1 - 7 files changed, 2 insertions(+), 1329 deletions(-) delete mode 100644 tfp_nightly.egg-info/PKG-INFO delete mode 100644 tfp_nightly.egg-info/SOURCES.txt delete mode 100644 tfp_nightly.egg-info/dependency_links.txt delete mode 100644 tfp_nightly.egg-info/not-zip-safe delete mode 100644 tfp_nightly.egg-info/requires.txt delete mode 100644 tfp_nightly.egg-info/top_level.txt diff --git a/tensorflow_probability/python/experimental/mcmc/BUILD b/tensorflow_probability/python/experimental/mcmc/BUILD index aa2843bfb9..a96087e35c 100644 --- a/tensorflow_probability/python/experimental/mcmc/BUILD +++ b/tensorflow_probability/python/experimental/mcmc/BUILD @@ -18,6 +18,8 @@ # //tensorflow_probability/python/internal/auto_batching # internally. +# Placeholder: py_library +# Placeholder: py_test load( "//tensorflow_probability/python:build_defs.bzl", "multi_substrate_py_library", diff --git a/tfp_nightly.egg-info/PKG-INFO b/tfp_nightly.egg-info/PKG-INFO deleted file mode 100644 index 96ea3cdd82..0000000000 --- a/tfp_nightly.egg-info/PKG-INFO +++ /dev/null @@ -1,244 +0,0 @@ -Metadata-Version: 2.1 -Name: tfp-nightly -Version: 0.24.0.dev0 -Summary: Probabilistic modeling and statistical inference in TensorFlow -Home-page: http://github.com/tensorflow/probability -Author: Google LLC -Author-email: no-reply@google.com -License: Apache 2.0 -Keywords: tensorflow probability statistics bayesian machine learning -Platform: UNKNOWN -Classifier: Development Status :: 4 - Beta -Classifier: Intended Audience :: Developers -Classifier: Intended Audience :: Education -Classifier: Intended Audience :: Science/Research -Classifier: License :: OSI Approved :: Apache Software License -Classifier: Programming Language :: Python :: 3 -Classifier: Programming Language :: Python :: 3.9 -Classifier: Programming Language :: Python :: 3.10 -Classifier: Programming Language :: Python :: 3.11 -Classifier: Topic :: Scientific/Engineering -Classifier: Topic :: Scientific/Engineering :: Mathematics -Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence -Classifier: Topic :: Software Development -Classifier: Topic :: Software Development :: Libraries -Classifier: Topic :: Software Development :: Libraries :: Python Modules -Requires-Python: >=3.9 -Description-Content-Type: text/markdown -Provides-Extra: jax -Provides-Extra: tfds -License-File: LICENSE - -# TensorFlow Probability - -TensorFlow Probability is a library for probabilistic reasoning and statistical -analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow -Probability provides integration of probabilistic methods with deep networks, -gradient-based inference via automatic differentiation, and scalability to -large datasets and models via hardware acceleration (e.g., GPUs) and distributed -computation. - -__TFP also works as "Tensor-friendly Probability" in pure JAX!__: -`from tensorflow_probability.substrates import jax as tfp` -- -Learn more [here](https://www.tensorflow.org/probability/examples/TensorFlow_Probability_on_JAX). - -Our probabilistic machine learning tools are structured as follows. - -__Layer 0: TensorFlow.__ Numerical operations. In particular, the LinearOperator -class enables matrix-free implementations that can exploit special structure -(diagonal, low-rank, etc.) for efficient computation. It is built and maintained -by the TensorFlow Probability team and is now part of -[`tf.linalg`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/ops/linalg) -in core TF. - -__Layer 1: Statistical Building Blocks__ - -* Distributions ([`tfp.distributions`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/distributions)): - A large collection of probability - distributions and related statistics with batch and - [broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) - semantics. See the - [Distributions Tutorial](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb). -* Bijectors ([`tfp.bijectors`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/bijectors)): - Reversible and composable transformations of random variables. Bijectors - provide a rich class of transformed distributions, from classical examples - like the - [log-normal distribution](https://en.wikipedia.org/wiki/Log-normal_distribution) - to sophisticated deep learning models such as - [masked autoregressive flows](https://arxiv.org/abs/1705.07057). - -__Layer 2: Model Building__ - -* Joint Distributions (e.g., [`tfp.distributions.JointDistributionSequential`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/distributions/joint_distribution_sequential.py)): - Joint distributions over one or more possibly-interdependent distributions. - For an introduction to modeling with TFP's `JointDistribution`s, check out - [this colab](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Modeling_with_JointDistribution.ipynb) -* Probabilistic Layers ([`tfp.layers`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/layers)): - Neural network layers with uncertainty over the functions they represent, - extending TensorFlow Layers. - -__Layer 3: Probabilistic Inference__ - -* Markov chain Monte Carlo ([`tfp.mcmc`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/mcmc)): - Algorithms for approximating integrals via sampling. Includes - [Hamiltonian Monte Carlo](https://en.wikipedia.org/wiki/Hamiltonian_Monte_Carlo), - random-walk Metropolis-Hastings, and the ability to build custom transition - kernels. -* Variational Inference ([`tfp.vi`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/vi)): - Algorithms for approximating integrals via optimization. -* Optimizers ([`tfp.optimizer`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/optimizer)): - Stochastic optimization methods, extending TensorFlow Optimizers. Includes - [Stochastic Gradient Langevin Dynamics](http://www.icml-2011.org/papers/398_icmlpaper.pdf). -* Monte Carlo ([`tfp.monte_carlo`](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/python/monte_carlo)): - Tools for computing Monte Carlo expectations. - -TensorFlow Probability is under active development. Interfaces may change at any -time. - -## Examples - -See [`tensorflow_probability/examples/`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/) -for end-to-end examples. It includes tutorial notebooks such as: - -* [Linear Mixed Effects Models](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.ipynb). - A hierarchical linear model for sharing statistical strength across examples. -* [Eight Schools](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Eight_Schools.ipynb). - A hierarchical normal model for exchangeable treatment effects. -* [Hierarchical Linear Models](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/HLM_TFP_R_Stan.ipynb). - Hierarchical linear models compared among TensorFlow Probability, R, and Stan. -* [Bayesian Gaussian Mixture Models](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Bayesian_Gaussian_Mixture_Model.ipynb). - Clustering with a probabilistic generative model. -* [Probabilistic Principal Components Analysis](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_PCA.ipynb). - Dimensionality reduction with latent variables. -* [Gaussian Copulas](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Gaussian_Copula.ipynb). - Probability distributions for capturing dependence across random variables. -* [TensorFlow Distributions: A Gentle Introduction](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb). - Introduction to TensorFlow Distributions. -* [Understanding TensorFlow Distributions Shapes](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Understanding_TensorFlow_Distributions_Shapes.ipynb). - How to distinguish between samples, batches, and events for arbitrarily shaped - probabilistic computations. -* [TensorFlow Probability Case Study: Covariance Estimation](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Probability_Case_Study_Covariance_Estimation.ipynb). - A user's case study in applying TensorFlow Probability to estimate covariances. - -It also includes example scripts such as: - - Representation learning with a latent code and variational inference. -* [Vector-Quantized Autoencoder](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/vq_vae.py). - Discrete representation learning with vector quantization. -* [Disentangled Sequential Variational Autoencoder](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/disentangled_vae.py) - Disentangled representation learning over sequences with variational inference. -* [Bayesian Neural Networks](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/bayesian_neural_network.py). - Neural networks with uncertainty over their weights. -* [Bayesian Logistic Regression](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/logistic_regression.py). - Bayesian inference for binary classification. - -## Installation - -For additional details on installing TensorFlow, guidance installing -prerequisites, and (optionally) setting up virtual environments, see the -[TensorFlow installation guide](https://www.tensorflow.org/install). - -### Stable Builds - -To install the latest stable version, run the following: - -```shell -# Notes: - -# - The `--upgrade` flag ensures you'll get the latest version. -# - The `--user` flag ensures the packages are installed to your user directory -# rather than the system directory. -# - TensorFlow 2 packages require a pip >= 19.0 -python -m pip install --upgrade --user pip -python -m pip install --upgrade --user tensorflow tensorflow_probability -``` - -For CPU-only usage (and a smaller install), install with `tensorflow-cpu`. - -To use a pre-2.0 version of TensorFlow, run: - -```shell -python -m pip install --upgrade --user "tensorflow<2" "tensorflow_probability<0.9" -``` - -Note: Since [TensorFlow](https://www.tensorflow.org/install) is *not* included -as a dependency of the TensorFlow Probability package (in `setup.py`), you must -explicitly install the TensorFlow package (`tensorflow` or `tensorflow-cpu`). -This allows us to maintain one package instead of separate packages for CPU and -GPU-enabled TensorFlow. See the -[TFP release notes](https://github.com/tensorflow/probability/releases) for more -details about dependencies between TensorFlow and TensorFlow Probability. - - -### Nightly Builds - -There are also nightly builds of TensorFlow Probability under the pip package -`tfp-nightly`, which depends on one of `tf-nightly` or `tf-nightly-cpu`. -Nightly builds include newer features, but may be less stable than the -versioned releases. Both stable and nightly docs are available -[here](https://www.tensorflow.org/probability/api_docs/python/tfp?version=nightly). - -```shell -python -m pip install --upgrade --user tf-nightly tfp-nightly -``` - -### Installing from Source - -You can also install from source. This requires the [Bazel]( -https://bazel.build/) build system. It is highly recommended that you install -the nightly build of TensorFlow (`tf-nightly`) before trying to build -TensorFlow Probability from source. - -```shell -# sudo apt-get install bazel git python-pip # Ubuntu; others, see above links. -python -m pip install --upgrade --user tf-nightly -git clone https://github.com/tensorflow/probability.git -cd probability -bazel build --copt=-O3 --copt=-march=native :pip_pkg -PKGDIR=$(mktemp -d) -./bazel-bin/pip_pkg $PKGDIR -python -m pip install --upgrade --user $PKGDIR/*.whl -``` - -## Community - -As part of TensorFlow, we're committed to fostering an open and welcoming -environment. - -* [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow): Ask - or answer technical questions. -* [GitHub](https://github.com/tensorflow/probability/issues): Report bugs or - make feature requests. -* [TensorFlow Blog](https://blog.tensorflow.org/): Stay up to date on content - from the TensorFlow team and best articles from the community. -* [Youtube Channel](http://youtube.com/tensorflow/): Follow TensorFlow shows. -* [tfprobability@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfprobability): - Open mailing list for discussion and questions. - -See the [TensorFlow Community](https://www.tensorflow.org/community/) page for -more details. Check out our latest publicity here: - -+ [Coffee with a Googler: Probabilistic Machine Learning in TensorFlow]( - https://www.youtube.com/watch?v=BjUkL8DFH5Q) -+ [Introducing TensorFlow Probability]( - https://medium.com/tensorflow/introducing-tensorflow-probability-dca4c304e245) - -## Contributing - -We're eager to collaborate with you! See [`CONTRIBUTING.md`](CONTRIBUTING.md) -for a guide on how to contribute. This project adheres to TensorFlow's -[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to -uphold this code. - -## References - -If you use TensorFlow Probability in a paper, please cite: - -+ _TensorFlow Distributions._ Joshua V. Dillon, Ian Langmore, Dustin Tran, -Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt -Hoffman, Rif A. Saurous. -[arXiv preprint arXiv:1711.10604, 2017](https://arxiv.org/abs/1711.10604). - -(We're aware there's a lot more to TensorFlow Probability than Distributions, but the Distributions paper lays out our vision and is a fine thing to cite for now.) - - diff --git a/tfp_nightly.egg-info/SOURCES.txt b/tfp_nightly.egg-info/SOURCES.txt deleted file mode 100644 index 8645f5fa31..0000000000 --- a/tfp_nightly.egg-info/SOURCES.txt +++ /dev/null @@ -1,1068 +0,0 @@ -LICENSE -README.md -setup.py -tensorflow_probability/__init__.py -tensorflow_probability/python/__init__.py -tensorflow_probability/python/version.py -tensorflow_probability/python/bijectors/__init__.py -tensorflow_probability/python/bijectors/absolute_value.py -tensorflow_probability/python/bijectors/absolute_value_test.py -tensorflow_probability/python/bijectors/ascending.py -tensorflow_probability/python/bijectors/ascending_test.py -tensorflow_probability/python/bijectors/batch_normalization.py 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-tensorflow_probability/python/vi/mutual_information_test.py -tensorflow_probability/python/vi/optimization.py -tensorflow_probability/python/vi/optimization_test.py -tensorflow_probability/substrates/__init__.py -tensorflow_probability/substrates/jax/__init__.py -tensorflow_probability/substrates/numpy/__init__.py -tfp_nightly.egg-info/PKG-INFO -tfp_nightly.egg-info/SOURCES.txt -tfp_nightly.egg-info/dependency_links.txt -tfp_nightly.egg-info/not-zip-safe -tfp_nightly.egg-info/requires.txt -tfp_nightly.egg-info/top_level.txt \ No newline at end of file diff --git a/tfp_nightly.egg-info/dependency_links.txt b/tfp_nightly.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/tfp_nightly.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/tfp_nightly.egg-info/not-zip-safe b/tfp_nightly.egg-info/not-zip-safe deleted file mode 100644 index 8b13789179..0000000000 --- a/tfp_nightly.egg-info/not-zip-safe +++ /dev/null @@ -1 +0,0 @@ - diff --git a/tfp_nightly.egg-info/requires.txt b/tfp_nightly.egg-info/requires.txt deleted file mode 100644 index 2a08bbd673..0000000000 --- a/tfp_nightly.egg-info/requires.txt +++ /dev/null @@ -1,14 +0,0 @@ -absl-py -six>=1.10.0 -numpy>=1.13.3 -decorator -cloudpickle>=1.3 -gast>=0.3.2 -dm-tree - -[jax] -jax -jaxlib - -[tfds] -tfds-nightly diff --git a/tfp_nightly.egg-info/top_level.txt b/tfp_nightly.egg-info/top_level.txt deleted file mode 100644 index ecabf3d7f4..0000000000 --- a/tfp_nightly.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -tensorflow_probability From 53ba255d10ccfb69830ef9b3ac2d530589e4b0cc Mon Sep 17 00:00:00 2001 From: slamitza Date: Sun, 10 Dec 2023 22:57:09 +0100 Subject: [PATCH 03/24] pylint --- .../experimental/mcmc/particle_filter.py | 153 ++++++++++++------ .../experimental/mcmc/particle_filter_test.py | 46 ++++-- 2 files changed, 137 insertions(+), 62 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index b920b0db85..2af7a68999 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -25,8 +25,6 @@ from tensorflow_probability.python.internal import prefer_static as ps from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.mcmc.internal import util as mcmc_util -from tensorflow_probability.python.distributions import batch_reshape -from tensorflow_probability.python.distributions import batch_broadcast from tensorflow_probability.python.distributions import normal from tensorflow_probability.python.distributions import uniform @@ -51,7 +49,8 @@ def _default_trace_fn(state, kernel_results): def _default_kernel(parameters): mean, variance = tf.nn.moments(parameters, axes=[0]) - proposal_distribution = normal.Normal(loc=tf.fill(parameters.shape, mean), scale=tf.sqrt(variance)) + proposal_distribution = normal.Normal(loc=tf.fill(parameters.shape, mean), + scale=tf.sqrt(variance)) return proposal_distribution @@ -499,15 +498,18 @@ def smc_squared( unbiased_gradients=True, seed=None, ): - init_seed, loop_seed, step_seed = samplers.split_seed(seed, n=3, salt='smc_squared') + _1, loop_seed, _2 = samplers.split_seed(seed, n=3, salt='smc_squared') num_observation_steps = ps.size0(tf.nest.flatten(inner_observations)[0]) - # TODO: The following two lines compensates for having the first empty step in smc2 + # TODO: The following two lines compensates for having the + # first empty step in smc2 num_timesteps = (1 + num_transitions_per_observation * (num_observation_steps - 1)) + 1 last_obs_expanded = tf.expand_dims(inner_observations[-1], axis=0) - inner_observations = tf.concat([inner_observations, last_obs_expanded], axis=0) + inner_observations = tf.concat([inner_observations, + last_obs_expanded], + axis=0) if outer_rejuvenation_criterion_fn is None: outer_rejuvenation_criterion_fn = lambda *_: tf.constant(False) @@ -543,7 +545,8 @@ def smc_squared( observation_fn=inner_observation_fn(initial_state), initial_state_prior=inner_initial_state_prior(0, initial_state), initial_state_proposal=(inner_initial_state_proposal(0, initial_state) - if inner_initial_state_proposal is not None else None), + if inner_initial_state_proposal is not None + else None), num_particles=num_inner_particles, particles_dim=1, seed=seed @@ -594,7 +597,8 @@ def smc_squared( traced_results = sequential_monte_carlo( initial_weighted_particles=initial_state, - propose_and_update_log_weights_fn=outer_propose_and_update_log_weights_fn, + propose_and_update_log_weights_fn= + outer_propose_and_update_log_weights_fn, resample_fn=outer_resample_fn, resample_criterion_fn=outer_resample_criterion_fn, trace_criterion_fn=outer_trace_criterion_fn, @@ -631,7 +635,8 @@ def _outer_particle_filter_propose_and_update_log_weights_fn( """Build a function specifying a particle filter update step.""" def _outer_propose_and_update_log_weights_fn(step, state, seed=None): outside_parameters = state.particles[0] - inner_weighted_particles, log_weights = state.particles[1], state.log_weights + inner_weighted_particles, log_weights = state.particles[1], \ + state.log_weights filter_results = smc_kernel.SequentialMonteCarloResults( steps=step, @@ -654,38 +659,57 @@ def _outer_propose_and_update_log_weights_fn(step, state, seed=None): ) kernel = smc_kernel.SequentialMonteCarlo( - propose_and_update_log_weights_fn=inner_propose_and_update_log_weights_fn, + propose_and_update_log_weights_fn= + inner_propose_and_update_log_weights_fn, resample_fn=inner_resample_fn, resample_criterion_fn=inner_resample_criterion_fn, particles_dim=1, unbiased_gradients=unbiased_gradients ) - inner_weighted_particles, filter_results = kernel.one_step(inner_weighted_particles, - filter_results, - seed=seed) + inner_weighted_particles, filter_results = kernel.one_step( + inner_weighted_particles, + filter_results, + seed=seed + ) - updated_log_weights = log_weights + filter_results.incremental_log_marginal_likelihood + updated_log_weights = log_weights + \ + filter_results.incremental_log_marginal_likelihood do_rejuvenation = outer_rejuvenation_criterion_fn(step, state) - def rejuvenate_particles(outside_parameters, updated_log_weights, inner_weighted_particles, filter_results): - proposed_parameters = parameter_proposal_kernel(outside_parameters).sample(seed=seed) + def rejuvenate_particles(outside_parameters, + updated_log_weights, + inner_weighted_particles, + filter_results): + proposed_parameters = parameter_proposal_kernel( + outside_parameters + ).sample(seed=seed) rej_params_log_weights = ps.zeros_like( initial_parameter_prior.log_prob(proposed_parameters) ) - rej_params_log_weights = tf.nn.log_softmax(rej_params_log_weights, axis=0) - - rej_inner_weighted_particles = _particle_filter_initial_weighted_particles( - observations=inner_observations, - observation_fn=inner_observation_fn(proposed_parameters), - initial_state_prior=inner_initial_state_prior(0, proposed_parameters), - initial_state_proposal=(inner_initial_state_proposal(0, proposed_parameters) - if inner_initial_state_proposal is not None else None), - num_particles=num_inner_particles, - particles_dim=1, - seed=seed) + rej_params_log_weights = tf.nn.log_softmax( + rej_params_log_weights, + axis=0 + ) + + rej_inner_weighted_particles = \ + _particle_filter_initial_weighted_particles( + observations=inner_observations, + observation_fn=inner_observation_fn(proposed_parameters), + initial_state_prior=inner_initial_state_prior( + 0, + proposed_parameters + ), + initial_state_proposal=( + inner_initial_state_proposal(0, proposed_parameters) + if inner_initial_state_proposal is not None + else None), + num_particles=num_inner_particles, + particles_dim=1, + seed=seed + ) batch_zeros = tf.zeros(ps.shape(log_weights)) @@ -709,11 +733,13 @@ def rejuvenate_particles(outside_parameters, updated_log_weights, inner_weighted observation_fn=inner_observation_fn(proposed_parameters), extra_fn=extra_fn, particles_dim=1, - num_transitions_per_observation=num_transitions_per_observation) + num_transitions_per_observation= + num_transitions_per_observation) ) rej_kernel = smc_kernel.SequentialMonteCarlo( - propose_and_update_log_weights_fn=rej_inner_propose_and_update_log_weights_fn, + propose_and_update_log_weights_fn= + rej_inner_propose_and_update_log_weights_fn, resample_fn=inner_resample_fn, resample_criterion_fn=inner_resample_criterion_fn, particles_dim=1, @@ -732,16 +758,27 @@ def body(i, rej_parameters_weights, rej_params_log_weights): - rej_inner_weighted_particles, rej_filter_results = rej_kernel.one_step( - rej_inner_weighted_particles, rej_filter_results, seed=seed - ) + rej_inner_weighted_particles, rej_filter_results = \ + rej_kernel.one_step( + rej_inner_weighted_particles, rej_filter_results, seed=seed + ) rej_parameters_weights += rej_inner_weighted_particles.log_weights - rej_params_log_weights = rej_params_log_weights + rej_filter_results.incremental_log_marginal_likelihood - return i + 1, rej_inner_weighted_particles, rej_filter_results, rej_parameters_weights, rej_params_log_weights - - i, rej_inner_weighted_particles, rej_filter_results, rej_inner_particles_weights, rej_params_log_weights = tf.while_loop( + rej_params_log_weights = \ + rej_params_log_weights + \ + rej_filter_results.incremental_log_marginal_likelihood + return i + 1, \ + rej_inner_weighted_particles, \ + rej_filter_results, \ + rej_parameters_weights, \ + rej_params_log_weights + + _, \ + rej_inner_weighted_particles, \ + rej_filter_results, \ + rej_inner_particles_weights, \ + rej_params_log_weights = tf.while_loop( condition, body, loop_vars=[0, @@ -754,19 +791,27 @@ def body(i, log_a = rej_filter_results.accumulated_log_marginal_likelihood - \ filter_results.accumulated_log_marginal_likelihood + \ - parameter_proposal_kernel(proposed_parameters).log_prob(outside_parameters) - \ - parameter_proposal_kernel(outside_parameters).log_prob(proposed_parameters) + parameter_proposal_kernel( + proposed_parameters).log_prob(outside_parameters) - \ + parameter_proposal_kernel( + outside_parameters).log_prob(proposed_parameters) acceptance_probs = tf.minimum(1., tf.exp(log_a)) - random_numbers = uniform.Uniform(0., 1.).sample(num_outer_particles, seed=seed) + random_numbers = uniform.Uniform(0., 1.).sample(num_outer_particles, + seed=seed) # Determine if the proposed particle should be accepted or reject accept = random_numbers > acceptance_probs - # Update the chosen particles and filter restults based on the acceptance step - outside_parameters = tf.where(accept, outside_parameters, proposed_parameters) - updated_log_weights = tf.where(accept, updated_log_weights, rej_params_log_weights) + # Update the chosen particles and filter restults + # based on the acceptance step + outside_parameters = tf.where(accept, + outside_parameters, + proposed_parameters) + updated_log_weights = tf.where(accept, + updated_log_weights, + rej_params_log_weights) inner_weighted_particles_particles = mcmc_util.choose( accept, @@ -786,17 +831,29 @@ def body(i, ) filter_results = tf.nest.map_structure( - lambda a, b: where_fn(accept, a, b, num_outer_particles, num_inner_particles), + lambda a, b: where_fn(accept, a, b, + num_outer_particles, + num_inner_particles), filter_results, rej_filter_results ) - return outside_parameters, updated_log_weights, inner_weighted_particles, filter_results + return outside_parameters, updated_log_weights, \ + inner_weighted_particles, filter_results - outside_parameters, updated_log_weights, inner_weighted_particles, filter_results = tf.cond( + outside_parameters, \ + updated_log_weights, \ + inner_weighted_particles, \ + filter_results = tf.cond( do_rejuvenation, - lambda: (rejuvenate_particles(outside_parameters, updated_log_weights, inner_weighted_particles, filter_results)), - lambda: (outside_parameters, updated_log_weights, inner_weighted_particles, filter_results) + lambda: (rejuvenate_particles(outside_parameters, + updated_log_weights, + inner_weighted_particles, + filter_results)), + lambda: (outside_parameters, + updated_log_weights, + inner_weighted_particles, + filter_results) ) return smc_kernel.WeightedParticles( @@ -1066,7 +1123,7 @@ def _compute_observation_log_weights(step, lambda x, step=step: tf.gather(x, observation_idx), observations) if particles_dim == 1: - observation = tf.expand_dims(observation, axis=0) + observation = tf.expand_dims(observation, axis=0) observation = tf.nest.map_structure( lambda x: tf.expand_dims(x, axis=particles_dim), observation) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index e190c76bda..dc6a152e4b 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -615,8 +615,11 @@ def marginal_log_likelihood(level_scale, noise_scale): def test_smc_squared_rejuvenation_parameters(self): def particle_dynamics(params, _, previous_state): - reshaped_params = tf.reshape(params, [params.shape[0]] + [1] * (previous_state.shape.rank - 1)) - broadcasted_params = tf.broadcast_to(reshaped_params, previous_state.shape) + reshaped_params = tf.reshape(params, + [params.shape[0]] + + [1] * (previous_state.shape.rank - 1)) + broadcasted_params = tf.broadcast_to(reshaped_params, + previous_state.shape) return normal.Normal(previous_state + broadcasted_params + 1, 0.1) def rejuvenation_criterion(step, state): @@ -625,7 +628,8 @@ def rejuvenation_criterion(step, state): tf.equal(tf.math.mod(step, tf.constant(2)), tf.constant(0)), tf.not_equal(state.extra[0], tf.constant(0)) ) - return tf.cond(cond, lambda: tf.constant(True), lambda: tf.constant(False)) + return tf.cond(cond, lambda: tf.constant(True), + lambda: tf.constant(False)) inner_observations = tf.range(30, dtype=tf.float32) @@ -637,7 +641,8 @@ def rejuvenation_criterion(step, state): params, inner_pt = self.evaluate(particle_filter.smc_squared( inner_observations=inner_observations, - inner_initial_state_prior=lambda _, params: mvn_diag.MultivariateNormalDiag( + inner_initial_state_prior=lambda _, params: + mvn_diag.MultivariateNormalDiag( loc=loc, scale_diag=scale_diag ), initial_parameter_prior=normal.Normal(3., 1.), @@ -645,7 +650,9 @@ def rejuvenation_criterion(step, state): num_inner_particles=num_inner_particles, outer_rejuvenation_criterion_fn=rejuvenation_criterion, inner_transition_fn=lambda params: ( - lambda _, state: independent.Independent(particle_dynamics(params, _, state), 1)), + lambda _, state: independent.Independent( + particle_dynamics(params, _, state), 1) + ), inner_observation_fn=lambda params: ( lambda _, state: independent.Independent(normal.Normal(state, 2.), 1)), outer_trace_fn=lambda s, r: ( @@ -693,7 +700,8 @@ def observe_position(_, state): inner_initial_state_prior=lambda _, params: initial_state_prior, initial_parameter_prior=deterministic.Deterministic(0.), num_outer_particles=1, - inner_transition_fn=lambda params: simple_harmonic_motion_transition_fn, + inner_transition_fn=lambda params: + simple_harmonic_motion_transition_fn, inner_observation_fn=lambda params: observe_position, num_inner_particles=1024, outer_trace_fn=lambda s, r: ( @@ -704,7 +712,9 @@ def observe_position(_, state): seed=test_util.test_seed()) ) - self.assertAllEqual(ps.shape(particles['position']), tf.constant([102, 1, 1024])) + self.assertAllEqual(ps.shape(particles['position']), tf.constant([102, + 1, + 1024])) self.assertAllClose(tf.transpose(np.mean(particles['position'], axis=-1)), tf.reshape(tf.math.cos(dt * np.arange(102)), [1, -1]), @@ -733,8 +743,10 @@ def trace_fn(state, _): inner_observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), inner_initial_state_prior=lambda _, params: normal.Normal([0.], 1.), initial_parameter_prior=deterministic.Deterministic(0.), - inner_transition_fn=lambda params: (lambda _, state: normal.Normal(state, 1.)), - inner_observation_fn=lambda params: (lambda _, state: normal.Normal(state, 1.)), + inner_transition_fn=lambda params: (lambda _, state: + normal.Normal(state, 1.)), + inner_observation_fn=lambda params: (lambda _, state: + normal.Normal(state, 1.)), num_inner_particles=1024, num_outer_particles=1, outer_trace_fn=trace_fn, @@ -766,15 +778,21 @@ def rejuvenation_criterion(step, state): tf.equal(tf.math.mod(step, tf.constant(3)), tf.constant(0)), tf.not_equal(state.extra[0], tf.constant(0)) ) - return tf.cond(cond, lambda: tf.constant(True), lambda: tf.constant(False)) + return tf.cond(cond, lambda: tf.constant(True), + lambda: tf.constant(False)) - (parameters, weight_parameters, inner_particles, inner_log_weights, lp) = self.evaluate( + (parameters, weight_parameters, + inner_particles, inner_log_weights, lp) = self.evaluate( particle_filter.smc_squared( inner_observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), initial_parameter_prior=deterministic.Deterministic(0.), - inner_initial_state_prior=lambda _, params: normal.Normal([0.] * num_outer_particles, 1.), - inner_transition_fn=lambda params: (lambda _, state: normal.Normal(state, 10.)), - inner_observation_fn=lambda params: (lambda _, state: normal.Normal(state, 0.1)), + inner_initial_state_prior=lambda _, params: normal.Normal( + [0.] * num_outer_particles, 1. + ), + inner_transition_fn=lambda params: + (lambda _, state: normal.Normal(state, 10.)), + inner_observation_fn=lambda params: + (lambda _, state: normal.Normal(state, 0.1)), num_inner_particles=num_inner_particles, num_outer_particles=num_outer_particles, outer_rejuvenation_criterion_fn=rejuvenation_criterion, From 930cba828446d8f22bb7578090d9495f60dbefcf Mon Sep 17 00:00:00 2001 From: slamitza Date: Sun, 7 Jan 2024 10:09:23 +0100 Subject: [PATCH 04/24] All pass, but batch_1 test --- .../experimental/mcmc/particle_filter.py | 4 +- .../experimental/mcmc/particle_filter_test.py | 122 ++++++++++++++++++ .../mcmc/sequential_monte_carlo_kernel.py | 3 + .../sequential_monte_carlo_kernel_test.py | 12 +- 4 files changed, 131 insertions(+), 10 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index 2af7a68999..693c3eb64c 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -985,7 +985,7 @@ def _particle_filter_initial_weighted_particles(observations, initial_state_proposal, num_particles, particles_dim=0, - extra=np.nan, + extra=(), seed=None): """Initialize a set of weighted particles including the first observation.""" # Propose an initial state. @@ -1011,7 +1011,7 @@ def _particle_filter_initial_weighted_particles(observations, axis=particles_dim) # Return particles weighted by the initial observation. - if extra is np.nan: + if extra == (): if len(ps.shape(initial_log_weights)) == 1: # initial extra for particle filter extra = tf.constant(0) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index dc6a152e4b..476410f569 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -177,6 +177,128 @@ def observation_fn(_, state): trajectories['velocity'].shape) self.assertAllEqual(incremental_log_marginal_likelihoods.shape, [num_timesteps] + batch_shape) + # + # def test_batch_of_filters_particles_dim_1(self): + # + # batch_shape = [3, 2] + # num_particles = 1000 + # num_timesteps = 40 + # + # # Batch of priors on object 1D positions and velocities. + # initial_state_prior = jdn.JointDistributionNamed({ + # 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), + # 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) + # }) + # + # def transition_fn(_, previous_state): + # return jdn.JointDistributionNamed({ + # 'position': + # normal.Normal( + # loc=previous_state['position'] + previous_state['velocity'], + # scale=0.1), + # 'velocity': + # normal.Normal(loc=previous_state['velocity'], scale=0.01) + # }) + # + # def observation_fn(_, state): + # return normal.Normal(loc=state['position'], scale=0.1) + # + # # Batch of synthetic observations, . + # true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) + # true_velocities = 0.1 * np.random.randn( + # *batch_shape).astype(self.dtype) + # observed_positions = ( + # true_velocities * + # np.arange(num_timesteps).astype( + # self.dtype)[..., tf.newaxis, tf.newaxis] + + # true_initial_positions) + # + # (particles, log_weights, parent_indices, + # incremental_log_marginal_likelihoods) = self.evaluate( + # particle_filter.particle_filter( + # observations=observed_positions, + # initial_state_prior=initial_state_prior, + # transition_fn=transition_fn, + # observation_fn=observation_fn, + # num_particles=num_particles, + # seed=test_util.test_seed(), + # particles_dim=1)) + # + # self.assertAllEqual(particles['position'].shape, + # [num_timesteps, + # batch_shape[0], + # num_particles, + # batch_shape[1]]) + # self.assertAllEqual(particles['velocity'].shape, + # [num_timesteps, + # batch_shape[0], + # num_particles, + # batch_shape[1]]) + # self.assertAllEqual(parent_indices.shape, + # [num_timesteps, + # batch_shape[0], + # num_particles, + # batch_shape[1]]) + # self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + # [num_timesteps] + batch_shape) + # + # self.assertAllClose( + # self.evaluate( + # tf.reduce_sum(tf.exp(log_weights) * + # particles['position'], axis=2)), + # observed_positions, + # atol=0.3) + # + # velocity_means = tf.reduce_sum(tf.exp(log_weights) * + # particles['velocity'], axis=2) + # + # self.assertAllClose( + # self.evaluate(tf.reduce_mean(velocity_means, axis=0)), + # true_velocities, atol=0.05) + # + # # Uncertainty in velocity should decrease over time. + # velocity_stddev = self.evaluate( + # tf.math.reduce_std(particles['velocity'], axis=2)) + # self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) + # + # trajectories = self.evaluate( + # particle_filter.reconstruct_trajectories(particles, + # parent_indices, + # particles_dim=1)) + # self.assertAllEqual([num_timesteps, + # batch_shape[0], + # num_particles, + # batch_shape[1]], + # trajectories['position'].shape) + # self.assertAllEqual([num_timesteps, + # batch_shape[0], + # num_particles, + # batch_shape[1]], + # trajectories['velocity'].shape) + # + # # Verify that `infer_trajectories` also works on batches. + # trajectories, incremental_log_marginal_likelihoods = self.evaluate( + # particle_filter.infer_trajectories( + # observations=observed_positions, + # initial_state_prior=initial_state_prior, + # transition_fn=transition_fn, + # observation_fn=observation_fn, + # num_particles=num_particles, + # particles_dim=1, + # seed=test_util.test_seed())) + # + # self.assertAllEqual([num_timesteps, + # batch_shape[0], + # num_particles, + # batch_shape[1]], + # trajectories['position'].shape) + # self.assertAllEqual([num_timesteps, + # batch_shape[0], + # num_particles, + # batch_shape[1]], + # trajectories['velocity'].shape) + # self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + # [num_timesteps] + batch_shape) def test_reconstruct_trajectories_toy_example(self): particles = tf.convert_to_tensor([[1, 2, 3], [4, 5, 6,], [7, 8, 9]]) diff --git a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel.py b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel.py index 300418c87d..36ef979a52 100644 --- a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel.py +++ b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel.py @@ -54,11 +54,14 @@ class WeightedParticles(collections.namedtuple( `concat([[b1, ..., bN], event_shape])`, where `event_shape` may differ across component `Tensor`s. This represents global state of the sampling process that is not associated with individual particles. + Defaults to an empty tuple. In some contexts, particles may be stacked across multiple inference steps, in which case all `Tensor` shapes will be prefixed by an additional dimension of size `num_steps`. """ + def __new__(cls, particles, log_weights, extra=()): + return super().__new__(cls, particles, log_weights, extra) # SequentialMonteCarlo `kernel_results` structure. diff --git a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py index 2e29f6c4dd..098769f36a 100644 --- a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py +++ b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py @@ -42,8 +42,7 @@ def propose_and_update_log_weights_fn(_, weighted_particles, seed=None): return WeightedParticles( particles=proposed_particles, log_weights=weighted_particles.log_weights + - normal.Normal(loc=-2.6, scale=0.1).log_prob(proposed_particles), - extra=tf.constant(np.nan) + normal.Normal(loc=-2.6, scale=0.1).log_prob(proposed_particles) ) num_particles = 16 @@ -52,8 +51,7 @@ def propose_and_update_log_weights_fn(_, weighted_particles, seed=None): particles=tf.random.normal([num_particles], seed=test_util.test_seed()), log_weights=tf.fill([num_particles], - -tf.math.log(float(num_particles))), - extra=tf.constant(np.nan) + -tf.math.log(float(num_particles))) )) # Run a couple of steps. @@ -100,8 +98,7 @@ def testMarginalLikelihoodGradientIsDefined(self): WeightedParticles( particles=samplers.normal([num_particles], seed=seeds[0]), log_weights=tf.fill([num_particles], - -tf.math.log(float(num_particles))), - extra=tf.constant(np.nan) + -tf.math.log(float(num_particles))) )) def propose_and_update_log_weights_fn(_, @@ -116,8 +113,7 @@ def propose_and_update_log_weights_fn(_, particles=proposed_particles, log_weights=(weighted_particles.log_weights + transition_dist.log_prob(proposed_particles) - - proposal_dist.log_prob(proposed_particles)), - extra=tf.constant(np.nan) + proposal_dist.log_prob(proposed_particles)) ) def marginal_logprob(transition_scale): From 249852bbd2e44a46eff6325b6c80a0dc968133c6 Mon Sep 17 00:00:00 2001 From: slamitza Date: Mon, 8 Jan 2024 16:35:21 +0100 Subject: [PATCH 05/24] removed extra from inner pf in smc2 and pf --- .../experimental/mcmc/particle_filter.py | 20 ++++--------------- .../experimental/mcmc/particle_filter_test.py | 18 ----------------- 2 files changed, 4 insertions(+), 34 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index 693c3eb64c..d1f2a18fdb 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -653,8 +653,7 @@ def _outer_propose_and_update_log_weights_fn(step, state, seed=None): if inner_proposal_fn is not None else None), observation_fn=inner_observation_fn(outside_parameters), particles_dim=1, - num_transitions_per_observation=num_transitions_per_observation, - extra_fn=extra_fn + num_transitions_per_observation=num_transitions_per_observation ) ) @@ -731,7 +730,6 @@ def rejuvenate_particles(outside_parameters, proposal_fn=(inner_proposal_fn(proposed_parameters) if inner_proposal_fn is not None else None), observation_fn=inner_observation_fn(proposed_parameters), - extra_fn=extra_fn, particles_dim=1, num_transitions_per_observation= num_transitions_per_observation) @@ -875,7 +873,6 @@ def particle_filter(observations, transition_fn, observation_fn, num_particles, - extra_fn=_default_extra_fn, initial_state_proposal=None, proposal_fn=None, resample_fn=weighted_resampling.resample_systematic, @@ -960,8 +957,7 @@ def particle_filter(observations, particles_dim=particles_dim, proposal_fn=proposal_fn, observation_fn=observation_fn, - num_transitions_per_observation=num_transitions_per_observation, - extra_fn=extra_fn + num_transitions_per_observation=num_transitions_per_observation )) return sequential_monte_carlo( @@ -1010,15 +1006,12 @@ def _particle_filter_initial_weighted_particles(observations, initial_log_weights = tf.nn.log_softmax(initial_log_weights, axis=particles_dim) - # Return particles weighted by the initial observation. if extra == (): if len(ps.shape(initial_log_weights)) == 1: # initial extra for particle filter extra = tf.constant(0) - else: - # initial extra for inner particles of smc_squared - extra = tf.constant(0, shape=ps.shape(initial_log_weights)) + # Return particles weighted by the initial observation. return smc_kernel.WeightedParticles( particles=initial_state, log_weights=initial_log_weights + _compute_observation_log_weights( @@ -1035,7 +1028,6 @@ def _particle_filter_propose_and_update_log_weights_fn( transition_fn, proposal_fn, observation_fn, - extra_fn, num_transitions_per_observation=1, particles_dim=0): """Build a function specifying a particle filter update step.""" @@ -1066,10 +1058,6 @@ def propose_and_update_log_weights_fn(step, state, seed=None): else: proposed_particles = transition_dist.sample(seed=seed) - updated_extra = extra_fn(step, - state, - seed) - with tf.control_dependencies(assertions): return smc_kernel.WeightedParticles( particles=proposed_particles, @@ -1077,7 +1065,7 @@ def propose_and_update_log_weights_fn(step, state, seed=None): step + 1, proposed_particles, observations, observation_fn, num_transitions_per_observation=num_transitions_per_observation, particles_dim=particles_dim), - extra=updated_extra) + extra=state.extra) return propose_and_update_log_weights_fn diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 476410f569..a5b821b214 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -934,24 +934,6 @@ def rejuvenation_criterion(step, state): self.assertAllEqual(ps.shape(inner_log_weights), [6, 7, 13]) self.assertAllEqual(ps.shape(lp), [6]) - def test_extra(self): - def step_hundred(step, state, seed): - return step * 2 - - results = self.evaluate( - particle_filter.particle_filter( - observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - initial_state_prior=normal.Normal(0., 1.), - transition_fn=lambda _, state: normal.Normal(state, 1.), - observation_fn=lambda _, state: normal.Normal(state, 1.), - num_particles=1024, - extra_fn=step_hundred, - trace_fn=lambda s, r: s.extra, - seed=test_util.test_seed()) - ) - - self.assertAllEqual(results, [0, 0, 2, 4, 6]) - # TODO(b/186068104): add tests with dynamic shapes. class ParticleFilterTestFloat32(_ParticleFilterTest): From a8640ad1fac2a87a261987f5cc2fcbe6cf98a6da Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 9 Jan 2024 10:46:42 +0100 Subject: [PATCH 06/24] more fixes --- .../experimental/mcmc/particle_filter.py | 33 ++++++++++--------- 1 file changed, 18 insertions(+), 15 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index d1f2a18fdb..63945451a3 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -498,7 +498,7 @@ def smc_squared( unbiased_gradients=True, seed=None, ): - _1, loop_seed, _2 = samplers.split_seed(seed, n=3, salt='smc_squared') + params_seed, particles_seed, smc_seed = samplers.split_seed(seed, n=3, salt='smc_squared') num_observation_steps = ps.size0(tf.nest.flatten(inner_observations)[0]) @@ -525,12 +525,12 @@ def smc_squared( if initial_parameter_proposal is None: initial_state = initial_parameter_prior.sample(num_outer_particles, - seed=seed) + seed=params_seed) initial_log_weights = ps.zeros_like( initial_parameter_prior.log_prob(initial_state)) else: initial_state = initial_parameter_proposal.sample(num_outer_particles, - seed=seed) + seed=params_seed) initial_log_weights = ( initial_parameter_prior.log_prob(initial_state) - initial_parameter_proposal.log_prob(initial_state) @@ -549,7 +549,7 @@ def smc_squared( else None), num_particles=num_inner_particles, particles_dim=1, - seed=seed + seed=particles_seed ) init_state = smc_kernel.WeightedParticles(*inner_weighted_particles) @@ -590,8 +590,7 @@ def smc_squared( inner_initial_state_prior=inner_initial_state_prior, inner_initial_state_proposal=inner_initial_state_proposal, num_inner_particles=num_inner_particles, - num_outer_particles=num_outer_particles, - extra_fn=extra_fn + num_outer_particles=num_outer_particles ) ) @@ -608,7 +607,7 @@ def smc_squared( num_steps=num_timesteps, particles_dim=0, trace_fn=outer_trace_fn, - seed=loop_seed + seed=smc_seed ) return traced_results @@ -629,20 +628,24 @@ def _outer_particle_filter_propose_and_update_log_weights_fn( unbiased_gradients, parameter_proposal_kernel, num_inner_particles, - num_outer_particles, - extra_fn + num_outer_particles ): """Build a function specifying a particle filter update step.""" def _outer_propose_and_update_log_weights_fn(step, state, seed=None): outside_parameters = state.particles[0] - inner_weighted_particles, log_weights = state.particles[1], \ - state.log_weights + (params, + inner_particles, + inner_parent_indices, + inner_incremental_likelihood, + inner_accumulated_likelihood + ) = state.particles + log_weights = state.log_weights filter_results = smc_kernel.SequentialMonteCarloResults( steps=step, - parent_indices=state.particles[2], - incremental_log_marginal_likelihood=state.particles[3], - accumulated_log_marginal_likelihood=state.particles[4], + parent_indices=inner_parent_indices, + incremental_log_marginal_likelihood=inner_incremental_likelihood, + accumulated_log_marginal_likelihood=inner_accumulated_likelihood, seed=state.extra[1]) inner_propose_and_update_log_weights_fn = ( @@ -667,7 +670,7 @@ def _outer_propose_and_update_log_weights_fn(step, state, seed=None): ) inner_weighted_particles, filter_results = kernel.one_step( - inner_weighted_particles, + inner_particles, filter_results, seed=seed ) From c22c07f1ea1d24fcbf9dffc2512cd0c58a8dcfc0 Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 9 Jan 2024 11:18:16 +0100 Subject: [PATCH 07/24] fixed extra --- .../experimental/mcmc/particle_filter.py | 27 ++++++++++--------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index 63945451a3..830079d98d 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -570,8 +570,7 @@ def smc_squared( initial_filter_results.incremental_log_marginal_likelihood, initial_filter_results.accumulated_log_marginal_likelihood), log_weights=initial_log_weights, - extra=(tf.constant(0), - initial_filter_results.seed) + extra=initial_filter_results.seed ) outer_propose_and_update_log_weights_fn = ( @@ -633,12 +632,13 @@ def _outer_particle_filter_propose_and_update_log_weights_fn( """Build a function specifying a particle filter update step.""" def _outer_propose_and_update_log_weights_fn(step, state, seed=None): outside_parameters = state.particles[0] - (params, - inner_particles, - inner_parent_indices, - inner_incremental_likelihood, - inner_accumulated_likelihood - ) = state.particles + ( + params, + inner_particles, + inner_parent_indices, + inner_incremental_likelihood, + inner_accumulated_likelihood + ) = state.particles log_weights = state.log_weights filter_results = smc_kernel.SequentialMonteCarloResults( @@ -646,7 +646,7 @@ def _outer_propose_and_update_log_weights_fn(step, state, seed=None): parent_indices=inner_parent_indices, incremental_log_marginal_likelihood=inner_incremental_likelihood, accumulated_log_marginal_likelihood=inner_accumulated_likelihood, - seed=state.extra[1]) + seed=state.extra) inner_propose_and_update_log_weights_fn = ( _particle_filter_propose_and_update_log_weights_fn( @@ -675,8 +675,9 @@ def _outer_propose_and_update_log_weights_fn(step, state, seed=None): seed=seed ) - updated_log_weights = log_weights + \ - filter_results.incremental_log_marginal_likelihood + updated_log_weights = ( + log_weights + filter_results.incremental_log_marginal_likelihood + ) do_rejuvenation = outer_rejuvenation_criterion_fn(step, state) @@ -864,8 +865,8 @@ def body(i, filter_results.incremental_log_marginal_likelihood, filter_results.accumulated_log_marginal_likelihood), log_weights=updated_log_weights, - extra=(step, - filter_results.seed)) + extra=filter_results.seed + ) return _outer_propose_and_update_log_weights_fn From 31e8d3120b5b5d460a9f688fb71fd909a8b2282d Mon Sep 17 00:00:00 2001 From: slamitza Date: Wed, 10 Jan 2024 16:26:38 +0100 Subject: [PATCH 08/24] smc2 fixes --- .../experimental/mcmc/particle_filter.py | 569 +++++++++--------- .../experimental/mcmc/particle_filter_test.py | 244 ++++---- 2 files changed, 398 insertions(+), 415 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index 830079d98d..ca3c8df1ac 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -54,33 +54,6 @@ def _default_kernel(parameters): return proposal_distribution -def _default_extra_fn(step, - state, - seed - ): - return state.extra - - -def where_fn(accept, a, b, num_outer_particles, num_inner_particles): - is_scalar = tf.rank(a) == tf.constant(0) - is_nan = tf.math.is_nan(tf.cast(a, tf.float32)) - is_all_nan = tf.reduce_all(is_nan) - if is_scalar and is_all_nan: - return a - elif a.shape == 2 and b.shape == 2: - # extra - return a - elif a.shape == num_outer_particles and b.shape == num_outer_particles: - return mcmc_util.choose(accept, a, b) - elif a.shape == [num_outer_particles, num_inner_particles] and \ - b.shape == [num_outer_particles, num_inner_particles]: - return mcmc_util.choose(accept, a, b) - elif a.shape == () and b.shape == (): - return a - else: - raise ValueError("Unexpected tensor shapes") - - particle_filter_arg_str = """\ Each latent state is a `Tensor` or nested structure of `Tensor`s, as defined by the `initial_state_prior`. @@ -473,30 +446,29 @@ def seeded_one_step(seed_state_results, _): def smc_squared( - inner_observations, - initial_parameter_prior, - num_outer_particles, - inner_initial_state_prior, - inner_transition_fn, - inner_observation_fn, - num_inner_particles, - outer_trace_fn=_default_trace_fn, - outer_rejuvenation_criterion_fn=None, - outer_resample_criterion_fn=None, - outer_resample_fn=weighted_resampling.resample_systematic, - inner_resample_criterion_fn=smc_kernel.ess_below_threshold, - inner_resample_fn=weighted_resampling.resample_systematic, - extra_fn=_default_extra_fn, - parameter_proposal_kernel=_default_kernel, - inner_proposal_fn=None, - inner_initial_state_proposal=None, - outer_trace_criterion_fn=_always_trace, - parallel_iterations=1, - num_transitions_per_observation=1, - static_trace_allocation_size=None, - initial_parameter_proposal=None, - unbiased_gradients=True, - seed=None, + inner_observations, + initial_parameter_prior, + num_outer_particles, + inner_initial_state_prior, + inner_transition_fn, + inner_observation_fn, + num_inner_particles, + outer_trace_fn=_default_trace_fn, + outer_rejuvenation_criterion_fn=None, + outer_resample_criterion_fn=None, + outer_resample_fn=weighted_resampling.resample_systematic, + inner_resample_criterion_fn=smc_kernel.ess_below_threshold, + inner_resample_fn=weighted_resampling.resample_systematic, + parameter_proposal_kernel=_default_kernel, + inner_proposal_fn=None, + inner_initial_state_proposal=None, + outer_trace_criterion_fn=_always_trace, + parallel_iterations=1, + num_transitions_per_observation=1, + static_trace_allocation_size=None, + initial_parameter_proposal=None, + unbiased_gradients=True, + seed=None, ): params_seed, particles_seed, smc_seed = samplers.split_seed(seed, n=3, salt='smc_squared') @@ -512,29 +484,29 @@ def smc_squared( axis=0) if outer_rejuvenation_criterion_fn is None: - outer_rejuvenation_criterion_fn = lambda *_: tf.constant(False) + outer_rejuvenation_criterion_fn = lambda *_: tf.constant(False) if outer_resample_criterion_fn is None: - outer_resample_criterion_fn = lambda *_: tf.constant(False) + outer_resample_criterion_fn = lambda *_: tf.constant(False) # If trace criterion is `None`, we'll return only the final results. never_trace = lambda *_: False if outer_trace_criterion_fn is None: - static_trace_allocation_size = 0 - outer_trace_criterion_fn = never_trace + static_trace_allocation_size = 0 + outer_trace_criterion_fn = never_trace if initial_parameter_proposal is None: - initial_state = initial_parameter_prior.sample(num_outer_particles, - seed=params_seed) - initial_log_weights = ps.zeros_like( - initial_parameter_prior.log_prob(initial_state)) + initial_state = initial_parameter_prior.sample(num_outer_particles, + seed=params_seed) + initial_log_weights = ps.zeros_like( + initial_parameter_prior.log_prob(initial_state)) else: - initial_state = initial_parameter_proposal.sample(num_outer_particles, - seed=params_seed) - initial_log_weights = ( - initial_parameter_prior.log_prob(initial_state) - - initial_parameter_proposal.log_prob(initial_state) - ) + initial_state = initial_parameter_proposal.sample(num_outer_particles, + seed=params_seed) + initial_log_weights = ( + initial_parameter_prior.log_prob(initial_state) - + initial_parameter_proposal.log_prob(initial_state) + ) # Normalize the initial weights. If we used a proposal, the weights are # normalized in expectation, but actually normalizing them reduces variance. @@ -613,260 +585,271 @@ def smc_squared( def _outer_particle_filter_propose_and_update_log_weights_fn( - inner_observations, - inner_transition_fn, - inner_proposal_fn, - inner_observation_fn, - initial_parameter_prior, - inner_initial_state_prior, - inner_initial_state_proposal, - num_transitions_per_observation, - inner_resample_fn, - inner_resample_criterion_fn, - outer_rejuvenation_criterion_fn, - unbiased_gradients, - parameter_proposal_kernel, - num_inner_particles, - num_outer_particles + inner_observations, + inner_transition_fn, + inner_proposal_fn, + inner_observation_fn, + initial_parameter_prior, + inner_initial_state_prior, + inner_initial_state_proposal, + num_transitions_per_observation, + inner_resample_fn, + inner_resample_criterion_fn, + outer_rejuvenation_criterion_fn, + unbiased_gradients, + parameter_proposal_kernel, + num_inner_particles, + num_outer_particles ): """Build a function specifying a particle filter update step.""" def _outer_propose_and_update_log_weights_fn(step, state, seed=None): - outside_parameters = state.particles[0] - ( - params, - inner_particles, - inner_parent_indices, - inner_incremental_likelihood, - inner_accumulated_likelihood - ) = state.particles - log_weights = state.log_weights + outside_parameters = state.particles[0] + ( + params, + inner_particles, + inner_parent_indices, + inner_incremental_likelihood, + inner_accumulated_likelihood + ) = state.particles + log_weights = state.log_weights + + filter_results = smc_kernel.SequentialMonteCarloResults( + steps=step, + parent_indices=inner_parent_indices, + incremental_log_marginal_likelihood=inner_incremental_likelihood, + accumulated_log_marginal_likelihood=inner_accumulated_likelihood, + seed=state.extra) + + inner_propose_and_update_log_weights_fn = ( + _particle_filter_propose_and_update_log_weights_fn( + observations=inner_observations, + transition_fn=inner_transition_fn(outside_parameters), + proposal_fn=(inner_proposal_fn(outside_parameters) + if inner_proposal_fn is not None else None), + observation_fn=inner_observation_fn(outside_parameters), + particles_dim=1, + num_transitions_per_observation=num_transitions_per_observation + ) + ) - filter_results = smc_kernel.SequentialMonteCarloResults( - steps=step, - parent_indices=inner_parent_indices, - incremental_log_marginal_likelihood=inner_incremental_likelihood, - accumulated_log_marginal_likelihood=inner_accumulated_likelihood, - seed=state.extra) + kernel = smc_kernel.SequentialMonteCarlo( + propose_and_update_log_weights_fn= + inner_propose_and_update_log_weights_fn, + resample_fn=inner_resample_fn, + resample_criterion_fn=inner_resample_criterion_fn, + particles_dim=1, + unbiased_gradients=unbiased_gradients + ) + + inner_weighted_particles, filter_results = kernel.one_step( + inner_particles, + filter_results, + seed=seed + ) + + updated_log_weights = ( + log_weights + filter_results.incremental_log_marginal_likelihood + ) + + do_rejuvenation = outer_rejuvenation_criterion_fn(step, state) + + def rejuvenate_particles(outside_parameters, + updated_log_weights, + inner_weighted_particles, + filter_results): + proposed_parameters = parameter_proposal_kernel( + outside_parameters + ).sample(seed=seed) + + rej_params_log_weights = ps.zeros_like( + initial_parameter_prior.log_prob(proposed_parameters) + ) + rej_params_log_weights = tf.nn.log_softmax( + rej_params_log_weights, + axis=0 + ) + + rej_inner_weighted_particles = \ + _particle_filter_initial_weighted_particles( + observations=inner_observations, + observation_fn=inner_observation_fn(proposed_parameters), + initial_state_prior=inner_initial_state_prior( + 0, + proposed_parameters + ), + initial_state_proposal=( + inner_initial_state_proposal(0, proposed_parameters) + if inner_initial_state_proposal is not None + else None), + num_particles=num_inner_particles, + particles_dim=1, + seed=seed + ) - inner_propose_and_update_log_weights_fn = ( + batch_zeros = tf.zeros(ps.shape(log_weights)) + + rej_filter_results = smc_kernel.SequentialMonteCarloResults( + steps=tf.constant(0, dtype=tf.int32), + parent_indices=smc_kernel._dummy_indices_like( + rej_inner_weighted_particles.log_weights + ), + incremental_log_marginal_likelihood=batch_zeros, + accumulated_log_marginal_likelihood=batch_zeros, + seed=samplers.zeros_seed() + ) + + rej_inner_particles_weights = rej_inner_weighted_particles.log_weights + + rej_inner_propose_and_update_log_weights_fn = ( _particle_filter_propose_and_update_log_weights_fn( observations=inner_observations, - transition_fn=inner_transition_fn(outside_parameters), - proposal_fn=(inner_proposal_fn(outside_parameters) - if inner_proposal_fn is not None else None), - observation_fn=inner_observation_fn(outside_parameters), + transition_fn=inner_transition_fn(proposed_parameters), + proposal_fn=(inner_proposal_fn(proposed_parameters) + if inner_proposal_fn is not None else None), + observation_fn=inner_observation_fn(proposed_parameters), particles_dim=1, - num_transitions_per_observation=num_transitions_per_observation - ) + num_transitions_per_observation= + num_transitions_per_observation) ) - kernel = smc_kernel.SequentialMonteCarlo( + rej_kernel = smc_kernel.SequentialMonteCarlo( propose_and_update_log_weights_fn= - inner_propose_and_update_log_weights_fn, + rej_inner_propose_and_update_log_weights_fn, resample_fn=inner_resample_fn, resample_criterion_fn=inner_resample_criterion_fn, particles_dim=1, unbiased_gradients=unbiased_gradients ) - inner_weighted_particles, filter_results = kernel.one_step( - inner_particles, - filter_results, - seed=seed - ) - - updated_log_weights = ( - log_weights + filter_results.incremental_log_marginal_likelihood - ) - - do_rejuvenation = outer_rejuvenation_criterion_fn(step, state) - - def rejuvenate_particles(outside_parameters, - updated_log_weights, - inner_weighted_particles, - filter_results): - proposed_parameters = parameter_proposal_kernel( - outside_parameters - ).sample(seed=seed) - - rej_params_log_weights = ps.zeros_like( - initial_parameter_prior.log_prob(proposed_parameters) - ) - rej_params_log_weights = tf.nn.log_softmax( - rej_params_log_weights, - axis=0 - ) - - rej_inner_weighted_particles = \ - _particle_filter_initial_weighted_particles( - observations=inner_observations, - observation_fn=inner_observation_fn(proposed_parameters), - initial_state_prior=inner_initial_state_prior( - 0, - proposed_parameters - ), - initial_state_proposal=( - inner_initial_state_proposal(0, proposed_parameters) - if inner_initial_state_proposal is not None - else None), - num_particles=num_inner_particles, - particles_dim=1, - seed=seed + def condition(i, + rej_inner_weighted_particles, + rej_filter_results, + rej_parameters_weights, + rej_params_log_weights): + return tf.less_equal(i, step) + + def body(i, + rej_inner_weighted_particles, + rej_filter_results, + rej_parameters_weights, + rej_params_log_weights + ): + + rej_inner_weighted_particles, rej_filter_results = \ + rej_kernel.one_step( + rej_inner_weighted_particles, rej_filter_results, seed=seed ) - batch_zeros = tf.zeros(ps.shape(log_weights)) - - rej_filter_results = smc_kernel.SequentialMonteCarloResults( - steps=tf.constant(0, dtype=tf.int32), - parent_indices=smc_kernel._dummy_indices_like( - rej_inner_weighted_particles.log_weights - ), - incremental_log_marginal_likelihood=batch_zeros, - accumulated_log_marginal_likelihood=batch_zeros, - seed=samplers.zeros_seed()) - - rej_inner_particles_weights = rej_inner_weighted_particles.log_weights + rej_parameters_weights += rej_inner_weighted_particles.log_weights + + rej_params_log_weights = \ + rej_params_log_weights + \ + rej_filter_results.incremental_log_marginal_likelihood + return i + 1, \ + rej_inner_weighted_particles, \ + rej_filter_results, \ + rej_parameters_weights, \ + rej_params_log_weights + + _, \ + rej_inner_weighted_particles, \ + rej_filter_results, \ + rej_inner_particles_weights, \ + rej_params_log_weights = tf.while_loop( + condition, + body, + loop_vars=[0, + rej_inner_weighted_particles, + rej_filter_results, + rej_inner_particles_weights, + rej_params_log_weights + ] + ) - rej_inner_propose_and_update_log_weights_fn = ( - _particle_filter_propose_and_update_log_weights_fn( - observations=inner_observations, - transition_fn=inner_transition_fn(proposed_parameters), - proposal_fn=(inner_proposal_fn(proposed_parameters) - if inner_proposal_fn is not None else None), - observation_fn=inner_observation_fn(proposed_parameters), - particles_dim=1, - num_transitions_per_observation= - num_transitions_per_observation) - ) + log_a = rej_filter_results.accumulated_log_marginal_likelihood - \ + filter_results.accumulated_log_marginal_likelihood + \ + parameter_proposal_kernel( + proposed_parameters).log_prob(outside_parameters) - \ + parameter_proposal_kernel( + outside_parameters).log_prob(proposed_parameters) + + acceptance_probs = tf.minimum(1., tf.exp(log_a)) + + random_numbers = uniform.Uniform(0., 1.).sample(num_outer_particles, + seed=seed) + + # Determine if the proposed particle should be accepted or reject + accept = random_numbers > acceptance_probs + + # Update the chosen particles and filter restults + # based on the acceptance step + outside_parameters = tf.where(accept, + outside_parameters, + proposed_parameters) + updated_log_weights = tf.where(accept, + updated_log_weights, + rej_params_log_weights) + + inner_weighted_particles_particles = mcmc_util.choose( + accept, + inner_weighted_particles.particles, + rej_inner_weighted_particles.particles + ) + inner_weighted_particles_log_weights = mcmc_util.choose( + accept, + inner_weighted_particles.log_weights, + rej_inner_weighted_particles.log_weights + ) - rej_kernel = smc_kernel.SequentialMonteCarlo( - propose_and_update_log_weights_fn= - rej_inner_propose_and_update_log_weights_fn, - resample_fn=inner_resample_fn, - resample_criterion_fn=inner_resample_criterion_fn, - particles_dim=1, - unbiased_gradients=unbiased_gradients) - - def condition(i, - rej_inner_weighted_particles, - rej_filter_results, - rej_parameters_weights, - rej_params_log_weights): - return tf.less_equal(i, step) - - def body(i, - rej_inner_weighted_particles, - rej_filter_results, - rej_parameters_weights, - rej_params_log_weights): - - rej_inner_weighted_particles, rej_filter_results = \ - rej_kernel.one_step( - rej_inner_weighted_particles, rej_filter_results, seed=seed - ) - - rej_parameters_weights += rej_inner_weighted_particles.log_weights - - rej_params_log_weights = \ - rej_params_log_weights + \ - rej_filter_results.incremental_log_marginal_likelihood - return i + 1, \ - rej_inner_weighted_particles, \ - rej_filter_results, \ - rej_parameters_weights, \ - rej_params_log_weights - - _, \ - rej_inner_weighted_particles, \ - rej_filter_results, \ - rej_inner_particles_weights, \ - rej_params_log_weights = tf.while_loop( - condition, - body, - loop_vars=[0, - rej_inner_weighted_particles, - rej_filter_results, - rej_inner_particles_weights, - rej_params_log_weights - ] - ) + inner_weighted_particles = smc_kernel.WeightedParticles( + particles=inner_weighted_particles_particles, + log_weights=inner_weighted_particles_log_weights, + extra=inner_weighted_particles.extra + ) - log_a = rej_filter_results.accumulated_log_marginal_likelihood - \ - filter_results.accumulated_log_marginal_likelihood + \ - parameter_proposal_kernel( - proposed_parameters).log_prob(outside_parameters) - \ - parameter_proposal_kernel( - outside_parameters).log_prob(proposed_parameters) - - acceptance_probs = tf.minimum(1., tf.exp(log_a)) - - random_numbers = uniform.Uniform(0., 1.).sample(num_outer_particles, - seed=seed) - - # Determine if the proposed particle should be accepted or reject - accept = random_numbers > acceptance_probs - - # Update the chosen particles and filter restults - # based on the acceptance step - outside_parameters = tf.where(accept, - outside_parameters, - proposed_parameters) - updated_log_weights = tf.where(accept, - updated_log_weights, - rej_params_log_weights) - - inner_weighted_particles_particles = mcmc_util.choose( - accept, - inner_weighted_particles.particles, - rej_inner_weighted_particles.particles - ) - inner_weighted_particles_log_weights = mcmc_util.choose( - accept, - inner_weighted_particles.log_weights, - rej_inner_weighted_particles.log_weights - ) + parent_indices, incremental_log_marginal_likelihood, accumulated_log_marginal_likelihood = mcmc_util.choose( + accept, + (filter_results.parent_indices, filter_results.incremental_log_marginal_likelihood, + filter_results.accumulated_log_marginal_likelihood), + (rej_filter_results.parent_indices, rej_filter_results.incremental_log_marginal_likelihood, + rej_filter_results.accumulated_log_marginal_likelihood) + ) - inner_weighted_particles = smc_kernel.WeightedParticles( - particles=inner_weighted_particles_particles, - log_weights=inner_weighted_particles_log_weights, - extra=inner_weighted_particles.extra - ) + filter_results = smc_kernel.SequentialMonteCarloResults( + steps=filter_results.steps, + parent_indices=parent_indices, + incremental_log_marginal_likelihood=incremental_log_marginal_likelihood, + accumulated_log_marginal_likelihood=accumulated_log_marginal_likelihood, + seed=filter_results.seed + ) - filter_results = tf.nest.map_structure( - lambda a, b: where_fn(accept, a, b, - num_outer_particles, - num_inner_particles), - filter_results, - rej_filter_results - ) + return outside_parameters, updated_log_weights, \ + inner_weighted_particles, filter_results + + outside_parameters, \ + updated_log_weights, \ + inner_weighted_particles, \ + filter_results = tf.cond( + do_rejuvenation, + lambda: (rejuvenate_particles(outside_parameters, + updated_log_weights, + inner_weighted_particles, + filter_results)), + lambda: (outside_parameters, + updated_log_weights, + inner_weighted_particles, + filter_results) + ) - return outside_parameters, updated_log_weights, \ - inner_weighted_particles, filter_results - - outside_parameters, \ - updated_log_weights, \ - inner_weighted_particles, \ - filter_results = tf.cond( - do_rejuvenation, - lambda: (rejuvenate_particles(outside_parameters, - updated_log_weights, - inner_weighted_particles, - filter_results)), - lambda: (outside_parameters, - updated_log_weights, + return smc_kernel.WeightedParticles( + particles=(outside_parameters, inner_weighted_particles, - filter_results) - ) - - return smc_kernel.WeightedParticles( - particles=(outside_parameters, - inner_weighted_particles, - filter_results.parent_indices, - filter_results.incremental_log_marginal_likelihood, - filter_results.accumulated_log_marginal_likelihood), - log_weights=updated_log_weights, - extra=filter_results.seed - ) + filter_results.parent_indices, + filter_results.incremental_log_marginal_likelihood, + filter_results.accumulated_log_marginal_likelihood), + log_weights=updated_log_weights, + extra=filter_results.seed + ) return _outer_propose_and_update_log_weights_fn diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index a5b821b214..5c564b489a 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -177,128 +177,128 @@ def observation_fn(_, state): trajectories['velocity'].shape) self.assertAllEqual(incremental_log_marginal_likelihoods.shape, [num_timesteps] + batch_shape) - # - # def test_batch_of_filters_particles_dim_1(self): - # - # batch_shape = [3, 2] - # num_particles = 1000 - # num_timesteps = 40 - # - # # Batch of priors on object 1D positions and velocities. - # initial_state_prior = jdn.JointDistributionNamed({ - # 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), - # 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) - # }) - # - # def transition_fn(_, previous_state): - # return jdn.JointDistributionNamed({ - # 'position': - # normal.Normal( - # loc=previous_state['position'] + previous_state['velocity'], - # scale=0.1), - # 'velocity': - # normal.Normal(loc=previous_state['velocity'], scale=0.01) - # }) - # - # def observation_fn(_, state): - # return normal.Normal(loc=state['position'], scale=0.1) - # - # # Batch of synthetic observations, . - # true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) - # true_velocities = 0.1 * np.random.randn( - # *batch_shape).astype(self.dtype) - # observed_positions = ( - # true_velocities * - # np.arange(num_timesteps).astype( - # self.dtype)[..., tf.newaxis, tf.newaxis] + - # true_initial_positions) - # - # (particles, log_weights, parent_indices, - # incremental_log_marginal_likelihoods) = self.evaluate( - # particle_filter.particle_filter( - # observations=observed_positions, - # initial_state_prior=initial_state_prior, - # transition_fn=transition_fn, - # observation_fn=observation_fn, - # num_particles=num_particles, - # seed=test_util.test_seed(), - # particles_dim=1)) - # - # self.assertAllEqual(particles['position'].shape, - # [num_timesteps, - # batch_shape[0], - # num_particles, - # batch_shape[1]]) - # self.assertAllEqual(particles['velocity'].shape, - # [num_timesteps, - # batch_shape[0], - # num_particles, - # batch_shape[1]]) - # self.assertAllEqual(parent_indices.shape, - # [num_timesteps, - # batch_shape[0], - # num_particles, - # batch_shape[1]]) - # self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - # [num_timesteps] + batch_shape) - # - # self.assertAllClose( - # self.evaluate( - # tf.reduce_sum(tf.exp(log_weights) * - # particles['position'], axis=2)), - # observed_positions, - # atol=0.3) - # - # velocity_means = tf.reduce_sum(tf.exp(log_weights) * - # particles['velocity'], axis=2) - # - # self.assertAllClose( - # self.evaluate(tf.reduce_mean(velocity_means, axis=0)), - # true_velocities, atol=0.05) - # - # # Uncertainty in velocity should decrease over time. - # velocity_stddev = self.evaluate( - # tf.math.reduce_std(particles['velocity'], axis=2)) - # self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) - # - # trajectories = self.evaluate( - # particle_filter.reconstruct_trajectories(particles, - # parent_indices, - # particles_dim=1)) - # self.assertAllEqual([num_timesteps, - # batch_shape[0], - # num_particles, - # batch_shape[1]], - # trajectories['position'].shape) - # self.assertAllEqual([num_timesteps, - # batch_shape[0], - # num_particles, - # batch_shape[1]], - # trajectories['velocity'].shape) - # - # # Verify that `infer_trajectories` also works on batches. - # trajectories, incremental_log_marginal_likelihoods = self.evaluate( - # particle_filter.infer_trajectories( - # observations=observed_positions, - # initial_state_prior=initial_state_prior, - # transition_fn=transition_fn, - # observation_fn=observation_fn, - # num_particles=num_particles, - # particles_dim=1, - # seed=test_util.test_seed())) - # - # self.assertAllEqual([num_timesteps, - # batch_shape[0], - # num_particles, - # batch_shape[1]], - # trajectories['position'].shape) - # self.assertAllEqual([num_timesteps, - # batch_shape[0], - # num_particles, - # batch_shape[1]], - # trajectories['velocity'].shape) - # self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - # [num_timesteps] + batch_shape) + + def test_batch_of_filters_particles_dim_1(self): + + batch_shape = [3, 2] + num_particles = 1000 + num_timesteps = 40 + + # Batch of priors on object 1D positions and velocities. + initial_state_prior = jdn.JointDistributionNamed({ + 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), + 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) + }) + + def transition_fn(_, previous_state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=previous_state['position'] + previous_state['velocity'], + scale=0.1), + 'velocity': + normal.Normal(loc=previous_state['velocity'], scale=0.01) + }) + + def observation_fn(_, state): + return normal.Normal(loc=state['position'], scale=0.1) + + # Batch of synthetic observations, . + true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) + true_velocities = 0.1 * np.random.randn( + *batch_shape).astype(self.dtype) + observed_positions = ( + true_velocities * + np.arange(num_timesteps).astype( + self.dtype)[..., tf.newaxis, tf.newaxis] + + true_initial_positions) + + (particles, log_weights, parent_indices, + incremental_log_marginal_likelihoods) = self.evaluate( + particle_filter.particle_filter( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + seed=test_util.test_seed(), + particles_dim=1)) + + self.assertAllEqual(particles['position'].shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) + self.assertAllEqual(particles['velocity'].shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) + self.assertAllEqual(parent_indices.shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) + + self.assertAllClose( + self.evaluate( + tf.reduce_sum(tf.exp(log_weights) * + particles['position'], axis=2)), + observed_positions, + atol=0.3) + + velocity_means = tf.reduce_sum(tf.exp(log_weights) * + particles['velocity'], axis=2) + + self.assertAllClose( + self.evaluate(tf.reduce_mean(velocity_means, axis=0)), + true_velocities, atol=0.05) + + # Uncertainty in velocity should decrease over time. + velocity_stddev = self.evaluate( + tf.math.reduce_std(particles['velocity'], axis=2)) + self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) + + trajectories = self.evaluate( + particle_filter.reconstruct_trajectories(particles, + parent_indices, + particles_dim=1)) + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['velocity'].shape) + + # Verify that `infer_trajectories` also works on batches. + trajectories, incremental_log_marginal_likelihoods = self.evaluate( + particle_filter.infer_trajectories( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + particles_dim=1, + seed=test_util.test_seed())) + + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['velocity'].shape) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) def test_reconstruct_trajectories_toy_example(self): particles = tf.convert_to_tensor([[1, 2, 3], [4, 5, 6,], [7, 8, 9]]) From 7012580ba9831f03255976a00a059d004e19886c Mon Sep 17 00:00:00 2001 From: slamitza Date: Thu, 11 Jan 2024 17:29:43 +0100 Subject: [PATCH 09/24] pyleint --- .../python/experimental/mcmc/particle_filter_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 5c564b489a..7ef8d2a148 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -761,7 +761,7 @@ def rejuvenation_criterion(step, state): loc = tf.broadcast_to([0., 0.], [num_outer_particles, 2]) scale_diag = tf.broadcast_to([0.05, 0.05], [num_outer_particles, 2]) - params, inner_pt = self.evaluate(particle_filter.smc_squared( + params, _ = self.evaluate(particle_filter.smc_squared( inner_observations=inner_observations, inner_initial_state_prior=lambda _, params: mvn_diag.MultivariateNormalDiag( From 14e6d8485ae78b2051820b6ee827db8ddb82ccf1 Mon Sep 17 00:00:00 2001 From: slamitza Date: Thu, 11 Jan 2024 23:11:20 +0100 Subject: [PATCH 10/24] pylint --- .../experimental/mcmc/particle_filter.py | 20 +++++++++++++------ .../experimental/mcmc/particle_filter_test.py | 4 +++- 2 files changed, 17 insertions(+), 7 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index ca3c8df1ac..134c35514d 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -470,7 +470,9 @@ def smc_squared( unbiased_gradients=True, seed=None, ): - params_seed, particles_seed, smc_seed = samplers.split_seed(seed, n=3, salt='smc_squared') + params_seed, particles_seed, smc_seed = samplers.split_seed( + seed, n=3, salt='smc_squared' + ) num_observation_steps = ps.size0(tf.nest.flatten(inner_observations)[0]) @@ -807,19 +809,25 @@ def body(i, extra=inner_weighted_particles.extra ) - parent_indices, incremental_log_marginal_likelihood, accumulated_log_marginal_likelihood = mcmc_util.choose( + parent_indices, \ + incremental_log_marginal_likelihood, \ + accumulated_log_marginal_likelihood = mcmc_util.choose( accept, - (filter_results.parent_indices, filter_results.incremental_log_marginal_likelihood, + (filter_results.parent_indices, + filter_results.incremental_log_marginal_likelihood, filter_results.accumulated_log_marginal_likelihood), - (rej_filter_results.parent_indices, rej_filter_results.incremental_log_marginal_likelihood, + (rej_filter_results.parent_indices, + rej_filter_results.incremental_log_marginal_likelihood, rej_filter_results.accumulated_log_marginal_likelihood) ) filter_results = smc_kernel.SequentialMonteCarloResults( steps=filter_results.steps, parent_indices=parent_indices, - incremental_log_marginal_likelihood=incremental_log_marginal_likelihood, - accumulated_log_marginal_likelihood=accumulated_log_marginal_likelihood, + incremental_log_marginal_likelihood= + incremental_log_marginal_likelihood, + accumulated_log_marginal_likelihood= + accumulated_log_marginal_likelihood, seed=filter_results.seed ) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 7ef8d2a148..8e9a140999 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -776,7 +776,9 @@ def rejuvenation_criterion(step, state): particle_dynamics(params, _, state), 1) ), inner_observation_fn=lambda params: ( - lambda _, state: independent.Independent(normal.Normal(state, 2.), 1)), + lambda _, state: independent.Independent( + normal.Normal(state, 2.), 1) + ), outer_trace_fn=lambda s, r: ( s.particles[0], s.particles[1] From 79ca7d9339a8caf5a4cd27817d2aac308a9b537a Mon Sep 17 00:00:00 2001 From: slamitza Date: Thu, 11 Jan 2024 23:40:41 +0100 Subject: [PATCH 11/24] pylint --- .../python/experimental/mcmc/particle_filter.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index 134c35514d..2322d5a5ec 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -605,9 +605,8 @@ def _outer_particle_filter_propose_and_update_log_weights_fn( ): """Build a function specifying a particle filter update step.""" def _outer_propose_and_update_log_weights_fn(step, state, seed=None): - outside_parameters = state.particles[0] ( - params, + outside_parameters, inner_particles, inner_parent_indices, inner_incremental_likelihood, From 183fdf902d3baeefb9b824bbde16cddee5ffa8ea Mon Sep 17 00:00:00 2001 From: slamitza Date: Sun, 21 Jan 2024 17:30:23 +0100 Subject: [PATCH 12/24] fixed test --- .../experimental/mcmc/particle_filter.py | 17 +- .../experimental/mcmc/particle_filter_test.py | 854 +----------------- 2 files changed, 20 insertions(+), 851 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index 2322d5a5ec..c5060747ee 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -446,7 +446,7 @@ def seeded_one_step(seed_state_results, _): def smc_squared( - inner_observations, + observations, initial_parameter_prior, num_outer_particles, inner_initial_state_prior, @@ -474,14 +474,14 @@ def smc_squared( seed, n=3, salt='smc_squared' ) - num_observation_steps = ps.size0(tf.nest.flatten(inner_observations)[0]) + num_observation_steps = ps.size0(tf.nest.flatten(observations)[0]) # TODO: The following two lines compensates for having the # first empty step in smc2 num_timesteps = (1 + num_transitions_per_observation * (num_observation_steps - 1)) + 1 - last_obs_expanded = tf.expand_dims(inner_observations[-1], axis=0) - inner_observations = tf.concat([inner_observations, + last_obs_expanded = tf.expand_dims(observations[-1], axis=0) + inner_observations = tf.concat([observations, last_obs_expanded], axis=0) @@ -1104,12 +1104,13 @@ def _compute_observation_log_weights(step, observation = tf.nest.map_structure( lambda x, step=step: tf.gather(x, observation_idx), observations) - if particles_dim == 1: - observation = tf.expand_dims(observation, axis=0) - observation = tf.nest.map_structure( - lambda x: tf.expand_dims(x, axis=particles_dim), observation) + if particles_dim != 1: + observation = tf.nest.map_structure( + lambda x: tf.expand_dims(x, axis=particles_dim), observation + ) log_weights = observation_fn(step, particles).log_prob(observation) + return tf.where(step_has_observation, log_weights, tf.zeros_like(log_weights)) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 8e9a140999..235ef57237 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -41,700 +41,6 @@ @test_util.test_all_tf_execution_regimes class _ParticleFilterTest(test_util.TestCase): - def test_random_walk(self): - initial_state_prior = jdn.JointDistributionNamed( - {'position': deterministic.Deterministic(0.)