Hierarchical partial pooling with tfprobability

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Hierarchical partial pooling with tfprobability


Earlier than we leap into the technicalities: This put up is, after all, devoted to McElreath who wrote one in all most intriguing books on Bayesian (or ought to we simply say – scientific?) modeling we’re conscious of. In the event you haven’t learn Statistical Rethinking, and are curious about modeling, you would possibly positively need to test it out. On this put up, we’re not going to attempt to re-tell the story: Our clear focus will, as an alternative, be an illustration of the best way to do MCMC with tfprobability.

Concretely, this put up has two elements. The primary is a fast overview of the best way to use tfd_joint_sequential_distribution to assemble a mannequin, after which pattern from it utilizing Hamiltonian Monte Carlo. This half may be consulted for fast code look-up, or as a frugal template of the entire course of.
The second half then walks by a multi-level mannequin in additional element, displaying the best way to extract, post-process and visualize sampling in addition to diagnostic outputs.

Reedfrogs

The info comes with the rethinking bundle.

'knowledge.body':   48 obs. of  5 variables:
 $ density : int  10 10 10 10 10 10 10 10 10 10 ...
 $ pred    : Issue w/ 2 ranges "no","pred": 1 1 1 1 1 1 1 1 2 2 ...
 $ measurement    : Issue w/ 2 ranges "large","small": 1 1 1 1 2 2 2 2 1 1 ...
 $ surv    : int  9 10 7 10 9 9 10 9 4 9 ...
 $ propsurv: num  0.9 1 0.7 1 0.9 0.9 1 0.9 0.4 0.9 ...

The duty is modeling survivor counts amongst tadpoles, the place tadpoles are held in tanks of various sizes (equivalently, completely different numbers of inhabitants). Every row within the dataset describes one tank, with its preliminary rely of inhabitants (density) and variety of survivors (surv).
Within the technical overview half, we construct a easy unpooled mannequin that describes each tank in isolation. Then, within the detailed walk-through, we’ll see the best way to assemble a various intercepts mannequin that permits for data sharing between tanks.

Establishing fashions with tfd_joint_distribution_sequential

tfd_joint_distribution_sequential represents a mannequin as a listing of conditional distributions.
That is best to see on an actual instance, so we’ll leap proper in, creating an unpooled mannequin of the tadpole knowledge.

That is the how the mannequin specification would look in Stan:

mannequin{
    vector[48] p;
    a ~ regular( 0 , 1.5 );
    for ( i in 1:48 ) {
        p[i] = a[tank[i]];
        p[i] = inv_logit(p[i]);
    }
    S ~ binomial( N , p );
}

And right here is tfd_joint_distribution_sequential:

library(tensorflow)

# ensure you have at the least model 0.7 of TensorFlow Likelihood 
# as of this writing, it's required of set up the grasp department:
# install_tensorflow(model = "nightly")
library(tfprobability)

n_tadpole_tanks <- nrow(d)
n_surviving <- d$surv
n_start <- d$density

m1 <- tfd_joint_distribution_sequential(
  record(
    # regular prior of per-tank logits
    tfd_multivariate_normal_diag(
      loc = rep(0, n_tadpole_tanks),
      scale_identity_multiplier = 1.5),
    # binomial distribution of survival counts
    operate(l)
      tfd_independent(
        tfd_binomial(total_count = n_start, logits = l),
        reinterpreted_batch_ndims = 1
      )
  )
)

The mannequin consists of two distributions: Prior means and variances for the 48 tadpole tanks are specified by tfd_multivariate_normal_diag; then tfd_binomial generates survival counts for every tank.
Be aware how the primary distribution is unconditional, whereas the second will depend on the primary. Be aware too how the second must be wrapped in tfd_independent to keep away from improper broadcasting. (That is a facet of tfd_joint_distribution_sequential utilization that deserves to be documented extra systematically, which is definitely going to occur. Simply suppose that this performance was added to TFP grasp solely three weeks in the past!)

As an apart, the mannequin specification right here finally ends up shorter than in Stan as tfd_binomial optionally takes logits as parameters.

