Improve interface around model specification

NAMESPACE

@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export(mcstate_model)
+export(mcstate_model_properties)
 export(mcstate_rng)
 export(mcstate_rng_distributed_pointer)
 export(mcstate_rng_distributed_state)

R/model.R

@@ -1,63 +1,235 @@
-##' Create a basic `mcstate` model.  Currently nothing here is
-##' validated, and it's likely that users will never actually use this
-##' directly.  Contains data and methods that define a basic model
-##' object, so that we can implement samplers against.  Not all models
-##' will support everything here, and we'll add additional
-##' fields/traits over time to advertise what a model can do.  For
-##' example, models will need to advertise that they are capable of
-##' being differentiated, or that they are stochastic in order to be
-##' used with different methods.
+##' Describe properties of a model.  Use of this function is optional,
+##' but you can pass the return value of this as the `properties`
+##' argument to enforce that your model does actually have these
+##' properties.
 ##'
-##' @title Create basic model
+##' @title Describe model properties
+##'
+##' @param has_gradient Logical, indicating if the model has a
+##'   `gradient` method.  Use `NULL` (the default) to detect this from
+##'   the model.
+##'
+##' @param has_direct_sample Logical, indicating if the model has a
+##'   `direct_sample` method.  Use `NULL` (the default) to detect this
+##'   from the model.
+##'
+##' @return A list of class `mcstate_model_properties` which should
+##'   not be modified.
+##'
+##' @export
+mcstate_model_properties <- function(has_gradient = NULL,
+                                     has_direct_sample = NULL) {
+  ret <- list(has_gradient = has_gradient,
+              has_direct_sample = has_direct_sample)
+  class(ret) <- "mcstate_model_properties"
+  ret
+}
+
+##' Create a basic `mcstate` model.  This takes a user-supplied object
+##' that minimally can compute a probability density (via a `density`
+##' function) and information about parameters; with this we can
+##' sample from the model using `MCMC` using [mcstate_sample].  We
+##' don't imagine that many users will call this function directly,
+##' but that this will be glue used by packages.
+##'
+##' The `model` argument can be a list or environment (something
+##' indexable by `$`) and have elements:
 ##'
-##' @param parameters Names of the parameters.  Every parameter is
-##'   named, and for now every parameter is a scalar.  We might relax
-##'   this later to support an `odin`-style structured parameter list,
-##'   but that might just generate a suitable vector of parameter
-##'   names perhaps?  In any case, once we start doing inference it's
-##'   naturally in the R^n, and here n is defined as the length of
-##'   this vector of names.
+##' * `density`: A function that will compute some probability
+##'   density.  It must take an argument representing a parameter
+##'   vector (a numeric vector) and return a single value.  This is
+##'   the posterior probability density in Bayesian inference, but it
+##'   could be anything really.  Models can return `-Inf` if things
+##'   are impossible, and we'll try and cope gracefully with that
+##'   wherever possible.
 ##'
-##' @param direct_sample A function to sample directly from the
+##' * `parameters`: A character vector of parameter names.  This can
+##'   be overridden by the `parameters` argument to this function.
+##'   This vector is the source of truth for the length of the
+##'   parameter vector.
+##'
+##' * `domain`: Information on the parameter domain.  This can be
+##'   overridden by the `domain` argument to this function.  This is a
+##'   two column matrix with `length(parameters)` rows representing
+##'   each parameter.  The parameter minimum and maximum bounds are
+##'   given as the first and second column.  Infinite values (`-Inf`
+##'   or `Inf`) should be used where the parameter has infinite domain
+##'   up or down.  Currently used to translate from a bounded to
+##'   unbounded space for HMC, but we might also use this for
+##'   reflecting proposals in MCMC too.  If not present (and if not