}) - - # Biased random walk. - def particle_dynamics(_, previous_state): - state_shape = ps.shape(previous_state['position']) - return jdn.JointDistributionNamed({ - 'position': - transformed_distribution.TransformedDistribution( - bernoulli.Bernoulli( - probs=tf.fill(state_shape, 0.75), dtype=self.dtype), - shift.Shift(previous_state['position'])) - }) - - # Completely uninformative observations allowing a test - # of the pure dynamics. - def particle_observations(_, state): - state_shape = ps.shape(state['position']) - return uniform.Uniform( - low=tf.fill(state_shape, -100.), high=tf.fill(state_shape, 100.)) - - observations = tf.zeros((9,), dtype=self.dtype) - trajectories, _ = self.evaluate( - particle_filter.infer_trajectories( - observations=observations, - initial_state_prior=initial_state_prior, - transition_fn=particle_dynamics, - observation_fn=particle_observations, - num_particles=16384, - seed=test_util.test_seed())) - position = trajectories['position'] - - # The trajectories have the following properties: - # 1. they lie completely in the range [0, 8] - self.assertAllInRange(position, 0., 8.) - # 2. each step lies in the range [0, 1] - self.assertAllInRange(position[1:] - position[:-1], 0., 1.) - # 3. the expectation and variance of the final positions are 6 and 1.5. - self.assertAllClose(tf.reduce_mean(position[-1]), 6., atol=0.1) - self.assertAllClose(tf.math.reduce_variance(position[-1]), 1.5, atol=0.1) - - def test_batch_of_filters(self): - - batch_shape = [3, 2] - num_particles = 1000 - num_timesteps = 40 - - # Batch of priors on object 1D positions and velocities. - initial_state_prior = jdn.JointDistributionNamed({ - 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), - 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) - }) - - def transition_fn(_, previous_state): - return jdn.JointDistributionNamed({ - 'position': - normal.Normal( - loc=previous_state['position'] + previous_state['velocity'], - scale=0.1), - 'velocity': - normal.Normal(loc=previous_state['velocity'], scale=0.01) - }) - - def observation_fn(_, state): - return normal.Normal(loc=state['position'], scale=0.1) - - # Batch of synthetic observations, . - true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) - true_velocities = 0.1 * np.random.randn( - *batch_shape).astype(self.dtype) - observed_positions = ( - true_velocities * - np.arange(num_timesteps).astype( - self.dtype)[..., tf.newaxis, tf.newaxis] + - true_initial_positions) - - (particles, log_weights, parent_indices, - incremental_log_marginal_likelihoods) = self.evaluate( - particle_filter.particle_filter( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - seed=test_util.test_seed())) - - self.assertAllEqual(particles['position'].shape, - [num_timesteps, num_particles] + batch_shape) - self.assertAllEqual(particles['velocity'].shape, - [num_timesteps, num_particles] + batch_shape) - self.assertAllEqual(parent_indices.shape, - [num_timesteps, num_particles] + batch_shape) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - - self.assertAllClose( - self.evaluate( - tf.reduce_sum(tf.exp(log_weights) * - particles['position'], axis=1)), - observed_positions, - atol=0.1) - - velocity_means = tf.reduce_sum(tf.exp(log_weights) * - particles['velocity'], axis=1) - self.assertAllClose( - self.evaluate(tf.reduce_mean(velocity_means, axis=0)), - true_velocities, atol=0.05) - - # Uncertainty in velocity should decrease over time. - velocity_stddev = self.evaluate( - tf.math.reduce_std(particles['velocity'], axis=1)) - self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) - - trajectories = self.evaluate( - particle_filter.reconstruct_trajectories(particles, parent_indices)) - self.assertAllEqual([num_timesteps, num_particles] + batch_shape, - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, num_particles] + batch_shape, - trajectories['velocity'].shape) - - # Verify that `infer_trajectories` also works on batches. - trajectories, incremental_log_marginal_likelihoods = self.evaluate( - particle_filter.infer_trajectories( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - seed=test_util.test_seed())) - self.assertAllEqual([num_timesteps, num_particles] + batch_shape, - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, num_particles] + batch_shape, - trajectories['velocity'].shape) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - - def test_batch_of_filters_particles_dim_1(self): - - batch_shape = [3, 2] - num_particles = 1000 - num_timesteps = 40 - - # Batch of priors on object 1D positions and velocities. - initial_state_prior = jdn.JointDistributionNamed({ - 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), - 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) - }) - - def transition_fn(_, previous_state): - return jdn.JointDistributionNamed({ - 'position': - normal.Normal( - loc=previous_state['position'] + previous_state['velocity'], - scale=0.1), - 'velocity': - normal.Normal(loc=previous_state['velocity'], scale=0.01) - }) - - def observation_fn(_, state): - return normal.Normal(loc=state['position'], scale=0.1) - - # Batch of synthetic observations, . - true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) - true_velocities = 0.1 * np.random.randn( - *batch_shape).astype(self.dtype) - observed_positions = ( - true_velocities * - np.arange(num_timesteps).astype( - self.dtype)[..., tf.newaxis, tf.newaxis] + - true_initial_positions) - - (particles, log_weights, parent_indices, - incremental_log_marginal_likelihoods) = self.evaluate( - particle_filter.particle_filter( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - seed=test_util.test_seed(), - particles_dim=1)) - - self.assertAllEqual(particles['position'].shape, - [num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]]) - self.assertAllEqual(particles['velocity'].shape, - [num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]]) - self.assertAllEqual(parent_indices.shape, - [num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]]) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - - self.assertAllClose( - self.evaluate( - tf.reduce_sum(tf.exp(log_weights) * - particles['position'], axis=2)), - observed_positions, - atol=0.3) - - velocity_means = tf.reduce_sum(tf.exp(log_weights) * - particles['velocity'], axis=2) - - self.assertAllClose( - self.evaluate(tf.reduce_mean(velocity_means, axis=0)), - true_velocities, atol=0.05) - - # Uncertainty in velocity should decrease over time. - velocity_stddev = self.evaluate( - tf.math.reduce_std(particles['velocity'], axis=2)) - self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) - - trajectories = self.evaluate( - particle_filter.reconstruct_trajectories(particles, - parent_indices, - particles_dim=1)) - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['velocity'].shape) - - # Verify that `infer_trajectories` also works on batches. - trajectories, incremental_log_marginal_likelihoods = self.evaluate( - particle_filter.infer_trajectories( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - particles_dim=1, - seed=test_util.test_seed())) - - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['velocity'].shape) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - - def test_reconstruct_trajectories_toy_example(self): - particles = tf.convert_to_tensor([[1, 2, 3], [4, 5, 6,], [7, 8, 9]]) - # 1 -- 4 -- 7 - # 2 \/ 5 .- 8 - # 3 /\ 6 /-- 9 - parent_indices = tf.convert_to_tensor([[0, 1, 2], [0, 2, 1], [0, 2, 2]]) - - trajectories = self.evaluate( - particle_filter.reconstruct_trajectories(particles, parent_indices)) - self.assertAllEqual( - np.array([[1, 2, 2], [4, 6, 6], [7, 8, 9]]), trajectories) - - def test_epidemiological_model(self): - # A toy, discrete version of an SIR (Susceptible, Infected, Recovered) - # model (https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology) - - population_size = 1000 - infection_rate = tf.convert_to_tensor(1.1) - infectious_period = tf.convert_to_tensor(8.0) - - initial_state_prior = jdn.JointDistributionNamed({ - 'susceptible': deterministic.Deterministic(999.), - 'infected': deterministic.Deterministic(1.), - 'new_infections': deterministic.Deterministic(1.), - 'new_recoveries': deterministic.Deterministic(0.) - }) - - # Dynamics model: new infections and recoveries are given by the SIR - # model with Poisson noise. - def infection_dynamics(_, previous_state): - new_infections = poisson.Poisson( - infection_rate * previous_state['infected'] * - previous_state['susceptible'] / population_size) - new_recoveries = poisson.Poisson(previous_state['infected'] / - infectious_period) - - def susceptible(new_infections): - return deterministic.Deterministic( - ps.maximum(0., previous_state['susceptible'] - new_infections)) - - def infected(new_infections, new_recoveries): - return deterministic.Deterministic( - ps.maximum( - 0., - previous_state['infected'] + new_infections - new_recoveries)) - - return jdn.JointDistributionNamed({ - 'new_infections': new_infections, - 'new_recoveries': new_recoveries, - 'susceptible': susceptible, - 'infected': infected - }) - - # Observation model: each day we detect new cases, noisily. - def infection_observations(_, state): - return poisson.Poisson(state['infected']) - - # pylint: disable=bad-whitespace - observations = tf.convert_to_tensor([ - 0., 4., 1., 5., 23., 27., 75., 127., 248., 384., 540., 683., - 714., 611., 561., 493., 385., 348., 300., 277., 249., 219., 216., 174., - 132., 122., 115., 99., 76., 84., 77., 56., 42., 56., 46., 38., - 34., 44., 25., 27.]) - # pylint: enable=bad-whitespace - - trajectories, _ = self.evaluate( - particle_filter.infer_trajectories( - observations=observations, - initial_state_prior=initial_state_prior, - transition_fn=infection_dynamics, - observation_fn=infection_observations, - num_particles=100, - seed=test_util.test_seed())) - - # The susceptible population should decrease over time. - self.assertAllLessEqual( - trajectories['susceptible'][1:, ...] - - trajectories['susceptible'][:-1, ...], - 0.0) - - def test_data_driven_proposal(self): - - num_particles = 100 - observations = tf.convert_to_tensor([60., -179.2, 1337.42]) - - # Define a system constrained primarily by observations, where proposing - # from the dynamics would be a bad fit. - initial_state_prior = normal.Normal(loc=0., scale=1e6) - transition_fn = ( - lambda _, previous_state: normal.Normal(loc=previous_state, scale=1e6)) - observation_fn = lambda _, state: normal.Normal(loc=state, scale=0.1) - initial_state_proposal = normal.Normal(loc=observations[0], scale=0.1) - proposal_fn = ( - lambda step, state: normal.Normal( # pylint: disable=g-long-lambda - loc=tf.ones_like(state) * observations[step + 1], - scale=1.0)) - - trajectories, _ = self.evaluate( - particle_filter.infer_trajectories( - observations=observations, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - initial_state_proposal=initial_state_proposal, - proposal_fn=proposal_fn, - seed=test_util.test_seed())) - self.assertAllClose(trajectories, - tf.convert_to_tensor( - tf.convert_to_tensor( - observations)[..., tf.newaxis] * - tf.ones([num_particles])), atol=1.0) - - def test_estimated_prob_approximates_true_prob(self): - - # Draw simulated data from a 2D linear Gaussian system. - initial_state_prior = mvn_diag.MultivariateNormalDiag( - loc=0., scale_diag=(1., 1.)) - transition_matrix = tf.convert_to_tensor([[1., -0.5], [0.4, -1.]]) - transition_noise = mvn_tril.MultivariateNormalTriL( - loc=1., scale_tril=tf.convert_to_tensor([[0.3, 0], [-0.1, 0.2]])) - observation_matrix = tf.convert_to_tensor([[0.1, 1.], [1., 0.2]]) - observation_noise = mvn_tril.MultivariateNormalTriL( - loc=-0.3, scale_tril=tf.convert_to_tensor([[0.5, 0], [0.1, 0.5]])) - model = lgssm.LinearGaussianStateSpaceModel( - num_timesteps=20, - initial_state_prior=initial_state_prior, - transition_matrix=transition_matrix, - transition_noise=transition_noise, - observation_matrix=observation_matrix, - observation_noise=observation_noise) - observations = self.evaluate( - model.sample(seed=test_util.test_seed())) - (lps, filtered_means, - _, _, _, _, _) = self.evaluate(model.forward_filter(observations)) - - # Approximate the filtering means and marginal likelihood(s) using - # the particle filter. - # pylint: disable=g-long-lambda - (particles, log_weights, _, - estimated_incremental_log_marginal_likelihoods) = self.evaluate( - particle_filter.particle_filter( - observations=observations, - initial_state_prior=initial_state_prior, - transition_fn=lambda _, previous_state: mvn_tril. - MultivariateNormalTriL( - loc=transition_noise.loc + tf.linalg.matvec( - transition_matrix, previous_state), - scale_tril=transition_noise.scale_tril), - observation_fn=lambda _, state: mvn_tril.MultivariateNormalTriL( - loc=observation_noise.loc + tf.linalg.matvec( - observation_matrix, state), - scale_tril=observation_noise.scale_tril), - num_particles=1024, - seed=test_util.test_seed())) - # pylint: enable=g-long-lambda - - particle_means = np.sum( - particles * np.exp(log_weights)[..., np.newaxis], axis=1) - self.assertAllClose(filtered_means, particle_means, atol=0.1, rtol=0.1) - - self.assertAllClose( - lps, estimated_incremental_log_marginal_likelihoods, atol=0.6) - - def test_proposal_weights_dont_affect_marginal_likelihood(self): - observation = np.array([-1.3, 0.7]).astype(self.dtype) - # This particle filter has proposals different from the dynamics, - # so internally it will use proposal weights in addition to observation - # weights. It should still get the observation likelihood correct. - _, lps = self.evaluate( - particle_filter.infer_trajectories( - observation, - initial_state_prior=normal.Normal(loc=0., scale=1.), - transition_fn=lambda _, x: normal.Normal(loc=x, scale=1.), - observation_fn=lambda _, x: normal.Normal(loc=x, scale=1.), - initial_state_proposal=normal.Normal(loc=0., scale=5.), - proposal_fn=lambda _, x: normal.Normal(loc=x, scale=5.), - num_particles=2048, - seed=test_util.test_seed())) - - # Compare marginal likelihood against that - # from the true (jointly normal) marginal distribution. - y1_marginal_dist = normal.Normal(loc=0., scale=np.sqrt(1. + 1.)) - y2_conditional_dist = ( - lambda y1: normal.Normal(loc=y1 / 2., scale=np.sqrt(5. / 2.))) - true_lps = tf.stack( - [y1_marginal_dist.log_prob(observation[0]), - y2_conditional_dist(observation[0]).log_prob(observation[1])], - axis=0) - # The following line passes at atol = 0.01 if num_particles = 32768. - self.assertAllClose(true_lps, lps, atol=0.2) - - def test_can_step_dynamics_faster_than_observations(self): - initial_state_prior = jdn.JointDistributionNamed({ - 'position': deterministic.Deterministic(1.), - 'velocity': deterministic.Deterministic(0.) - }) - - # Use 100 steps between observations to integrate a simple harmonic - # oscillator. - dt = 0.01 - def simple_harmonic_motion_transition_fn(_, state): - return jdn.JointDistributionNamed({ - 'position': - normal.Normal( - loc=state['position'] + dt * state['velocity'], - scale=dt * 0.01), - 'velocity': - normal.Normal( - loc=state['velocity'] - dt * state['position'], - scale=dt * 0.01) - }) - - def observe_position(_, state): - return normal.Normal(loc=state['position'], scale=0.01) - - particles, _, _, lps = self.evaluate( - particle_filter.particle_filter( - # 'Observing' the values we'd expect from a proper integrator should - # give high likelihood if our discrete approximation is good. - observations=tf.convert_to_tensor( - [tf.math.cos(0.), tf.math.cos(1.)]), - initial_state_prior=initial_state_prior, - transition_fn=simple_harmonic_motion_transition_fn, - observation_fn=observe_position, - num_particles=1024, - num_transitions_per_observation=100, - seed=test_util.test_seed())) - - self.assertLen(particles['position'], 101) - self.assertAllClose(np.mean(particles['position'], axis=-1), - tf.math.cos(dt * np.arange(101)), - atol=0.04) - self.assertLen(lps, 101) - self.assertGreater(lps[0], 3.) - self.assertGreater(lps[-1], 3.) - - def test_custom_trace_fn(self): - - def trace_fn(state, _): - # Traces the mean and stddev of the particle population at each step. - weights = tf.exp(state.log_weights) - mean = tf.reduce_sum(weights * state.particles, axis=0) - variance = tf.reduce_sum( - weights * (state.particles - mean[tf.newaxis, ...])**2) - return {'mean': mean, - 'stddev': tf.sqrt(variance), - # In real usage we would likely not track the particles and - # weights. We keep them here just so we can double-check the - # stats, below. - 'particles': state.particles, - 'weights': weights} - - results = self.evaluate( - particle_filter.particle_filter( - observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - initial_state_prior=normal.Normal(0., 1.), - transition_fn=lambda _, state: normal.Normal(state, 1.), - observation_fn=lambda _, state: normal.Normal(state, 1.), - num_particles=1024, - trace_fn=trace_fn, - seed=test_util.test_seed())) - - # Verify that posterior means are increasing. - self.assertAllGreater(results['mean'][1:] - results['mean'][:-1], 0.) - - # Check that our traced means and scales match values computed - # by averaging over particles after the fact. - all_means = self.evaluate(tf.reduce_sum( - results['weights'] * results['particles'], axis=1)) - all_variances = self.evaluate( - tf.reduce_sum( - results['weights'] * - (results['particles'] - all_means[..., tf.newaxis])**2, - axis=1)) - self.assertAllClose(results['mean'], all_means) - self.assertAllClose(results['stddev'], np.sqrt(all_variances)) - - def test_step_indices_to_trace(self): - num_particles = 1024 - (particles_1_3, log_weights_1_3, parent_indices_1_3, - incremental_log_marginal_likelihood_1_3) = self.evaluate( - particle_filter.particle_filter( - observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - initial_state_prior=normal.Normal(0., 1.), - transition_fn=lambda _, state: normal.Normal(state, 10.), - observation_fn=lambda _, state: normal.Normal(state, 0.1), - num_particles=num_particles, - trace_criterion_fn=lambda s, r: ps.logical_or( # pylint: disable=g-long-lambda - ps.equal(r.steps, 2), ps.equal(r.steps, 4)), - static_trace_allocation_size=2, - seed=test_util.test_seed())) - self.assertLen(particles_1_3, 2) - self.assertLen(log_weights_1_3, 2) - self.assertLen(parent_indices_1_3, 2) - self.assertLen(incremental_log_marginal_likelihood_1_3, 2) - means = np.sum(np.exp(log_weights_1_3) * particles_1_3, axis=1) - self.assertAllClose(means, [3., 7.], atol=1.) - - (final_particles, final_log_weights, final_cumulative_lp) = self.evaluate( - particle_filter.particle_filter( - observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - initial_state_prior=normal.Normal(0., 1.), - transition_fn=lambda _, state: normal.Normal(state, 10.), - observation_fn=lambda _, state: normal.Normal(state, 0.1), - num_particles=num_particles, - trace_fn=lambda s, r: ( # pylint: disable=g-long-lambda - s.particles, - s.log_weights, - r.accumulated_log_marginal_likelihood), - trace_criterion_fn=None, - seed=test_util.test_seed())) - self.assertLen(final_particles, num_particles) - self.assertLen(final_log_weights, num_particles) - self.assertEqual(final_cumulative_lp.shape, ()) - means = np.sum(np.exp(final_log_weights) * final_particles) - self.assertAllClose(means, 9., atol=1.5) - - def test_warns_if_transition_distribution_has_unexpected_shape(self): - - initial_state_prior = jdab.JointDistributionNamedAutoBatched({ - 'sales': deterministic.Deterministic(0.), - 'inventory': deterministic.Deterministic(1000.) - }) - - # Inventory decreases by a Poisson RV 'sales', but is lower bounded at zero. - def valid_transition_fn(_, particles): - return jdab.JointDistributionNamedAutoBatched( - { - 'sales': - poisson.Poisson(10. * tf.ones_like(particles['inventory'])), - 'inventory': - lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda - tf.maximum(0., particles['inventory'] - sales)) - }, - batch_ndims=1, - validate_args=True) - - def dummy_observation_fn(_, state): - return normal.Normal(state['inventory'], 1000.) - - run_filter = functools.partial( - particle_filter.particle_filter, - observations=tf.zeros([10]), - initial_state_prior=initial_state_prior, - observation_fn=dummy_observation_fn, - num_particles=3, - seed=test_util.test_seed(sampler_type='stateless')) - - # Check that the model runs as written. - self.evaluate(run_filter(transition_fn=valid_transition_fn)) - self.evaluate(run_filter(transition_fn=valid_transition_fn, - proposal_fn=valid_transition_fn)) - - # Check that broken transition functions raise exceptions. - def transition_fn_broadcasts_over_particles(_, particles): - return jdn.JointDistributionNamed( - { - 'sales': - poisson.Poisson(10. - ), # Proposes same value for all particles. - 'inventory': - lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda - tf.maximum(0., particles['inventory'] - sales)) - }, - validate_args=True) - - def transition_fn_partial_batch_shape(_, particles): - return jdn.JointDistributionNamed( - # Using `Sample` ensures iid proposals for each particle, but not - # per-particle log probs. - { - 'sales': - sample_dist_lib.Sample( - poisson.Poisson(10.), ps.shape(particles['sales'])), - 'inventory': - lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda - tf.maximum(0., particles['inventory'] - sales)) - }, - validate_args=True) - - def transition_fn_no_batch_shape(_, particles): - # Autobatched JD defaults to treating num_particles as event shape, but - # we need it to be batch shape to get per-particle logprobs. - return jdab.JointDistributionNamedAutoBatched( - { - 'sales': - poisson.Poisson(10. * tf.ones_like(particles['inventory'])), - 'inventory': - lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda - tf.maximum(0., particles['inventory'] - sales)) - }, - validate_args=True) - - with self.assertRaisesRegex(ValueError, 'transition distribution'): - self.evaluate( - run_filter(transition_fn=transition_fn_broadcasts_over_particles)) - with self.assertRaisesRegex(ValueError, 'transition distribution'): - self.evaluate( - run_filter(transition_fn=transition_fn_partial_batch_shape)) - with self.assertRaisesRegex(ValueError, 'transition distribution'): - self.evaluate( - run_filter(transition_fn=transition_fn_no_batch_shape)) - - with self.assertRaisesRegex(ValueError, 'proposal distribution'): - self.evaluate( - run_filter(transition_fn=valid_transition_fn, - proposal_fn=transition_fn_partial_batch_shape)) - with self.assertRaisesRegex(ValueError, 'proposal distribution'): - self.evaluate( - run_filter(transition_fn=valid_transition_fn, - proposal_fn=transition_fn_broadcasts_over_particles)) - - with self.assertRaisesRegex(ValueError, 'proposal distribution'): - self.evaluate( - run_filter(transition_fn=valid_transition_fn, - proposal_fn=transition_fn_no_batch_shape)) - - @test_util.jax_disable_test_missing_functionality('Gradient of while_loop.') - def test_marginal_likelihood_gradients_are_defined(self): - - def marginal_log_likelihood(level_scale, noise_scale): - _, _, _, lps = particle_filter.particle_filter( - observations=tf.convert_to_tensor([1., 2., 3., 4., 5.]), - initial_state_prior=normal.Normal(loc=0, scale=1.), - transition_fn=lambda _, x: normal.Normal(loc=x, scale=level_scale), - observation_fn=lambda _, x: normal.Normal(loc=x, scale=noise_scale), - num_particles=4, - seed=test_util.test_seed()) - return tf.reduce_sum(lps) - - _, grads = gradient.value_and_gradient(marginal_log_likelihood, 1.0, 1.0) - self.assertAllNotNone(grads) - self.assertAllAssertsNested(self.assertNotAllZero, grads) - def test_smc_squared_rejuvenation_parameters(self): def particle_dynamics(params, _, previous_state): reshaped_params = tf.reshape(params, @@ -742,7 +48,12 @@ def particle_dynamics(params, _, previous_state): [1] * (previous_state.shape.rank - 1)) broadcasted_params = tf.broadcast_to(reshaped_params, previous_state.shape) - return normal.Normal(previous_state + broadcasted_params + 1, 0.1) + reshaped_dist = independent.Independent( + normal.Normal(previous_state + broadcasted_params + 1, 0.1), + reinterpreted_batch_ndims=1 + ) + + return reshaped_dist def rejuvenation_criterion(step, state): # Rejuvenation every 2 steps @@ -753,7 +64,8 @@ def rejuvenation_criterion(step, state): return tf.cond(cond, lambda: tf.constant(True), lambda: tf.constant(False)) - inner_observations = tf.range(30, dtype=tf.float32) + observations = tf.stack([tf.range(30, dtype=tf.float32), + tf.range(30, dtype=tf.float32)], axis=1) num_outer_particles = 3 num_inner_particles = 7 @@ -762,7 +74,7 @@ def rejuvenation_criterion(step, state): scale_diag = tf.broadcast_to([0.05, 0.05], [num_outer_particles, 2]) params, _ = self.evaluate(particle_filter.smc_squared( - inner_observations=inner_observations, + observations=observations, inner_initial_state_prior=lambda _, params: mvn_diag.MultivariateNormalDiag( loc=loc, scale_diag=scale_diag @@ -771,10 +83,8 @@ def rejuvenation_criterion(step, state): num_outer_particles=num_outer_particles, num_inner_particles=num_inner_particles, outer_rejuvenation_criterion_fn=rejuvenation_criterion, - inner_transition_fn=lambda params: ( - lambda _, state: independent.Independent( - particle_dynamics(params, _, state), 1) - ), + inner_transition_fn=lambda params: + lambda _, state: particle_dynamics(params, _, state), inner_observation_fn=lambda params: ( lambda _, state: independent.Independent( normal.Normal(state, 2.), 1) @@ -794,148 +104,6 @@ def rejuvenation_criterion(step, state): self.assertAllTrue(mask_parameters) - def test_smc_squared_can_step_dynamics_faster_than_observations(self): - initial_state_prior = jdn.JointDistributionNamed({ - 'position': deterministic.Deterministic([1.]), - 'velocity': deterministic.Deterministic([0.]) - }) - - # Use 100 steps between observations to integrate a simple harmonic - # oscillator. - dt = 0.01 - def simple_harmonic_motion_transition_fn(_, state): - return jdn.JointDistributionNamed({ - 'position': - normal.Normal( - loc=state['position'] + dt * state['velocity'], - scale=dt * 0.01), - 'velocity': - normal.Normal( - loc=state['velocity'] - dt * state['position'], - scale=dt * 0.01) - }) - - def observe_position(_, state): - return normal.Normal(loc=state['position'], scale=0.01) - - particles, lps = self.evaluate(particle_filter.smc_squared( - inner_observations=tf.convert_to_tensor( - [tf.math.cos(0.), tf.math.cos(1.)]), - inner_initial_state_prior=lambda _, params: initial_state_prior, - initial_parameter_prior=deterministic.Deterministic(0.), - num_outer_particles=1, - inner_transition_fn=lambda params: - simple_harmonic_motion_transition_fn, - inner_observation_fn=lambda params: observe_position, - num_inner_particles=1024, - outer_trace_fn=lambda s, r: ( - s.particles[1].particles, - s.particles[3] - ), - num_transitions_per_observation=100, - seed=test_util.test_seed()) - ) - - self.assertAllEqual(ps.shape(particles['position']), tf.constant([102, - 1, - 1024])) - - self.assertAllClose(tf.transpose(np.mean(particles['position'], axis=-1)), - tf.reshape(tf.math.cos(dt * np.arange(102)), [1, -1]), - atol=0.04) - - self.assertAllEqual(ps.shape(lps), [102, 1]) - self.assertGreater(lps[1][0], 1.) - self.assertGreater(lps[-1][0], 3.) - - def test_smc_squared_custom_outer_trace_fn(self): - def trace_fn(state, _): - # Traces the mean and stddev of the particle population at each step. - weights = tf.exp(state[0][1].log_weights[0]) - mean = tf.reduce_sum(weights * state[0][1].particles[0], axis=0) - variance = tf.reduce_sum( - weights * (state[0][1].particles[0] - mean[tf.newaxis, ...]) ** 2) - return {'mean': mean, - 'stddev': tf.sqrt(variance), - # In real usage we would likely not track the particles and - # weights. We keep them here just so we can double-check the - # stats, below. - 'particles': state[0][1].particles[0], - 'weights': weights} - - results = self.evaluate(particle_filter.smc_squared( - inner_observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - inner_initial_state_prior=lambda _, params: normal.Normal([0.], 1.), - initial_parameter_prior=deterministic.Deterministic(0.), - inner_transition_fn=lambda params: (lambda _, state: - normal.Normal(state, 1.)), - inner_observation_fn=lambda params: (lambda _, state: - normal.Normal(state, 1.)), - num_inner_particles=1024, - num_outer_particles=1, - outer_trace_fn=trace_fn, - seed=test_util.test_seed()) - ) - - # Verify that posterior means are increasing. - self.assertAllGreater(results['mean'][1:] - results['mean'][:-1], 0.) - - # Check that our traced means and scales match values computed - # by averaging over particles after the fact. - all_means = self.evaluate(tf.reduce_sum( - results['weights'] * results['particles'], axis=1)) - all_variances = self.evaluate( - tf.reduce_sum( - results['weights'] * - (results['particles'] - all_means[..., tf.newaxis])**2, - axis=1)) - self.assertAllClose(results['mean'], all_means) - self.assertAllClose(results['stddev'], np.sqrt(all_variances)) - - def test_smc_squared_indices_to_trace(self): - num_outer_particles = 7 - num_inner_particles = 13 - - def rejuvenation_criterion(step, state): - # Rejuvenation every 3 steps - cond = tf.logical_and( - tf.equal(tf.math.mod(step, tf.constant(3)), tf.constant(0)), - tf.not_equal(state.extra[0], tf.constant(0)) - ) - return tf.cond(cond, lambda: tf.constant(True), - lambda: tf.constant(False)) - - (parameters, weight_parameters, - inner_particles, inner_log_weights, lp) = self.evaluate( - particle_filter.smc_squared( - inner_observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - initial_parameter_prior=deterministic.Deterministic(0.), - inner_initial_state_prior=lambda _, params: normal.Normal( - [0.] * num_outer_particles, 1. - ), - inner_transition_fn=lambda params: - (lambda _, state: normal.Normal(state, 10.)), - inner_observation_fn=lambda params: - (lambda _, state: normal.Normal(state, 0.1)), - num_inner_particles=num_inner_particles, - num_outer_particles=num_outer_particles, - outer_rejuvenation_criterion_fn=rejuvenation_criterion, - outer_trace_fn=lambda s, r: ( # pylint: disable=g-long-lambda - s.particles[0], - s.log_weights, - s.particles[1].particles, - s.particles[1].log_weights, - r.accumulated_log_marginal_likelihood), - seed=test_util.test_seed()) - ) - - # TODO: smc_squared at the moment starts his run with an empty step - self.assertAllEqual(ps.shape(parameters), [6, 7]) - self.assertAllEqual(ps.shape(weight_parameters), [6, 7]) - self.assertAllEqual(ps.shape(inner_particles), [6, 7, 13]) - self.assertAllEqual(ps.shape(inner_log_weights), [6, 7, 13]) - self.assertAllEqual(ps.shape(lp), [6]) - # TODO(b/186068104): add tests with dynamic shapes. class ParticleFilterTestFloat32(_ParticleFilterTest): From 4c5f86e3cec9781647c64aa229ffc5ace44c187e Mon Sep 17 00:00:00 2001 From: slamitza Date: Sun, 21 Jan 2024 17:32:43 +0100 Subject: [PATCH 13/24] pylint tests --- .../experimental/mcmc/particle_filter_test.py | 836 ++++++++++++++++++ 1 file changed, 836 insertions(+) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 235ef57237..7efbf9a286 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -41,6 +41,700 @@ @test_util.test_all_tf_execution_regimes class _ParticleFilterTest(test_util.TestCase): + def test_random_walk(self): + initial_state_prior = jdn.JointDistributionNamed( + {'position': deterministic.Deterministic(0.)