As with each TFP distribution, you are able to do a fast performance test by sampling from the mannequin:

# pattern a batch of two values 
# we get samples for each distribution within the mannequin
s <- m1 %>% tfd_sample(2)
[[1]]
Tensor("MultivariateNormalDiag/pattern/affine_linear_operator/ahead/add:0",
form=(2, 48), dtype=float32)

[[2]]
Tensor("IndependentJointDistributionSequential/pattern/Beta/pattern/Reshape:0",
form=(2, 48), dtype=float32)

and computing log chances:

# we must always get solely the general log likelihood of the mannequin
m1 %>% tfd_log_prob(s)
t[[1]]
Tensor("MultivariateNormalDiag/pattern/affine_linear_operator/ahead/add:0",
form=(2, 48), dtype=float32)

[[2]]
Tensor("IndependentJointDistributionSequential/pattern/Beta/pattern/Reshape:0",
form=(2, 48), dtype=float32)

Now, let’s see how we are able to pattern from this mannequin utilizing Hamiltonian Monte Carlo.

Operating Hamiltonian Monte Carlo in TFP

We outline a Hamiltonian Monte Carlo kernel with dynamic step measurement adaptation based mostly on a desired acceptance likelihood.

# variety of steps to run burnin
n_burnin <- 500

# optimization goal is the chance of the logits given the information
logprob <- operate(l)
  m1 %>% tfd_log_prob(record(l, n_surviving))

hmc <- mcmc_hamiltonian_monte_carlo(
  target_log_prob_fn = logprob,
  num_leapfrog_steps = 3,
  step_size = 0.1,
) %>%
  mcmc_simple_step_size_adaptation(
    target_accept_prob = 0.8,
    num_adaptation_steps = n_burnin
  )

We then run the sampler, passing in an preliminary state. If we need to run (n) chains, that state must be of size (n), for each parameter within the mannequin (right here we’ve got only one).

The sampling operate, mcmc_sample_chain, might optionally be handed a trace_fn that tells TFP which sorts of meta data to avoid wasting. Right here we save acceptance ratios and step sizes.

# variety of steps after burnin
n_steps <- 500
# variety of chains
n_chain <- 4

# get beginning values for the parameters
# their form implicitly determines the variety of chains we are going to run
# see current_state parameter handed to mcmc_sample_chain beneath
c(initial_logits, .) %<-% (m1 %>% tfd_sample(n_chain))

# inform TFP to maintain monitor of acceptance ratio and step measurement
trace_fn <- operate(state, pkr) {
  record(pkr$inner_results$is_accepted,
       pkr$inner_results$accepted_results$step_size)
}

res <- hmc %>% mcmc_sample_chain(
  num_results = n_steps,
  num_burnin_steps = n_burnin,
  current_state = initial_logits,
  trace_fn = trace_fn
)

When sampling is completed, we are able to entry the samples as res$all_states:

mcmc_trace <- res$all_states
mcmc_trace
Tensor("mcmc_sample_chain/trace_scan/TensorArrayStack/TensorArrayGatherV3:0",
form=(500, 4, 48), dtype=float32)

That is the form of the samples for l, the 48 per-tank logits: 500 samples occasions 4 chains occasions 48 parameters.

From these samples, we are able to compute efficient pattern measurement and (rhat) (alias mcmc_potential_scale_reduction):

# Tensor("Imply:0", form=(48,), dtype=float32)
ess <- mcmc_effective_sample_size(mcmc_trace) %>% tf$reduce_mean(axis = 0L)

# Tensor("potential_scale_reduction/potential_scale_reduction_single_state/sub_1:0", form=(48,), dtype=float32)
rhat <- mcmc_potential_scale_reduction(mcmc_trace)

Whereas diagnostic data is on the market in res$hint:

# Tensor("mcmc_sample_chain/trace_scan/TensorArrayStack_1/TensorArrayGatherV3:0",
# form=(500, 4), dtype=bool)
is_accepted <- res$hint[[1]] 

# Tensor("mcmc_sample_chain/trace_scan/TensorArrayStack_2/TensorArrayGatherV3:0",
# form=(500,), dtype=float32)
step_size <- res$hint[[2]]

After this fast define, let’s transfer on to the subject promised within the title: multi-level modeling, or partial pooling. This time, we’ll additionally take a more in-depth take a look at sampling outcomes and diagnostic outputs.

Multi-level tadpoles

The multi-level mannequin – or various intercepts mannequin, on this case: we’ll get to various slopes in a later put up – provides a hyperprior to the mannequin. As a substitute of deciding on a imply and variance of the traditional prior the logits are drawn from, we let the mannequin be taught means and variances for particular person tanks.
These per-tank means, whereas being priors for the binomial logits, are assumed to be usually distributed, and are themselves regularized by a traditional prior for the imply and an exponential prior for the variance.