+##'   overridden by `domain`) we assume that the model is valid
+##'   everywhere (i.e., that all parameters are valid from `-Inf` to
+##'   `Inf`.
+##'
+##' * `direct_sample`: A function to sample directly from the
 ##'   parameter space, given an [mcstate_rng] object to sample from.
 ##'   In the case where a model returns a posterior (e.g., in Bayesian
 ##'   inference), this is assumed to be sampling from the prior.
 ##'   We'll use this for generating initial conditions for MCMC where
-##'   those are not given, and possibly other uses.
-##'
-##' @param density Compute the model density for a vector of parameter
-##'   values; this is the posterior probability in the case of
-##'   Bayesian inference, but it could be anything really.  Models can
-##'   return `-Inf` if things are impossible, and we'll try and cope
-##'   gracefully with that wherever possible.
-##'
-##' @param gradient Compute the gradient of `density` with respect to
-##'   the parameter vector; takes a parameter vector and returns a
-##'   vector the same length.  For efficiency, the model may want to
-##'   be stateful so that gradients can be efficiently calculated
-##'   after a density calculation, or density after gradient, where
-##'   these are called with the same parameters.
-##'
-##' @param domain Information on the parameter domain.  This is a two
-##'   column matrix with `length(parameters)` rows representing each
-##'   parameter.  The parameter minimum and maximum bounds are given
-##'   as the first and second column.  Infinite values (`-Inf` or
-##'   `Inf`) should be used where the parameter has infinite domain up
-##'   or down.  Currently used to translate from a bounded to
-##'   unbounded space for HMC, but we might also use this for
-##'   reflecting proposals in MCMC too.
+##'   those are not given, and possibly other uses.  If not given then
+##'   when using [mcstate_sample()] the user will have to provide a
+##'   vector of initial states.
+##'
+##' * `gradient`: A function to compute the gradient of `density` with
+##'   respect to the parameter vector; takes a parameter vector and
+##'   returns a vector the same length.  For efficiency, the model may
+##'   want to be stateful so that gradients can be efficiently
+##'   calculated after a density calculation, or density after
+##'   gradient, where these are called with the same parameters.  This
+##'   function is optional (and may not be well defined or possible to
+##'   define).
 ##'
-##' @return An object of class `mcstate_model`, which can be used with
-##'   a sampler.
+##' @title Create basic model
+##'
+##' @param model A list or environment with elements as described in
+##'   Details.
+##'
+##' @param properties Optionally, a [mcstate_model_properties] object,
+##'   used to enforce or clarify properties of the model.
+##'
+##' @return An object of class `mcstate_model`.  This will have elements:
+##'
+##' * `model`: The model as provided
+##' * `parameters`: The parameter name vector
+##' * `domain`: The parameter domain matrix
+##' * `direct_sample`: The `direct_sample` function, if provided by the model
+##' * `gradient`: The `gradient` function, if provided by the model
+##' * `properties`: A list of properties of the model;
+##'   see [mcstate_model_properties()].  Currently this contains:
+##'     * `has_gradient`: the model can compute its gradient
+##'     * `has_direct_sample`: the model can sample from parameters space
 ##'
 ##' @export
-mcstate_model <- function(parameters, direct_sample, density, gradient,
-                          domain) {
-  ret <- list(parameters = parameters,
-              direct_sample = direct_sample,
+mcstate_model <- function(model, properties = NULL) {
+  call <- environment() # for nicer stack traces
+  parameters <- validate_model_parameters(model, call)
+  domain <- validate_model_domain(model, call)
+  density <- validate_model_density(model, call)
+
+  properties <- validate_model_properties(properties, call)
+  gradient <- validate_model_gradient(model, properties, call)
+  direct_sample <- validate_model_direct_sample(model, properties, call)
+