}) + + # Biased random walk. + def particle_dynamics(_, previous_state): + state_shape = ps.shape(previous_state['position']) + return jdn.JointDistributionNamed({ + 'position': + transformed_distribution.TransformedDistribution( + bernoulli.Bernoulli( + probs=tf.fill(state_shape, 0.75), dtype=self.dtype), + shift.Shift(previous_state['position'])) + }) + + # Completely uninformative observations allowing a test + # of the pure dynamics. + def particle_observations(_, state): + state_shape = ps.shape(state['position']) + return uniform.Uniform( + low=tf.fill(state_shape, -100.), high=tf.fill(state_shape, 100.)) + + observations = tf.zeros((9,), dtype=self.dtype) + trajectories, _ = self.evaluate( + particle_filter.infer_trajectories( + observations=observations, + initial_state_prior=initial_state_prior, + transition_fn=particle_dynamics, + observation_fn=particle_observations, + num_particles=16384, + seed=test_util.test_seed())) + position = trajectories['position'] + + # The trajectories have the following properties: + # 1. they lie completely in the range [0, 8] + self.assertAllInRange(position, 0., 8.) + # 2. each step lies in the range [0, 1] + self.assertAllInRange(position[1:] - position[:-1], 0., 1.) + # 3. the expectation and variance of the final positions are 6 and 1.5. + self.assertAllClose(tf.reduce_mean(position[-1]), 6., atol=0.1) + self.assertAllClose(tf.math.reduce_variance(position[-1]), 1.5, atol=0.1) + + def test_batch_of_filters(self): + + batch_shape = [3, 2] + num_particles = 1000 + num_timesteps = 40 + + # Batch of priors on object 1D positions and velocities. + initial_state_prior = jdn.JointDistributionNamed({ + 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), + 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) + }) + + def transition_fn(_, previous_state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=previous_state['position'] + previous_state['velocity'], + scale=0.1), + 'velocity': + normal.Normal(loc=previous_state['velocity'], scale=0.01) + }) + + def observation_fn(_, state): + return normal.Normal(loc=state['position'], scale=0.1) + + # Batch of synthetic observations, . + true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) + true_velocities = 0.1 * np.random.randn( + *batch_shape).astype(self.dtype) + observed_positions = ( + true_velocities * + np.arange(num_timesteps).astype( + self.dtype)[..., tf.newaxis, tf.newaxis] + + true_initial_positions) + + (particles, log_weights, parent_indices, + incremental_log_marginal_likelihoods) = self.evaluate( + particle_filter.particle_filter( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + seed=test_util.test_seed())) + + self.assertAllEqual(particles['position'].shape, + [num_timesteps, num_particles] + batch_shape) + self.assertAllEqual(particles['velocity'].shape, + [num_timesteps, num_particles] + batch_shape) + self.assertAllEqual(parent_indices.shape, + [num_timesteps, num_particles] + batch_shape) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) + + self.assertAllClose( + self.evaluate( + tf.reduce_sum(tf.exp(log_weights) * + particles['position'], axis=1)), + observed_positions, + atol=0.1) + + velocity_means = tf.reduce_sum(tf.exp(log_weights) * + particles['velocity'], axis=1) + self.assertAllClose( + self.evaluate(tf.reduce_mean(velocity_means, axis=0)), + true_velocities, atol=0.05) + + # Uncertainty in velocity should decrease over time. + velocity_stddev = self.evaluate( + tf.math.reduce_std(particles['velocity'], axis=1)) + self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) + + trajectories = self.evaluate( + particle_filter.reconstruct_trajectories(particles, parent_indices)) + self.assertAllEqual([num_timesteps, num_particles] + batch_shape, + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, num_particles] + batch_shape, + trajectories['velocity'].shape) + + # Verify that `infer_trajectories` also works on batches. + trajectories, incremental_log_marginal_likelihoods = self.evaluate( + particle_filter.infer_trajectories( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + seed=test_util.test_seed())) + self.assertAllEqual([num_timesteps, num_particles] + batch_shape, + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, num_particles] + batch_shape, + trajectories['velocity'].shape) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) + + def test_batch_of_filters_particles_dim_1(self): + + batch_shape = [3, 2] + num_particles = 1000 + num_timesteps = 40 + + # Batch of priors on object 1D positions and velocities. + initial_state_prior = jdn.JointDistributionNamed({ + 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), + 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) + }) + + def transition_fn(_, previous_state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=previous_state['position'] + previous_state['velocity'], + scale=0.1), + 'velocity': + normal.Normal(loc=previous_state['velocity'], scale=0.01) + }) + + def observation_fn(_, state): + return normal.Normal(loc=state['position'], scale=0.1) + + # Batch of synthetic observations, . + true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) + true_velocities = 0.1 * np.random.randn( + *batch_shape).astype(self.dtype) + observed_positions = ( + true_velocities * + np.arange(num_timesteps).astype( + self.dtype)[..., tf.newaxis, tf.newaxis] + + true_initial_positions) + + (particles, log_weights, parent_indices, + incremental_log_marginal_likelihoods) = self.evaluate( + particle_filter.particle_filter( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + seed=test_util.test_seed(), + particles_dim=1)) + + self.assertAllEqual(particles['position'].shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) + self.assertAllEqual(particles['velocity'].shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) + self.assertAllEqual(parent_indices.shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) + + self.assertAllClose( + self.evaluate( + tf.reduce_sum(tf.exp(log_weights) * + particles['position'], axis=2)), + observed_positions, + atol=0.3) + + velocity_means = tf.reduce_sum(tf.exp(log_weights) * + particles['velocity'], axis=2) + + self.assertAllClose( + self.evaluate(tf.reduce_mean(velocity_means, axis=0)), + true_velocities, atol=0.05) + + # Uncertainty in velocity should decrease over time. + velocity_stddev = self.evaluate( + tf.math.reduce_std(particles['velocity'], axis=2)) + self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) + + trajectories = self.evaluate( + particle_filter.reconstruct_trajectories(particles, + parent_indices, + particles_dim=1)) + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['velocity'].shape) + + # Verify that `infer_trajectories` also works on batches. + trajectories, incremental_log_marginal_likelihoods = self.evaluate( + particle_filter.infer_trajectories( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + particles_dim=1, + seed=test_util.test_seed())) + + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['velocity'].shape) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) + + def test_reconstruct_trajectories_toy_example(self): + particles = tf.convert_to_tensor([[1, 2, 3], [4, 5, 6,], [7, 8, 9]]) + # 1 -- 4 -- 7 + # 2 \/ 5 .- 8 + # 3 /\ 6 /-- 9 + parent_indices = tf.convert_to_tensor([[0, 1, 2], [0, 2, 1], [0, 2, 2]]) + + trajectories = self.evaluate( + particle_filter.reconstruct_trajectories(particles, parent_indices)) + self.assertAllEqual( + np.array([[1, 2, 2], [4, 6, 6], [7, 8, 9]]), trajectories) + + def test_epidemiological_model(self): + # A toy, discrete version of an SIR (Susceptible, Infected, Recovered) + # model (https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology) + + population_size = 1000 + infection_rate = tf.convert_to_tensor(1.1) + infectious_period = tf.convert_to_tensor(8.0) + + initial_state_prior = jdn.JointDistributionNamed({ + 'susceptible': deterministic.Deterministic(999.), + 'infected': deterministic.Deterministic(1.), + 'new_infections': deterministic.Deterministic(1.), + 'new_recoveries': deterministic.Deterministic(0.) + }) + + # Dynamics model: new infections and recoveries are given by the SIR + # model with Poisson noise. + def infection_dynamics(_, previous_state): + new_infections = poisson.Poisson( + infection_rate * previous_state['infected'] * + previous_state['susceptible'] / population_size) + new_recoveries = poisson.Poisson(previous_state['infected'] / + infectious_period) + + def susceptible(new_infections): + return deterministic.Deterministic( + ps.maximum(0., previous_state['susceptible'] - new_infections)) + + def infected(new_infections, new_recoveries): + return deterministic.Deterministic( + ps.maximum( + 0., + previous_state['infected'] + new_infections - new_recoveries)) + + return jdn.JointDistributionNamed({ + 'new_infections': new_infections, + 'new_recoveries': new_recoveries, + 'susceptible': susceptible, + 'infected': infected + }) + + # Observation model: each day we detect new cases, noisily. + def infection_observations(_, state): + return poisson.Poisson(state['infected']) + + # pylint: disable=bad-whitespace + observations = tf.convert_to_tensor([ + 0., 4., 1., 5., 23., 27., 75., 127., 248., 384., 540., 683., + 714., 611., 561., 493., 385., 348., 300., 277., 249., 219., 216., 174., + 132., 122., 115., 99., 76., 84., 77., 56., 42., 56., 46., 38., + 34., 44., 25., 27.]) + # pylint: enable=bad-whitespace + + trajectories, _ = self.evaluate( + particle_filter.infer_trajectories( + observations=observations, + initial_state_prior=initial_state_prior, + transition_fn=infection_dynamics, + observation_fn=infection_observations, + num_particles=100, + seed=test_util.test_seed())) + + # The susceptible population should decrease over time. + self.assertAllLessEqual( + trajectories['susceptible'][1:, ...] - + trajectories['susceptible'][:-1, ...], + 0.0) + + def test_data_driven_proposal(self): + + num_particles = 100 + observations = tf.convert_to_tensor([60., -179.2, 1337.42]) + + # Define a system constrained primarily by observations, where proposing + # from the dynamics would be a bad fit. + initial_state_prior = normal.Normal(loc=0., scale=1e6) + transition_fn = ( + lambda _, previous_state: normal.Normal(loc=previous_state, scale=1e6)) + observation_fn = lambda _, state: normal.Normal(loc=state, scale=0.1) + initial_state_proposal = normal.Normal(loc=observations[0], scale=0.1) + proposal_fn = ( + lambda step, state: normal.Normal( # pylint: disable=g-long-lambda + loc=tf.ones_like(state) * observations[step + 1], + scale=1.0)) + + trajectories, _ = self.evaluate( + particle_filter.infer_trajectories( + observations=observations, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + initial_state_proposal=initial_state_proposal, + proposal_fn=proposal_fn, + seed=test_util.test_seed())) + self.assertAllClose(trajectories, + tf.convert_to_tensor( + tf.convert_to_tensor( + observations)[..., tf.newaxis] * + tf.ones([num_particles])), atol=1.0) + + def test_estimated_prob_approximates_true_prob(self): + + # Draw simulated data from a 2D linear Gaussian system. + initial_state_prior = mvn_diag.MultivariateNormalDiag( + loc=0., scale_diag=(1., 1.)) + transition_matrix = tf.convert_to_tensor([[1., -0.5], [0.4, -1.]]) + transition_noise = mvn_tril.MultivariateNormalTriL( + loc=1., scale_tril=tf.convert_to_tensor([[0.3, 0], [-0.1, 0.2]])) + observation_matrix = tf.convert_to_tensor([[0.1, 1.], [1., 0.2]]) + observation_noise = mvn_tril.MultivariateNormalTriL( + loc=-0.3, scale_tril=tf.convert_to_tensor([[0.5, 0], [0.1, 0.5]])) + model = lgssm.LinearGaussianStateSpaceModel( + num_timesteps=20, + initial_state_prior=initial_state_prior, + transition_matrix=transition_matrix, + transition_noise=transition_noise, + observation_matrix=observation_matrix, + observation_noise=observation_noise) + observations = self.evaluate( + model.sample(seed=test_util.test_seed())) + (lps, filtered_means, + _, _, _, _, _) = self.evaluate(model.forward_filter(observations)) + + # Approximate the filtering means and marginal likelihood(s) using + # the particle filter. + # pylint: disable=g-long-lambda + (particles, log_weights, _, + estimated_incremental_log_marginal_likelihoods) = self.evaluate( + particle_filter.particle_filter( + observations=observations, + initial_state_prior=initial_state_prior, + transition_fn=lambda _, previous_state: mvn_tril. + MultivariateNormalTriL( + loc=transition_noise.loc + tf.linalg.matvec( + transition_matrix, previous_state), + scale_tril=transition_noise.scale_tril), + observation_fn=lambda _, state: mvn_tril.MultivariateNormalTriL( + loc=observation_noise.loc + tf.linalg.matvec( + observation_matrix, state), + scale_tril=observation_noise.scale_tril), + num_particles=1024, + seed=test_util.test_seed())) + # pylint: enable=g-long-lambda + + particle_means = np.sum( + particles * np.exp(log_weights)[..., np.newaxis], axis=1) + self.assertAllClose(filtered_means, particle_means, atol=0.1, rtol=0.1) + + self.assertAllClose( + lps, estimated_incremental_log_marginal_likelihoods, atol=0.6) + + def test_proposal_weights_dont_affect_marginal_likelihood(self): + observation = np.array([-1.3, 0.7]).astype(self.dtype) + # This particle filter has proposals different from the dynamics, + # so internally it will use proposal weights in addition to observation + # weights. It should still get the observation likelihood correct. + _, lps = self.evaluate( + particle_filter.infer_trajectories( + observation, + initial_state_prior=normal.Normal(loc=0., scale=1.), + transition_fn=lambda _, x: normal.Normal(loc=x, scale=1.), + observation_fn=lambda _, x: normal.Normal(loc=x, scale=1.), + initial_state_proposal=normal.Normal(loc=0., scale=5.), + proposal_fn=lambda _, x: normal.Normal(loc=x, scale=5.), + num_particles=2048, + seed=test_util.test_seed())) + + # Compare marginal likelihood against that + # from the true (jointly normal) marginal distribution. + y1_marginal_dist = normal.Normal(loc=0., scale=np.sqrt(1. + 1.)) + y2_conditional_dist = ( + lambda y1: normal.Normal(loc=y1 / 2., scale=np.sqrt(5. / 2.))) + true_lps = tf.stack( + [y1_marginal_dist.log_prob(observation[0]), + y2_conditional_dist(observation[0]).log_prob(observation[1])], + axis=0) + # The following line passes at atol = 0.01 if num_particles = 32768. + self.assertAllClose(true_lps, lps, atol=0.2) + + def test_can_step_dynamics_faster_than_observations(self): + initial_state_prior = jdn.JointDistributionNamed({ + 'position': deterministic.Deterministic(1.), + 'velocity': deterministic.Deterministic(0.) + }) + + # Use 100 steps between observations to integrate a simple harmonic + # oscillator. + dt = 0.01 + def simple_harmonic_motion_transition_fn(_, state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=state['position'] + dt * state['velocity'], + scale=dt * 0.01), + 'velocity': + normal.Normal( + loc=state['velocity'] - dt * state['position'], + scale=dt * 0.01) + }) + + def observe_position(_, state): + return normal.Normal(loc=state['position'], scale=0.01) + + particles, _, _, lps = self.evaluate( + particle_filter.particle_filter( + # 'Observing' the values we'd expect from a proper integrator should + # give high likelihood if our discrete approximation is good. + observations=tf.convert_to_tensor( + [tf.math.cos(0.), tf.math.cos(1.)]), + initial_state_prior=initial_state_prior, + transition_fn=simple_harmonic_motion_transition_fn, + observation_fn=observe_position, + num_particles=1024, + num_transitions_per_observation=100, + seed=test_util.test_seed())) + + self.assertLen(particles['position'], 101) + self.assertAllClose(np.mean(particles['position'], axis=-1), + tf.math.cos(dt * np.arange(101)), + atol=0.04) + self.assertLen(lps, 101) + self.assertGreater(lps[0], 3.) + self.assertGreater(lps[-1], 3.) + + def test_custom_trace_fn(self): + + def trace_fn(state, _): + # Traces the mean and stddev of the particle population at each step. + weights = tf.exp(state.log_weights) + mean = tf.reduce_sum(weights * state.particles, axis=0) + variance = tf.reduce_sum( + weights * (state.particles - mean[tf.newaxis, ...])**2) + return {'mean': mean, + 'stddev': tf.sqrt(variance), + # In real usage we would likely not track the particles and + # weights. We keep them here just so we can double-check the + # stats, below. + 'particles': state.particles, + 'weights': weights} + + results = self.evaluate( + particle_filter.particle_filter( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_state_prior=normal.Normal(0., 1.), + transition_fn=lambda _, state: normal.Normal(state, 1.), + observation_fn=lambda _, state: normal.Normal(state, 1.), + num_particles=1024, + trace_fn=trace_fn, + seed=test_util.test_seed())) + + # Verify that posterior means are increasing. + self.assertAllGreater(results['mean'][1:] - results['mean'][:-1], 0.) + + # Check that our traced means and scales match values computed + # by averaging over particles after the fact. + all_means = self.evaluate(tf.reduce_sum( + results['weights'] * results['particles'], axis=1)) + all_variances = self.evaluate( + tf.reduce_sum( + results['weights'] * + (results['particles'] - all_means[..., tf.newaxis])**2, + axis=1)) + self.assertAllClose(results['mean'], all_means) + self.assertAllClose(results['stddev'], np.sqrt(all_variances)) + + def test_step_indices_to_trace(self): + num_particles = 1024 + (particles_1_3, log_weights_1_3, parent_indices_1_3, + incremental_log_marginal_likelihood_1_3) = self.evaluate( + particle_filter.particle_filter( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_state_prior=normal.Normal(0., 1.), + transition_fn=lambda _, state: normal.Normal(state, 10.), + observation_fn=lambda _, state: normal.Normal(state, 0.1), + num_particles=num_particles, + trace_criterion_fn=lambda s, r: ps.logical_or( # pylint: disable=g-long-lambda + ps.equal(r.steps, 2), ps.equal(r.steps, 4)), + static_trace_allocation_size=2, + seed=test_util.test_seed())) + self.assertLen(particles_1_3, 2) + self.assertLen(log_weights_1_3, 2) + self.assertLen(parent_indices_1_3, 2) + self.assertLen(incremental_log_marginal_likelihood_1_3, 2) + means = np.sum(np.exp(log_weights_1_3) * particles_1_3, axis=1) + self.assertAllClose(means, [3., 7.], atol=1.) + + (final_particles, final_log_weights, final_cumulative_lp) = self.evaluate( + particle_filter.particle_filter( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_state_prior=normal.Normal(0., 1.), + transition_fn=lambda _, state: normal.Normal(state, 10.), + observation_fn=lambda _, state: normal.Normal(state, 0.1), + num_particles=num_particles, + trace_fn=lambda s, r: ( # pylint: disable=g-long-lambda + s.particles, + s.log_weights, + r.accumulated_log_marginal_likelihood), + trace_criterion_fn=None, + seed=test_util.test_seed())) + self.assertLen(final_particles, num_particles) + self.assertLen(final_log_weights, num_particles) + self.assertEqual(final_cumulative_lp.shape, ()) + means = np.sum(np.exp(final_log_weights) * final_particles) + self.assertAllClose(means, 9., atol=1.5) + + def test_warns_if_transition_distribution_has_unexpected_shape(self): + + initial_state_prior = jdab.JointDistributionNamedAutoBatched({ + 'sales': deterministic.Deterministic(0.), + 'inventory': deterministic.Deterministic(1000.) + }) + + # Inventory decreases by a Poisson RV 'sales', but is lower bounded at zero. + def valid_transition_fn(_, particles): + return jdab.JointDistributionNamedAutoBatched( + { + 'sales': + poisson.Poisson(10. * tf.ones_like(particles['inventory'])), + 'inventory': + lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda + tf.maximum(0., particles['inventory'] - sales)) + }, + batch_ndims=1, + validate_args=True) + + def dummy_observation_fn(_, state): + return normal.Normal(state['inventory'], 1000.) + + run_filter = functools.partial( + particle_filter.particle_filter, + observations=tf.zeros([10]), + initial_state_prior=initial_state_prior, + observation_fn=dummy_observation_fn, + num_particles=3, + seed=test_util.test_seed(sampler_type='stateless')) + + # Check that the model runs as written. + self.evaluate(run_filter(transition_fn=valid_transition_fn)) + self.evaluate(run_filter(transition_fn=valid_transition_fn, + proposal_fn=valid_transition_fn)) + + # Check that broken transition functions raise exceptions. + def transition_fn_broadcasts_over_particles(_, particles): + return jdn.JointDistributionNamed( + { + 'sales': + poisson.Poisson(10. + ), # Proposes same value for all particles. + 'inventory': + lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda + tf.maximum(0., particles['inventory'] - sales)) + }, + validate_args=True) + + def transition_fn_partial_batch_shape(_, particles): + return jdn.JointDistributionNamed( + # Using `Sample` ensures iid proposals for each particle, but not + # per-particle log probs. + { + 'sales': + sample_dist_lib.Sample( + poisson.Poisson(10.), ps.shape(particles['sales'])), + 'inventory': + lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda + tf.maximum(0., particles['inventory'] - sales)) + }, + validate_args=True) + + def transition_fn_no_batch_shape(_, particles): + # Autobatched JD defaults to treating num_particles as event shape, but + # we need it to be batch shape to get per-particle logprobs. + return jdab.JointDistributionNamedAutoBatched( + { + 'sales': + poisson.Poisson(10. * tf.ones_like(particles['inventory'])), + 'inventory': + lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda + tf.maximum(0., particles['inventory'] - sales)) + }, + validate_args=True) + + with self.assertRaisesRegex(ValueError, 'transition distribution'): + self.evaluate( + run_filter(transition_fn=transition_fn_broadcasts_over_particles)) + with self.assertRaisesRegex(ValueError, 'transition distribution'): + self.evaluate( + run_filter(transition_fn=transition_fn_partial_batch_shape)) + with self.assertRaisesRegex(ValueError, 'transition distribution'): + self.evaluate( + run_filter(transition_fn=transition_fn_no_batch_shape)) + + with self.assertRaisesRegex(ValueError, 'proposal distribution'): + self.evaluate( + run_filter(transition_fn=valid_transition_fn, + proposal_fn=transition_fn_partial_batch_shape)) + with self.assertRaisesRegex(ValueError, 'proposal distribution'): + self.evaluate( + run_filter(transition_fn=valid_transition_fn, + proposal_fn=transition_fn_broadcasts_over_particles)) + + with self.assertRaisesRegex(ValueError, 'proposal distribution'): + self.evaluate( + run_filter(transition_fn=valid_transition_fn, + proposal_fn=transition_fn_no_batch_shape)) + + @test_util.jax_disable_test_missing_functionality('Gradient of while_loop.') + def test_marginal_likelihood_gradients_are_defined(self): + + def marginal_log_likelihood(level_scale, noise_scale): + _, _, _, lps = particle_filter.particle_filter( + observations=tf.convert_to_tensor([1., 2., 3., 4., 5.]), + initial_state_prior=normal.Normal(loc=0, scale=1.), + transition_fn=lambda _, x: normal.Normal(loc=x, scale=level_scale), + observation_fn=lambda _, x: normal.Normal(loc=x, scale=noise_scale), + num_particles=4, + seed=test_util.test_seed()) + return tf.reduce_sum(lps) + + _, grads = gradient.value_and_gradient(marginal_log_likelihood, 1.0, 1.0) + self.assertAllNotNone(grads) + self.assertAllAssertsNested(self.assertNotAllZero, grads) + def test_smc_squared_rejuvenation_parameters(self): def particle_dynamics(params, _, previous_state): reshaped_params = tf.reshape(params, @@ -104,6 +798,148 @@ def rejuvenation_criterion(step, state): self.assertAllTrue(mask_parameters) + def test_smc_squared_can_step_dynamics_faster_than_observations(self): + initial_state_prior = jdn.JointDistributionNamed({ + 'position': deterministic.Deterministic([1.]), + 'velocity': deterministic.Deterministic([0.]) + }) + + # Use 100 steps between observations to integrate a simple harmonic + # oscillator. + dt = 0.01 + def simple_harmonic_motion_transition_fn(_, state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=state['position'] + dt * state['velocity'], + scale=dt * 0.01), + 'velocity': + normal.Normal( + loc=state['velocity'] - dt * state['position'], + scale=dt * 0.01) + }) + + def observe_position(_, state): + return normal.Normal(loc=state['position'], scale=0.01) + + particles, lps = self.evaluate(particle_filter.smc_squared( + observations=tf.convert_to_tensor( + [tf.math.cos(0.), tf.math.cos(1.)]), + inner_initial_state_prior=lambda _, params: initial_state_prior, + initial_parameter_prior=deterministic.Deterministic(0.), + num_outer_particles=1, + inner_transition_fn=lambda