For the Stan-savvy, right here is the Stan formulation of this mannequin.

record(
    # a_bar, the prior for the imply of the traditional distribution of per-tank logits
    tfd_normal(loc = 0, scale = 1.5),
    # sigma, the prior for the variance of the traditional distribution of per-tank logits
    tfd_exponential(price = 1),
    # regular distribution of per-tank logits
    # parameters sigma and a_bar confer with the outputs of the above two distributions
    operate(sigma, a_bar) 
      tfd_sample_distribution(
        tfd_normal(loc = a_bar, scale = sigma),
        sample_shape = record(n_tadpole_tanks)
      ), 
    # binomial distribution of survival counts
    # parameter l refers back to the output of the traditional distribution instantly above
    operate(l)
      tfd_independent(
        tfd_binomial(total_count = n_start, logits = l),
        reinterpreted_batch_ndims = 1
      )
  )
)

Technically, dependencies in tfd_joint_distribution_sequential are outlined through spatial proximity within the record: Within the discovered prior for the logits

operate(sigma, a_bar) 
      tfd_sample_distribution(
        tfd_normal(loc = a_bar, scale = sigma),
        sample_shape = record(n_tadpole_tanks)
      )

sigma refers back to the distribution instantly above, and a_bar to the one above that.

Analogously, within the distribution of survival counts

operate(l)
      tfd_independent(
        tfd_binomial(total_count = n_start, logits = l),
        reinterpreted_batch_ndims = 1
      )

l refers back to the distribution instantly previous its personal definition.

Once more, let’s pattern from this mannequin to see if shapes are appropriate.

s <- m2 %>% tfd_sample(2)
s

They’re.

[[1]]
Tensor("Regular/sample_1/Reshape:0", form=(2,), dtype=float32)

[[2]]
Tensor("Exponential/sample_1/Reshape:0", form=(2,), dtype=float32)

[[3]]
Tensor("SampleJointDistributionSequential/sample_1/Regular/pattern/Reshape:0",
form=(2, 48), dtype=float32)

[[4]]
Tensor("IndependentJointDistributionSequential/sample_1/Beta/pattern/Reshape:0",
form=(2, 48), dtype=float32)

And to verify we get one total log_prob per batch:

Tensor("JointDistributionSequential/log_prob/add_3:0", form=(2,), dtype=float32)

Coaching this mannequin works like earlier than, besides that now the preliminary state contains three parameters, a_bar, sigma and l:

c(initial_a, initial_s, initial_logits, .) %<-% (m2 %>% tfd_sample(n_chain))

Right here is the sampling routine:

# the joint log likelihood now's based mostly on three parameters
logprob <- operate(a, s, l)
  m2 %>% tfd_log_prob(record(a, s, l, n_surviving))

hmc <- mcmc_hamiltonian_monte_carlo(
  target_log_prob_fn = logprob,
  num_leapfrog_steps = 3,
  # one step measurement for every parameter
  step_size = record(0.1, 0.1, 0.1),
) %>%
  mcmc_simple_step_size_adaptation(target_accept_prob = 0.8,
                                   num_adaptation_steps = n_burnin)

run_mcmc <- operate(kernel) {
  kernel %>% mcmc_sample_chain(
    num_results = n_steps,
    num_burnin_steps = n_burnin,
    current_state = record(initial_a, tf$ones_like(initial_s), initial_logits),
    trace_fn = trace_fn
  )
}

res <- hmc %>% run_mcmc()
 
mcmc_trace <- res$all_states

This time, mcmc_trace is a listing of three: We’ve got

[[1]]
Tensor("mcmc_sample_chain/trace_scan/TensorArrayStack/TensorArrayGatherV3:0",
form=(500, 4), dtype=float32)

[[2]]
Tensor("mcmc_sample_chain/trace_scan/TensorArrayStack_1/TensorArrayGatherV3:0",
form=(500, 4), dtype=float32)

[[3]]
Tensor("mcmc_sample_chain/trace_scan/TensorArrayStack_2/TensorArrayGatherV3:0",
form=(500, 4, 48), dtype=float32)

Now let’s create graph nodes for the outcomes and data we’re curious about.

