From 7bfe96ef4da68d8d7b6ce694d2fb8794c0b1f31e Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Tue, 12 Nov 2024 16:35:18 -0800 Subject: [PATCH 01/44] add dbscan file using hier_clust.R as template --- R/dbscan.R | 193 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 193 insertions(+) create mode 100644 R/dbscan.R diff --git a/R/dbscan.R b/R/dbscan.R new file mode 100644 index 0000000..338ce84 --- /dev/null +++ b/R/dbscan.R @@ -0,0 +1,193 @@ +#' Density-Based Spatial Clustering of Applications with Noise +#' +#' @description +#' +#' `dbscan` defines a model that fits clusters based on areas with observations +#' that are densly packed together +#' +#' There are different ways to fit this model, and the method of estimation is +#' chosen by setting the model engine. The engine-specific pages for this model +#' are listed below. +#' +#' - \link[=details_hier_clust_stats]{stats} +#' +#' @param mode A single character string for the type of model. The only +#' possible value for this model is "partition". +#' @param engine A single character string specifying what computational engine +#' to use for fitting. Possible engines are listed below. The default for this +#' model is `"stats"`. +#' @param num_clusters Positive integer, number of clusters in model (optional). +#' @param cut_height Positive double, height at which to cut dendrogram to +#' obtain cluster assignments (only used if `num_clusters` is `NULL`) +#' @param linkage_method the agglomeration method to be used. This should be (an +#' unambiguous abbreviation of) one of `"ward.D"`, `"ward.D2"`, `"single"`, +#' `"complete"`, `"average"` (= UPGMA), `"mcquitty"` (= WPGMA), `"median"` (= +#' WPGMC) or `"centroid"` (= UPGMC). +#' +#' @details +#' +#' ## What does it mean to predict? +#' +#' To predict the cluster assignment for a new observation, we find the closest +#' cluster. How we measure “closeness” is dependent on the specified type of +#' linkage in the model: +#' +#' - *single linkage*: The new observation is assigned to the same cluster as +#' its nearest observation from the training data. +#' - *complete linkage*: The new observation is assigned to the cluster with the +#' smallest maximum distances between training observations and the new +#' observation. +#' - *average linkage*: The new observation is assigned to the cluster with the +#' smallest average distances between training observations and the new +#' observation. +#' - *centroid method*: The new observation is assigned to the cluster with the +#' closest centroid, as in prediction for k_means. +#' - *Ward’s method*: The new observation is assigned to the cluster with the +#' smallest increase in **error sum of squares (ESS)** due to the new +#' addition. The ESS is computed as the sum of squared distances between +#' observations in a cluster, and the centroid of the cluster. +#' +#' @return A `dbscan` cluster specification. +#' +#' @examples +#' # Show all engines +#' modelenv::get_from_env("dbscan") +#' +#' dbscan() +#' @export +dbscan <- + function(mode = "partition", + engine = "dbscan", + radius = NULL, + minpts = NULL) { + args <- list( + radius = enquo(radius), + minpts = enquo(minpts) + ) + + new_cluster_spec( + "dbscan", + args = args, + eng_args = NULL, + mode = mode, + method = NULL, + engine = engine + ) + } + +#' @export +print.dbscan <- function(x, ...) { + cat("DBSCAN Clustering Specification (", x$mode, ")\n\n", sep = "") + model_printer(x, ...) + + if (!is.null(x$method$fit$args)) { + cat("Model fit template:\n") + print(show_call(x)) + } + + invisible(x) +} + +# ------------------------------------------------------------------------------ + +#' @method update dbscan +#' @rdname tidyclust_update +#' @export +update.dbscan <- function(object, + parameters = NULL, + radius = NULL, + minpts = NULL, + fresh = FALSE, ...) { + eng_args <- parsnip::update_engine_parameters( + object$eng_args, + fresh = fresh, ... + ) + + if (!is.null(parameters)) { + parameters <- parsnip::check_final_param(parameters) + } + args <- list( + num_clusters = enquo(num_clusters), + cut_height = enquo(cut_height), + linkage_method = enquo(linkage_method) + ) + + args <- parsnip::update_main_parameters(args, parameters) + + if (fresh) { + object$args <- args + object$eng_args <- eng_args + } else { + null_args <- map_lgl(args, null_value) + if (any(null_args)) { + args <- args[!null_args] + } + if (length(args) > 0) { + object$args[names(args)] <- args + } + if (length(eng_args) > 0) { + object$eng_args[names(eng_args)] <- eng_args + } + } + + new_cluster_spec( + "dbscan", + args = object$args, + eng_args = object$eng_args, + mode = object$mode, + method = NULL, + engine = object$engine + ) +} + +# # ---------------------------------------------------------------------------- + +#' @export +check_args.dbscan <- function(object) { + args <- lapply(object$args, rlang::eval_tidy) + + if (all(is.numeric(args$num_clusters)) && any(args$num_clusters < 0)) { + rlang::abort("The number of centers should be >= 0.") + } + + invisible(object) +} + +#' @export +translate_tidyclust.hier_clust <- function(x, engine = x$engine, ...) { + x <- translate_tidyclust.default(x, engine, ...) + x +} + +# ------------------------------------------------------------------------------ + +#' Simple Wrapper around hclust function +#' +#' This wrapper prepares the data into a distance matrix to send to +#' `stats::hclust` and retains the parameters `num_clusters` or `h` as an +#' attribute. +#' +#' @param x matrix or data frame +#' @param num_clusters the number of clusters +#' @param cut_height the height to cut the dendrogram +#' @param linkage_method the agglomeration method to be used. This should be (an +#' unambiguous abbreviation of) one of `"ward.D"`, `"ward.D2"`, `"single"`, +#' `"complete"`, `"average"` (= UPGMA), `"mcquitty"` (= WPGMA), `"median"` (= +#' WPGMC) or `"centroid"` (= UPGMC). +#' @param dist_fun A distance function to use +#' +#' @return A dendrogram +#' @keywords internal +#' @export +.hier_clust_fit_stats <- function(x, + num_clusters = NULL, + cut_height = NULL, + linkage_method = NULL, + dist_fun = Rfast::Dist) { + dmat <- dist_fun(x) + res <- stats::hclust(stats::as.dist(dmat), method = linkage_method) + attr(res, "num_clusters") <- num_clusters + attr(res, "cut_height") <- cut_height + attr(res, "training_data") <- x + return(res) +} From a3f3d73065852fe0e0c1f6776ec3c022a2f231ac Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 16 Nov 2024 14:31:59 -0800 Subject: [PATCH 02/44] dbscan --> db_clust + add documentation --- R/db_clust.R | 169 ++++++++++++++++++++++++++++++++++++++++++++ R/dbscan.R | 193 --------------------------------------------------- 2 files changed, 169 insertions(+), 193 deletions(-) create mode 100644 R/db_clust.R delete mode 100644 R/dbscan.R diff --git a/R/db_clust.R b/R/db_clust.R new file mode 100644 index 0000000..28ce960 --- /dev/null +++ b/R/db_clust.R @@ -0,0 +1,169 @@ +#' Density-Based Spatial Clustering of Applications with Noise +#' +#' @description +#' +#' `db_clust` defines a model that fits clusters based on areas with observations +#' that are densely packed together +#' +#' There are different ways to fit this model, and the method of estimation is +#' chosen by setting the model engine. The engine-specific pages for this model +#' are listed below. +#' +#' - \link[=details_hier_clust_stats]{stats} +#' +#' @param mode A single character string for the type of model. The only +#' possible value for this model is "partition". +#' @param engine A single character string specifying what computational engine +#' to use for fitting. The engine for this model is `"dbscan"`. +#' @param radius Positive integer, Radius used to determine core-points and cluster points together (required). +#' @param minpts Positive double, Minimum number of points needed to form a cluster (required) +#' +#' +#' @details +#' +#' ## What does it mean to predict? +#' +#' To predict the cluster assignment for a new observation, we determine if a point +#' is within the radius of a core point. If so, we predict the same cluster as the core point. +#' If not, we predict the observation to be an outlier. +#' +#' +#' @return A `db_clust` cluster specification. +#' +#' @examples +#' # Show all engines +#' modelenv::get_from_env("db_clust") +#' +#' dbscan() +#' @export +db_clust <- + function(mode = "partition", + engine = "dbscan", + radius = NULL, + minpts = NULL) { + args <- list( + radius = enquo(radius), + minpts = enquo(minpts) + ) + + new_cluster_spec( + "db_clust", + args = args, + eng_args = NULL, + mode = mode, + method = NULL, + engine = engine + ) + } + +#' @export +print.db_clust <- function(x, ...) { + cat("DBSCAN Clustering Specification (", x$mode, ")\n\n", sep = "") + model_printer(x, ...) + + if (!is.null(x$method$fit$args)) { + cat("Model fit template:\n") + print(show_call(x)) + } + + invisible(x) +} + +# ------------------------------------------------------------------------------ + +#' @method update db_clust +#' @rdname tidyclust_update +#' @export +update.db_clust <- function(object, + parameters = NULL, + radius = NULL, + minpts = NULL, + fresh = FALSE, ...) { + eng_args <- parsnip::update_engine_parameters( + object$eng_args, + fresh = fresh, ... + ) + + if (!is.null(parameters)) { + parameters <- parsnip::check_final_param(parameters) + } + args <- list( + radius = enquo(radius), + minpts = enquo(minpts), + ) + + args <- parsnip::update_main_parameters(args, parameters) + + if (fresh) { + object$args <- args + object$eng_args <- eng_args + } else { + null_args <- map_lgl(args, null_value) + if (any(null_args)) { + args <- args[!null_args] + } + if (length(args) > 0) { + object$args[names(args)] <- args + } + if (length(eng_args) > 0) { + object$eng_args[names(eng_args)] <- eng_args + } + } + + new_cluster_spec( + "db_clust", + args = object$args, + eng_args = object$eng_args, + mode = object$mode, + method = NULL, + engine = object$engine + ) +} + +# # ---------------------------------------------------------------------------- + +#' @export +check_args.db_clust <- function(object) { + args <- lapply(object$args, rlang::eval_tidy) + + if (all(is.numeric(args$minpts)) && any(args$minpts < 0)) { + rlang::abort("The number of points in a cluster should