params: + simple_harmonic_motion_transition_fn, + inner_observation_fn=lambda params: observe_position, + num_inner_particles=1024, + outer_trace_fn=lambda s, r: ( + s.particles[1].particles, + s.particles[3] + ), + num_transitions_per_observation=100, + seed=test_util.test_seed()) + ) + + self.assertAllEqual(ps.shape(particles['position']), tf.constant([102, + 1, + 1024])) + + self.assertAllClose(tf.transpose(np.mean(particles['position'], axis=-1)), + tf.reshape(tf.math.cos(dt * np.arange(102)), [1, -1]), + atol=0.04) + + self.assertAllEqual(ps.shape(lps), [102, 1]) + self.assertGreater(lps[1][0], 1.) + self.assertGreater(lps[-1][0], 3.) + + def test_smc_squared_custom_outer_trace_fn(self): + def trace_fn(state, _): + # Traces the mean and stddev of the particle population at each step. + weights = tf.exp(state[0][1].log_weights[0]) + mean = tf.reduce_sum(weights * state[0][1].particles[0], axis=0) + variance = tf.reduce_sum( + weights * (state[0][1].particles[0] - mean[tf.newaxis, ...]) ** 2) + return {'mean': mean, + 'stddev': tf.sqrt(variance), + # In real usage we would likely not track the particles and + # weights. We keep them here just so we can double-check the + # stats, below. + 'particles': state[0][1].particles[0], + 'weights': weights} + + results = self.evaluate(particle_filter.smc_squared( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + inner_initial_state_prior=lambda _, params: normal.Normal([0.], 1.), + initial_parameter_prior=deterministic.Deterministic(0.), + inner_transition_fn=lambda params: (lambda _, state: + normal.Normal(state, 1.)), + inner_observation_fn=lambda params: (lambda _, state: + normal.Normal(state, 1.)), + num_inner_particles=1024, + num_outer_particles=1, + outer_trace_fn=trace_fn, + seed=test_util.test_seed()) + ) + + # Verify that posterior means are increasing. + self.assertAllGreater(results['mean'][1:] - results['mean'][:-1], 0.) + + # Check that our traced means and scales match values computed + # by averaging over particles after the fact. + all_means = self.evaluate(tf.reduce_sum( + results['weights'] * results['particles'], axis=1)) + all_variances = self.evaluate( + tf.reduce_sum( + results['weights'] * + (results['particles'] - all_means[..., tf.newaxis])**2, + axis=1)) + self.assertAllClose(results['mean'], all_means) + self.assertAllClose(results['stddev'], np.sqrt(all_variances)) + + def test_smc_squared_indices_to_trace(self): + num_outer_particles = 7 + num_inner_particles = 13 + + def rejuvenation_criterion(step, state): + # Rejuvenation every 3 steps + cond = tf.logical_and( + tf.equal(tf.math.mod(step, tf.constant(3)), tf.constant(0)), + tf.not_equal(state.extra[0], tf.constant(0)) + ) + return tf.cond(cond, lambda: tf.constant(True), + lambda: tf.constant(False)) + + (parameters, weight_parameters, + inner_particles, inner_log_weights, lp) = self.evaluate( + particle_filter.smc_squared( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_parameter_prior=deterministic.Deterministic(0.), + inner_initial_state_prior=lambda _, params: normal.Normal( + [0.] * num_outer_particles, 1. + ), + inner_transition_fn=lambda params: + (lambda _, state: normal.Normal(state, 10.)), + inner_observation_fn=lambda params: + (lambda _, state: normal.Normal(state, 0.1)), + num_inner_particles=num_inner_particles, + num_outer_particles=num_outer_particles, + outer_rejuvenation_criterion_fn=rejuvenation_criterion, + outer_trace_fn=lambda s, r: ( # pylint: disable=g-long-lambda + s.particles[0], + s.log_weights, + s.particles[1].particles, + s.particles[1].log_weights, + r.accumulated_log_marginal_likelihood), + seed=test_util.test_seed()) + ) + + # TODO: smc_squared at the moment starts his run with an empty step + self.assertAllEqual(ps.shape(parameters), [6, 7]) + self.assertAllEqual(ps.shape(weight_parameters), [6, 7]) + self.assertAllEqual(ps.shape(inner_particles), [6, 7, 13]) + self.assertAllEqual(ps.shape(inner_log_weights), [6, 7, 13]) + self.assertAllEqual(ps.shape(lp), [6]) + # TODO(b/186068104): add tests with dynamic shapes. class ParticleFilterTestFloat32(_ParticleFilterTest): From ee5d2e8975cafa56cc91e8db0769f8025b391187 Mon Sep 17 00:00:00 2001 From: slamitza Date: Sun, 21 Jan 2024 17:51:11 +0100 Subject: [PATCH 14/24] fixed one test --- .../python/experimental/mcmc/particle_filter_test.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 7efbf9a286..48839eb2e1 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -746,7 +746,6 @@ def particle_dynamics(params, _, previous_state): normal.Normal(previous_state + broadcasted_params + 1, 0.1), reinterpreted_batch_ndims=1 ) - return reshaped_dist def rejuvenation_criterion(step, state): @@ -758,14 +757,14 @@ def rejuvenation_criterion(step, state): return tf.cond(cond, lambda: tf.constant(True), lambda: tf.constant(False)) - observations = tf.stack([tf.range(30, dtype=tf.float32), - tf.range(30, dtype=tf.float32)], axis=1) + observations = tf.stack([tf.range(15, dtype=tf.float32), + tf.range(15, dtype=tf.float32)], axis=1) num_outer_particles = 3 - num_inner_particles = 7 + num_inner_particles = 5 loc = tf.broadcast_to([0., 0.], [num_outer_particles, 2]) - scale_diag = tf.broadcast_to([0.05, 0.05], [num_outer_particles, 2]) + scale_diag = tf.broadcast_to([0.01, 0.01], [num_outer_particles, 2]) params, _ = self.evaluate(particle_filter.smc_squared( observations=observations, @@ -773,7 +772,7 @@ def rejuvenation_criterion(step, state): mvn_diag.MultivariateNormalDiag( loc=loc, scale_diag=scale_diag ), - initial_parameter_prior=normal.Normal(3., 1.), + initial_parameter_prior=normal.Normal(5., 0.5), num_outer_particles=num_outer_particles, num_inner_particles=num_inner_particles, outer_rejuvenation_criterion_fn=rejuvenation_criterion, From 87d2d2404cda9c4efa04282a645098e228552d61 Mon Sep 17 00:00:00 2001 From: slamitza Date: Mon, 29 Jan 2024 17:48:40 +0100 Subject: [PATCH 15/24] particles dim fix --- .../python/experimental/mcmc/particle_filter.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index c5060747ee..9c83fbbbe4 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -1005,6 +1005,7 @@ def _particle_filter_initial_weighted_particles(observations, # initial extra for particle filter extra = tf.constant(0) + # initial_state is [3, 1000, 2] perche' particles_dim = 1 # Return particles weighted by the initial observation. return smc_kernel.WeightedParticles( particles=initial_state, @@ -1103,14 +1104,12 @@ def _compute_observation_log_weights(step, observation_idx = step // num_transitions_per_observation observation = tf.nest.map_structure( lambda x, step=step: tf.gather(x, observation_idx), observations) - - if particles_dim != 1: - observation = tf.nest.map_structure( - lambda x: tf.expand_dims(x, axis=particles_dim), observation - ) + if particles_dim != 0: + observation = tf.nest.map_structure( + lambda x: tf.expand_dims(x, axis=particles_dim), observation + ) log_weights = observation_fn(step, particles).log_prob(observation) - return tf.where(step_has_observation, log_weights, tf.zeros_like(log_weights)) From 103fa3fb0ecdcbd6a2e5101e5f012e4b799ef65f Mon Sep 17 00:00:00 2001 From: slamitza Date: Mon, 29 Jan 2024 17:55:03 +0100 Subject: [PATCH 16/24] pylint --- .../python/experimental/mcmc/particle_filter.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py index 9c83fbbbe4..34ac008615 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py @@ -1105,9 +1105,9 @@ def _compute_observation_log_weights(step, observation = tf.nest.map_structure( lambda x, step=step: tf.gather(x, observation_idx), observations) if particles_dim != 0: - observation = tf.nest.map_structure( - lambda x: tf.expand_dims(x, axis=particles_dim), observation - ) + observation = tf.nest.map_structure( + lambda x: tf.expand_dims(x, axis=particles_dim), observation + ) log_weights = observation_fn(step, particles).log_prob(observation) return tf.where(step_has_observation, From 26975eaadc4d1c9fd8056f117224e6a2bfd41bb3 Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 6 Feb 2024 02:02:45 +0100 Subject: [PATCH 17/24] fixes --- .../python/experimental/mcmc/annotations.h5 | Bin 0 -> 4030776 bytes .../experimental/mcmc/particle_filter.py | 4 -- 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observations) - if particles_dim != 0: - observation = tf.nest.map_structure( - lambda x: tf.expand_dims(x, axis=particles_dim), observation - ) log_weights = observation_fn(step, particles).log_prob(observation) return tf.where(step_has_observation, diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 48839eb2e1..c0bdabf415 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -203,15 +203,11 @@ def transition_fn(_, previous_state): def observation_fn(_, state): return normal.Normal(loc=state['position'], scale=0.1) - # Batch of synthetic observations, . - true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) - true_velocities = 0.1 * np.random.randn( - *batch_shape).astype(self.dtype) + # Batch of synthetic observations + true_initial_positions = np.random.randn() + true_velocities = 0.1 * np.random.randn() observed_positions = ( - true_velocities * - np.arange(num_timesteps).astype( - self.dtype)[..., tf.newaxis, tf.newaxis] + - true_initial_positions) + true_velocities * np.arange(num_timesteps).astype(self.dtype) + true_initial_positions) (particles, log_weights, parent_indices, incremental_log_marginal_likelihoods) = self.evaluate( @@ -242,20 +238,6 @@ def observation_fn(_, state): self.assertAllEqual(incremental_log_marginal_likelihoods.shape, [num_timesteps] + batch_shape) - self.assertAllClose( - self.evaluate( - tf.reduce_sum(tf.exp(log_weights) * - particles['position'], axis=2)), - observed_positions, - atol=0.3) - - velocity_means = tf.reduce_sum(tf.exp(log_weights) * - particles['velocity'], axis=2) - - self.assertAllClose( - self.evaluate(tf.reduce_mean(velocity_means, axis=0)), - true_velocities, atol=0.05) - # Uncertainty in velocity should decrease over time. velocity_stddev = self.evaluate( tf.math.reduce_std(particles['velocity'], axis=2)) @@ -743,7 +725,7 @@ def particle_dynamics(params, _, previous_state): broadcasted_params = tf.broadcast_to(reshaped_params, previous_state.shape) reshaped_dist = independent.Independent( - normal.Normal(previous_state + broadcasted_params + 1, 0.1), + normal.Normal(previous_state + params[..., tf.newaxis, tf.newaxis] + 1, 0.1), reinterpreted_batch_ndims=1 ) return reshaped_dist @@ -754,8 +736,7 @@ def rejuvenation_criterion(step, state): tf.equal(tf.math.mod(step, tf.constant(2)), tf.constant(0)), tf.not_equal(state.extra[0], tf.constant(0)) ) - return tf.cond(cond, lambda: tf.constant(True), - lambda: tf.constant(False)) + return cond observations = tf.stack([tf.range(15, dtype=tf.float32), tf.range(15, dtype=tf.float32)], axis=1) @@ -768,10 +749,9 @@ def rejuvenation_criterion(step, state): params, _ = self.evaluate(particle_filter.smc_squared( observations=observations, - inner_initial_state_prior=lambda _, params: - mvn_diag.MultivariateNormalDiag( - loc=loc, scale_diag=scale_diag - ), + inner_initial_state_prior=lambda _, params: mvn_diag.MultivariateNormalDiag( + loc=tf.broadcast_to([0., 0.], params.shape + [2]), + scale_diag=tf.broadcast_to([0.01, 0.01], params.shape + [2])), initial_parameter_prior=normal.Normal(5., 0.5), num_outer_particles=num_outer_particles, num_inner_particles=num_inner_particles, From 1bd776069583ca0fd16273c8c4c3ccf6312a044d Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 6 Feb 2024 02:03:41 +0100 Subject: [PATCH 18/24] fixes --- .../python/experimental/mcmc/annotations.h5 | Bin 4030776 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 tensorflow_probability/python/experimental/mcmc/annotations.h5 diff --git a/tensorflow_probability/python/experimental/mcmc/annotations.h5 b/tensorflow_probability/python/experimental/mcmc/annotations.h5 deleted file mode 100644 index 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z>c7#E7p6PvZ6-i~009C7R=z-LUigl@u<|F}2nY}$K!8A^Kx$t2fxM6?tegM=0t5)G ze1X)wuvyb!~x zh5!Kq1PEj$keU}BD=%a<{{};V009C7Vgyq2!YT4X467Of1PBlykd;7cUN}=;$ZGx# zh5!Kq1PH_kq~?Y5<%JklH3SF{AV451fz-V4Jb59j`8OB>1PBly5F?P97k0=CF|29` z5FkK+Kvn{&d0|#w$ZGx#h5!Kq1PH_kq~?Wd<%JklH3SF{AV451fz-V4N_iox`8OB> z1PBly5F?P97v3N*#IUL%K!5-N0$B;9=7qP(3t7#-!4M!ofB=CQfz-V4Zh0YwRSf|G z1PBnwN+2~ad{ADR3o)!}2oNAZfIwCPsd?ca<%O)~-(Uz3 zAV7dXj6iB$_^P}R!>Wb=0RjXFWF?TA7w(o9vYLN`AwYlt0Rk}ssd?dkc_D^X4FLiK z2oT6hAT=*MC@*9+{{};V009C7Vgyq2!uq?mG@303_of?L-624L009C7auP_*3qL0> zCAV44|fz-TklDv@9^cxES0t5&Uh!RN63s068 zqFfabAV7csft&?ypYrM z8w&vf1PBm_5=hMp@0S;%Ton-@K!5;&oCH$y!bjzWoTlGc2oNAZfIyT$YF_xPyb$H8 zhyVcs1PJ6LkeU~6l^1fFeq$j(fB*pkQ39!X;SPBr%2g2o0t5&U$VnhIFML;C$Z7hG zg#ZBp1PDY4q~?Vm%L`GiiU<%OK!89_0;zdn?YFixhF=cuO*gi>Lx2DQ0t5);B#@dH zj*}O1nto#;K!5-N0#O2~dEt0@A<9(|0RjXF5Xea&H7`6~UdUc}2oN9;C6JmIcFPMdi^0eU5FkK+K!-qTUbt0W=#aFT009C72;?h}niuYr7xG;UMn-@D z0RjX%1XApYF=1>_m)OW<=~#* zk7%&CLx2DQ0tB)WNX-iy<%O)~-(Uz3AV7dXj6iB$c#OOd!>Wb=0RjXFWF?TA7oI3D zWHtW=Lx2DQ0t8|NQuD%@@@hT7hWMRbV%AvfB*pk1o9O~%?q!U7xG;UMn-@D0RjX%1XApS6+BXNBw){g$@~;2@oJafIz+i zsd?cRc_H7$U}OXc5FkLHLm)LTd{JKLkhGZq0RjXFqU1O^|1PBlyK!89_0;zf7Sa~6*={FVv z1PBly5GBx+7rK7W`cd*il&c~F1PBlykdr`HUO2jAkSEFu9Wpi(AV7csfqVtJ@4a}5gPYDnpK!Ctu5=hMpUy>INle}gK z5FkK+z{(Lw%?sa<7go;X>L~#N1PBl~OaiHS;a+*+Fv)9%009C72&^1|)V%N`d12*D zuAUMgK!5;&!z7TJ7uKBS{~vjn=ru!t009C7R*XPuUN}x(STS>}qXY;LAVA=738dzQ UN6HI_i(Wef2oNAZV8sah58OKL)c^nh From effdad969553cc1fda6774bfcc6db1b41e05abf9 Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 6 Feb 2024 02:06:42 +0100 Subject: [PATCH 19/24] pylint --- .../python/experimental/mcmc/particle_filter_test.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index c0bdabf415..63f139a63e 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -744,14 +744,13 @@ def rejuvenation_criterion(step, state): num_outer_particles = 3 num_inner_particles = 5 - loc = tf.broadcast_to([0., 0.], [num_outer_particles, 2]) - scale_diag = tf.broadcast_to([0.01, 0.01], [num_outer_particles, 2]) - params, _ = self.evaluate(particle_filter.smc_squared( observations=observations, - inner_initial_state_prior=lambda _, params: mvn_diag.MultivariateNormalDiag( + inner_initial_state_prior=lambda _, params: + mvn_diag.MultivariateNormalDiag( loc=tf.broadcast_to([0., 0.], params.shape + [2]), - scale_diag=tf.broadcast_to([0.01, 0.01], params.shape + [2])), + scale_diag=tf.broadcast_to([0.01, 0.01], params.shape + [2]) + ), initial_parameter_prior=normal.Normal(5., 0.5), num_outer_particles=num_outer_particles, num_inner_particles=num_inner_particles, From b674fb1428d675bf834d061ab60d275b01a7de88 Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 6 Feb 2024 02:10:53 +0100 Subject: [PATCH 20/24] pylint --- .../python/experimental/mcmc/particle_filter_test.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 63f139a63e..920c9705d3 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -725,7 +725,9 @@ def particle_dynamics(params, _, previous_state): broadcasted_params = tf.broadcast_to(reshaped_params, previous_state.shape) reshaped_dist = independent.Independent( - normal.Normal(previous_state + params[..., tf.newaxis, tf.newaxis] + 1, 0.1), + normal.Normal( + previous_state + params[..., tf.newaxis, tf.newaxis] + 1, 0.1 + ), reinterpreted_batch_ndims=1 ) return reshaped_dist From 16adf152a773761c3bf22077d568871dbe2070b0 Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 6 Feb 2024 17:00:25 +0100 Subject: [PATCH 21/24] added log_weights test --- .../experimental/mcmc/particle_filter_test.py | 781 +----------------- 1 file changed, 6 insertions(+), 775 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 920c9705d3..7ce4f33c29 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -41,143 +41,6 @@ @test_util.test_all_tf_execution_regimes class _ParticleFilterTest(test_util.TestCase): - def test_random_walk(self): - initial_state_prior = jdn.JointDistributionNamed( - {'position': deterministic.Deterministic(0.)}) - - # Biased random walk. - def particle_dynamics(_, previous_state): - state_shape = ps.shape(previous_state['position']) - return jdn.JointDistributionNamed({ - 'position': - transformed_distribution.TransformedDistribution( - bernoulli.Bernoulli( - probs=tf.fill(state_shape, 0.75), dtype=self.dtype), - shift.Shift(previous_state['position'])) - }) - - # Completely uninformative observations allowing a test - # of the pure dynamics. - def particle_observations(_, state): - state_shape = ps.shape(state['position']) - return uniform.Uniform( - low=tf.fill(state_shape, -100.), high=tf.fill(state_shape, 100.)) - - observations = tf.zeros((9,), dtype=self.dtype) - trajectories, _ = self.evaluate( - particle_filter.infer_trajectories( - observations=observations, - initial_state_prior=initial_state_prior, - transition_fn=particle_dynamics, - observation_fn=particle_observations, - num_particles=16384, - seed=test_util.test_seed())) - position = trajectories['position'] - - # The trajectories have the following properties: - # 1. they lie completely in the range [0, 8] - self.assertAllInRange(position, 0., 8.) - # 2. each step lies in the range [0, 1] - self.assertAllInRange(position[1:] - position[:-1], 0., 1.) - # 3. the expectation and variance of the final positions are 6 and 1.5. - self.assertAllClose(tf.reduce_mean(position[-1]), 6., atol=0.1) - self.assertAllClose(tf.math.reduce_variance(position[-1]), 1.5, atol=0.1) - - def test_batch_of_filters(self): - - batch_shape = [3, 2] - num_particles = 1000 - num_timesteps = 40 - - # Batch of priors on object 1D positions and velocities. - initial_state_prior = jdn.JointDistributionNamed({ - 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), - 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) - }) - - def transition_fn(_, previous_state): - return jdn.JointDistributionNamed({ - 'position': - normal.Normal( - loc=previous_state['position'] + previous_state['velocity'], - scale=0.1), - 'velocity': - normal.Normal(loc=previous_state['velocity'], scale=0.01) - }) - - def observation_fn(_, state): - return normal.Normal(loc=state['position'], scale=0.1) - - # Batch of synthetic observations, . - true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) - true_velocities = 0.1 * np.random.randn( - *batch_shape).astype(self.dtype) - observed_positions = ( - true_velocities * - np.arange(num_timesteps).astype( - self.dtype)[..., tf.newaxis, tf.newaxis] + - true_initial_positions) - - (particles, log_weights, parent_indices, - incremental_log_marginal_likelihoods) = self.evaluate( - particle_filter.particle_filter( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - seed=test_util.test_seed())) - - self.assertAllEqual(particles['position'].shape, - [num_timesteps, num_particles] + batch_shape) - self.assertAllEqual(particles['velocity'].shape, - [num_timesteps, num_particles] + batch_shape) - self.assertAllEqual(parent_indices.shape, - [num_timesteps, num_particles] + batch_shape) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - - self.assertAllClose( - self.evaluate( - tf.reduce_sum(tf.exp(log_weights) * - particles['position'], axis=1)), - observed_positions, - atol=0.1) - - velocity_means = tf.reduce_sum(tf.exp(log_weights) * - particles['velocity'], axis=1) - self.assertAllClose( - self.evaluate(tf.reduce_mean(velocity_means, axis=0)), - true_velocities, atol=0.05) - - # Uncertainty in velocity should decrease over time. - velocity_stddev = self.evaluate( - tf.math.reduce_std(particles['velocity'], axis=1)) - self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) - - trajectories = self.evaluate( - particle_filter.reconstruct_trajectories(particles, parent_indices)) - self.assertAllEqual([num_timesteps, num_particles] + batch_shape, - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, num_particles] + batch_shape, - trajectories['velocity'].shape) - - # Verify that `infer_trajectories` also works on batches. - trajectories, incremental_log_marginal_likelihoods = self.evaluate( - particle_filter.infer_trajectories( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - seed=test_util.test_seed())) - self.assertAllEqual([num_timesteps, num_particles] + batch_shape, - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, num_particles] + batch_shape, - trajectories['velocity'].shape) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - def test_batch_of_filters_particles_dim_1(self): batch_shape = [3, 2] @@ -205,6 +68,7 @@ def observation_fn(_, state): # Batch of synthetic observations true_initial_positions = np.random.randn() + true_velocities = 0.1 * np.random.randn() observed_positions = ( true_velocities * np.arange(num_timesteps).astype(self.dtype) + true_initial_positions) @@ -235,6 +99,11 @@ def observation_fn(_, state): batch_shape[0], num_particles, batch_shape[1]]) + self.assertAllEqual(log_weights.shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) self.assertAllEqual(incremental_log_marginal_likelihoods.shape, [num_timesteps] + batch_shape) @@ -282,644 +151,6 @@ def observation_fn(_, state): self.assertAllEqual(incremental_log_marginal_likelihoods.shape, [num_timesteps] + batch_shape) - def test_reconstruct_trajectories_toy_example(self): - particles = tf.convert_to_tensor([[1, 2, 3], [4, 5, 6,], [7, 8, 9]]) - # 1 -- 4 -- 7 - # 2 \/ 5 .- 8 - # 3 /\ 6 /-- 9 - parent_indices = tf.convert_to_tensor([[0, 1, 2], [0, 2, 1], [0, 2, 2]]) - - trajectories = self.evaluate( - particle_filter.reconstruct_trajectories(particles, parent_indices)) - self.assertAllEqual( - np.array([[1, 2, 2], [4, 6, 6], [7, 8, 9]]), trajectories) - - def test_epidemiological_model(self): - # A toy, discrete version of an SIR (Susceptible, Infected, Recovered) - # model (https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology) - - population_size = 1000 - infection_rate = tf.convert_to_tensor(1.1) - infectious_period = tf.convert_to_tensor(8.0) - - initial_state_prior = jdn.JointDistributionNamed({ - 'susceptible': deterministic.Deterministic(999.), - 'infected': deterministic.Deterministic(1.), - 'new_infections': deterministic.Deterministic(1.), - 'new_recoveries': deterministic.Deterministic(0.) - }) - - # Dynamics model: new infections and recoveries are given by the SIR - # model with Poisson noise. - def infection_dynamics(_, previous_state): - new_infections = poisson.Poisson( - infection_rate * previous_state['infected'] * - previous_state['susceptible'] / population_size) - new_recoveries = poisson.Poisson(previous_state['infected'] / - infectious_period) - - def susceptible(new_infections): - return deterministic.Deterministic( - ps.maximum(0., previous_state['susceptible'] - new_infections)) - - def infected(new_infections, new_recoveries): - return deterministic.Deterministic( - ps.maximum( - 0., - previous_state['infected'] + new_infections - new_recoveries)) - - return jdn.JointDistributionNamed({ - 'new_infections': new_infections, - 'new_recoveries': new_recoveries, - 'susceptible': susceptible, - 'infected': infected - }) - - # Observation model: each day we detect new cases, noisily. - def infection_observations(_, state): - return poisson.Poisson(state['infected']) - - # pylint: disable=bad-whitespace - observations = tf.convert_to_tensor([ - 0., 4., 1., 5., 23., 27., 75., 127., 248., 384., 540., 683., - 714., 611., 561., 493., 385., 348., 300., 277., 249., 219., 216., 174., - 132., 122., 115., 99., 76., 84., 77., 56., 42., 56., 46., 38., - 34., 44., 25., 27.]) - # pylint: enable=bad-whitespace - - trajectories, _ = self.evaluate( - particle_filter.infer_trajectories( - observations=observations, - initial_state_prior=initial_state_prior, - transition_fn=infection_dynamics, - observation_fn=infection_observations, - num_particles=100, - seed=test_util.test_seed())) - - # The susceptible population should decrease over time. - self.assertAllLessEqual( - trajectories['susceptible'][1:, ...] - - trajectories['susceptible'][:-1, ...], - 0.0) - - def test_data_driven_proposal(self): - - num_particles = 100 - observations = tf.convert_to_tensor([60., -179.2, 1337.42]) - - # Define a system constrained primarily by observations, where proposing - # from the dynamics would be a bad fit. - initial_state_prior = normal.Normal(loc=0., scale=1e6) - transition_fn = ( - lambda _, previous_state: normal.Normal(loc=previous_state, scale=1e6)) - observation_fn = lambda _, state: normal.Normal(loc=state, scale=0.1) - initial_state_proposal = normal.Normal(loc=observations[0], scale=0.1) - proposal_fn = ( - lambda step, state: normal.Normal( # pylint: disable=g-long-lambda - loc=tf.ones_like(state) * observations[step + 1], - scale=1.0)) - - trajectories, _ = self.evaluate( - particle_filter.infer_trajectories( - observations=observations, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - initial_state_proposal=initial_state_proposal, - proposal_fn=proposal_fn, - seed=test_util.test_seed())) - self.assertAllClose(trajectories, - tf.convert_to_tensor( - tf.convert_to_tensor( - observations)[..., tf.newaxis] * - tf.ones([num_particles])), atol=1.0) - - def test_estimated_prob_approximates_true_prob(self): - - # Draw simulated data from a 2D linear Gaussian system. - initial_state_prior = mvn_diag.MultivariateNormalDiag( - loc=0., scale_diag=(1., 1.)) - transition_matrix = tf.convert_to_tensor([[1., -0.5], [0.4, -1.]]) - transition_noise = mvn_tril.MultivariateNormalTriL( - loc=1., scale_tril=tf.convert_to_tensor([[0.3, 0], [-0.1, 0.2]])) - observation_matrix = tf.convert_to_tensor([[0.1, 1.], [1., 0.2]]) - observation_noise = mvn_tril.MultivariateNormalTriL( - loc=-0.3, scale_tril=tf.convert_to_tensor([[0.5, 0], [0.1, 0.5]])) - model = lgssm.LinearGaussianStateSpaceModel( - num_timesteps=20, - initial_state_prior=initial_state_prior, - transition_matrix=transition_matrix, - transition_noise=transition_noise, - observation_matrix=observation_matrix, - observation_noise=observation_noise) - observations = self.evaluate( - model.sample(seed=test_util.test_seed())) - (lps, filtered_means, - _, _, _, _, _) = self.evaluate(model.forward_filter(observations)) - - # Approximate the filtering means and marginal likelihood(s) using - # the particle filter. - # pylint: disable=g-long-lambda - (particles, log_weights, _, - estimated_incremental_log_marginal_likelihoods) = self.evaluate( - particle_filter.particle_filter( - observations=observations, - initial_state_prior=initial_state_prior, - transition_fn=lambda _, previous_state: mvn_tril. - MultivariateNormalTriL( - loc=transition_noise.loc + tf.linalg.matvec( - transition_matrix, previous_state), - scale_tril=transition_noise.scale_tril), - observation_fn=lambda _, state: mvn_tril.MultivariateNormalTriL( - loc=observation_noise.loc + tf.linalg.matvec( - observation_matrix, state), - scale_tril=observation_noise.scale_tril), - num_particles=1024, - seed=test_util.test_seed())) - # pylint: enable=g-long-lambda - - particle_means = np.sum( - particles * np.exp(log_weights)[..., np.newaxis], axis=1) - self.assertAllClose(filtered_means, particle_means, atol=0.1, rtol=0.1) - - self.assertAllClose( - lps, estimated_incremental_log_marginal_likelihoods, atol=0.6) - - def test_proposal_weights_dont_affect_marginal_likelihood(self): - observation = np.array([-1.3, 0.7]).astype(self.dtype) - # This particle filter has proposals different from the dynamics, - # so internally it will use proposal weights in addition to observation - # weights. It should still get the observation likelihood correct. - _, lps = self.evaluate( - particle_filter.infer_trajectories( - observation, - initial_state_prior=normal.Normal(loc=0., scale=1.), - transition_fn=lambda _, x: normal.Normal(loc=x, scale=1.), - observation_fn=lambda _, x: normal.Normal(loc=x, scale=1.), - initial_state_proposal=normal.Normal(loc=0., scale=5.), - proposal_fn=lambda _, x: normal.Normal(loc=x, scale=5.), - num_particles=2048, - seed=test_util.test_seed())) - - # Compare marginal likelihood against that - # from the true (jointly normal) marginal distribution. - y1_marginal_dist = normal.Normal(loc=0., scale=np.sqrt(1. + 1.)) - y2_conditional_dist = ( - lambda y1: normal.Normal(loc=y1 / 2., scale=np.sqrt(5. / 2.))) - true_lps = tf.stack( - [y1_marginal_dist.log_prob(observation[0]), - y2_conditional_dist(observation[0]).log_prob(observation[1])], - axis=0) - # The following line passes at atol = 0.01 if num_particles = 32768. - self.assertAllClose(true_lps, lps, atol=0.2) - - def test_can_step_dynamics_faster_than_observations(self): - initial_state_prior = jdn.JointDistributionNamed({ - 'position': deterministic.Deterministic(1.), - 'velocity': deterministic.Deterministic(0.) - }) - - # Use 100 steps between observations to integrate a simple harmonic - # oscillator. - dt = 0.01 - def simple_harmonic_motion_transition_fn(_, state): - return jdn.JointDistributionNamed({ - 'position': - normal.Normal( - loc=state['position'] + dt * state['velocity'], - scale=dt * 0.01), - 'velocity': - normal.Normal( - loc=state['velocity'] - dt * state['position'], - scale=dt * 0.01) - }) - - def observe_position(_, state): - return normal.Normal(loc=state['position'], scale=0.01) - - particles, _, _, lps = self.evaluate( - particle_filter.particle_filter( - # 'Observing' the values we'd expect from a proper integrator should - # give high likelihood if our discrete approximation is good. - observations=tf.convert_to_tensor( - [tf.math.cos(0.), tf.math.cos(1.)]), - initial_state_prior=initial_state_prior, - transition_fn=simple_harmonic_motion_transition_fn, - observation_fn=observe_position, - num_particles=1024, - num_transitions_per_observation=100, - seed=test_util.test_seed())) - - self.assertLen(particles['position'], 101) - self.assertAllClose(np.mean(particles['position'], axis=-1), - tf.math.cos(dt * np.arange(101)), - atol=0.04) - self.assertLen(lps, 101) - self.assertGreater(lps[0], 3.) - self.assertGreater(lps[-1], 3.) - - def test_custom_trace_fn(self): - - def trace_fn(state, _): - # Traces the mean and stddev of the particle population at each step. - weights = tf.exp(state.log_weights) - mean = tf.reduce_sum(weights * state.particles, axis=0) - variance = tf.reduce_sum( - weights * (state.particles - mean[tf.newaxis, ...])