# as above, that is the uncooked consequence
mcmc_trace_ <- res$all_states

# we carry out some reshaping operations immediately in tensorflow
all_samples_ <-
  tf$concat(
    record(
      mcmc_trace_[[1]] %>% tf$expand_dims(axis = -1L),
      mcmc_trace_[[2]]  %>% tf$expand_dims(axis = -1L),
      mcmc_trace_[[3]]
    ),
    axis = -1L
  ) %>%
  tf$reshape(record(2000L, 50L))

# diagnostics, additionally as above
is_accepted_ <- res$hint[[1]]
step_size_ <- res$hint[[2]]

# efficient pattern measurement
# once more we use tensorflow to get conveniently formed outputs
ess_ <- mcmc_effective_sample_size(mcmc_trace) 
ess_ <- tf$concat(
  record(
    ess_[[1]] %>% tf$expand_dims(axis = -1L),
    ess_[[2]]  %>% tf$expand_dims(axis = -1L),
    ess_[[3]]
  ),
  axis = -1L
) 

# rhat, conveniently post-processed
rhat_ <- mcmc_potential_scale_reduction(mcmc_trace)
rhat_ <- tf$concat(
  record(
    rhat_[[1]] %>% tf$expand_dims(axis = -1L),
    rhat_[[2]]  %>% tf$expand_dims(axis = -1L),
    rhat_[[3]]
  ),
  axis = -1L
)

And we’re prepared to really run the chains.

# thus far, no sampling has been finished!
# the precise sampling occurs after we create a Session 
# and run the above-defined nodes
sess <- tf$Session()
eval <- operate(...) sess$run(record(...))

c(mcmc_trace, all_samples, is_accepted, step_size, ess, rhat) %<-%
  eval(mcmc_trace_, all_samples_, is_accepted_, step_size_, ess_, rhat_)

This time, let’s truly examine these outcomes.

Multi-level tadpoles: Outcomes

First, how do the chains behave?

Hint plots

Extract the samples for a_bar and sigma, in addition to one of many discovered priors for the logits:

Right here’s a hint plot for a_bar:

prep_tibble <- operate(samples) {
  as_tibble(samples, .name_repair = ~ c("chain_1", "chain_2", "chain_3", "chain_4")) %>% 
    add_column(pattern = 1:500) %>%
    collect(key = "chain", worth = "worth", -pattern)
}

plot_trace <- operate(samples, param_name) {
  prep_tibble(samples) %>% 
    ggplot(aes(x = pattern, y = worth, coloration = chain)) +
    geom_line() + 
    ggtitle(param_name)
}

plot_trace(a_bar, "a_bar")

And right here for sigma and a_1:

How in regards to the posterior distributions of the parameters, initially, the various intercepts a_1 … a_48?

Posterior distributions

plot_posterior <- operate(samples) {
  prep_tibble(samples) %>% 
    ggplot(aes(x = worth, coloration = chain)) +
    geom_density() +
    theme_classic() +
    theme(legend.place = "none",
          axis.title = element_blank(),
          axis.textual content = element_blank(),
          axis.ticks = element_blank())
    
}

plot_posteriors <- operate(sample_array, num_params) {
  plots <- purrr::map(1:num_params, ~ plot_posterior(sample_array[ , , .x] %>% as.matrix()))
  do.name(grid.prepare, plots)
}

plot_posteriors(mcmc_trace[[3]], dim(mcmc_trace[[3]])[3])

Now let’s see the corresponding posterior means and highest posterior density intervals.
(The beneath code consists of the hyperpriors in abstract as we’ll need to show a whole summary-like output quickly.)

Posterior means and HPDIs

all_samples <- all_samples %>%
  as_tibble(.name_repair = ~ c("a_bar", "sigma", paste0("a_", 1:48))) 

means <- all_samples %>% 
  summarise_all(record (~ imply)) %>% 
  collect(key = "key", worth = "imply")

sds <- all_samples %>% 
  summarise_all(record (~ sd)) %>% 
  collect(key = "key", worth = "sd")

hpdis <-
  all_samples %>%
  summarise_all(record(~ record(hdi(.) %>% t() %>% as_tibble()))) %>% 
  unnest() 

hpdis_lower <- hpdis %>% choose(-incorporates("higher")) %>%
  rename(lower0 = decrease) %>%
  collect(key = "key", worth = "decrease") %>% 
  prepare(as.integer(str_sub(key, 6))) %>%
  mutate(key = c("a_bar", "sigma", paste0("a_", 1:48)))

