package prbnmcn-dagger

Sequential Monte Carlo: examples

Example: Nonlinear polynomial regression

The example consists in guessing the coefficients of a polynomial by observing a sequence of noisy values at increasing times. The degree of the polynomial is not known in advance. The full code is available in the test/poly.ml file of the source distribution.

Let us start by defining some module for handling polynomials:

module Poly :
sig
  type t = float array
  val zero : unit -> t
  val degree : t -> int
  val get : t -> int -> float
  val add : t -> t -> t
  val smul : float -> t -> t
  val eval : t -> float -> float
  val init : int -> (int -> float) -> t
  val truncate : int -> t -> t
  val pp : Format.formatter -> t -> unit
end

In our model, each particle represents a candidate polynomial, represented by a vector of coefficients.

open Dagger.Smc_inference

module Smc_types = struct
  type particle_output = Poly.t

  type resampling_state = unit
end

module Smc = Make (Smc_types) ()
module Dists = Stats.Gen

At each observation, these polynomials will "mutate". This mutation function performs a symmetric random walk on the degree of the polynomial and adds a bit of gaussian noise on each coefficient.

(* A random walk on polynomials *)
let mutate (p : Poly.t) =
  let open Smc in
  let open Infix in
  let current_degree = Poly.degree p in
  let* degree =
    sample
      (Stats_dist.uniform
         [| Int.max 0 (current_degree - 1);
            current_degree;
            current_degree + 1
         |])
  in
  let* noise =
    map_array
      (Poly.init degree (fun _ ->
           sample (Stats_dist.gaussian ~mean:0.0 ~std:1.0)))
      Fun.id
  in
  return (Poly.add noise p |> Poly.truncate degree)

The model proceeds sequentially on observations. After mutation, each particle is scored by estimating how close it fits to the data observed so far, with an additional term penalizing high-degree polynomials. This scoring is followed by a resampling step.

let model observations =
  let open Smc in
  let open Infix in
  let rec loop observed acc prev_coeffs =
    match observed with
    | [] -> return ()
    | next :: ys ->
        let* coeffs = mutate prev_coeffs in
        let acc = next :: acc in
        (* Score the quality of the fit *)
        let* () =
          List_ops.iter
            (fun (x, y) ->
              let estimate = Poly.eval coeffs x in
              log_score @@ Stats_dist.Pdfs.gaussian_ln ~mean:y ~std:1.0 estimate)
            acc
        in
        (* Penalize high-degree polynomials *)
        let* () =
          log_score
          @@ Stats_dist.Pdfs.exponential_ln
               ~rate:0.5
               (float (Poly.degree coeffs))
        in
        let* () = yield coeffs in
        loop ys acc coeffs
  in
  loop observations [] (Poly.zero ())

Running this model on some observations produces a sequence of Dagger.Smc_inference.S.population. The function Dagger.Smc_inference.S.run is used to produce this sequence. Let's examine its arguments one by one.

• We use systematic resampling, a predefined resampling strategy.
• Resampling steps act as state transformers on the type Smc_types.resampling_state. Here, the state is trivial and equal to unit, hence the second argument is ().
• The number of particles is set to be equal to 10000. A higher number of particles allows to better explore the state space (here, the space of polynomials). There's a direct tradeoff between the number of particles and the runtime of the algorithm.
• The penultimate argument is the model itself.
• The last one is the random generator state.

let run_model observations rng_state : Poly.t Seq.t =
  Smc.run
    (systematic_resampling ~ess_threshold:0.5)
    ()
    ~npart:10_000
    (model observations)
    rng_state
  |> Seq.filter_map (fun pop ->
         if Array.length pop.Smc.active = 0 then None
         else
           let itotal = 1. /. pop.total_mass in
           Array.fold_left
             (fun acc (coeff, w) -> Poly.(add acc (smul (w *. itotal) coeff)))
             (Poly.zero ())
             pop.active
           |> Option.some)
  |> Seq.memoize

The raw outcome of Smc.run is a sequence of unnormalized weighted outputs of particles. We perform two transformations on this sequence:

• We're only interested in the intermediate populations, not in the final one so we filter it out.
• We normalize and average each population, yielding the mean polynomial at that step.

