Full code available at github.com/mrandri19/smolppl/tree/sampling

## Introduction

This post is the continuation of “A probabilistic programming language in 70 lines of Python”. Today, we extend the library built in the last post by creating an API for sampling values from the prior and posterior distributions.

At the end we will have build an API like this one:

x = LatentVariable("x", Normal, [0.0, 3.0])
y = ObservedVariable("y", Normal, [x, 4.0], observed=1.5)

prior_sample(y)
posterior_sample(y, {"x": -2.0})


As users of a Probabilistic Programming Language (PPL), we are interested in sampling the distributions defined by our model. We do it, for example, to estimate means and variances via Monte Carlo integration. This is how we would estimate the prior and posteriror mean and standard deviation of the y variable using the new API:

prior_samples = [prior_sample(y) for _ in range(10_000)]
np.mean(prior_samples), np.std(prior_samples)
# => (0.0, 5.0)

posterior_samples = [posterior_sample(y, {"x": -2.0}) for _ in range(10_000)]
np.mean(posterior_samples), np.std(posterior_samples)
# => (-2.0, 4.0)


Mathematically speaking, the two snippets above define this probability model:

$x \sim \text{Normal}(0, 3)$ $y \sim \text{Normal}(x, 4)$

and compute the following expectations on it:

$E(y) = 0$ $\text{std}(y) = 5$ $E(y|x=-2) = -2$ $\text{std}(y|x=-2) = 4$

Most probabilistic programming languages implement sampling APIs. We will briefly see how Stan and PyMC, two of the most popular PPLs, allow sampling. Just skip this section if you only care about the implementation.

Stan’s sampling interface is lower-level compared to other PPLs. Sampling from both the prior and the posterior must be implemented manually by the user. To perform prior predictive sampling, the user must copy code from the model section into the generated_quantities sections and replace all distribution calls (like normal, binomial) with their _rng versions. For posterior predictive sampling, the procedure is similar, but model parameters are sampled from the posterior chain rather than from a Random Number Generator (RNG). In practice, this means that it is enough to keep using the same parameters as in the model section.

In PyMC the process is much simpler from a user’s perspective. PyMC uses its knowledge of the probabilistic DAG to automatically generate implementations for the the likelihood, prior sampling, and posterior sampling. In practice, this means calling pm.sample_prior_predictive() to get prior samples and pm.sample_posterior_predictive(posterior_trace) to get posterior samples.

## Implementation

### DAG traversals

To understand the main challenge in sampling from a DAG let’s see an example. Consider this probabilistic model:

$x \sim \text{Normal}(0, 3)$ $y \sim \text{Normal}(x, 4)$

in code:

x = LatentVariable("x", Normal, [0.0, 3.0])
y = ObservedVariable("y", Normal, [x, 4.0], observed=1.5)


and its corresponding DAG:

Looking at the DAG we see that, to sample the x variable, we need the floats 0 and 3 respectively for the mean and standard deviation. To sample the y variable we need the value of x and the float value 4. This means that we need the values of a variable’s children before being able to sample its value. As a consequence, simple depth-first search (DFS) is not be enough. Otherwise, it could happen that we try sampling the value of y without knowing its mean x.

The solution is called post-order DFS. Post-order traversal has the property that a node will only get visited after all of its children have. Simple DFS, on the other hand, does not have this property and does a pre-order traversal. To better understand the differences, check out the figure below. With pre-order traversal the root node $$a$$ is always evaluated before the children $$b, c$$, and in one case $$b$$ is evaluated before its child $$c$$ is.

The API we implement is heavily inspired by PyMC. The function prior_sample samples one value from the prior distribution. The function posterior_sample samples one value from the posterior, given a dictionary of latent values from the posterior. Inside these functions we traverse the probabilistic DAG and do what a Stan user would do. In prior_sample replace all variables with a new random value from their respective distribution. In posterior_sample replace all ObservedVariables with a new random value from its distribution, and replace all LatentVariables with values from the posterior chain. Let’s see how to do it.