be > 0.") + } + + if (all(is.numeric(args$radius)) && any(args$radius < 0)) { + rlang::abort("The radius used to create a cluster should be > 0.") + } + + invisible(object) +} + +#' @export +translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { + x <- translate_tidyclust.default(x, engine, ...) + x +} + +# ------------------------------------------------------------------------------ + +#' Simple Wrapper around dbscan function +#' +#' This wrapper prepares the data into a distance matrix to send to +#' `dbscan::dbscan` and retains the parameters `radius` or `minpts` as an +#' attribute. +#' +#' @param x matrix or data frame +#' @param radius Radius used to determine core-points and cluster points together +#' @param minpts Minimum number of points needed to form a cluster +#' +#' @return dbscan object +#' @keywords internal +#' @export +.db_clust_fit_dbscan <- function(x, + radius = NULL, + minpts = NULL) { + res <- dbscan::dbscan(x, eps = raidus, minPts = minPts) + attr(res, "radius") <- radius + attr(res, "minpts") <- minpts + attr(res, "training_data") <- x + return(res) +} diff --git a/R/dbscan.R b/R/dbscan.R deleted file mode 100644 index 338ce84..0000000 --- a/R/dbscan.R +++ /dev/null @@ -1,193 +0,0 @@ -#' Density-Based Spatial Clustering of Applications with Noise -#' -#' @description -#' -#' `dbscan` defines a model that fits clusters based on areas with observations -#' that are densly packed together -#' -#' There are different ways to fit this model, and the method of estimation is -#' chosen by setting the model engine. The engine-specific pages for this model -#' are listed below. -#' -#' - \link[=details_hier_clust_stats]{stats} -#' -#' @param mode A single character string for the type of model. The only -#' possible value for this model is "partition". -#' @param engine A single character string specifying what computational engine -#' to use for fitting. Possible engines are listed below. The default for this -#' model is `"stats"`. -#' @param num_clusters Positive integer, number of clusters in model (optional). -#' @param cut_height Positive double, height at which to cut dendrogram to -#' obtain cluster assignments (only used if `num_clusters` is `NULL`) -#' @param linkage_method the agglomeration method to be used. This should be (an -#' unambiguous abbreviation of) one of `"ward.D"`, `"ward.D2"`, `"single"`, -#' `"complete"`, `"average"` (= UPGMA), `"mcquitty"` (= WPGMA), `"median"` (= -#' WPGMC) or `"centroid"` (= UPGMC). -#' -#' @details -#' -#' ## What does it mean to predict? -#' -#' To predict the cluster assignment for a new observation, we find the closest -#' cluster. How we measure “closeness” is dependent on the specified type of -#' linkage in the model: -#' -#' - *single linkage*: The new observation is assigned to the same cluster as -#' its nearest observation from the training data. -#' - *complete linkage*: The new observation is assigned to the cluster with the -#' smallest maximum distances between training observations and the new -#' observation. -#' - *average linkage*: The new observation is assigned to the cluster with the -#' smallest average distances between training observations and the new -#' observation. -#' - *centroid method*: The new observation is assigned to the cluster with the -#' closest centroid, as in prediction for k_means. -#' - *Ward’s method*: The new observation is assigned to the cluster with the -#' smallest increase in **error sum of squares (ESS)** due to the new -#' addition. The ESS is computed as the sum of squared distances between -#' observations in a cluster, and the centroid of the cluster. -#' -#' @return A `dbscan` cluster specification. -#' -#' @examples -#' # Show all engines -#' modelenv::get_from_env("dbscan") -#' -#' dbscan() -#' @export -dbscan <- - function(mode = "partition", - engine = "dbscan", - radius = NULL, - minpts = NULL) { - args <- list( - radius = enquo(radius), - minpts = enquo(minpts) - ) - - new_cluster_spec( - "dbscan", - args = args, - eng_args = NULL, - mode = mode, - method = NULL, - engine = engine - ) - } - -#' @export -print.dbscan <- function(x, ...) { - cat("DBSCAN Clustering Specification (", x$mode, ")\n\n", sep = "") - model_printer(x, ...) - - if (!is.null(x$method$fit$args)) { - cat("Model fit template:\n") - print(show_call(x)) - } - - invisible(x) -} - -# ------------------------------------------------------------------------------ - -#' @method update dbscan -#' @rdname tidyclust_update -#' @export -update.dbscan <- function(object, - parameters = NULL, - radius = NULL, - minpts = NULL, - fresh = FALSE, ...) { - eng_args <- parsnip::update_engine_parameters( - object$eng_args, - fresh = fresh, ... - ) - - if (!is.null(parameters)) { - parameters <- parsnip::check_final_param(parameters) - } - args <- list( - num_clusters = enquo(num_clusters), - cut_height = enquo(cut_height), - linkage_method = enquo(linkage_method) - ) - - args <- parsnip::update_main_parameters(args, parameters) - - if (fresh) { - object$args <- args - object$eng_args <- eng_args - } else { - null_args <- map_lgl(args, null_value) - if (any(null_args)) { - args <- args[!null_args] - } - if (length(args) > 0) { - object$args[names(args)] <- args - } - if (length(eng_args) > 0) { - object$eng_args[names(eng_args)] <- eng_args - } - } - - new_cluster_spec( - "dbscan", - args = object$args, - eng_args = object$eng_args, - mode = object$mode, - method = NULL, - engine = object$engine - ) -} - -# # ---------------------------------------------------------------------------- - -#' @export -check_args.dbscan <- function(object) { - args <- lapply(object$args, rlang::eval_tidy) - - if (all(is.numeric(args$num_clusters)) && any(args$num_clusters < 0)) { - rlang::abort("The number of centers should be >= 0.") - } - - invisible(object) -} - -#' @export -translate_tidyclust.hier_clust <- function(x, engine = x$engine, ...) { - x <- translate_tidyclust.default(x, engine, ...) - x -} - -# ------------------------------------------------------------------------------ - -#' Simple Wrapper around hclust function -#' -#' This wrapper prepares the data into a distance matrix to send to -#' `stats::hclust` and retains the parameters `num_clusters` or `h` as an -#' attribute. -#' -#' @param x matrix or data frame -#' @param num_clusters the number of clusters -#' @param cut_height the height to cut the dendrogram -#' @param linkage_method the agglomeration method to be used. This should be (an -#' unambiguous abbreviation of) one of `"ward.D"`, `"ward.D2"`, `"single"`, -#' `"complete"`, `"average"` (= UPGMA), `"mcquitty"` (= WPGMA), `"median"` (= -#' WPGMC) or `"centroid"` (= UPGMC). -#' @param dist_fun A distance function to use -#' -#' @return A dendrogram -#' @keywords internal -#' @export -.hier_clust_fit_stats <- function(x, - num_clusters = NULL, - cut_height = NULL, - linkage_method = NULL, - dist_fun = Rfast::Dist) { - dmat <- dist_fun(x) - res <- stats::hclust(stats::as.dist(dmat), method = linkage_method) - attr(res, "num_clusters") <- num_clusters - attr(res, "cut_height") <- cut_height - attr(res, "training_data") <- x - return(res) -} From 27084036d1159b4fafc3292735343f6c2fda086f Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 16 Nov 2024 15:04:39 -0800 Subject: [PATCH 03/44] cluster assignment and predict helpers --- R/extract_cluster_assignment.R | 49 ++++++++++++++++++++++++++++++++++ R/predict_helpers.R | 9 +++++++ 2 files changed, 58 insertions(+) diff --git a/R/extract_cluster_assignment.R b/R/extract_cluster_assignment.R index af18ce3..0fceb06 100644 --- a/R/extract_cluster_assignment.R +++ b/R/extract_cluster_assignment.R @@ -157,6 +157,55 @@ extract_cluster_assignment.hclust <- function(object, cluster_assignment_tibble(clusters, length(unique(clusters)), ...) } +#' @export +#' +#' STILL GOTTA DO STUFF HERE +extract_cluster_assignment.db_clust <- function(object, + ..., + call = rlang::caller_env(0)) { + + args <- list(...) + + # if (!is.null(args[["h"]])) { + # rlang::abort( + # paste( + # "Using `h` argument is not supported.", + # "Please use `cut_height` instead." + # ), + # call = call + # ) + # } + # + # if (!is.null(args[["k"]])) { + # rlang::abort( + # paste( + # "Using `k` argument is not supported.", + # "Please use `num_clusters` instead." + # ), + # call = call + # ) + # } + + # if (!("num_clusters" %in% names(args) || "cut_height" %in% names(args))) { + # num_clusters <- attr(object, "num_clusters") + # cut_height <- attr(object, "cut_height") + # } else { + # num_clusters <- args[["num_clusters"]] + # cut_height <- args[["cut_height"]] + # } + # + # if (is.null(num_clusters) && is.null(cut_height)) { + # rlang::abort( + # "Please specify either `num_clusters` or `cut_height`.", + # call = call + # ) + # } + + clusters <- object %$% cluster + cluster_assignment_tibble(clusters, length(unique(clusters)), ...) +} + + # ------------------------------------------------------------------------------ cluster_assignment_tibble <- function(clusters, diff --git a/R/predict_helpers.R b/R/predict_helpers.R index f4780ae..bbf9c54 100644 --- a/R/predict_helpers.R +++ b/R/predict_helpers.R @@ -152,3 +152,12 @@ make_predictions <- function(x, prefix, n_clusters) { pred_clusts } + +.db_clust_predict_dbscan <- function(object, new_data, prefix = "Cluster_") { + + clusters <- predict(object, newdata = new_data, data = object$training_data) + n_clusters <- length(db$cluster %>% unique()) + + make_predictions(clusters, prefix, n_clusters) +} + From 4ff1ad4bad9d77340f116a87611d69fde2f270b9 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 16 Nov 2024 15:12:44 -0800 Subject: [PATCH 04/44] minPts -> minpts --- R/db_clust.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/db_clust.R b/R/db_clust.R index 28ce960..59d1a04 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -161,7 +161,7 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { .db_clust_fit_dbscan <- function(x, radius = NULL, minpts = NULL) { - res <- dbscan::dbscan(x, eps = raidus, minPts = minPts) + res <- dbscan::dbscan(x, eps = raidus, minPts = minpts) attr(res, "radius") <- radius attr(res, "minpts") <- minpts attr(res, "training_data") <- x From 8106e43b4e1973b5928a1ebe22933d9821b22893 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 16 Nov 2024 15:21:12 -0800 Subject: [PATCH 05/44] fix function name references --- R/db_clust.