+  ## Update properties based on what we found:
+  properties$has_gradient <- !is.null(gradient)
+  properties$has_direct_sample <- !is.null(direct_sample)
+
+  ret <- list(model = model,
+              parameters = parameters,
+              domain = domain,
               density = density,
               gradient = gradient,
-              domain = domain)
+              direct_sample = direct_sample,
+              properties = properties)
   class(ret) <- "mcstate_model"
   ret
 }
+
+
+
+validate_model_properties <- function(properties, call = NULL) {
+  if (is.null(properties)) {
+    return(mcstate_model_properties())
+  }
+
+  if (!inherits(properties, "mcstate_model_properties")) {
+    cli::cli_abort(
+      "Expected 'properties' to be a 'mcstate_model_properties' object",
+      arg = "properties", call = call)
+  }
+
+  properties
+}
+
+
+validate_model_parameters <- function(model, call = NULL) {
+  if (!is.character(model$parameters)) {
+    cli::cli_abort("Expected 'model$parameters' to be a character vector",
+                   arg = "model", call = call)
+  }
+  model$parameters
+}
+
+
+validate_model_domain <- function(model, call = NULL) {
+  n_pars <- length(model$parameters)
+  if (is.null(model$domain)) {
+    domain <- cbind(rep(-Inf, n_pars), rep(Inf, n_pars))
+  } else {
+    domain <- model$domain
+    if (!is.matrix(domain)) {
+      cli::cli_abort("Expected 'model$domain' to be a matrix if non-NULL")
+    }
+    if (nrow(domain) != n_pars) {
+      cli::cli_abort(paste(
+        "Expected 'model$domain' to have {n_pars} row{?s},",
+        "but it had {nrow(domain)}"))
+    }
+    if (ncol(domain) != 2) {
+      cli::cli_abort(paste(
+        "Expected 'model$domain' to have 2 columns,",
+        "but it had {ncol(domain)}"))
+    }
+  }
+  domain
+}
+
+
+validate_model_density <- function(model, call = NULL) {
+  if (!is.function(model$density)) {
+    cli::cli_abort("Expected 'model$density' to be a function",
+                   arg = "model", call = call)
+  }
+  model$density
+}
+
+
+validate_model_gradient <- function(model, properties, call) {
+  if (isFALSE(properties$has_gradient)) {
+    return(NULL)
+  }
+  gradient <- model$gradient
+  if (isTRUE(properties$has_gradient) && !is.function(gradient)) {
+    cli::cli_abort(
+      paste("Did not find a function 'gradient' within your model, but",
+            "your properties say that it should do"),
+      arg = "model", call = call)
+  }
+  if (!is.null(gradient) && !is.function(gradient)) {
+    cli::cli_abort(
+      "Expected 'model$gradient' to be a function if non-NULL",
+      arg = "model", call = call)
+  }
+  gradient
+}
+
+
+validate_model_direct_sample <- function(model, properties, call) {
+  if (isFALSE(properties$has_direct_sample)) {
+    return(NULL)
+  }
+  direct_sample <- model$direct_sample
+  if (isTRUE(properties$has_direct_sample) && !is.function(direct_sample)) {
+    cli::cli_abort(
+      paste("Did not find a function 'direct_sample' within your model, but",
+            "your properties say that it should do"),
+      arg = "model", call = call)
+  }
+  if (!is.null(direct_sample) && !is.function(direct_sample)) {
+    cli::cli_abort(
+      "Expected 'model$direct_sample' to be a function if non-NULL",
+      arg = "model", call = call)
+  }
+  direct_sample
+}
+
+
+require_direct_sample <- function(model, message, ...) {
+  if (!model$properties$has_direct_sample) {
+    cli::cli_abort(
+      c(message,
+        i = paste("This model does not provide 'direct_sample()', so we",
+                  "cannot directly sample from its parameter space")),
+      ...)
+  }
+}

R/sample.R

@@ -73,6 +73,9 @@ initial_parameters <- function(initial, model, rng, call = NULL) {
   n_pars <- length(model$parameters)
   n_chains <- length(rng)
   if (is.null(initial)) {
+    require_direct_sample(model,
+                          "'initial' must be provided with this model",
+                          arg = "initial", call = environment())
     ## Really this would just be from the prior; we can't directly
     ## sample from the posterior!
     initial <- lapply(rng, function(r) model$direct_sample(r))