celery: A Common API to Clustering

#' Model Specification Information
#'
#' @description
#'
#' An object with class "cluster_spec" is a container for information about a
#' model that will be fit.
#'
#' @details
#'
#' The following model types are implemented in tidyclust:
#'
#' - K-Means in [k_means()]
#'
#' - Hierarchical (Agglomerative) Clustering in [hier_clust()]
#'
#' The main elements of the object are:
#'
#' * `args`: A vector of the main arguments for the model. The
#' names of these arguments may be different from their counterparts n the
#' underlying model function. For example, for a `k_means()` model, the argument
#' name for the number of clusters are called "num_clusters" instead of "k" to
#' make it more general and usable across different types of models (and to not
#' be specific to a particular model function). The elements of `args` can
#' `tune()` with the use in [tune_cluster()].
#'
#' For more information see <https://www.tidymodels.org/start/tuning/>. If left
#' to their defaults (`NULL`), the arguments will use the underlying model
#' functions default value. As discussed below, the arguments in `args` are
#' captured as quosures and are not immediately executed.
#'
#' * `...`: Optional model-function-specific parameters. As with `args`, these
#'   will be quosures and can be `tune()`.
#'
#' * `mode`: The type of model, such as "partition". Other modes will be added
#'   once the package adds more functionality.
#'
#' * `method`: This is a slot that is filled in later by the model's constructor
#'   function. It generally contains lists of information that are used to
#'   create the fit and prediction code as well as required packages and similar
#'   data.
#'
#' * `engine`: This character string declares exactly what software will be
#'   used. It can be a package name or a technology type.
#'
#' This class and structure is the basis for how \pkg{tidyclust} stores model
#' objects prior to seeing the data.
#'
#' @section Argument Details:
#'
#' An important detail to understand when creating model specifications is that
#' they are intended to be functionally independent of the data. While it is
#' true that some tuning parameters are _data dependent_, the model
#' specification does not interact with the data at all.
#'
#' For example, most R functions immediately evaluate their arguments. For
#' example, when calling `mean(dat_vec)`, the object `dat_vec` is immediately
#' evaluated inside of the function.
#'
#' `tidyclust` model functions do not do this. For example, using
#'
#' \preformatted{
#'   k_means(num_clusters = ncol(mtcars) / 5)
#' }
#'
#' **does not** execute `ncol(mtcars) / 5` when creating the specification.
#' This can be seen in the output:
#'
#' \preformatted{
#'   > k_means(num_clusters = ncol(mtcars) / 5)
#'   K Means Cluster Specification (partition)
#'
#'   Main Arguments:
#'     num_clusters = ncol(mtcars)/5
#'
#'   Computational engine: stats
#' }
#'
#' The model functions save the argument _expressions_ and their associated
#' environments (a.k.a. a quosure) to be evaluated later when either
#' [fit.cluster_spec()] or [fit_xy.cluster_spec()] are called with the actual
#' data.
#'
#' The consequence of this strategy is that any data required to get the
#' parameter values must be available when the model is fit. The two main ways
#' that this can fail is if:
#'
#' \enumerate{
#'   \item The data have been modified between the creation of the model
#'   specification and when the model fit function is invoked.
#'
#'   \item If the model specification is saved and loaded into a new session
#'   where those same data objects do not exist.
#' }
#'
#' The best way to avoid these issues is to not reference any data objects in
#' the global environment but to use data descriptors such as `.cols()`. Another
#' way of writing the previous specification is
#'
#' \preformatted{
#'   k_means(num_clusters = .cols() / 5)
#' }
#'
#' This is not dependent on any specific data object and is evaluated
#' immediately before the model fitting process begins.
#'
#' One less advantageous approach to solving this issue is to use
#' quasiquotation. This would insert the actual R object into the model
#' specification and might be the best idea when the data object is small. For
#' example, using
#'
#' \preformatted{
#'   k_means(num_clusters = ncol(!!mtcars) - 1)
#' }
#'
#' would work (and be reproducible between sessions) but embeds the entire
#' mtcars data set into the `num_clusters` expression:
#'
#' \preformatted{
#'  > k_means(num_clusters = ncol(!!mtcars) / 5)
#'  K Means Cluster Specification (partition)
#'
#'  Main Arguments:
#'    num_clusters = ncol(structure(list(mpg = c(21, 21, 22.8, 21.4, 18.7,<snip>
#'
#'  Computational engine: stats
#' }
#'
#' However, if there were an object with the number of columns in it, this
#' wouldn't be too bad:
#'
#'\preformatted{
#'  > num_clusters_val <- ncol(mtcars) / 5
#'  > num_clusters_val
#'  [1] 10
#'  > k_means(num_clusters = !!num_clusters_val)
#'  K Means Cluster Specification (partition)
#'
#'  Main Arguments:
#'    num_clusters = 2.2
#'}
#'
#' More information on quosures and quasiquotation can be found at
#' \url{https://adv-r.hadley.nz/quasiquotation.html}.
#'
#' @rdname cluster_spec
#' @name cluster_spec
NULL

#' Model Fit Object Information
#'
#' @description
#'
#' An object with class "cluster_fit" is a container for information about a
#' model that has been fit to the data.
#'
#' @details
#'
#' The following model types are implemented in tidyclust:
#'
#' - K-Means in [k_means()]
#'
#' - Hierarchical (Agglomerative) Clustering in [hier_clust()]
#'
#' The main elements of the object are:
#'
#'   * `spec`: A [`cluster_spec`] object.
#'
#'   * `fit`: The object produced by the fitting function.
#'
#'   * `preproc`: This contains any data-specific information required to
#'     process new a sample point for prediction. For example, if the underlying
#'     model function requires arguments `x` and the user passed a formula to
#'     `fit`, the `preproc` object would contain items such as the terms object
#'     and so on. When no information is required, this is `NA`.
#'
#' As discussed in the documentation for [`cluster_spec`], the original
#' arguments to the specification are saved as quosures. These are evaluated for
#' the `cluster_fit` object prior to fitting. If the resulting model object
#' prints its call, any user-defined options are shown in the call preceded by a
#' tilde (see the example below). This is a result of the use of quosures in the
#' specification.
#'
#' This class and structure is the basis for how \pkg{tidyclust} stores model
#' objects after seeing the data and applying a model.
#' @rdname cluster_fit
#' @name cluster_fit
NULL