hpdis_upper <- hpdis %>% choose(-incorporates("decrease")) %>%
  rename(upper0 = higher) %>%
  collect(key = "key", worth = "higher") %>% 
  prepare(as.integer(str_sub(key, 6))) %>%
  mutate(key = c("a_bar", "sigma", paste0("a_", 1:48)))

abstract <- means %>% 
  inner_join(sds, by = "key") %>% 
  inner_join(hpdis_lower, by = "key") %>%
  inner_join(hpdis_upper, by = "key")


abstract %>% 
  filter(!key %in% c("a_bar", "sigma")) %>%
  mutate(key_fct = issue(key, ranges = distinctive(key))) %>%
  ggplot(aes(x = key_fct, y = imply, ymin = decrease, ymax = higher)) +
   geom_pointrange() + 
   coord_flip() +  
   xlab("") + ylab("put up. imply and HPDI") +
   theme_minimal()

Now for an equal to summary. We already computed means, normal deviations and the HPDI interval.
Let’s add n_eff, the efficient variety of samples, and rhat, the Gelman-Rubin statistic.

Complete abstract (a.okay.a. “summary”)

is_accepted <- is_accepted %>% as.integer() %>% imply()
step_size <- purrr::map(step_size, imply)

ess <- apply(ess, 2, imply)

summary_with_diag <- abstract %>% add_column(ess = ess, rhat = rhat)
summary_with_diag
# A tibble: 50 x 7
   key    imply    sd  decrease higher   ess  rhat
   <chr> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
 1 a_bar  1.35 0.266  0.792  1.87 405.   1.00
 2 sigma  1.64 0.218  1.23   2.05  83.6  1.00
 3 a_1    2.14 0.887  0.451  3.92  33.5  1.04
 4 a_2    3.16 1.13   1.09   5.48  23.7  1.03
 5 a_3    1.01 0.698 -0.333  2.31  65.2  1.02
 6 a_4    3.02 1.04   1.06   5.05  31.1  1.03
 7 a_5    2.11 0.843  0.625  3.88  49.0  1.05
 8 a_6    2.06 0.904  0.496  3.87  39.8  1.03
 9 a_7    3.20 1.27   1.11   6.12  14.2  1.02
10 a_8    2.21 0.894  0.623  4.18  44.7  1.04
# ... with 40 extra rows

For the various intercepts, efficient pattern sizes are fairly low, indicating we’d need to examine doable causes.

Let’s additionally show posterior survival chances, analogously to determine 13.2 within the guide.

Posterior survival chances

sim_tanks <- rnorm(8000, a_bar, sigma)
tibble(x = sim_tanks) %>% ggplot(aes(x = x)) + geom_density() + xlab("distribution of per-tank logits")

# our ordinary sigmoid by one other identify (undo the logit)
logistic <- operate(x) 1/(1 + exp(-x))
probs <- map_dbl(sim_tanks, logistic)
tibble(x = probs) %>% ggplot(aes(x = x)) + geom_density() + xlab("likelihood of survival")

Lastly, we need to be certain we see the shrinkage habits displayed in determine 13.1 within the guide.

Shrinkage

abstract %>% 
  filter(!key %in% c("a_bar", "sigma")) %>%
  choose(key, imply) %>%
  mutate(est_survival = logistic(imply)) %>%
  add_column(act_survival = d$propsurv) %>%
  choose(-imply) %>%
  collect(key = "kind", worth = "worth", -key) %>%
  ggplot(aes(x = key, y = worth, coloration = kind)) +
  geom_point() +
  geom_hline(yintercept = imply(d$propsurv), measurement = 0.5, coloration = "cyan" ) +
  xlab("") +
  ylab("") +
  theme_minimal() +
  theme(axis.textual content.x = element_blank())

We see outcomes comparable in spirit to McElreath’s: estimates are shrunken to the imply (the cyan-colored line). Additionally, shrinkage appears to be extra energetic in smaller tanks, that are the lower-numbered ones on the left of the plot.

Outlook

On this put up, we noticed the best way to assemble a various intercepts mannequin with tfprobability, in addition to the best way to extract sampling outcomes and related diagnostics. In an upcoming put up, we’ll transfer on to various slopes.
With non-negligible likelihood, our instance will construct on one in all Mc Elreath’s once more…
Thanks for studying!

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