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/db_clust.R b/R/db_clust.R index 59d1a04..d77ed65 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -1,4 +1,4 @@ -#' Density-Based Spatial Clustering of Applications with Noise +#' Density-Based Spatial Clustering of Applications with Noise (DBSCAN) #' #' @description #' @@ -9,7 +9,7 @@ #' chosen by setting the model engine. The engine-specific pages for this model #' are listed below. #' -#' - \link[=details_hier_clust_stats]{stats} +#' - \link[=details_db_clust_dbscan]{dbscan} #' #' @param mode A single character string for the type of model. The only #' possible value for this model is "partition". @@ -34,7 +34,7 @@ #' # Show all engines #' modelenv::get_from_env("db_clust") #' -#' dbscan() +#' db_clust() #' @export db_clust <- function(mode = "partition", From c2cc44e7060de4e6f7ccb7194fb9877706a2853b Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Tue, 4 Feb 2025 19:12:28 -0800 Subject: [PATCH 06/44] db_clust and gm_clust fit, extract clusters, and predict --- NAMESPACE | 14 + R/db_clust.R | 31 +- R/db_clust_data.R | 85 +++++ R/db_clust_dbscan.R | 11 + R/extract_cluster_assignment.R | 74 ++-- R/gm_clust.R | 164 +++++++++ R/gm_clust_data.R | 77 ++++ R/gm_clust_mclust.R | 11 + R/predict_helpers.R | 25 +- R/zzz.R | 2 + man/db_clust.Rd | 47 +++ man/details_db_clust_dbscan.Rd | 9 + man/details_gm_clust_mclust.Rd | 9 + man/dot-db_clust_fit_dbscan.Rd | 24 ++ man/dot-gm_clust_fit_mclust.Rd | 22 ++ man/gm_clust.Rd | 44 +++ man/tidyclust_update.Rd | 28 +- tests/testthat/_snaps/k_means.new.md | 85 +++++ .../_snaps/reconcile_clusterings.new.md | 17 + vignettes/articles/db_clust.Rmd | 338 ++++++++++++++++++ vignettes/articles/gm_clust.Rmd | 130 +++++++ 21 files changed, 1187 insertions(+), 60 deletions(-) create mode 100644 R/db_clust_data.R create mode 100644 R/db_clust_dbscan.R create mode 100644 R/gm_clust.R create mode 100644 R/gm_clust_data.R create mode 100644 R/gm_clust_mclust.R create mode 100644 man/db_clust.Rd create mode 100644 man/details_db_clust_dbscan.Rd create mode 100644 man/details_gm_clust_mclust.Rd create mode 100644 man/dot-db_clust_fit_dbscan.Rd create mode 100644 man/dot-gm_clust_fit_mclust.Rd create mode 100644 man/gm_clust.Rd create mode 100644 tests/testthat/_snaps/k_means.new.md create mode 100644 tests/testthat/_snaps/reconcile_clusterings.new.md create mode 100644 vignettes/articles/db_clust.Rmd create mode 100644 vignettes/articles/gm_clust.Rmd diff --git a/NAMESPACE b/NAMESPACE index 9960b43..759e97a 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,12 +2,16 @@ S3method(as_tibble,cluster_metric_set) S3method(augment,cluster_fit) +S3method(check_args,db_clust) S3method(check_args,default) +S3method(check_args,gm_clust) S3method(check_args,hier_clust) S3method(check_args,k_means) S3method(extract_cluster_assignment,KMeansCluster) +S3method(extract_cluster_assignment,Mclust) S3method(extract_cluster_assignment,cluster_fit) S3method(extract_cluster_assignment,cluster_spec) +S3method(extract_cluster_assignment,dbscan) S3method(extract_cluster_assignment,hclust) S3method(extract_cluster_assignment,kmeans) S3method(extract_cluster_assignment,kmodes) @@ -37,6 +41,8 @@ S3method(print,cluster_fit) S3method(print,cluster_metric_set) S3method(print,cluster_spec) S3method(print,control_cluster) +S3method(print,db_clust) +S3method(print,gm_clust) S3method(print,hier_clust) S3method(print,k_means) S3method(required_pkgs,cluster_fit) @@ -57,7 +63,9 @@ S3method(sse_within_total,cluster_fit) S3method(sse_within_total,cluster_spec) S3method(sse_within_total,workflow) S3method(tidy,cluster_fit) +S3method(translate_tidyclust,db_clust) S3method(translate_tidyclust,default) +S3method(translate_tidyclust,gm_clust) S3method(translate_tidyclust,hier_clust) S3method(translate_tidyclust,k_means) S3method(tunable,cluster_spec) @@ -66,9 +74,13 @@ S3method(tune_args,cluster_spec) S3method(tune_cluster,cluster_spec) S3method(tune_cluster,default) S3method(tune_cluster,workflow) +S3method(update,db_clust) +S3method(update,gm_clust) S3method(update,hier_clust) S3method(update,k_means) export("%>%") +export(.db_clust_fit_dbscan) +export(.gm_clust_fit_mclust) export(.hier_clust_fit_stats) export(.k_means_fit_ClusterR) export(.k_means_fit_clustMixType) @@ -78,6 +90,7 @@ export(augment) export(cluster_metric_set) export(control_cluster) export(cut_height) +export(db_clust) export(extract_centroids) export(extract_cluster_assignment) export(extract_fit_engine) @@ -94,6 +107,7 @@ export(fit_xy) export(fit_xy.cluster_spec) export(get_tidyclust_colors) export(glance) +export(gm_clust) export(hier_clust) export(k_means) export(knit_engine_docs) diff --git a/R/db_clust.R b/R/db_clust.R index d77ed65..d2c8e94 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -159,11 +159,30 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { #' @keywords internal #' @export .db_clust_fit_dbscan <- function(x, - radius = NULL, - minpts = NULL) { - res <- dbscan::dbscan(x, eps = raidus, minPts = minpts) - attr(res, "radius") <- radius - attr(res, "minpts") <- minpts + eps = NULL, + minPts = NULL, + ...) { + if (is.null(eps)) { + rlang::abort( + "Please specify `radius` to be able to fit specification.", + call = call("fit") + ) + } + + if (is.null(minPts)) { + rlang::abort( + "Please specify `minpts` to be able to fit specification.", + call = call("fit") + ) + } + + res <- dbscan::dbscan(x, eps = eps, minPts = minPts) + attr(res, "radius") <- eps + attr(res, "minpts") <- minPts attr(res, "training_data") <- x - return(res) + is_core <- is.corepoint(x, eps = eps, minPts = minPts) + attr(res, "core_points") <- x[is_core, ] + attr(res, "cp_clusters") <- res$cluster[is_core] + + res } diff --git a/R/db_clust_data.R b/R/db_clust_data.R new file mode 100644 index 0000000..1330f5b --- /dev/null +++ b/R/db_clust_data.R @@ -0,0 +1,85 @@ +# nocov start + +make_db_clust <- function() { + modelenv::set_new_model("db_clust") + + modelenv::set_model_mode("db_clust", "partition") + + # ---------------------------------------------------------------------------- + + modelenv::set_model_engine("db_clust", "partition", "dbscan") + modelenv::set_dependency( + model = "db_clust", + mode = "partition", + eng = "dbscan", + pkg = "dbscan" + ) + modelenv::set_dependency( + model = "db_clust", + mode = "partition", + eng = "dbscan", + pkg = "tidyclust" + ) + + modelenv::set_fit( + model = "db_clust", + eng = "dbscan", + mode = "partition", + value = list( + interface = "matrix", + protect = c("data"), + func = c(pkg = "tidyclust", fun = ".db_clust_fit_dbscan"), + defaults = list() + ) + ) + + modelenv::set_encoding( + model = "db_clust", + eng = "dbscan", + mode = "partition", + options = list( + predictor_indicators = "traditional", + compute_intercept = TRUE, + remove_intercept = TRUE, + allow_sparse_x = FALSE + ) + ) + + modelenv::set_model_arg( + model = "db_clust", + eng = "dbscan", + exposed = "radius", + original = "eps", + func = list(pkg = "dials", fun = "raidus"), + has_submodel = TRUE + ) + + modelenv::set_model_arg( + model = "db_clust", + eng = "dbscan", + exposed = "minpts", + original = "minPts", + func = list(pkg = "dials", fun = "minpts"), + has_submodel = TRUE + ) + + modelenv::set_pred( + model = "db_clust", + eng = "dbscan", + mode = "partition", + type = "cluster", + value = list( + pre = NULL, + post = NULL, + func = c(fun = ".db_clust_predict_dbscan"), + args = + list( + object = rlang::expr(object$fit), + new_data = rlang::expr(new_data) + ) + ) + ) + +} + +# nocov end diff --git a/R/db_clust_dbscan.R b/R/db_clust_dbscan.R new file mode 100644 index 0000000..56f4846 --- /dev/null +++ b/R/db_clust_dbscan.R @@ -0,0 +1,11 @@ +#' Density-Based Spatial Clustering of Applications with Noise (DBSCAN) via dbscan +#' +#' [db_clust()] creates DBSCAN model. +#' +#' @includeRmd man/rmd/db_clust_dbscan.md details +#' +#' @name details_db_clust_dbscan +#' @keywords internal +NULL + +# See inst/README-DOCS.md for a description of how these files are processed diff --git a/R/extract_cluster_assignment.R b/R/extract_cluster_assignment.R index 0fceb06..3c02a8d 100644 --- a/R/extract_cluster_assignment.R +++ b/R/extract_cluster_assignment.R @@ -158,51 +158,17 @@ extract_cluster_assignment.hclust <- function(object, } #' @export -#' -#' STILL GOTTA DO STUFF HERE -extract_cluster_assignment.db_clust <- function(object, - ..., - call = rlang::caller_env(0)) { - - args <- list(...) +extract_cluster_assignment.dbscan <- function(object, ...) { + clusters <- object$cluster + n_clusters <- length(unique(clusters)) + cluster_assignment_tibble_w_outliers(clusters, n_clusters, ...) +} - # if (!is.null(args[["h"]])) { - # rlang::abort( - # paste( - # "Using `h` argument is not supported.", - # "Please use `cut_height` instead." - # ), - # call = call - # ) - # } - # - # if (!is.null(args[["k"]])) { - # rlang::abort( - # paste( - # "Using `k` argument is not supported.", - # "Please use `num_clusters` instead." - # ), - # call = call - # ) - # } - - # if (!