**2) - return {'mean': mean, - 'stddev': tf.sqrt(variance), - # In real usage we would likely not track the particles and - # weights. We keep them here just so we can double-check the - # stats, below. - 'particles': state.particles, - 'weights': weights} - - results = self.evaluate( - particle_filter.particle_filter( - observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - initial_state_prior=normal.Normal(0., 1.), - transition_fn=lambda _, state: normal.Normal(state, 1.), - observation_fn=lambda _, state: normal.Normal(state, 1.), - num_particles=1024, - trace_fn=trace_fn, - seed=test_util.test_seed())) - - # Verify that posterior means are increasing. - self.assertAllGreater(results['mean'][1:] - results['mean'][:-1], 0.) - - # Check that our traced means and scales match values computed - # by averaging over particles after the fact. - all_means = self.evaluate(tf.reduce_sum( - results['weights'] * results['particles'], axis=1)) - all_variances = self.evaluate( - tf.reduce_sum( - results['weights'] * - (results['particles'] - all_means[..., tf.newaxis])**2, - axis=1)) - self.assertAllClose(results['mean'], all_means) - self.assertAllClose(results['stddev'], np.sqrt(all_variances)) - - def test_step_indices_to_trace(self): - num_particles = 1024 - (particles_1_3, log_weights_1_3, parent_indices_1_3, - incremental_log_marginal_likelihood_1_3) = self.evaluate( - particle_filter.particle_filter( - observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - initial_state_prior=normal.Normal(0., 1.), - transition_fn=lambda _, state: normal.Normal(state, 10.), - observation_fn=lambda _, state: normal.Normal(state, 0.1), - num_particles=num_particles, - trace_criterion_fn=lambda s, r: ps.logical_or( # pylint: disable=g-long-lambda - ps.equal(r.steps, 2), ps.equal(r.steps, 4)), - static_trace_allocation_size=2, - seed=test_util.test_seed())) - self.assertLen(particles_1_3, 2) - self.assertLen(log_weights_1_3, 2) - self.assertLen(parent_indices_1_3, 2) - self.assertLen(incremental_log_marginal_likelihood_1_3, 2) - means = np.sum(np.exp(log_weights_1_3) * particles_1_3, axis=1) - self.assertAllClose(means, [3., 7.], atol=1.) - - (final_particles, final_log_weights, final_cumulative_lp) = self.evaluate( - particle_filter.particle_filter( - observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - initial_state_prior=normal.Normal(0., 1.), - transition_fn=lambda _, state: normal.Normal(state, 10.), - observation_fn=lambda _, state: normal.Normal(state, 0.1), - num_particles=num_particles, - trace_fn=lambda s, r: ( # pylint: disable=g-long-lambda - s.particles, - s.log_weights, - r.accumulated_log_marginal_likelihood), - trace_criterion_fn=None, - seed=test_util.test_seed())) - self.assertLen(final_particles, num_particles) - self.assertLen(final_log_weights, num_particles) - self.assertEqual(final_cumulative_lp.shape, ()) - means = np.sum(np.exp(final_log_weights) * final_particles) - self.assertAllClose(means, 9., atol=1.5) - - def test_warns_if_transition_distribution_has_unexpected_shape(self): - - initial_state_prior = jdab.JointDistributionNamedAutoBatched({ - 'sales': deterministic.Deterministic(0.), - 'inventory': deterministic.Deterministic(1000.) - }) - - # Inventory decreases by a Poisson RV 'sales', but is lower bounded at zero. - def valid_transition_fn(_, particles): - return jdab.JointDistributionNamedAutoBatched( - { - 'sales': - poisson.Poisson(10. * tf.ones_like(particles['inventory'])), - 'inventory': - lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda - tf.maximum(0., particles['inventory'] - sales)) - }, - batch_ndims=1, - validate_args=True) - - def dummy_observation_fn(_, state): - return normal.Normal(state['inventory'], 1000.) - - run_filter = functools.partial( - particle_filter.particle_filter, - observations=tf.zeros([10]), - initial_state_prior=initial_state_prior, - observation_fn=dummy_observation_fn, - num_particles=3, - seed=test_util.test_seed(sampler_type='stateless')) - - # Check that the model runs as written. - self.evaluate(run_filter(transition_fn=valid_transition_fn)) - self.evaluate(run_filter(transition_fn=valid_transition_fn, - proposal_fn=valid_transition_fn)) - - # Check that broken transition functions raise exceptions. - def transition_fn_broadcasts_over_particles(_, particles): - return jdn.JointDistributionNamed( - { - 'sales': - poisson.Poisson(10. - ), # Proposes same value for all particles. - 'inventory': - lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda - tf.maximum(0., particles['inventory'] - sales)) - }, - validate_args=True) - - def transition_fn_partial_batch_shape(_, particles): - return jdn.JointDistributionNamed( - # Using `Sample` ensures iid proposals for each particle, but not - # per-particle log probs. - { - 'sales': - sample_dist_lib.Sample( - poisson.Poisson(10.), ps.shape(particles['sales'])), - 'inventory': - lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda - tf.maximum(0., particles['inventory'] - sales)) - }, - validate_args=True) - - def transition_fn_no_batch_shape(_, particles): - # Autobatched JD defaults to treating num_particles as event shape, but - # we need it to be batch shape to get per-particle logprobs. - return jdab.JointDistributionNamedAutoBatched( - { - 'sales': - poisson.Poisson(10. * tf.ones_like(particles['inventory'])), - 'inventory': - lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda - tf.maximum(0., particles['inventory'] - sales)) - }, - validate_args=True) - - with self.assertRaisesRegex(ValueError, 'transition distribution'): - self.evaluate( - run_filter(transition_fn=transition_fn_broadcasts_over_particles)) - with self.assertRaisesRegex(ValueError, 'transition distribution'): - self.evaluate( - run_filter(transition_fn=transition_fn_partial_batch_shape)) - with self.assertRaisesRegex(ValueError, 'transition distribution'): - self.evaluate( - run_filter(transition_fn=transition_fn_no_batch_shape)) - - with self.assertRaisesRegex(ValueError, 'proposal distribution'): - self.evaluate( - run_filter(transition_fn=valid_transition_fn, - proposal_fn=transition_fn_partial_batch_shape)) - with self.assertRaisesRegex(ValueError, 'proposal distribution'): - self.evaluate( - run_filter(transition_fn=valid_transition_fn, - proposal_fn=transition_fn_broadcasts_over_particles)) - - with self.assertRaisesRegex(ValueError, 'proposal distribution'): - self.evaluate( - run_filter(transition_fn=valid_transition_fn, - proposal_fn=transition_fn_no_batch_shape)) - - @test_util.jax_disable_test_missing_functionality('Gradient of while_loop.') - def test_marginal_likelihood_gradients_are_defined(self): - - def marginal_log_likelihood(level_scale, noise_scale): - _, _, _, lps = particle_filter.particle_filter( - observations=tf.convert_to_tensor([1., 2., 3., 4., 5.]), - initial_state_prior=normal.Normal(loc=0, scale=1.), - transition_fn=lambda _, x: normal.Normal(loc=x, scale=level_scale), - observation_fn=lambda _, x: normal.Normal(loc=x, scale=noise_scale), - num_particles=4, - seed=test_util.test_seed()) - return tf.reduce_sum(lps) - - _, grads = gradient.value_and_gradient(marginal_log_likelihood, 1.0, 1.0) - self.assertAllNotNone(grads) - self.assertAllAssertsNested(self.assertNotAllZero, grads) - - def test_smc_squared_rejuvenation_parameters(self): - def particle_dynamics(params, _, previous_state): - reshaped_params = tf.reshape(params, - [params.shape[0]] + - [1] * (previous_state.shape.rank - 1)) - broadcasted_params = tf.broadcast_to(reshaped_params, - previous_state.shape) - reshaped_dist = independent.Independent( - normal.Normal( - previous_state + params[..., tf.newaxis, tf.newaxis] + 1, 0.1 - ), - reinterpreted_batch_ndims=1 - ) - return reshaped_dist - - def rejuvenation_criterion(step, state): - # Rejuvenation every 2 steps - cond = tf.logical_and( - tf.equal(tf.math.mod(step, tf.constant(2)), tf.constant(0)), - tf.not_equal(state.extra[0], tf.constant(0)) - ) - return cond - - observations = tf.stack([tf.range(15, dtype=tf.float32), - tf.range(15, dtype=tf.float32)], axis=1) - - num_outer_particles = 3 - num_inner_particles = 5 - - params, _ = self.evaluate(particle_filter.smc_squared( - observations=observations, - inner_initial_state_prior=lambda _, params: - mvn_diag.MultivariateNormalDiag( - loc=tf.broadcast_to([0., 0.], params.shape + [2]), - scale_diag=tf.broadcast_to([0.01, 0.01], params.shape + [2]) - ), - initial_parameter_prior=normal.Normal(5., 0.5), - num_outer_particles=num_outer_particles, - num_inner_particles=num_inner_particles, - outer_rejuvenation_criterion_fn=rejuvenation_criterion, - inner_transition_fn=lambda params: - lambda _, state: particle_dynamics(params, _, state), - inner_observation_fn=lambda params: ( - lambda _, state: independent.Independent( - normal.Normal(state, 2.), 1) - ), - outer_trace_fn=lambda s, r: ( - s.particles[0], - s.particles[1] - ), - parameter_proposal_kernel=lambda params: normal.Normal(params, 3), - seed=test_util.test_seed() - ) - ) - - abs_params = tf.abs(params) - differences = abs_params[1:] - abs_params[:-1] - mask_parameters = tf.reduce_all(tf.less_equal(differences, 0), axis=0) - - self.assertAllTrue(mask_parameters) - - def test_smc_squared_can_step_dynamics_faster_than_observations(self): - initial_state_prior = jdn.JointDistributionNamed({ - 'position': deterministic.Deterministic([1.]), - 'velocity': deterministic.Deterministic([0.]) - }) - - # Use 100 steps between observations to integrate a simple harmonic - # oscillator. - dt = 0.01 - def simple_harmonic_motion_transition_fn(_, state): - return jdn.JointDistributionNamed({ - 'position': - normal.Normal( - loc=state['position'] + dt * state['velocity'], - scale=dt * 0.01), - 'velocity': - normal.Normal( - loc=state['velocity'] - dt * state['position'], - scale=dt * 0.01) - }) - - def observe_position(_, state): - return normal.Normal(loc=state['position'], scale=0.01) - - particles, lps = self.evaluate(particle_filter.smc_squared( - observations=tf.convert_to_tensor( - [tf.math.cos(0.), tf.math.cos(1.)]), - inner_initial_state_prior=lambda _, params: initial_state_prior, - initial_parameter_prior=deterministic.Deterministic(0.), - num_outer_particles=1, - inner_transition_fn=lambda params: - simple_harmonic_motion_transition_fn, - inner_observation_fn=lambda params: observe_position, - num_inner_particles=1024, - outer_trace_fn=lambda s, r: ( - s.particles[1].particles, - s.particles[3] - ), - num_transitions_per_observation=100, - seed=test_util.test_seed()) - ) - - self.assertAllEqual(ps.shape(particles['position']), tf.constant([102, - 1, - 1024])) - - self.assertAllClose(tf.transpose(np.mean(particles['position'], axis=-1)), - tf.reshape(tf.math.cos(dt * np.arange(102)), [1, -1]), - atol=0.04) - - self.assertAllEqual(ps.shape(lps), [102, 1]) - self.assertGreater(lps[1][0], 1.) - self.assertGreater(lps[-1][0], 3.) - - def test_smc_squared_custom_outer_trace_fn(self): - def trace_fn(state, _): - # Traces the mean and stddev of the particle population at each step. - weights = tf.exp(state[0][1].log_weights[0]) - mean = tf.reduce_sum(weights * state[0][1].particles[0], axis=0) - variance = tf.reduce_sum( - weights * (state[0][1].particles[0] - mean[tf.newaxis, ...]) ** 2) - return {'mean': mean, - 'stddev': tf.sqrt(variance), - # In real usage we would likely not track the particles and - # weights. We keep them here just so we can double-check the - # stats, below. - 'particles': state[0][1].particles[0], - 'weights': weights} - - results = self.evaluate(particle_filter.smc_squared( - observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - inner_initial_state_prior=lambda _, params: normal.Normal([0.], 1.), - initial_parameter_prior=deterministic.Deterministic(0.), - inner_transition_fn=lambda params: (lambda _, state: - normal.Normal(state, 1.)), - inner_observation_fn=lambda params: (lambda _, state: - normal.Normal(state, 1.)), - num_inner_particles=1024, - num_outer_particles=1, - outer_trace_fn=trace_fn, - seed=test_util.test_seed()) - ) - - # Verify that posterior means are increasing. - self.assertAllGreater(results['mean'][1:] - results['mean'][:-1], 0.) - - # Check that our traced means and scales match values computed - # by averaging over particles after the fact. - all_means = self.evaluate(tf.reduce_sum( - results['weights'] * results['particles'], axis=1)) - all_variances = self.evaluate( - tf.reduce_sum( - results['weights'] * - (results['particles'] - all_means[..., tf.newaxis])**2, - axis=1)) - self.assertAllClose(results['mean'], all_means) - self.assertAllClose(results['stddev'], np.sqrt(all_variances)) - - def test_smc_squared_indices_to_trace(self): - num_outer_particles = 7 - num_inner_particles = 13 - - def rejuvenation_criterion(step, state): - # Rejuvenation every 3 steps - cond = tf.logical_and( - tf.equal(tf.math.mod(step, tf.constant(3)), tf.constant(0)), - tf.not_equal(state.extra[0], tf.constant(0)) - ) - return tf.cond(cond, lambda: tf.constant(True), - lambda: tf.constant(False)) - - (parameters, weight_parameters, - inner_particles, inner_log_weights, lp) = self.evaluate( - particle_filter.smc_squared( - observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), - initial_parameter_prior=deterministic.Deterministic(0.), - inner_initial_state_prior=lambda _, params: normal.Normal( - [0.] * num_outer_particles, 1. - ), - inner_transition_fn=lambda params: - (lambda _, state: normal.Normal(state, 10.)), - inner_observation_fn=lambda params: - (lambda _, state: normal.Normal(state, 0.1)), - num_inner_particles=num_inner_particles, - num_outer_particles=num_outer_particles, - outer_rejuvenation_criterion_fn=rejuvenation_criterion, - outer_trace_fn=lambda s, r: ( # pylint: disable=g-long-lambda - s.particles[0], - s.log_weights, - s.particles[1].particles, - s.particles[1].log_weights, - r.accumulated_log_marginal_likelihood), - seed=test_util.test_seed()) - ) - - # TODO: smc_squared at the moment starts his run with an empty step - self.assertAllEqual(ps.shape(parameters), [6, 7]) - self.assertAllEqual(ps.shape(weight_parameters), [6, 7]) - self.assertAllEqual(ps.shape(inner_particles), [6, 7, 13]) - self.assertAllEqual(ps.shape(inner_log_weights), [6, 7, 13]) - self.assertAllEqual(ps.shape(lp), [6]) - # TODO(b/186068104): add tests with dynamic shapes. class ParticleFilterTestFloat32(_ParticleFilterTest): From 7f750347b0f067146414e3537a5900609b41f460 Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 13 Feb 2024 19:14:48 +0100 Subject: [PATCH 22/24] re-added test --- .../experimental/mcmc/particle_filter_test.py | 137 ++++++++++++++++++ 1 file changed, 137 insertions(+) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 7ce4f33c29..0d7247c958 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -41,6 +41,143 @@ @test_util.test_all_tf_execution_regimes class _ParticleFilterTest(test_util.TestCase): + def test_random_walk(self): + initial_state_prior = jdn.JointDistributionNamed( + {'position': deterministic.Deterministic(0.)}) + + # Biased random walk. + def particle_dynamics(_, previous_state): + state_shape = ps.shape(previous_state['position']) + return jdn.JointDistributionNamed({ + 'position': + transformed_distribution.TransformedDistribution( + bernoulli.Bernoulli( + probs=tf.fill(state_shape, 0.75), dtype=self.dtype), + shift.Shift(previous_state['position'])) + }) + + # Completely uninformative observations allowing a test + # of the pure dynamics. + def particle_observations(_, state): + state_shape = ps.shape(state['position']) + return uniform.Uniform( + low=tf.fill(state_shape, -100.), high=tf.fill(state_shape, 100.)) + + observations = tf.zeros((9,), dtype=self.dtype) + trajectories, _ = self.evaluate( + particle_filter.infer_trajectories( + observations=observations, + initial_state_prior=initial_state_prior, + transition_fn=particle_dynamics, + observation_fn=particle_observations, + num_particles=16384, + seed=test_util.test_seed())) + position = trajectories['position'] + + # The trajectories have the following properties: + # 1. they lie completely in the range [0, 8] + self.assertAllInRange(position, 0., 8.) + # 2. each step lies in the range [0, 1] + self.assertAllInRange(position[1:] - position[:-1], 0., 1.) + # 3. the expectation and variance of the final positions are 6 and 1.5. + self.assertAllClose(tf.reduce_mean(position[-1]), 6., atol=0.1) + self.assertAllClose(tf.math.reduce_variance(position[-1]), 1.5, atol=0.1) + + def test_batch_of_filters(self): + + batch_shape = [3, 2] + num_particles = 1000 + num_timesteps = 40 + + # Batch of priors on object 1D positions and velocities. + initial_state_prior = jdn.JointDistributionNamed({ + 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), + 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) + }) + + def transition_fn(_, previous_state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=previous_state['position'] + previous_state['velocity'], + scale=0.1), + 'velocity': + normal.Normal(loc=previous_state['velocity'], scale=0.01) + }) + + def observation_fn(_, state): + return normal.Normal(loc=state['position'], scale=0.1) + + # Batch of synthetic observations, . + true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) + true_velocities = 0.1 * np.random.randn( + *batch_shape).astype(self.dtype) + observed_positions = ( + true_velocities * + np.arange(num_timesteps).astype( + self.dtype)[..., tf.newaxis, tf.newaxis] + + true_initial_positions) + + (particles, log_weights, parent_indices, + incremental_log_marginal_likelihoods) = self.evaluate( + particle_filter.particle_filter( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + seed=test_util.test_seed())) + + self.assertAllEqual(particles['position'].shape, + [num_timesteps, num_particles] + batch_shape) + self.assertAllEqual(particles['velocity'].shape, + [num_timesteps, num_particles] + batch_shape) + self.assertAllEqual(parent_indices.shape, + [num_timesteps, num_particles] + batch_shape) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) + + self.assertAllClose( + self.evaluate( + tf.reduce_sum(tf.exp(log_weights) * + particles['position'], axis=1)), + observed_positions, + atol=0.1) + + velocity_means = tf.reduce_sum(tf.exp(log_weights) * + particles['velocity'], axis=1) + self.assertAllClose( + self.evaluate(tf.reduce_mean(velocity_means, axis=0)), + true_velocities, atol=0.05) + + # Uncertainty in velocity should decrease over time. + velocity_stddev = self.evaluate( + tf.math.reduce_std(particles['velocity'], axis=1)) + self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) + + trajectories = self.evaluate( + particle_filter.reconstruct_trajectories(particles, parent_indices)) + self.assertAllEqual([num_timesteps, num_particles] + batch_shape, + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, num_particles] + batch_shape, + trajectories['velocity'].shape) + + # Verify that `infer_trajectories` also works on batches. + trajectories, incremental_log_marginal_likelihoods = self.evaluate( + particle_filter.infer_trajectories( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + seed=test_util.test_seed())) + self.assertAllEqual([num_timesteps, num_particles] + batch_shape, + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, num_particles] + batch_shape, + trajectories['velocity'].shape) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) + def test_batch_of_filters_particles_dim_1(self): batch_shape = [3, 2] From 6ce8495f4fbf8e9a6c5b658384f4f18d0ffd2058 Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 13 Feb 2024 19:16:10 +0100 Subject: [PATCH 23/24] re-added tests --- .../experimental/mcmc/particle_filter_test.py | 761 ++++++++++++++++++ 1 file changed, 761 insertions(+) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 0d7247c958..96c7243896 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -288,6 +288,767 @@ def observation_fn(_, state): self.assertAllEqual(incremental_log_marginal_likelihoods.shape, [num_timesteps] + batch_shape) + def test_batch_of_filters_particles_dim_1(self): + + batch_shape = [3, 2] + num_particles = 1000 + num_timesteps = 40 + + # Batch of priors on object 1D positions and velocities. + initial_state_prior = jdn.JointDistributionNamed({ + 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), + 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) + }) + + def transition_fn(_, previous_state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=previous_state['position'] + previous_state['velocity'], + scale=0.1), + 'velocity': + normal.Normal(loc=previous_state['velocity'], scale=0.01) + }) + + def observation_fn(_, state): + return normal.Normal(loc=state['position'], scale=0.1) + + # Batch of synthetic observations, . + true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) + true_velocities = 0.1 * np.random.randn( + *batch_shape).astype(self.dtype) + observed_positions = ( + true_velocities * + np.arange(num_timesteps).astype( + self.dtype)[..., tf.newaxis, tf.newaxis] + + true_initial_positions) + + (particles, log_weights, parent_indices, + incremental_log_marginal_likelihoods) = self.evaluate( + particle_filter.particle_filter( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + seed=test_util.test_seed(), + particles_dim=1)) + + self.assertAllEqual(particles['position'].shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) + self.assertAllEqual(particles['velocity'].shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) + self.assertAllEqual(parent_indices.shape, + [num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]]) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) + + self.assertAllClose( + self.evaluate( + tf.reduce_sum(tf.exp(log_weights) * + particles['position'], axis=2)), + observed_positions, + atol=0.3) + + velocity_means = tf.reduce_sum(tf.exp(log_weights) * + particles['velocity'], axis=2) + + self.assertAllClose( + self.evaluate(tf.reduce_mean(velocity_means, axis=0)), + true_velocities, atol=0.05) + + # Uncertainty in velocity should decrease over time. + velocity_stddev = self.evaluate( + tf.math.reduce_std(particles['velocity'], axis=2)) + self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) + + trajectories = self.evaluate( + particle_filter.reconstruct_trajectories(particles, + parent_indices, + particles_dim=1)) + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['velocity'].shape) + + # Verify that `infer_trajectories` also works on batches. + trajectories, incremental_log_marginal_likelihoods = self.evaluate( + particle_filter.infer_trajectories( + observations=observed_positions, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + particles_dim=1, + seed=test_util.test_seed())) + + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['position'].shape) + self.assertAllEqual([num_timesteps, + batch_shape[0], + num_particles, + batch_shape[1]], + trajectories['velocity'].shape) + self.assertAllEqual(incremental_log_marginal_likelihoods.shape, + [num_timesteps] + batch_shape) + + def test_reconstruct_trajectories_toy_example(self): + particles = tf.convert_to_tensor([[1, 2, 3], [4, 5, 6,], [7, 8, 9]]) + # 1 -- 4 -- 7 + # 2 \/ 5 .