In order to run this model we need to generate some synthetic data. We pick a degree-3 polynomial, evaluate it on some regularly spaced inputs and add some generous amount of gaussian noise on top.

let coeffs = [| 3.0; 25.0; -8.; 0.5 |]

let noisy_observations rng_state =
  List.init 150 (fun i ->
      let x = 0.1 *. float i in
      (x, Stats.Gen.gaussian ~mean:(Poly.eval coeffs x) ~std:10.0 rng_state))

let rng_state = Random.State.make [| 149572; 3891981; 48478478190758 |]

let observations = noisy_observations rng_state

Finally, we run the model.

let run () =
  let plot_predicted coeffs =
    List.map (fun (x, _) -> (x, Poly.eval coeffs x)) observations
  in
  let coeffs = run_model (noisy_observations rng_state) rng_state in
  let predicted =
    coeffs
    |> Seq.mapi (fun i elt -> (i, elt))
    |> Seq.filter (fun (i, _) -> i mod 10 = 0)
    |> Seq.map snd |> Seq.map plot_predicted |> List.of_seq
  in
  Seq.iteri (fun i coeff -> Format.printf "%d, %a@." i Poly.pp coeff) coeffs

You can have a look at the sequence of all 150 mean polynomials here. In addition, here is a plot of a subset of 15 mean polynomials together with the synthetic data.

Example: multinomial resampling

While the library proposes systematic resampling and stratified resampling as predefined strategies, users can also specify custom ones.

A resampling strategy is a function of type Resampling.strategy. It must produce a new population from a given one. The operations to access the current population and create the new one are entirely contained in the given particles first-class module.

Multinomial resampling is a basic strategy where new particles are selected randomly with a probability proportional to their weight in the current population. The implementation below constructs a sampler for the categorical distribution associated to the current population and iteratively takes card samples from it. Each sampled particle is added with an equal weight to the next population using the P.append function.

let rev_array_of_iter iter =
  let elts = ref [] in
  iter (fun elt w -> elts := (elt, w) :: !elts) ;
  Array.of_list !elts

let multinomial_resampling : (_, float, unit) Resampling.strategy =
  fun (type o)
      card
      ((module P) : (o, float) Resampling.particles)
      ()
      rng_state ->
   let elts = rev_array_of_iter P.iter in
   let sampler = Stats.Gen.categorical elts in
   let w = 1. /. (P.total ()) in
   for _i = 0 to card - 1 do
     let p = sampler rng_state in
     P.append p w
   done

In practice, resampling is necessary only when the population becomes degenerate. A classical metric for population degeneracy is the effective sample size (ESS). The P module provides an P.ess which can be used for that purpose. For instance, we can decide to only perform resampling when P.ess () / P.size () < 0.5 where P.size () returns the number of particles in the current population (note that P.ess varies between 0 and the number of particles).

let multinomial_resampling' : (_, float, unit) Resampling.strategy =
  fun (type o)
      card
      ((module P) : (o, float) Resampling.particles)
      ()
      rng_state ->
   if (P.ess () /. P.size ()) >= 0.5 then ()
   else
     let elts = rev_array_of_iter P.iter in
     let sampler = Stats.Gen.categorical elts in
     let w = 1. /. (P.total ()) in
     for _i = 0 to card - 1 do
       let p = sampler rng_state in
       P.append p w
     done

Observe that in the first branch of the conditional, we do nothing. In this case, the inference engine will detect that the resampling was a no-op, take the current population and use it for the next iteration.

As a final note, for some advanced inference algorithms based on SMC, it may be useful to carry a resampling_state forward between each resampling steps. In the example above, this resampling state is of type unit and is therefore trivial. An example making use of this feature is available in the examples/smc-abc directory of the source distribution.