("num_clusters" %in% names(args) || "cut_height" %in% names(args))) { - # num_clusters <- attr(object, "num_clusters") - # cut_height <- attr(object, "cut_height") - # } else { - # num_clusters <- args[["num_clusters"]] - # cut_height <- args[["cut_height"]] - # } - # - # if (is.null(num_clusters) && is.null(cut_height)) { - # rlang::abort( - # "Please specify either `num_clusters` or `cut_height`.", - # call = call - # ) - # } - - clusters <- object %$% cluster - cluster_assignment_tibble(clusters, length(unique(clusters)), ...) +#' @export +extract_cluster_assignment.Mclust <- function(object, ...) { + clusters <- object$classification + n_clusters <- length(unique(clusters)) + cluster_assignment_tibble(clusters, n_clusters, ...) } @@ -218,3 +184,23 @@ cluster_assignment_tibble <- function(clusters, tibble::tibble(.cluster = factor(res)) } + +cluster_assignment_tibble_w_outliers <- function(clusters, + n_clusters, + ..., + prefix = "Cluster_") { + reorder_clusts <- order(union(unique(clusters), 0:(n_clusters-1))) + names <- paste0(prefix, 0:(n_clusters-1)) + res <- names[reorder_clusts][clusters+1] + res[res == paste0(prefix, "0")] <- "Outlier" + # zero_count <- 0 + # res <- sapply(res, function(x) { + # if (x == "Cluster_0") { + # zero_count <<- zero_count + 1 + # paste0("Cluster_0_", zero_count) + # } else { + # x + # } + # }) + tibble::tibble(.cluster = factor(res)) +} diff --git a/R/gm_clust.R b/R/gm_clust.R new file mode 100644 index 0000000..9c65910 --- /dev/null +++ b/R/gm_clust.R @@ -0,0 +1,164 @@ +#' Gaussian Mixture Models (GMM) +#' +#' @description +#' +#' `gm_clust` defines a model that fits clusters based on fitting a specified number of +#' multivariate normal distributions to the data. +#' +#' There are different ways to fit this model, and the method of estimation is +#' chosen by setting the model engine. The engine-specific pages for this model +#' are listed below. +#' +#' - \link[=details_gm_clust_mclust]{mclust} +#' +#' @param mode A single character string for the type of model. The only +#' possible value for this model is "partition". +#' @param engine A single character string specifying what computational engine +#' to use for fitting. The engine for this model is `"mclust"`. +#' @param num_clusters Positive integer, number of clusters in model (required). +#' +#' +#' @details +#' +#' ## What does it mean to predict? +#' +#' To predict the cluster assignment for a new observation, we determine which cluster +#' a point has the highest probability of belonging to. +#' +#' +#' @return A `gm_clust` cluster specification. +#' +#' @examples +#' # Show all engines +#' modelenv::get_from_env("gm_clust") +#' +#' gm_clust() +#' @export +gm_clust <- + function(mode = "partition", + engine = "mclust", + num_clusters = NULL) { + args <- list( + num_clusters = enquo(num_clusters) + ) + + new_cluster_spec( + "gm_clust", + args = args, + eng_args = NULL, + mode = mode, + method = NULL, + engine = engine + ) + } + +#' @export +print.gm_clust <- function(x, ...) { + cat("GMM Clustering Specification (", x$mode, ")\n\n", sep = "") + model_printer(x, ...) + + if (!is.null(x$method$fit$args)) { + cat("Model fit template:\n") + print(show_call(x)) + } + + invisible(x) +} + +# ------------------------------------------------------------------------------ + +#' @method update gm_clust +#' @rdname tidyclust_update +#' @export +update.gm_clust <- function(object, + parameters = NULL, + num_clusters = NULL, + fresh = FALSE, ...) { + eng_args <- parsnip::update_engine_parameters( + object$eng_args, + fresh = fresh, ... + ) + + if (!is.null(parameters)) { + parameters <- parsnip::check_final_param(parameters) + } + args <- list( + num_clusters = enquo(num_clusters) + ) + + args <- parsnip::update_main_parameters(args, parameters) + + if (fresh) { + object$args <- args + object$eng_args <- eng_args + } else { + null_args <- map_lgl(args, null_value) + if (any(null_args)) { + args <- args[!null_args] + } + if (length(args) > 0) { + object$args[names(args)] <- args + } + if (length(eng_args) > 0) { + object$eng_args[names(eng_args)] <- eng_args + } + } + + new_cluster_spec( + "gm_clust", + args = object$args, + eng_args = object$eng_args, + mode = object$mode, + method = NULL, + engine = object$engine + ) +} + +# # ---------------------------------------------------------------------------- + +#' @export +check_args.gm_clust <- function(object) { + args <- lapply(object$args, rlang::eval_tidy) + + if (all(is.numeric(args$num_clusters)) && any(args$num_clusters <= 0)) { + rlang::abort("The number of clusters should be > 0.") + } + + invisible(object) +} + +#' @export +translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { + x <- translate_tidyclust.default(x, engine, ...) + x +} + +# ------------------------------------------------------------------------------ + +#' Simple Wrapper around Mclust function +#' +#' This wrapper prepares the data into a distance matrix to send to +#' `mclust::Mclust` and retains the parameters `num_clusters` as an +#' attribute. +#' +#' @param x matrix or data frame +#' @param num_clusters Number of clusters +#' +#' @return mclust object +#' @keywords internal +#' @export +.gm_clust_fit_mclust <- function(x, + G = NULL, + ...) { + if (is.null(G)) { + rlang::abort( + "Please specify `num_clusters` to be able to fit specification.", + call = call("fit") + ) + } + + res <- mclust::Mclust(x, G = G) + attr(res, "num_clusters") <- G + attr(res, "training_data") <- x + res +} diff --git a/R/gm_clust_data.R b/R/gm_clust_data.R new file mode 100644 index 0000000..08efe84 --- /dev/null +++ b/R/gm_clust_data.R @@ -0,0 +1,77 @@ +# nocov start + +make_gm_clust <- function() { + modelenv::set_new_model("gm_clust") + + modelenv::set_model_mode("gm_clust", "partition") + + # ---------------------------------------------------------------------------- + + modelenv::set_model_engine("gm_clust", "partition", "mclust") + modelenv::set_dependency( + model = "gm_clust", + mode = "partition", + eng = "mclust", + pkg = "mclust" + ) + modelenv::set_dependency( + model = "gm_clust", + mode = "partition", + eng = "mclust", + pkg = "tidyclust" + ) + + modelenv::set_fit( + model = "gm_clust", + eng = "mclust", + mode = "partition", + value = list( + interface = "matrix", + protect = c("data"), + func = c(pkg = "tidyclust", fun = ".gm_clust_fit_mclust"), + defaults = list() + ) + ) + + modelenv::set_encoding( + model = "gm_clust", + eng = "mclust", + mode = "partition", + options = list( + predictor_indicators = "traditional", + compute_intercept = TRUE, + remove_intercept = TRUE, + allow_sparse_x = FALSE + ) + ) + + modelenv::set_model_arg( + model = "gm_clust", + eng = "mclust", + exposed = "num_clusters", + original = "G", + func = list(pkg = "dials", fun = "num_clusters"), + has_submodel = TRUE + ) + + + modelenv::set_pred( + model = "gm_clust", + eng = "mclust", + mode = "partition", + type = "cluster", + value = list( + pre = NULL, + post = NULL, + func = c(fun = ".gm_clust_predict_mclust"), + args = + list( + object = rlang::expr(object$fit), + new_data = rlang::expr(new_data) + ) + ) + ) + +} + +# nocov end diff --git a/R/gm_clust_mclust.R b/R/gm_clust_mclust.R new file mode 100644 index 0000000..769298e --- /dev/null +++ b/R/gm_clust_mclust.R @@ -0,0 +1,11 @@ +#' Gaussian Mixture Model (GMM) via mclust +#' +#' [gm_clust()] creates GMM model. +#' +#' @includeRmd man/rmd/gm_clust_mclust.md details +#' +#' @name details_gm_clust_mclust +#' @keywords internal +NULL + +# See inst/README-DOCS.md for a description of how these files are processed diff --git a/R/predict_helpers.R b/R/predict_helpers.R index bbf9c54..29b520f 100644 --- a/R/predict_helpers.R +++ b/R/predict_helpers.R @@ -3,6 +3,13 @@ make_predictions <- function(x, prefix, n_clusters) { factor(x, levels = levels, labels = paste0(prefix, levels)) } +make_predictions_w_outliers <- function(x, prefix, n_clusters) { + levels <- seq_len(n_clusters)-1 + labels <- paste0(prefix, levels) + labels[labels == paste0(prefix, "0")] <- "Outlier" + factor(x, levels = levels, labels = labels) +} + .k_means_predict_stats <- function(object, new_data, prefix = "Cluster_") { res <- object$centers res <- flexclust::dist2(res, new_data) @@ -150,13 +157,25 @@ make_predictions <- function(x, prefix, n_clusters) { } pred_clusts <- unique(clusters$.cluster)[pred_clusts_num] - pred_clusts + pred_clust } .db_clust_predict_dbscan <- function(object, new_data, prefix = "Cluster_") { - clusters <- predict(object, newdata = new_data, data = object$training_data) - n_clusters <- length(db$cluster %>% unique()) + cp <- attr(object, "core_points") + cp_clusters <- attr(object, "cp_clusters") + eps <- attr(object, "radius") + + clusters <- dbscan:::.predict_frNN(newdata = new_data, data = cp, cp_clusters, eps = eps) + n_clusters <- length(unique(object$cluster)) + + make_predictions_w_outliers(clusters, prefix, n_clusters) +} + +.gm_clust_predict_mclust <- function(object, new_data, prefix = "Cluster_") { + + clusters <- predict(object, newdata = new_data)$classification + n_clusters <- length(clusters %>% unique()) make_predictions(clusters, prefix, n_clusters) } diff --git a/R/zzz.R b/R/zzz.R index 44938d0..2754a6d 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -3,6 +3,8 @@ .onLoad <- function(libname, pkgname) { make_hier_clust() make_k_means() + make_db_clust() + make_gm_clust() s3_register("generics::required_pkgs", "cluster_fit") s3_register("generics::required_pkgs", "cluster_spec") diff --git a/man/db_clust.Rd b/man/db_clust.Rd new file mode 100644 index 0000000..c352b9c --- /dev/null +++ b/man/db_clust.Rd @@ -0,0 +1,47 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/db_clust.R +\name{db_clust} +\alias{db_clust} +\title{Density-Based Spatial Clustering of Applications with Noise (DBSCAN)} +\usage{ +db_clust(mode = "partition", engine = "dbscan", radius = NULL, minpts = NULL) +} +\arguments{ +\item{mode}{A single character string for the type of model. The only +possible value for this model is "partition".} + +\item{engine}{A single character string specifying what computational engine +to use for fitting. The engine for this model is \code{"dbscan"}.} + +\item{radius}{Positive integer, Radius used to determine core-points and cluster points together (required).} + +\item{minpts}{Positive double, Minimum number of points needed to form a cluster (required)} +} +\value{ +A \code{db_clust} cluster specification. +} +\description{ +\code{db_clust} defines a model that fits clusters based on areas with observations +that are densely packed together + +There are different ways to fit this model, and the method of estimation is +chosen by setting the model engine. The engine-specific pages for this model +are listed below. +\itemize{ +\item \link[=details_db_clust_dbscan]{dbscan} +} +} +\details{ +\subsection{What does it mean to predict?