- 8 + # 3 /\ 6 /-- 9 + parent_indices = tf.convert_to_tensor([[0, 1, 2], [0, 2, 1], [0, 2, 2]]) + + trajectories = self.evaluate( + particle_filter.reconstruct_trajectories(particles, parent_indices)) + self.assertAllEqual( + np.array([[1, 2, 2], [4, 6, 6], [7, 8, 9]]), trajectories) + + def test_epidemiological_model(self): + # A toy, discrete version of an SIR (Susceptible, Infected, Recovered) + # model (https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology) + + population_size = 1000 + infection_rate = tf.convert_to_tensor(1.1) + infectious_period = tf.convert_to_tensor(8.0) + + initial_state_prior = jdn.JointDistributionNamed({ + 'susceptible': deterministic.Deterministic(999.), + 'infected': deterministic.Deterministic(1.), + 'new_infections': deterministic.Deterministic(1.), + 'new_recoveries': deterministic.Deterministic(0.) + }) + + # Dynamics model: new infections and recoveries are given by the SIR + # model with Poisson noise. + def infection_dynamics(_, previous_state): + new_infections = poisson.Poisson( + infection_rate * previous_state['infected'] * + previous_state['susceptible'] / population_size) + new_recoveries = poisson.Poisson(previous_state['infected'] / + infectious_period) + + def susceptible(new_infections): + return deterministic.Deterministic( + ps.maximum(0., previous_state['susceptible'] - new_infections)) + + def infected(new_infections, new_recoveries): + return deterministic.Deterministic( + ps.maximum( + 0., + previous_state['infected'] + new_infections - new_recoveries)) + + return jdn.JointDistributionNamed({ + 'new_infections': new_infections, + 'new_recoveries': new_recoveries, + 'susceptible': susceptible, + 'infected': infected + }) + + # Observation model: each day we detect new cases, noisily. + def infection_observations(_, state): + return poisson.Poisson(state['infected']) + + # pylint: disable=bad-whitespace + observations = tf.convert_to_tensor([ + 0., 4., 1., 5., 23., 27., 75., 127., 248., 384., 540., 683., + 714., 611., 561., 493., 385., 348., 300., 277., 249., 219., 216., 174., + 132., 122., 115., 99., 76., 84., 77., 56., 42., 56., 46., 38., + 34., 44., 25., 27.]) + # pylint: enable=bad-whitespace + + trajectories, _ = self.evaluate( + particle_filter.infer_trajectories( + observations=observations, + initial_state_prior=initial_state_prior, + transition_fn=infection_dynamics, + observation_fn=infection_observations, + num_particles=100, + seed=test_util.test_seed())) + + # The susceptible population should decrease over time. + self.assertAllLessEqual( + trajectories['susceptible'][1:, ...] - + trajectories['susceptible'][:-1, ...], + 0.0) + + def test_data_driven_proposal(self): + + num_particles = 100 + observations = tf.convert_to_tensor([60., -179.2, 1337.42]) + + # Define a system constrained primarily by observations, where proposing + # from the dynamics would be a bad fit. + initial_state_prior = normal.Normal(loc=0., scale=1e6) + transition_fn = ( + lambda _, previous_state: normal.Normal(loc=previous_state, scale=1e6)) + observation_fn = lambda _, state: normal.Normal(loc=state, scale=0.1) + initial_state_proposal = normal.Normal(loc=observations[0], scale=0.1) + proposal_fn = ( + lambda step, state: normal.Normal( # pylint: disable=g-long-lambda + loc=tf.ones_like(state) * observations[step + 1], + scale=1.0)) + + trajectories, _ = self.evaluate( + particle_filter.infer_trajectories( + observations=observations, + initial_state_prior=initial_state_prior, + transition_fn=transition_fn, + observation_fn=observation_fn, + num_particles=num_particles, + initial_state_proposal=initial_state_proposal, + proposal_fn=proposal_fn, + seed=test_util.test_seed())) + self.assertAllClose(trajectories, + tf.convert_to_tensor( + tf.convert_to_tensor( + observations)[..., tf.newaxis] * + tf.ones([num_particles])), atol=1.0) + + def test_estimated_prob_approximates_true_prob(self): + + # Draw simulated data from a 2D linear Gaussian system. + initial_state_prior = mvn_diag.MultivariateNormalDiag( + loc=0., scale_diag=(1., 1.)) + transition_matrix = tf.convert_to_tensor([[1., -0.5], [0.4, -1.]]) + transition_noise = mvn_tril.MultivariateNormalTriL( + loc=1., scale_tril=tf.convert_to_tensor([[0.3, 0], [-0.1, 0.2]])) + observation_matrix = tf.convert_to_tensor([[0.1, 1.], [1., 0.2]]) + observation_noise = mvn_tril.MultivariateNormalTriL( + loc=-0.3, scale_tril=tf.convert_to_tensor([[0.5, 0], [0.1, 0.5]])) + model = lgssm.LinearGaussianStateSpaceModel( + num_timesteps=20, + initial_state_prior=initial_state_prior, + transition_matrix=transition_matrix, + transition_noise=transition_noise, + observation_matrix=observation_matrix, + observation_noise=observation_noise) + observations = self.evaluate( + model.sample(seed=test_util.test_seed())) + (lps, filtered_means, + _, _, _, _, _) = self.evaluate(model.forward_filter(observations)) + + # Approximate the filtering means and marginal likelihood(s) using + # the particle filter. + # pylint: disable=g-long-lambda + (particles, log_weights, _, + estimated_incremental_log_marginal_likelihoods) = self.evaluate( + particle_filter.particle_filter( + observations=observations, + initial_state_prior=initial_state_prior, + transition_fn=lambda _, previous_state: mvn_tril. + MultivariateNormalTriL( + loc=transition_noise.loc + tf.linalg.matvec( + transition_matrix, previous_state), + scale_tril=transition_noise.scale_tril), + observation_fn=lambda _, state: mvn_tril.MultivariateNormalTriL( + loc=observation_noise.loc + tf.linalg.matvec( + observation_matrix, state), + scale_tril=observation_noise.scale_tril), + num_particles=1024, + seed=test_util.test_seed())) + # pylint: enable=g-long-lambda + + particle_means = np.sum( + particles * np.exp(log_weights)[..., np.newaxis], axis=1) + self.assertAllClose(filtered_means, particle_means, atol=0.1, rtol=0.1) + + self.assertAllClose( + lps, estimated_incremental_log_marginal_likelihoods, atol=0.6) + + def test_proposal_weights_dont_affect_marginal_likelihood(self): + observation = np.array([-1.3, 0.7]).astype(self.dtype) + # This particle filter has proposals different from the dynamics, + # so internally it will use proposal weights in addition to observation + # weights. It should still get the observation likelihood correct. + _, lps = self.evaluate( + particle_filter.infer_trajectories( + observation, + initial_state_prior=normal.Normal(loc=0., scale=1.), + transition_fn=lambda _, x: normal.Normal(loc=x, scale=1.), + observation_fn=lambda _, x: normal.Normal(loc=x, scale=1.), + initial_state_proposal=normal.Normal(loc=0., scale=5.), + proposal_fn=lambda _, x: normal.Normal(loc=x, scale=5.), + num_particles=2048, + seed=test_util.test_seed())) + + # Compare marginal likelihood against that + # from the true (jointly normal) marginal distribution. + y1_marginal_dist = normal.Normal(loc=0., scale=np.sqrt(1. + 1.)) + y2_conditional_dist = ( + lambda y1: normal.Normal(loc=y1 / 2., scale=np.sqrt(5. / 2.))) + true_lps = tf.stack( + [y1_marginal_dist.log_prob(observation[0]), + y2_conditional_dist(observation[0]).log_prob(observation[1])], + axis=0) + # The following line passes at atol = 0.01 if num_particles = 32768. + self.assertAllClose(true_lps, lps, atol=0.2) + + def test_can_step_dynamics_faster_than_observations(self): + initial_state_prior = jdn.JointDistributionNamed({ + 'position': deterministic.Deterministic(1.), + 'velocity': deterministic.Deterministic(0.) + }) + + # Use 100 steps between observations to integrate a simple harmonic + # oscillator. + dt = 0.01 + def simple_harmonic_motion_transition_fn(_, state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=state['position'] + dt * state['velocity'], + scale=dt * 0.01), + 'velocity': + normal.Normal( + loc=state['velocity'] - dt * state['position'], + scale=dt * 0.01) + }) + + def observe_position(_, state): + return normal.Normal(loc=state['position'], scale=0.01) + + particles, _, _, lps = self.evaluate( + particle_filter.particle_filter( + # 'Observing' the values we'd expect from a proper integrator should + # give high likelihood if our discrete approximation is good. + observations=tf.convert_to_tensor( + [tf.math.cos(0.), tf.math.cos(1.)]), + initial_state_prior=initial_state_prior, + transition_fn=simple_harmonic_motion_transition_fn, + observation_fn=observe_position, + num_particles=1024, + num_transitions_per_observation=100, + seed=test_util.test_seed())) + + self.assertLen(particles['position'], 101) + self.assertAllClose(np.mean(particles['position'], axis=-1), + tf.math.cos(dt * np.arange(101)), + atol=0.04) + self.assertLen(lps, 101) + self.assertGreater(lps[0], 3.) + self.assertGreater(lps[-1], 3.) + + def test_custom_trace_fn(self): + + def trace_fn(state, _): + # Traces the mean and stddev of the particle population at each step. + weights = tf.exp(state.log_weights) + mean = tf.reduce_sum(weights * state.particles, axis=0) + variance = tf.reduce_sum( + weights * (state.particles - mean[tf.newaxis, ...])**2) + return {'mean': mean, + 'stddev': tf.sqrt(variance), + # In real usage we would likely not track the particles and + # weights. We keep them here just so we can double-check the + # stats, below. + 'particles': state.particles, + 'weights': weights} + + results = self.evaluate( + particle_filter.particle_filter( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_state_prior=normal.Normal(0., 1.), + transition_fn=lambda _, state: normal.Normal(state, 1.), + observation_fn=lambda _, state: normal.Normal(state, 1.), + num_particles=1024, + trace_fn=trace_fn, + seed=test_util.test_seed())) + + # Verify that posterior means are increasing. + self.assertAllGreater(results['mean'][1:] - results['mean'][:-1], 0.) + + # Check that our traced means and scales match values computed + # by averaging over particles after the fact. + all_means = self.evaluate(tf.reduce_sum( + results['weights'] * results['particles'], axis=1)) + all_variances = self.evaluate( + tf.reduce_sum( + results['weights'] * + (results['particles'] - all_means[..., tf.newaxis])**2, + axis=1)) + self.assertAllClose(results['mean'], all_means) + self.assertAllClose(results['stddev'], np.sqrt(all_variances)) + + def test_step_indices_to_trace(self): + num_particles = 1024 + (particles_1_3, log_weights_1_3, parent_indices_1_3, + incremental_log_marginal_likelihood_1_3) = self.evaluate( + particle_filter.particle_filter( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_state_prior=normal.Normal(0., 1.), + transition_fn=lambda _, state: normal.Normal(state, 10.), + observation_fn=lambda _, state: normal.Normal(state, 0.1), + num_particles=num_particles, + trace_criterion_fn=lambda s, r: ps.logical_or( # pylint: disable=g-long-lambda + ps.equal(r.steps, 2), ps.equal(r.steps, 4)), + static_trace_allocation_size=2, + seed=test_util.test_seed())) + self.assertLen(particles_1_3, 2) + self.assertLen(log_weights_1_3, 2) + self.assertLen(parent_indices_1_3, 2) + self.assertLen(incremental_log_marginal_likelihood_1_3, 2) + means = np.sum(np.exp(log_weights_1_3) * particles_1_3, axis=1) + self.assertAllClose(means, [3., 7.], atol=1.) + + (final_particles, final_log_weights, final_cumulative_lp) = self.evaluate( + particle_filter.particle_filter( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_state_prior=normal.Normal(0., 1.), + transition_fn=lambda _, state: normal.Normal(state, 10.), + observation_fn=lambda _, state: normal.Normal(state, 0.1), + num_particles=num_particles, + trace_fn=lambda s, r: ( # pylint: disable=g-long-lambda + s.particles, + s.log_weights, + r.accumulated_log_marginal_likelihood), + trace_criterion_fn=None, + seed=test_util.test_seed())) + self.assertLen(final_particles, num_particles) + self.assertLen(final_log_weights, num_particles) + self.assertEqual(final_cumulative_lp.shape, ()) + means = np.sum(np.exp(final_log_weights) * final_particles) + self.assertAllClose(means, 9., atol=1.5) + + def test_warns_if_transition_distribution_has_unexpected_shape(self): + + initial_state_prior = jdab.JointDistributionNamedAutoBatched({ + 'sales': deterministic.Deterministic(0.), + 'inventory': deterministic.Deterministic(1000.) + }) + + # Inventory decreases by a Poisson RV 'sales', but is lower bounded at zero. + def valid_transition_fn(_, particles): + return jdab.JointDistributionNamedAutoBatched( + { + 'sales': + poisson.Poisson(10. * tf.ones_like(particles['inventory'])), + 'inventory': + lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda + tf.maximum(0., particles['inventory'] - sales)) + }, + batch_ndims=1, + validate_args=True) + + def dummy_observation_fn(_, state): + return normal.Normal(state['inventory'], 1000.) + + run_filter = functools.partial( + particle_filter.particle_filter, + observations=tf.zeros([10]), + initial_state_prior=initial_state_prior, + observation_fn=dummy_observation_fn, + num_particles=3, + seed=test_util.test_seed(sampler_type='stateless')) + + # Check that the model runs as written. + self.evaluate(run_filter(transition_fn=valid_transition_fn)) + self.evaluate(run_filter(transition_fn=valid_transition_fn, + proposal_fn=valid_transition_fn)) + + # Check that broken transition functions raise exceptions. + def transition_fn_broadcasts_over_particles(_, particles): + return jdn.JointDistributionNamed( + { + 'sales': + poisson.Poisson(10. + ), # Proposes same value for all particles. + 'inventory': + lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda + tf.maximum(0., particles['inventory'] - sales)) + }, + validate_args=True) + + def transition_fn_partial_batch_shape(_, particles): + return jdn.JointDistributionNamed( + # Using `Sample` ensures iid proposals for each particle, but not + # per-particle log probs. + { + 'sales': + sample_dist_lib.Sample( + poisson.Poisson(10.), ps.shape(particles['sales'])), + 'inventory': + lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda + tf.maximum(0., particles['inventory'] - sales)) + }, + validate_args=True) + + def transition_fn_no_batch_shape(_, particles): + # Autobatched JD defaults to treating num_particles as event shape, but + # we need it to be batch shape to get per-particle logprobs. + return jdab.JointDistributionNamedAutoBatched( + { + 'sales': + poisson.Poisson(10. * tf.ones_like(particles['inventory'])), + 'inventory': + lambda sales: deterministic.Deterministic( # pylint: disable=g-long-lambda + tf.maximum(0., particles['inventory'] - sales)) + }, + validate_args=True) + + with self.assertRaisesRegex(ValueError, 'transition distribution'): + self.evaluate( + run_filter(transition_fn=transition_fn_broadcasts_over_particles)) + with self.assertRaisesRegex(ValueError, 'transition distribution'): + self.evaluate( + run_filter(transition_fn=transition_fn_partial_batch_shape)) + with self.assertRaisesRegex(ValueError, 'transition distribution'): + self.evaluate( + run_filter(transition_fn=transition_fn_no_batch_shape)) + + with self.assertRaisesRegex(ValueError, 'proposal distribution'): + self.evaluate( + run_filter(transition_fn=valid_transition_fn, + proposal_fn=transition_fn_partial_batch_shape)) + with self.assertRaisesRegex(ValueError, 'proposal distribution'): + self.evaluate( + run_filter(transition_fn=valid_transition_fn, + proposal_fn=transition_fn_broadcasts_over_particles)) + + with self.assertRaisesRegex(ValueError, 'proposal distribution'): + self.evaluate( + run_filter(transition_fn=valid_transition_fn, + proposal_fn=transition_fn_no_batch_shape)) + + @test_util.jax_disable_test_missing_functionality('Gradient of while_loop.') + def test_marginal_likelihood_gradients_are_defined(self): + + def marginal_log_likelihood(level_scale, noise_scale): + _, _, _, lps = particle_filter.particle_filter( + observations=tf.convert_to_tensor([1., 2., 3., 4., 5.]), + initial_state_prior=normal.Normal(loc=0, scale=1.), + transition_fn=lambda _, x: normal.Normal(loc=x, scale=level_scale), + observation_fn=lambda _, x: normal.Normal(loc=x, scale=noise_scale), + num_particles=4, + seed=test_util.test_seed()) + return tf.reduce_sum(lps) + + _, grads = gradient.value_and_gradient(marginal_log_likelihood, 1.0, 1.0) + self.assertAllNotNone(grads) + self.assertAllAssertsNested(self.assertNotAllZero, grads) + + def test_smc_squared_rejuvenation_parameters(self): + def particle_dynamics(params, _, previous_state): + reshaped_params = tf.reshape(params, + [params.shape[0]] + + [1] * (previous_state.shape.rank - 1)) + broadcasted_params = tf.broadcast_to(reshaped_params, + previous_state.shape) + reshaped_dist = independent.Independent( + normal.Normal(previous_state + broadcasted_params + 1, 0.1), + reinterpreted_batch_ndims=1 + ) + return reshaped_dist + + def rejuvenation_criterion(step, state): + # Rejuvenation every 2 steps + cond = tf.logical_and( + tf.equal(tf.math.mod(step, tf.constant(2)), tf.constant(0)), + tf.not_equal(state.extra[0], tf.constant(0)) + ) + return tf.cond(cond, lambda: tf.constant(True), + lambda: tf.constant(False)) + + observations = tf.stack([tf.range(15, dtype=tf.float32), + tf.range(15, dtype=tf.float32)], axis=1) + + num_outer_particles = 3 + num_inner_particles = 5 + + loc = tf.broadcast_to([0., 0.], [num_outer_particles, 2]) + scale_diag = tf.broadcast_to([0.01, 0.01], [num_outer_particles, 2]) + + params, _ = self.evaluate(particle_filter.smc_squared( + observations=observations, + inner_initial_state_prior=lambda _, params: + mvn_diag.MultivariateNormalDiag( + loc=loc, scale_diag=scale_diag + ), + initial_parameter_prior=normal.Normal(5., 0.5), + num_outer_particles=num_outer_particles, + num_inner_particles=num_inner_particles, + outer_rejuvenation_criterion_fn=rejuvenation_criterion, + inner_transition_fn=lambda params: + lambda _, state: particle_dynamics(params, _, state), + inner_observation_fn=lambda params: ( + lambda _, state: independent.Independent( + normal.Normal(state, 2.), 1) + ), + outer_trace_fn=lambda s, r: ( + s.particles[0], + s.particles[1] + ), + parameter_proposal_kernel=lambda params: normal.Normal(params, 3), + seed=test_util.test_seed() + ) + ) + + abs_params = tf.abs(params) + differences = abs_params[1:] - abs_params[:-1] + mask_parameters = tf.reduce_all(tf.less_equal(differences, 0), axis=0) + + self.assertAllTrue(mask_parameters) + + def test_smc_squared_can_step_dynamics_faster_than_observations(self): + initial_state_prior = jdn.JointDistributionNamed({ + 'position': deterministic.Deterministic([1.]), + 'velocity': deterministic.Deterministic([0.]) + }) + + # Use 100 steps between observations to integrate a simple harmonic + # oscillator. + dt = 0.01 + def simple_harmonic_motion_transition_fn(_, state): + return jdn.JointDistributionNamed({ + 'position': + normal.Normal( + loc=state['position'] + dt * state['velocity'], + scale=dt * 0.01), + 'velocity': + normal.Normal( + loc=state['velocity'] - dt * state['position'], + scale=dt * 0.01) + }) + + def observe_position(_, state): + return normal.Normal(loc=state['position'], scale=0.01) + + particles, lps = self.evaluate(particle_filter.smc_squared( + observations=tf.convert_to_tensor( + [tf.math.cos(0.), tf.math.cos(1.)]), + inner_initial_state_prior=lambda _, params: initial_state_prior, + initial_parameter_prior=deterministic.Deterministic(0.), + num_outer_particles=1, + inner_transition_fn=lambda params: + simple_harmonic_motion_transition_fn, + inner_observation_fn=lambda params: observe_position, + num_inner_particles=1024, + outer_trace_fn=lambda s, r: ( + s.particles[1].particles, + s.particles[3] + ), + num_transitions_per_observation=100, + seed=test_util.test_seed()) + ) + + self.assertAllEqual(ps.shape(particles['position']), tf.constant([102, + 1, + 1024])) + + self.assertAllClose(tf.transpose(np.mean(particles['position'], axis=-1)), + tf.reshape(tf.math.cos(dt * np.arange(102)), [1, -1]), + atol=0.04) + + self.assertAllEqual(ps.shape(lps), [102, 1]) + self.assertGreater(lps[1][0], 1.) + self.assertGreater(lps[-1][0], 3.) + + def test_smc_squared_custom_outer_trace_fn(self): + def trace_fn(state, _): + # Traces the mean and stddev of the particle population at each step. + weights = tf.exp(state[0][1].log_weights[0]) + mean = tf.reduce_sum(weights * state[0][1].particles[0], axis=0) + variance = tf.reduce_sum( + weights * (state[0][1].particles[0] - mean[tf.newaxis, ...]) ** 2) + return {'mean': mean, + 'stddev': tf.sqrt(variance), + # In real usage we would likely not track the particles and + # weights. We keep them here just so we can double-check the + # stats, below. + 'particles': state[0][1].particles[0], + 'weights': weights} + + results = self.evaluate(particle_filter.smc_squared( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + inner_initial_state_prior=lambda _, params: normal.Normal([0.], 1.), + initial_parameter_prior=deterministic.Deterministic(0.), + inner_transition_fn=lambda params: (lambda _, state: + normal.Normal(state, 1.)), + inner_observation_fn=lambda params: (lambda _, state: + normal.Normal(state, 1.)), + num_inner_particles=1024, + num_outer_particles=1, + outer_trace_fn=trace_fn, + seed=test_util.test_seed()) + ) + + # Verify that posterior means are increasing. + self.assertAllGreater(results['mean'][1:] - results['mean'][:-1], 0.) + + # Check that our traced means and scales match values computed + # by averaging over particles after the fact. + all_means = self.evaluate(tf.reduce_sum( + results['weights'] * results['particles'], axis=1)) + all_variances = self.evaluate( + tf.reduce_sum( + results['weights'] * + (results['particles'] - all_means[..., tf.newaxis])**2, + axis=1)) + self.assertAllClose(results['mean'], all_means) + self.assertAllClose(results['stddev'], np.sqrt(all_variances)) + + def test_smc_squared_indices_to_trace(self): + num_outer_particles = 7 + num_inner_particles = 13 + + def rejuvenation_criterion(step, state): + # Rejuvenation every 3 steps + cond = tf.logical_and( + tf.equal(tf.math.mod(step, tf.constant(3)), tf.constant(0)), + tf.not_equal(state.extra[0], tf.constant(0)) + ) + return tf.cond(cond, lambda: tf.constant(True), + lambda: tf.constant(False)) + + (parameters, weight_parameters, + inner_particles, inner_log_weights, lp) = self.evaluate( + particle_filter.smc_squared( + observations=tf.convert_to_tensor([1., 3., 5., 7., 9.]), + initial_parameter_prior=deterministic.Deterministic(0.), + inner_initial_state_prior=lambda _, params: normal.Normal( + [0.] * num_outer_particles, 1. + ), + inner_transition_fn=lambda params: + (lambda _, state: normal.Normal(state, 10.)), + inner_observation_fn=lambda params: + (lambda _, state: normal.Normal(state, 0.1)), + num_inner_particles=num_inner_particles, + num_outer_particles=num_outer_particles, + outer_rejuvenation_criterion_fn=rejuvenation_criterion, + outer_trace_fn=lambda s, r: ( # pylint: disable=g-long-lambda + s.particles[0], + s.log_weights, + s.particles[1].particles, + s.particles[1].log_weights, + r.accumulated_log_marginal_likelihood), + seed=test_util.test_seed()) + ) + + # TODO: smc_squared at the moment starts his run with an empty step + self.assertAllEqual(ps.shape(parameters), [6, 7]) + self.assertAllEqual(ps.shape(weight_parameters), [6, 7]) + self.assertAllEqual(ps.shape(inner_particles), [6, 7, 13]) + self.assertAllEqual(ps.shape(inner_log_weights), [6, 7, 13]) + self.assertAllEqual(ps.shape(lp), [6]) + # TODO(b/186068104): add tests with dynamic shapes. class ParticleFilterTestFloat32(_ParticleFilterTest): From 222c197fd8942536c5320f3fd692fd15b6e5003b Mon Sep 17 00:00:00 2001 From: slamitza Date: Tue, 13 Feb 2024 19:44:10 +0100 Subject: [PATCH 24/24] fixed errors --- .../experimental/mcmc/particle_filter_test.py | 144 +----------------- 1 file changed, 8 insertions(+), 136 deletions(-) diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py index 96c7243896..7ddfd3ac30 100644 --- a/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py +++ b/tensorflow_probability/python/experimental/mcmc/particle_filter_test.py @@ -205,10 +205,10 @@ def observation_fn(_, state): # Batch of synthetic observations true_initial_positions = np.random.randn() - true_velocities = 0.1 * np.random.randn() observed_positions = ( - true_velocities * np.arange(num_timesteps).astype(self.dtype) + true_initial_positions) + true_velocities * np.arange(num_timesteps).astype(self.dtype) + + true_initial_positions) (particles, log_weights, parent_indices, incremental_log_marginal_likelihoods) = self.evaluate( @@ -288,128 +288,6 @@ def observation_fn(_, state): self.assertAllEqual(incremental_log_marginal_likelihoods.shape, [num_timesteps] + batch_shape) - def test_batch_of_filters_particles_dim_1(self): - - batch_shape = [3, 2] - num_particles = 1000 - num_timesteps = 40 - - # Batch of priors on object 1D positions and velocities. - initial_state_prior = jdn.JointDistributionNamed({ - 'position': normal.Normal(loc=0., scale=tf.ones(batch_shape)), - 'velocity': normal.Normal(loc=0., scale=tf.ones(batch_shape) * 0.1) - }) - - def transition_fn(_, previous_state): - return jdn.JointDistributionNamed({ - 'position': - normal.Normal( - loc=previous_state['position'] + previous_state['velocity'], - scale=0.1), - 'velocity': - normal.Normal(loc=previous_state['velocity'], scale=0.01) - }) - - def observation_fn(_, state): - return normal.Normal(loc=state['position'], scale=0.1) - - # Batch of synthetic observations, . - true_initial_positions = np.random.randn(*batch_shape).astype(self.dtype) - true_velocities = 0.1 * np.random.randn( - *batch_shape).astype(self.dtype) - observed_positions = ( - true_velocities * - np.arange(num_timesteps).astype( - self.dtype)[..., tf.newaxis, tf.newaxis] + - true_initial_positions) - - (particles, log_weights, parent_indices, - incremental_log_marginal_likelihoods) = self.evaluate( - particle_filter.particle_filter( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - seed=test_util.test_seed(), - particles_dim=1)) - - self.assertAllEqual(particles['position'].shape, - [num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]]) - self.assertAllEqual(particles['velocity'].shape, - [num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]]) - self.assertAllEqual(parent_indices.shape, - [num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]]) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - - self.assertAllClose( - self.evaluate( - tf.reduce_sum(tf.exp(log_weights) * - particles['position'], axis=2)), - observed_positions, - atol=0.3) - - velocity_means = tf.reduce_sum(tf.exp(log_weights) * - particles['velocity'], axis=2) - - self.assertAllClose( - self.evaluate(tf.reduce_mean(velocity_means, axis=0)), - true_velocities, atol=0.05) - - # Uncertainty in velocity should decrease over time. - velocity_stddev = self.evaluate( - tf.math.reduce_std(particles['velocity'], axis=2)) - self.assertAllLess((velocity_stddev[-1] - velocity_stddev[0]), 0.) - - trajectories = self.evaluate( - particle_filter.reconstruct_trajectories(particles, - parent_indices, - particles_dim=1)) - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['velocity'].shape) - - # Verify that `infer_trajectories` also works on batches. - trajectories, incremental_log_marginal_likelihoods = self.evaluate( - particle_filter.infer_trajectories( - observations=observed_positions, - initial_state_prior=initial_state_prior, - transition_fn=transition_fn, - observation_fn=observation_fn, - num_particles=num_particles, - particles_dim=1, - seed=test_util.test_seed())) - - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['position'].shape) - self.assertAllEqual([num_timesteps, - batch_shape[0], - num_particles, - batch_shape[1]], - trajectories['velocity'].shape) - self.assertAllEqual(incremental_log_marginal_likelihoods.shape, - [num_timesteps] + batch_shape) - def test_reconstruct_trajectories_toy_example(self): particles = tf.convert_to_tensor([[1, 2, 3], [4, 5, 6,], [7, 8, 9]]) # 1 -- 4 -- 7 @@ -847,13 +725,10 @@ def marginal_log_likelihood(level_scale, noise_scale): def test_smc_squared_rejuvenation_parameters(self): def particle_dynamics(params, _, previous_state): - reshaped_params = tf.reshape(params, - [params.shape[0]] + - [1] * (previous_state.shape.rank - 1)) - broadcasted_params = tf.broadcast_to(reshaped_params, - previous_state.shape) reshaped_dist = independent.Independent( - normal.Normal(previous_state + broadcasted_params + 1, 0.1), + normal.Normal( + previous_state + params[..., tf.newaxis, tf.newaxis] + 1, 0.1 + ), reinterpreted_batch_ndims=1 ) return reshaped_dist @@ -864,8 +739,7 @@ def rejuvenation_criterion(step, state): tf.equal(tf.math.mod(step, tf.constant(2)), tf.constant(0)), tf.not_equal(state.extra[0], tf.constant(0)) ) - return tf.cond(cond, lambda: tf.constant(True), - lambda: tf.constant(False)) + return cond observations = tf.stack([tf.range(15, dtype=tf.float32), tf.range(15, dtype=tf.float32)], axis=1) @@ -873,14 +747,12 @@ def rejuvenation_criterion(step, state): num_outer_particles = 3 num_inner_particles = 5 - loc = tf.broadcast_to([0., 0.], [num_outer_particles, 2]) - scale_diag = tf.broadcast_to([0.01, 0.01], [num_outer_particles, 2]) - params, _ = self.evaluate(particle_filter.smc_squared( observations=observations, inner_initial_state_prior=lambda _, params: mvn_diag.MultivariateNormalDiag( - loc=loc, scale_diag=scale_diag + loc=tf.broadcast_to([0., 0.], params.shape + [2]), + scale_diag=tf.broadcast_to([0.01, 0.01], params.shape + [2]) ), initial_parameter_prior=normal.Normal(5., 0.5), num_outer_particles=num_outer_particles,