# Hierarchical partial pooling with tfprobability

Earlier than we soar into the technicalities: This publish 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. Should you haven’t learn Statistical Rethinking, and are enthusiastic about modeling, you may undoubtedly wish to test it out. On this publish, we’re not going to attempt to re-tell the story: Our clear focus will, as an alternative, be an illustration of methods to do MCMC with tfprobability.

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

## Reedfrogs

The information 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, totally different numbers of inhabitants). Every row within the dataset describes one tank, with its preliminary depend 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 methods to assemble a various intercepts mannequin that permits for info sharing between tanks.

## Establishing fashions with `tfd_joint_distribution_sequential`

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

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

``````mannequin{
vector 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)

# be sure you have not less than 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_surviving <- d\$surv
n_start <- d\$density

m1 <- tfd_joint_distribution_sequential(
listing(
# 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
perform(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. Notice how the primary distribution is unconditional, whereas the second relies on the primary. Notice too how the second must be wrapped in `tfd_independent` to keep away from fallacious broadcasting. (That is a side of `tfd_joint_distribution_sequential` utilization that deserves to be documented extra systematically, which is unquestionably going to occur. Simply assume 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 examine 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)``````
``````[]
form=(2, 48), dtype=float32)

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

and computing log chances:

``````# we should always get solely the general log chance of the mannequin
m1 %>% tfd_log_prob(s)``````
``````t[]
form=(2, 48), dtype=float32)

[]
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.

## Working Hamiltonian Monte Carlo in TFP

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

``````# variety of steps to run burnin
n_burnin <- 500

# optimization goal is the probability of the logits given the info
logprob <- perform(l)
m1 %>% tfd_log_prob(listing(l, n_surviving))

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

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

The sampling perform, mcmc_sample_chain, might optionally be handed a `trace_fn` that tells TFP which sorts of meta info to save lots of. 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 <- perform(state, pkr) {
listing(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 instances 4 chains instances 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 info is offered in `res\$hint`:

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

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

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 better have a look at sampling outcomes and diagnostic outputs.

The multi-level mannequin – or various intercepts mannequin, on this case: we’ll get to various slopes in a later publish – provides a hyperprior to the mannequin. As a substitute of deciding on a imply and variance of the conventional prior the logits are drawn from, we let the mannequin study 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.

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

``````m2 <- tfd_joint_distribution_sequential(
``````listing(
# a_bar, the prior for the imply of the conventional distribution of per-tank logits
tfd_normal(loc = 0, scale = 1.5),
# sigma, the prior for the variance of the conventional distribution of per-tank logits
tfd_exponential(price = 1),
# regular distribution of per-tank logits
# parameters sigma and a_bar check with the outputs of the above two distributions
perform(sigma, a_bar)
tfd_sample_distribution(
tfd_normal(loc = a_bar, scale = sigma),
),
# binomial distribution of survival counts
# parameter l refers back to the output of the conventional distribution instantly above
perform(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 listing: Within the discovered prior for the logits

``````perform(sigma, a_bar)
tfd_sample_distribution(
tfd_normal(loc = a_bar, scale = sigma),
)``````

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

Analogously, within the distribution of survival counts

``````perform(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 right.

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

They’re.

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

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

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

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

And to ensure 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 includes 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 chance now's based mostly on three parameters
logprob <- perform(a, s, l)
m2 %>% tfd_log_prob(listing(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 = listing(0.1, 0.1, 0.1),
) %>%

run_mcmc <- perform(kernel) {
kernel %>% mcmc_sample_chain(
num_results = n_steps,
num_burnin_steps = n_burnin,
current_state = listing(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 an inventory of three: We have now

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

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

[]
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 knowledge we’re enthusiastic about.

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

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

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

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

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

And we’re prepared to truly run the chains.

``````# to this point, no sampling has been finished!
# the precise sampling occurs after we create a Session
# and run the above-defined nodes
sess <- tf\$Session()
eval <- perform(...) sess\$run(listing(...))

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 <- perform(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 <- perform(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, firstly, the various intercepts `a_1``a_48`?

### Posterior distributions

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

plot_posteriors(mcmc_trace[], dim(mcmc_trace[]))`````` Now let’s see the corresponding posterior means and highest posterior density intervals. (The beneath code contains the hyperpriors in `abstract` as we’ll wish 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(listing (~ imply)) %>%
collect(key = "key", worth = "imply")

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

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

hpdis_lower <- hpdis %>% choose(-incorporates("higher")) %>%
rename(lower0 = decrease) %>%
collect(key = "key", worth = "decrease") %>%
organize(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") %>%
organize(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("publish. imply and HPDI") +
theme_minimal() `````` Now for an equal to summary. We already computed means, customary deviations and the HPDI interval. Let’s add n_eff, the efficient variety of samples, and rhat, the Gelman-Rubin statistic.

### Complete abstract (a.ok.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 would wish to examine potential causes.

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

### 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 normal sigmoid by one other identify (undo the logit)
logistic <- perform(x) 1/(1 + exp(-x))
probs <- map_dbl(sim_tanks, logistic)
tibble(x = probs) %>% ggplot(aes(x = x)) + geom_density() + xlab("chance of survival")`````` Lastly, we wish to make certain we see the shrinkage conduct displayed in determine 13.1 within the ebook.

### 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 lively in smaller tanks, that are the lower-numbered ones on the left of the plot.

## Outlook

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