}{ + +To predict the cluster assignment for a new observation, we determine if a point +is within the radius of a core point. If so, we predict the same cluster as the core point. +If not, we predict the observation to be an outlier. +} +} +\examples{ +# Show all engines +modelenv::get_from_env("db_clust") + +db_clust() +} diff --git a/man/details_db_clust_dbscan.Rd b/man/details_db_clust_dbscan.Rd new file mode 100644 index 0000000..3ac9204 --- /dev/null +++ b/man/details_db_clust_dbscan.Rd @@ -0,0 +1,9 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/db_clust_dbscan.R +\name{details_db_clust_dbscan} +\alias{details_db_clust_dbscan} +\title{Density-Based Spatial Clustering of Applications with Noise (DBSCAN) via dbscan} +\description{ +\code{\link[=db_clust]{db_clust()}} creates DBSCAN model. +} +\keyword{internal} diff --git a/man/details_gm_clust_mclust.Rd b/man/details_gm_clust_mclust.Rd new file mode 100644 index 0000000..385a563 --- /dev/null +++ b/man/details_gm_clust_mclust.Rd @@ -0,0 +1,9 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/gm_clust_mclust.R +\name{details_gm_clust_mclust} +\alias{details_gm_clust_mclust} +\title{Gaussian Mixture Model (GMM) via mclust} +\description{ +\code{\link[=gm_clust]{gm_clust()}} creates GMM model. +} +\keyword{internal} diff --git a/man/dot-db_clust_fit_dbscan.Rd b/man/dot-db_clust_fit_dbscan.Rd new file mode 100644 index 0000000..4c2f6d9 --- /dev/null +++ b/man/dot-db_clust_fit_dbscan.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/db_clust.R +\name{.db_clust_fit_dbscan} +\alias{.db_clust_fit_dbscan} +\title{Simple Wrapper around dbscan function} +\usage{ +.db_clust_fit_dbscan(x, eps = NULL, minPts = NULL, ...) +} +\arguments{ +\item{x}{matrix or data frame} + +\item{radius}{Radius used to determine core-points and cluster points together} + +\item{minpts}{Minimum number of points needed to form a cluster} +} +\value{ +dbscan object +} +\description{ +This wrapper prepares the data into a distance matrix to send to +\code{dbscan::dbscan} and retains the parameters \code{radius} or \code{minpts} as an +attribute. +} +\keyword{internal} diff --git a/man/dot-gm_clust_fit_mclust.Rd b/man/dot-gm_clust_fit_mclust.Rd new file mode 100644 index 0000000..6a4ea76 --- /dev/null +++ b/man/dot-gm_clust_fit_mclust.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/gm_clust.R +\name{.gm_clust_fit_mclust} +\alias{.gm_clust_fit_mclust} +\title{Simple Wrapper around dbscan function} +\usage{ +.gm_clust_fit_mclust(x, G = NULL, ...) +} +\arguments{ +\item{x}{matrix or data frame} + +\item{num_clusters}{Number of clusters} +} +\value{ +mclust object +} +\description{ +This wrapper prepares the data into a distance matrix to send to +\code{mclust::Mclust} and retains the parameters \code{num_clusters} as an +attribute. +} +\keyword{internal} diff --git a/man/gm_clust.Rd b/man/gm_clust.Rd new file mode 100644 index 0000000..9daf6ac --- /dev/null +++ b/man/gm_clust.Rd @@ -0,0 +1,44 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/gm_clust.R +\name{gm_clust} +\alias{gm_clust} +\title{Gaussian Mixture Models (GMM)} +\usage{ +gm_clust(mode = "partition", engine = "mclust", num_clusters = NULL) +} +\arguments{ +\item{mode}{A single character string for the type of model. The only +possible value for this model is "partition".} + +\item{engine}{A single character string specifying what computational engine +to use for fitting. The engine for this model is \code{"mclust"}.} + +\item{num_clusters}{Positive integer, number of clusters in model (required).} +} +\value{ +A \code{gm_clust} cluster specification. +} +\description{ +\code{gm_clust} defines a model that fits clusters based on fitting a specified number of +multivariate normal distributions to the data. + +There are different ways to fit this model, and the method of estimation is +chosen by setting the model engine. The engine-specific pages for this model +are listed below. +\itemize{ +\item \link[=details_gm_clust_mclust]{mclust} +} +} +\details{ +\subsection{What does it mean to predict?}{ + +To predict the cluster assignment for a new observation, we determine which cluster +a point has the highest probability of belonging to. +} +} +\examples{ +# Show all engines +modelenv::get_from_env("gm_clust") + +gm_clust() +} diff --git a/man/tidyclust_update.Rd b/man/tidyclust_update.Rd index d11819f..2b6c906 100644 --- a/man/tidyclust_update.Rd +++ b/man/tidyclust_update.Rd @@ -1,11 +1,25 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/hier_clust.R, R/k_means.R, R/update.R -\name{update.hier_clust} +% Please edit documentation in R/db_clust.R, R/gm_clust.R, R/hier_clust.R, +% R/k_means.R, R/update.R +\name{update.db_clust} +\alias{update.db_clust} +\alias{update.gm_clust} \alias{update.hier_clust} \alias{update.k_means} \alias{tidyclust_update} \title{Update a cluster specification} \usage{ +\method{update}{db_clust}( + object, + parameters = NULL, + radius = NULL, + minpts = NULL, + fresh = FALSE, + ... +) + +\method{update}{gm_clust}(object, parameters = NULL, num_clusters = NULL, fresh = FALSE, ...) + \method{update}{hier_clust}( object, parameters = NULL, @@ -27,6 +41,11 @@ updating. If the main arguments are used, these will supersede the values in \code{parameters}. Also, using engine arguments in this object will result in an error.} +\item{fresh}{A logical for whether the arguments should be modified in-place +or replaced wholesale.} + +\item{...}{Not used for \code{update()}.} + \item{num_clusters}{Positive integer, number of clusters in model.} \item{cut_height}{Positive double, height at which to cut dendrogram to @@ -36,11 +55,6 @@ obtain cluster assignments (only used if \code{num_clusters} is \code{NULL})} unambiguous abbreviation of) one of \code{"ward.D"}, \code{"ward.D2"}, \code{"single"}, \code{"complete"}, \code{"average"} (= UPGMA), \code{"mcquitty"} (= WPGMA), \code{"median"} (= WPGMC) or \code{"centroid"} (= UPGMC).} - -\item{fresh}{A logical for whether the arguments should be modified in-place -or replaced wholesale.} - -\item{...}{Not used for \code{update()}.} } \value{ An updated cluster specification. diff --git a/tests/testthat/_snaps/k_means.new.md b/tests/testthat/_snaps/k_means.new.md new file mode 100644 index 0000000..51be933 --- /dev/null +++ b/tests/testthat/_snaps/k_means.new.md @@ -0,0 +1,85 @@ +# bad input + + Code + k_means(mode = "bogus") + Condition + Error in `modelenv::check_spec_mode_engine_val()`: + ! 'bogus' is not a known mode for model `k_means()`. + +--- + + Code + bt <- k_means(num_clusters = -1) %>% set_engine("stats") + fit(bt, mpg ~ ., mtcars) + Condition + Error in `check_args()`: + ! The number of centers should be >= 0. + +--- + + Code + translate_tidyclust(k_means(), engine = NULL) + Condition + Error in `translate_tidyclust.default()`: + ! Please set an engine. + +--- + + Code + translate_tidyclust(k_means(formula = ~x)) + Condition + Error in `k_means()`: + ! unused argument (formula = ~x) + +# printing + + Code + k_means() + Output + K Means Cluster Specification (partition) + + Computational engine: stats + + +--- + + Code + k_means(num_clusters = 10) + Output + K Means Cluster Specification (partition) + + Main Arguments: + num_clusters = 10 + + Computational engine: stats + + +# updating + + Code + k_means(num_clusters = 5) %>% update(num_clusters = tune()) + Output + K Means Cluster Specification (partition) + + Main Arguments: + num_clusters = tune() + + Computational engine: stats + + +# errors if `num_clust` isn't specified + + Code + k_means() %>% set_engine("stats") %>% fit(~., data = mtcars) + Condition + Error in `fit()`: + ! Please specify `num_clust` to be able to fit specification. + +--- + + Code + k_means() %>% set_engine("ClusterR") %>% fit(~., data = mtcars) + Condition + Error in `fit()`: + ! This engine requires some package installs: 'ClusterR' + diff --git a/tests/testthat/_snaps/reconcile_clusterings.new.md b/tests/testthat/_snaps/reconcile_clusterings.new.md new file mode 100644 index 0000000..8bb2168 --- /dev/null +++ b/tests/testthat/_snaps/reconcile_clusterings.new.md @@ -0,0 +1,17 @@ +# reconciliation works with uneven numbers + + Code + reconcile_clusterings_mapping(primary_cluster_assignment, + alt_cluster_assignment, one_to_one = TRUE) + Condition + Error in `reconcile_clusterings_mapping()`: + ! The package "RcppHungarian" is required. + +# reconciliation errors for uneven lengths + + Code + reconcile_clusterings_mapping(letters, letters[1:10]) + Condition + Error in `reconcile_clusterings_mapping()`: + ! The package "RcppHungarian" is required. + diff --git a/vignettes/articles/db_clust.Rmd b/vignettes/articles/db_clust.Rmd new file mode 100644 index 0000000..6839510 --- /dev/null +++ b/vignettes/articles/db_clust.Rmd @@ -0,0 +1,338 @@ +--- +title: "Density-Based Clustering" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Density-Based Clustering} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + + +## Setup +```{r} +library(workflows) +library(parsnip) +``` + +```{r} +library(tidyclust) +library(tidyverse) +library(tidymodels) +set.seed(838383) +``` + + +Load and clean a dataset: + +```{r} +data("penguins", package = "modeldata") + +penguins <- penguins %>% + select(bill_length_mm, bill_depth_mm) %>% + drop_na() + + +# shuffle rows +penguins <- penguins %>% + sample_n(nrow(penguins)) +``` + +At the end of this vignette, you will find a brief overview of the DBSCAN algorithm. + +## A brief introduction to density-based clustering + +Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a method of unsupervised learning that groups observations into clusters based on their density in multidimensional space. Unlike methods such as k-means, DBSCAN does not require specifying the number of clusters beforehand and can identify clusters of arbitrary shapes. Additionally, it can classify points that do not belong to any cluster as noise, allowing for greater flexibility in handling real-world data. + +In DBSCAN, observations are considered as locations in multidimensional space. The algorithm works by defining a cluster as a dense region of connected points. During the fitting process, points are classified as core points, border points, or noise based on their proximity to other points (**radius**) and a density threshold (**minpts**). + +```{r, include = FALSE} +penguins_std <- penguins %>% + mutate(bill_length_std = scale(bill_length_mm)[,1], + bill_depth_std = scale(bill_depth_mm)[,1]) %>% + select(bill_length_std, bill_depth_std) +``` + + +```{r, echo = FALSE} + +# penguins_std %>% + # ggplot() + + # geom_point(aes(x = bill_length_std, y = bill_depth_std)) + + # theme_minimal() + + # coord_fixed() + +``` + +1. For each observation in the data, determine whether each point is a core point. A core points is defined as an observation which has more than the specified **minpts** points (including the point itself) within the specified **radius** around the point. + + +```{r, include = FALSE} +library(dbscan) +eps <- 0.35 +minpts <- 10 +dbscan_fit <- dbscan(penguins_std, eps = eps, minPts = minpts) + +``` + +```{r, echo = FALSE} +library(ggforce) +penguins_std %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std)) + + theme_minimal() + + geom_circle(tibble(x = c(-0.79162259), y = c(1.79706436), radius = rep(eps,1)), + mapping = aes(x0 = x, y0 = y, r = eps), + color = "gray", linewidth = 0.8) + + geom_point(tibble(x = c(-0.79162259), y = c(1.79706436)), mapping = aes(x = x, y = y, color = c("1"))) + + coord_fixed() + + theme(legend.position = "none") + + scale_color_manual(values = c("red")) +``` + + + +```{r, echo = FALSE} +penguins_new <- penguins_std +penguins_new$cluster <- factor(dbscan_fit$cluster) +penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = minpts)) == 1, "Yes", "No"), levels = c("Yes", "No")) + +penguins_new %>% + ggplot(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + + geom_point() + + theme_minimal() + + coord_fixed() + + labs(color = "Is a core point?") + + scale_color_manual(values = c("red", "black")) + + theme(legend.position = "none") +``` + +2. For each Core point, assign it and all the points within the specified radius around the point ta + +```{r, echo = FALSE} +penguins_new %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + + theme_minimal() + + geom_circle(tibble(x = c(-1.10300165), y = c(0.73366185), radius = rep(eps, 1)), + mapping = aes(x0 = x, y0 = y, r = eps), + color = "gray", linewidth = 0.8) + + geom_point(tibble(x = c(-1.10300165), y = c(0.73366185)), mapping = aes(x = x, y = y, color = c("1"))) + + coord_fixed() + + theme(legend.position = "none") + + scale_color_manual(values = c("red", "black", "#619CFF")) +``` + + + +```{r, echo = FALSE} +penguins_new %>% + mutate(cp = if_else(sqrt((bill_length_std - -1.10300165)^2 + (bill_depth_std - 0.73366185)^2) <= eps, "1", cp)) %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + + theme_minimal() + + geom_circle(tibble(x = c(-1.10300165), y = c(0.73366185), radius = rep(eps, 1)), + mapping = aes(x0 = x, y0 = y, r = eps), + color = "gray", linewidth = 0.8) + + geom_point(tibble(x = c(-1.10300165), y = c(0.73366185)), mapping = aes(x = x, y = y, color = c("1"))) + + coord_fixed() + + theme(legend.position = "none") + + scale_color_manual(values = c("#619CFF", "black", "red")) +``` + + +```{r, echo = FALSE} +penguins_new %>% + mutate(cluster_temp = if_else(cluster == 1, "temp", cp)) %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster_temp)) + + theme_minimal() + + geom_circle(tibble(x = c(-1.25), y = c(-0.32), radius = rep(eps, 1)), + mapping = aes(x0 = x, y0 = y, r = eps), + color = "gray", linewidth = 0.8) + + geom_point(tibble(x = c(-1.25), y = c(-0.32)), mapping = aes(x = x, y = y, color = c("1"))) + + coord_fixed() + + theme(legend.position = "none") + + scale_color_manual(values = c("#619CFF", "black", "#619CFF", "red")) +``` + + +```{r, echo = FALSE} +penguins_new %>% + mutate(cluster_temp = case_when(cluster == 1 ~ "temp", + sqrt((bill_length_std - 1.2)^2 + (bill_depth_std - 1.05)^2) <= eps ~ "1", + TRUE ~ cp)) %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster_temp)) + + theme_minimal() + + geom_circle(tibble(x = c(1.2), y = c(1.05), radius = rep(eps, 1)), + mapping = aes(x0 = x, y0 = y, r = eps), + color = "gray", linewidth = 0.8) + + geom_point(tibble(x = c(1.2), y = c(1.05)), mapping = aes(x = x, y = y, color = c("1")), size = 1.8) + + coord_fixed() + + theme(legend.position = "none") + + scale_color_manual(values = c("#00BA38", "black", "#619CFF", "red")) +``` + + +```{r, echo = FALSE} +penguins_new %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster)) + + theme_minimal() + + coord_fixed() + + scale_color_manual(values = c("black", "#619CFF", "#F8766D", "#00BA38", "#C77CFF")) + + theme(legend.position = "none") + +``` + + +## db clust specification in {`tidyclust`} + +```{r} +db_clust_spec <- db_clust(radius = 0.35, minpts = 10) + +db_clust_spec +``` + +There is currently one engine: `dbscan::dbscan` (default) + +## Fitting db_clust models + +We fit the model to data in the usual way: + +```{r} +db_clust_fit <- db_clust_spec %>% + fit( ~ bill_length_std + bill_depth_std, + data = penguins_std + ) + +db_clust_fit %>% + summary() +``` + +We can also extract the standard `tidyclust` summary list: + + +```{r} +# no extract fit summary method for db_clust object yet + +# db_clust_summary <- db_clust_fit %>% extract_fit_summary() + +# db_clust_summary %>% str() +``` + +```{r} +db_clust_fit %>% extract_cluster_assignment() +``` + + +```{r} +penguins_new %>% + mutate(cluster2 = (db_clust_fit %>% extract_cluster_assignment())$.cluster) %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster2)) + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = "green", size = 2), data = penguins_new %>% .[144,]) + + theme_minimal() + + coord_fixed() + + scale_color_manual(values = c("#619CFF", "#F8766D", "#00BA38", "black", "green")) + + theme(legend.position = "none") +``` + + +If you have not yet read the `k_means` vignette, we recommend reading that first; +functions that are used in this vignette are explained in more detail there. + + +## Prediction + + + + +Since $DBSCAN$ algorithm ultimately assigns training observations to the +clusters based on proximity to core points, it is natural to "predict" that test +observations belong to a cluster if they are within $radius$ distance to a core point. + +The `predict()` function behaves as expected, producing cluster assignment +predictions on new data based on distance to the core points from the fitted model. + + +```{r} +new_penguin <- tibble( + bill_length_std = -1.2454235, + bill_depth_std = 0.5012132 +) + +db_clust_fit %>% + predict(new_penguin) +``` + +```{r} +set.seed(80) + +dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) +is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) +pred_data <- penguins_std[is_core,] + +pred <- dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) +``` + + +```{r} +test <- db_clust_fit %>% + predict(penguins_std) %>% + mutate(.fit_cluster = (db_clust_fit %>% extract_cluster_assignment())$.cluster, + .bc = pred, + .bc = case_when(.bc == 0 ~ "Outlier", + TRUE ~ paste0("Cluster_", .bc))) +# %>% +# filter(.pred_cluster != .fit_cluster) + +# want to assign to closer corepoint + + +which(test$.pred_cluster != test$.bc) +``` + + +```{r, include = FALSE} + +dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) +is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) +pred_data <- penguins_std[is_core,] + +pred <- dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) + +preds <- cbind( + db_clust_fit %>% extract_cluster_assignment(), + db_clust_fit %>% predict(penguins_std), + .pred_clust_adj = paste0("Cluster_", dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps)) + +``` +```{r} +preds %>% filter(.cluster != .pred_cluster) +``` + + +```{r} +cbind( + attr(db_clust_fit$fit, "training_data"), + attr(db_clust_fit$fit, "core_points") +) + +dist(db_clust_fit) + + + + +new_penguin + + +``` + diff --git a/vignettes/articles/gm_clust.Rmd b/vignettes/articles/gm_clust.Rmd new file mode 100644 index 0000000..bdae1f4 --- /dev/null +++ b/vignettes/articles/gm_clust.Rmd @@ -0,0 +1,130 @@ +--- +title: "Gaussian Model Clustering" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Gaussian Model Clustering} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + + +## Setup +```{r} +library(workflows) +library(parsnip) +library(mclust) +``` + +```{r} +library(tidyclust) +library(tidyverse) +library(tidymodels) +set.seed(838383) +``` + + +Load and clean a dataset: + +```{r} +data("penguins", package = "modeldata") + +penguins <- penguins %>% + select(bill_length_mm, bill_depth_mm) %>% + drop_na() + + +# shuffle rows +penguins <- penguins %>% + sample_n(nrow(penguins)) +``` + + + +At the end of this vignette, you will find a brief overview of the GMM algorithm. + + + +## gm clust specification in {`tidyclust`} + +```{r} +gm_clust_spec <- gm_clust(num_clusters = 3) + +gm_clust_spec +``` + +There is currently one engine: `mclust::Mclust` (default) + +## Fitting gm_clust models + +We fit the model to data in the usual way: + +```{r} +gm_clust_fit <- gm_clust_spec %>% + fit( ~ bill_length_mm + bill_depth_mm, + data = penguins + ) + +gm_clust_fit %>% + summary() +``` +We can also extract the standard `tidyclust` summary list: + + +```{r} +# gm_clust_summary <- gm_clust_fit %>% extract_fit_summary() + +# gm_clust_summary %>% str() +``` + + +```{r} +gm_clust_fit %>% extract_cluster_assignment() + + + +``` + +If you have not yet read the `k_means` vignette, we recommend reading that first; +functions that are used in this vignette are explained in more detail there. + + +## Prediction + + + + +write stuff here + +```{r} +new_penguin <- tibble( + bill_length_mm = 40, + bill_depth_mm = 20 +) + +gm_clust_fit %>% + predict(new_penguin) +``` + + +## A brief introduction to guassian mixture models + + +write and explain here + + + + + + + + + + + From e43433b8a5ed0418d70ea246634e3907a731e35d Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 22 Feb 2025 10:01:07 -0800 Subject: [PATCH 07/44] add helper to make db_clust fit deterministic --- R/db_clust.R | 47 ++++++++++- R/extract_cluster_assignment.R | 2 +- R/predict_helpers.R | 6 +- man/dbscan_helper.Rd | 18 ++++ man/dot-gm_clust_fit_mclust.Rd | 2 +- vignettes/articles/db_clust.Rmd | 144 ++++++++++++++++++++++++++------ vignettes/articles/gm_clust.Rmd | 45 +++++++++- 7 files changed, 230 insertions(+), 34 deletions(-) create mode 100644 man/dbscan_helper.Rd diff --git a/R/db_clust.R b/R/db_clust.R index d2c8e94..7b87c7d 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -181,8 +181,51 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { attr(res, "minpts") <- minPts attr(res, "training_data") <- x is_core <- is.corepoint(x, eps = eps, minPts = minPts) - attr(res, "core_points") <- x[is_core, ] - attr(res, "cp_clusters") <- res$cluster[is_core] + attr(res, "is_core") <- is_core res } + +#' dbscan fit helper function +#' +#' This function... +#' +#' @param x matrix or data frame +#' +#' @return dbscan object +#' @keywords