### Distributions

First of all, we need to add a sample method to our Distribution abstract class, and implement it for all of our distributions. Just like we did for the log-density, we use SciPy.

class Distribution:
<rest of class>

@staticmethod
def sample(params):
raise NotImplementedError("Must be implemented by a subclass")

class Normal(Distribution):
<rest of class>

@staticmethod
def sample(params):
return float(norm.rvs(loc=params[0], scale=params[1]))


### Sampling from the prior

We begin by implementing prior sampling as it is the simpler of the two. We implement post-order depth-first search in the collect_variables inner function. It’s very similar to pre-order DFS but, instead of appending variable to to variables before recursion, we do it after. This results in a variable being “visited”, only after we have reached a leaf of the DAG.

def prior_sample(root):
visited = set()
variables = []

def collect_variables(variable):
if isinstance(variable, float):
return

for arg in variable.dist_args:
if arg not in visited:
collect_variables(arg)

# post-order
variables.append(variable)

collect_variables(root)


Then, for every variable, we need to obtain the numeric value of every argument. float arguments are already numeric so we take them as they are. On the other hand, we will keep numeric values of variable in a dictionary called sampled_values. We are sure that we will never get a KeyError because of the post-order traversal. All children of a variable will be evaluated befoure evaluating the variable.

    sampled_values = {}
for variable in variables:
dist_params = []
for dist_arg in variable.dist_args:
if isinstance(dist_arg, float):
dist_params.append(dist_arg)
else:
dist_params.append(sampled_values[dist_arg.name])


With all the required arguments, we call the sample method of the variable’s distribution. For prior sampling, we sample all types of variable, both ObservedVariables and LatentVariables. The result its stored in the sampled_values dict, to be used by one of the variable’s parents (this is a DAG, we can have multiple parents).

        sampled_values[variable.name] = variable.dist_class.sample(
dist_params
)


Finally, we return the sampled value of our root variable.

    return sampled_values[root.name]


Let’s see an example:

x = LatentVariable("x", Normal, [5.0, 0.1])
y = ObservedVariable("y", Normal, [x, 1.0], observed=1.5)

prior_sample(x)
# => 5.09

prior_sample(y)
# => 6.40


### Sampling from the posterior

For posterior sampling we repeat the same procedure: traverse the DAG in post-order, starting at the root, accumulating variables inside variables. The only difference being latent_values, which has the same role as it had in evaluate_log_density: being a dictionary from latent variable names to their numeric values. When doing, for example, posterior predictive simulation we will use samples of the posterior chain for the latent_values dictionary.

def posterior_sample(root, latent_values):
visited = set()
variables = []

def collect_variables(variable):
if isinstance(variable, float):
return

for arg in variable.dist_args:
if arg not in visited:
collect_variables(arg)

# post-order
variables.append(variable)

collect_variables(root)

sampled_values = {}


Again, we either use floats as they are, or fetch sampled children from the sampled_values dictionary by their name.

    for variable in variables:
dist_params = []
for dist_arg in variable.dist_args:
if isinstance(dist_arg, float):
dist_params.append(dist_arg)
else:
dist_params.append(sampled_values[dist_arg.name])


And finally the new bit: instead of sampling both latent and observed variables, we only sample observed values. Latent variables instead come from the latent_values dictionary, just like they did in evaluate_log_density.

        if isinstance(variable, LatentVariable):
sampled_values[variable.name] = latent_values[variable.name]
if isinstance(variable, ObservedVariable):
sampled_values[variable.name] = variable.dist_class.sample(
dist_params
)

return sampled_values[root.name]


Let’s see an example:

x = LatentVariable("x", Normal, [5.0, 0.1])
y = ObservedVariable("y", Normal, [x, 1.0], observed=1.5)

posterior_sample(y, {"x": -2})
# => 3.19
posterior_sample(x, {"x": -2})
# => -2.00


## Conclusion

We continue our work on a small Probabilistic Programming Language, introducing and motivating the need for sampling. After analyzing pre-order and post-order DAG traversals, we discover that we need the latter to respect the DAG’s evaluation order. Finally, we to implement an API for prior and posterior sampling, comparing its implementation with the one for log density evaluation.

### Bonus: more on DAG traversals

For those who read the previous post, I want to point out that likelihood evaluation does not have this dependency structure. The likelihood of each variable can be computed independently (even in parallel!) because we know all the children’s values, either from latent_values or from variable.observed. Since the order does not matter, I decided to use DFS because of its simplicity.

For people familiar with DAG traversals, post-order traversal on our DAG is equivalent to performing a topological sorting of the transposed DAG. A topological ordering is the reversed post-ordering of a DAG, while in our implementation we are doing a post-ordering on the transposed DAG, without reversing at the end. Perhaps surprisingly these two actions are equivalent:
reverse-list ∘ post-order-traversal ≡ post-order-traversal ∘ transpose-DAG. Unsurprisingly, I was not the first to discover this. Check out these stackoverflow questions:

Also, for applications of DAG traversals, check out Eli’s blog.