internal +dbscan_helper <- function(object, + ...) { + + is_core <- attr(object, "is_core") + print(length(is_core)) + training_data <- data.frame(attr(object, "training_data")) + cp <- training_data[is_core,] + non_cp <- training_data[!is_core,] + cp_clusters <- object$cluster[is_core] + eps <- attr(object, "radius") + + # get fit values according to closest core point + non_cp_clusters <- dbscan:::.predict_frNN(newdata = non_cp, data = cp, cp_clusters, eps = eps) + + # join back separated fits into proper order in training data + non_cp_clusters <- data.frame(non_cp_clusters) + cp_clusters <- data.frame(cp_clusters) + + # create vars to join back results in proper order + training_data$overall_order <- 1:nrow(training_data) + training_data$is_core <- ifelse(is_core, "cp", "non cp") + non_cp_clusters$is_core <- "non cp" + cp_clusters$is_core <- "cp" + + training_data$id <- ave(training_data$is_core, training_data$is_core, FUN = seq_along) + non_cp_clusters$id <- 1:nrow(non_cp_clusters) + cp_clusters$id <- 1:nrow(cp_clusters) + + training_data <- merge(x = training_data, y = non_cp_clusters, by = c("id", "is_core"), all.x = TRUE) + training_data <- merge(x = training_data, y = cp_clusters, by = c("id", "is_core"), all.x = TRUE) + + training_data$cluster <- ifelse(!is.na(training_data$non_cp_clusters), training_data$non_cp_clusters, training_data$cp_clusters) + training_data$cluster[order(training_data$overall_order)] + +} diff --git a/R/extract_cluster_assignment.R b/R/extract_cluster_assignment.R index 3c02a8d..1ff9984 100644 --- a/R/extract_cluster_assignment.R +++ b/R/extract_cluster_assignment.R @@ -159,7 +159,7 @@ extract_cluster_assignment.hclust <- function(object, #' @export extract_cluster_assignment.dbscan <- function(object, ...) { - clusters <- object$cluster + clusters <- dbscan_helper(object) n_clusters <- length(unique(clusters)) cluster_assignment_tibble_w_outliers(clusters, n_clusters, ...) } diff --git a/R/predict_helpers.R b/R/predict_helpers.R index 29b520f..a991112 100644 --- a/R/predict_helpers.R +++ b/R/predict_helpers.R @@ -162,8 +162,10 @@ make_predictions_w_outliers <- function(x, prefix, n_clusters) { .db_clust_predict_dbscan <- function(object, new_data, prefix = "Cluster_") { - cp <- attr(object, "core_points") - cp_clusters <- attr(object, "cp_clusters") + is_core <- attr(object, "is_core") + training_data <- attr(object, "training_data") + cp <- training_data[is_core,] + cp_clusters <- object$cluster[is_core] eps <- attr(object, "radius") clusters <- dbscan:::.predict_frNN(newdata = new_data, data = cp, cp_clusters, eps = eps) diff --git a/man/dbscan_helper.Rd b/man/dbscan_helper.Rd new file mode 100644 index 0000000..9bdeef1 --- /dev/null +++ b/man/dbscan_helper.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/db_clust.R +\name{dbscan_helper} +\alias{dbscan_helper} +\title{dbscan fit helper function} +\usage{ +dbscan_helper(x, ...) +} +\arguments{ +\item{x}{matrix or data frame} +} +\value{ +dbscan object +} +\description{ +This function... +} +\keyword{internal} diff --git a/man/dot-gm_clust_fit_mclust.Rd b/man/dot-gm_clust_fit_mclust.Rd index 6a4ea76..aad9416 100644 --- a/man/dot-gm_clust_fit_mclust.Rd +++ b/man/dot-gm_clust_fit_mclust.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/gm_clust.R \name{.gm_clust_fit_mclust} \alias{.gm_clust_fit_mclust} -\title{Simple Wrapper around dbscan function} +\title{Simple Wrapper around Mclust function} \usage{ .gm_clust_fit_mclust(x, G = NULL, ...) } diff --git a/vignettes/articles/db_clust.Rmd b/vignettes/articles/db_clust.Rmd index 6839510..256a43d 100644 --- a/vignettes/articles/db_clust.Rmd +++ b/vignettes/articles/db_clust.Rmd @@ -97,7 +97,6 @@ penguins_std %>% ``` - ```{r, echo = FALSE} penguins_new <- penguins_std penguins_new$cluster <- factor(dbscan_fit$cluster) @@ -113,9 +112,9 @@ penguins_new %>% theme(legend.position = "none") ``` -2. For each Core point, assign it and all the points within the specified radius around the point ta +2. For each core point, assign it to a cluster as well as all the points within the specified **radius** of the point. -```{r, echo = FALSE} +```{r, echo = FALSE, include = FALSE} penguins_new %>% ggplot() + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + @@ -146,6 +145,7 @@ penguins_new %>% scale_color_manual(values = c("#619CFF", "black", "red")) ``` +3. For all the points added to the cluster, check if a point added is a core point. If a point is a core point recursively add all other points wihtin that core point's **radius** to the current cluster. ```{r, echo = FALSE} penguins_new %>% @@ -162,6 +162,7 @@ penguins_new %>% scale_color_manual(values = c("#619CFF", "black", "#619CFF", "red")) ``` +4. Repeat until all core points have been assigned clusters ```{r, echo = FALSE} penguins_new %>% @@ -180,6 +181,7 @@ penguins_new %>% scale_color_manual(values = c("#00BA38", "black", "#619CFF", "red")) ``` +5. Points not assigned to a cluster are considered outliers by the algorithm. ```{r, echo = FALSE} penguins_new %>% @@ -232,8 +234,17 @@ We can also extract the standard `tidyclust` summary list: db_clust_fit %>% extract_cluster_assignment() ``` +```{r} +penguins_new %>% + mutate(cluster2 = (db_clust_fit %>% extract_cluster_assignment())$.cluster) %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster2)) +``` + + ```{r} +#| include: false penguins_new %>% mutate(cluster2 = (db_clust_fit %>% extract_cluster_assignment())$.cluster) %>% ggplot() + @@ -263,6 +274,9 @@ The `predict()` function behaves as expected, producing cluster assignment predictions on new data based on distance to the core points from the fitted model. +*Need to make sure it predicts cluster of CLOSEST core point* + + ```{r} new_penguin <- tibble( bill_length_std = -1.2454235, @@ -273,8 +287,8 @@ db_clust_fit %>% predict(new_penguin) ``` -```{r} -set.seed(80) +```{r, include=FALSE} +# set.seed(80) dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) @@ -285,54 +299,132 @@ pred <- dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_ ```{r} +dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) +is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) +pred_data <- penguins_std[is_core,] + +pred <- dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) + +pred[144] +``` + + + + +```{r, include = FALSE} + + +dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) +is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) +pred_data <- penguins_std[is_core,] + +pred <- dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) + test <- db_clust_fit %>% predict(penguins_std) %>% mutate(.fit_cluster = (db_clust_fit %>% extract_cluster_assignment())$.cluster, .bc = pred, .bc = case_when(.bc == 0 ~ "Outlier", TRUE ~ paste0("Cluster_", .bc))) -# %>% -# filter(.pred_cluster != .fit_cluster) -# want to assign to closer corepoint - -which(test$.pred_cluster != test$.bc) +which(test$.fit_cluster != test$.bc) ``` -```{r, include = FALSE} +```{r} dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) -pred_data <- penguins_std[is_core,] +core_points <- penguins_std[is_core,] +non_core_points <- penguins_std[!is_core,] -pred <- dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) - -preds <- cbind( - db_clust_fit %>% extract_cluster_assignment(), - db_clust_fit %>% predict(penguins_std), - .pred_clust_adj = paste0("Cluster_", dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps)) - +non_core_point_fits <- dbscan:::.predict_frNN(newdata = non_core_points, data = core_points, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) +core_point_fits <- dbscan_adj$cluster[is_core] ``` + + ```{r} -preds %>% filter(.cluster != .pred_cluster) +penguins_temp <- penguins_std +ncps <- data.frame(non_core_point_fits) +cps <- data.frame(core_point_fits) + +core_point_fits <- dbscan_adj$cluster[is_core] + +penguins_std$is_core <- as.character(as.numeric(is_core)) + +penguins_std$id <- ave(penguins_std$is_core, penguins_std$is_core, FUN = seq_along) +ncps$id <- 1:nrow(ncps) +ncps$is_core <- "0" +cps$id <- 1:nrow(cps) +cps$is_core <- "1" + +penguins_temp <- merge(x = penguins_temp, y = ncps, by = c("id", "is_core"), all.x = TRUE) +penguins_temp <- merge(x = penguins_temp, y = cps, by = c("id", "is_core"), all.x = TRUE) + +penguins_temp$cluster <- ifelse(!is.na(penguins_temp$non_core_point_fits), penguins_temp$non_core_point_fits, penguins_temp$core_point_fits) + + ``` + ```{r} -cbind( - attr(db_clust_fit$fit, "training_data"), - attr(db_clust_fit$fit, "core_points") -) -dist(db_clust_fit) +dbscan_helper <- function(object, + ...) { + + is_core <- attr(object, "is_core") + print(length(is_core)) + training_data <- data.frame(attr(object, "training_data")) + cp <- training_data[is_core,] + non_cp <- training_data[!is_core,] + cp_clusters <- object$cluster[is_core] + eps <- attr(object, "radius") + # get fit values according to closest core point + non_cp_clusters <- dbscan:::.predict_frNN(newdata = non_cp, data = cp, cp_clusters, eps = eps) + # join back separated fits into proper order in training data + non_cp_clusters <- data.frame(non_cp_clusters) + cp_clusters <- data.frame(cp_clusters) -new_penguin + # create vars to join back results in proper order + training_data$is_core <- ifelse(is_core, "cp", "non cp") + non_cp_clusters$is_core <- "non cp" + cp_clusters$is_core <- "cp" + training_data$overall_order <- 1:nrow(training_data) + training_data$id <- ave(training_data$is_core, training_data$is_core, FUN = seq_along) + non_cp_clusters$id <- 1:nrow(non_cp_clusters) + cp_clusters$id <- 1:nrow(cp_clusters) + + training_data <- merge(x = training_data, y = non_cp_clusters, by = c("id", "is_core"), all.x = TRUE) + training_data <- merge(x = training_data, y = cp_clusters, by = c("id", "is_core"), all.x = TRUE) + + training_data$cluster <- ifelse(!is.na(training_data$non_cp_clusters), training_data$non_cp_clusters, training_data$cp_clusters) + + training_data$cluster[order(training_data$overall_order)] + +} ``` + +```{r} +dbscan_helper(db_clust_fit$fit) +``` + + +```{r} +penguins_new %>% + mutate(cluster2 = factor(dbscan_helper(db_clust_fit$fit))) %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster2)) + + +dbscan_helper(db_clust_fit$fit) +``` + + diff --git a/vignettes/articles/gm_clust.Rmd b/vignettes/articles/gm_clust.Rmd index bdae1f4..bece292 100644 --- a/vignettes/articles/gm_clust.Rmd +++ b/vignettes/articles/gm_clust.Rmd @@ -113,18 +113,59 @@ gm_clust_fit %>% ``` -## A brief introduction to guassian mixture models +## A brief introduction to density-based clustering -write and explain here +Gaussian Mixture Models (GMM) is a probabilistic unsupervised learning method that models data as a mixture of multiple Gaussian distributions. Unlike clustering methods such as k-means, GMM provides a soft clustering approach, where each observation has a probability of belonging to multiple clusters. This allows for more flexibility in capturing complex data distributions. +In GMM, observations are assumed to be generated from a combination of Gaussian distributions, each with its own mean and covariance. The algorithm works by iteratively estimating the parameters of these distributions using the Expectation-Maximization (EM) algorithm. During the fitting process, observations are assigned to clusters based on their probability of belonging to each Gaussian component (**num_clusters**), making GMM effective for identifying overlapping or elliptical clusters in data. +```{r} +penguins %>% + mutate(cluster = paste0("Cluster_", sample(c(1,2,3), size = nrow(penguins), replace = TRUE))) %>% + ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = cluster)) + + geom_point() + + stat_ellipse(level = .50, type = "norm", linetype = 1) + + stat_ellipse(level = .75, type = "norm", linetype = 1) + + stat_ellipse(level = .95, type = "norm", linetype = 1) + + theme_minimal() + + theme(legend.position = "none") + # scale_color_manual(values = c("red")) +``` + +```{r} +penguins %>% + mutate(cluster = (gm_clust_fit %>% extract_cluster_assignment())$.cluster) %>% + ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = cluster)) + + geom_point() + + stat_ellipse(level = .50, type = "norm", linetype = 1) + + stat_ellipse(level = .75, type = "norm", linetype = 1) + + stat_ellipse(level = .95, type = "norm", linetype = 1) + + theme_minimal() + + scale_color_manual(values = c("#F8766D", "#00BA38", "#619CFF")) + + theme(legend.position = "none") +``` + + + +```{r} +gm_clust_engine <- gm_clust_fit %>% extract_fit_engine() +``` + + +```{r} +gm_clust_engine$parameters +``` + +```{r} +plot(gm_clust_engine) +``` From 2112bd36cbb558b87ece67b58c89b73a5ccdb150 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Thu, 6 Mar 2025 12:18:00 -0800 Subject: [PATCH 08/44] fix gm clust bug + finalize vignettes --- NAMESPACE | 3 + R/db_clust.R | 3 +- R/extract_cluster_assignment.R | 10 +- R/extract_fit_summary.R | 64 ++++++ R/gm_clust.R | 1 + R/predict_helpers.R | 2 +- man/dbscan_helper.Rd | 2 +- vignettes/articles/db_clust.Rmd | 395 ++++++++------------------------ vignettes/articles/gm_clust.Rmd | 164 ++++++++++--- 9 files changed, 294 insertions(+), 350 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 759e97a..46eacc1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -19,8 +19,10 @@ S3method(extract_cluster_assignment,kproto) S3method(extract_cluster_assignment,workflow) S3method(extract_fit_engine,cluster_fit) S3method(extract_fit_summary,KMeansCluster) +S3method(extract_fit_summary,Mclust) S3method(extract_fit_summary,cluster_fit) S3method(extract_fit_summary,cluster_spec) +S3method(extract_fit_summary,dbscan) S3method(extract_fit_summary,hclust) S3method(extract_fit_summary,kmeans) S3method(extract_fit_summary,kmodes) @@ -161,6 +163,7 @@ importFrom(hardhat,extract_parameter_set_dials) importFrom(hardhat,extract_preprocessor) importFrom(hardhat,extract_spec_parsnip) importFrom(hardhat,tune) +importFrom(mclust,mclustBIC) importFrom(parsnip,make_call) importFrom(parsnip,maybe_data_frame) importFrom(parsnip,maybe_matrix) diff --git a/R/db_clust.R b/R/db_clust.R index 7b87c7d..0728d1e 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -180,7 +180,7 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { attr(res, "radius") <- eps attr(res, "minpts") <- minPts attr(res, "training_data") <- x - is_core <- is.corepoint(x, eps = eps, minPts = minPts) + is_core <- dbscan::is.corepoint(x, eps = eps, minPts = minPts) attr(res, "is_core") <- is_core res @@ -198,7 +198,6 @@ dbscan_helper <- function(object, ...) { is_core <- attr(object, "is_core") - print(length(is_core)) training_data <- data.frame(attr(object, "training_data")) cp <- training_data[is_core,] non_cp <- training_data[!is_core,] diff --git a/R/extract_cluster_assignment.R b/R/extract_cluster_assignment.R index 1ff9984..e989190 100644 --- a/R/extract_cluster_assignment.R +++ b/R/extract_cluster_assignment.R @@ -193,14 +193,6 @@ cluster_assignment_tibble_w_outliers <- function(clusters, names <- paste0(prefix, 0:(n_clusters-1)) res <- names[reorder_clusts][clusters+1] res[res == paste0(prefix, "0")] <- "Outlier" - # zero_count <- 0 - # res <- sapply(res, function(x) { - # if (x == "Cluster_0") { - # zero_count <<- zero_count + 1 - # paste0("Cluster_0_", zero_count) - # } else { - # x - # } - # }) + tibble::tibble(.cluster = factor(res)) } diff --git a/R/extract_fit_summary.R b/R/extract_fit_summary.R index ce6526a..71f11d4 100644 --- a/R/extract_fit_summary.R +++ b/R/extract_fit_summary.R @@ -179,3 +179,67 @@ extract_fit_summary.hclust <- function(object, ...) { cluster_assignments = clusts ) } + +#' @export +extract_fit_summary.dbscan <- function(object, ...) { + clusts <- extract_cluster_assignment(object, ...)$.cluster + n_clust <- dplyr::n_distinct(clusts)-1 + n_outliers <- length(clusts == "Outlier") + training_data <- attr(object, "training_data") + + overall_centroid <- colMeans(training_data) + + by_clust <- training_data %>% + tibble::as_tibble() %>% + dplyr::mutate( + .cluster = clusts + ) %>% + dplyr::filter(.cluster != "Outlier") %>% + dplyr::group_by(.cluster) %>% + tidyr::nest() + + centroids <- by_clust$data %>% + map(dplyr::summarize_all, mean) %>% + dplyr::bind_rows() + + + list( + cluster_names = unique(clusts), + centroids = centroids, + n_members = unname(as.integer(table(clusts))), + n_outliers = n_outliers, + orig_labels = NULL, + cluster_assignments = clusts + ) +} + +#' @export +extract_fit_summary.Mclust <- function(object, ...) { + clusts <- extract_cluster_assignment(object, ...)$.cluster + n_clust <- dplyr::n_distinct(clusts) + training_data <- attr(object, "training_data") + + overall_centroid <- colMeans(training_data) + + by_clust <- training_data %>% + tibble::as_tibble() %>% + dplyr::mutate( + .cluster = clusts + ) %>% + dplyr::filter(.cluster != "Outlier") %>% + dplyr::group_by(.cluster) %>% + tidyr::nest() + + centroids <- by_clust$data %>% + map(dplyr::summarize_all, mean) %>% + dplyr::bind_rows() + + + list( + cluster_names = unique(clusts), + centroids = centroids, + n_members = unname(as.integer(table(clusts))), + orig_labels = NULL, + cluster_assignments = clusts + ) +} diff --git a/R/gm_clust.R b/R/gm_clust.R index 9c65910..3b645e0 100644 --- a/R/gm_clust.R +++ b/R/gm_clust.R @@ -17,6 +17,7 @@ #' to use for fitting. The engine for this model is `"mclust"`. #' @param num_clusters Positive integer, number of clusters in model (required). #' +#' @importFrom mclust mclustBIC #' #' @details #' diff --git a/R/predict_helpers.R b/R/predict_helpers.R index a991112..23dd272 100644 --- a/R/predict_helpers.R +++ b/R/predict_helpers.R @@ -177,7 +177,7 @@ make_predictions_w_outliers <- function(x, prefix, n_clusters) { .gm_clust_predict_mclust <- function(object, new_data, prefix = "Cluster_") { clusters <- predict(object, newdata = new_data)$classification - n_clusters <- length(clusters %>% unique()) + n_clusters <- attr(object, "num_clusters") make_predictions(clusters, prefix, n_clusters) } diff --git a/man/dbscan_helper.Rd b/man/dbscan_helper.Rd index 9bdeef1..7a557ac 100644 --- a/man/dbscan_helper.Rd +++ b/man/dbscan_helper.Rd @@ -4,7 +4,7 @@ \alias{dbscan_helper} \title{dbscan fit helper function} \usage{ -dbscan_helper(x, ...) +dbscan_helper(object, ...) } \arguments{ \item{x}{matrix or data frame} diff --git a/vignettes/articles/db_clust.Rmd b/vignettes/articles/db_clust.Rmd index 256a43d..405d198 100644 --- a/vignettes/articles/db_clust.Rmd +++ b/vignettes/articles/db_clust.Rmd @@ -10,27 +10,37 @@ vignette: > ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, - comment = "#>" + comment = "#>", + message = FALSE, + warning = FALSE ) ``` ## Setup + ```{r} library(workflows) library(parsnip) ``` -```{r} +Load libraries: + +```{r setup} library(tidyclust) library(tidyverse) library(tidymodels) -set.seed(838383) ``` +```{r setup_secret, echo = FALSE} +library(ggforce) +set.seed(822) +``` + Load and clean a dataset: + ```{r} data("penguins", package = "modeldata") @@ -38,7 +48,6 @@ penguins <- penguins %>% select(bill_length_mm, bill_depth_mm) %>% drop_na() - # shuffle rows penguins <- penguins %>% sample_n(nrow(penguins)) @@ -46,156 +55,11 @@ penguins <- penguins %>% At the end of this vignette, you will find a brief overview of the DBSCAN algorithm. -## A brief introduction to density-based clustering - -Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a method of unsupervised learning that groups observations into clusters based on their density in multidimensional space. Unlike methods such as k-means, DBSCAN does not require specifying the number of clusters beforehand and can identify clusters of arbitrary shapes. Additionally, it can classify points that do not belong to any cluster as noise, allowing for greater flexibility in handling real-world data. - -In DBSCAN, observations are considered as locations in multidimensional space. The algorithm works by defining a cluster as a dense region of connected points. During the fitting process, points are classified as core points, border points, or noise based on their proximity to other points (**radius**) and a density threshold (**minpts**). - -```{r, include = FALSE} -penguins_std <- penguins %>% - mutate(bill_length_std = scale(bill_length_mm)[,1], - bill_depth_std = scale(bill_depth_mm)[,1]) %>% - select(bill_length_std, bill_depth_std) -``` - - -```{r, echo = FALSE} - -# penguins_std %>% - # ggplot() + - # geom_point(aes(x = bill_length_std, y = bill_depth_std)) + - # theme_minimal() + - # coord_fixed() - -``` - -1. For each observation in the data, determine whether each point is a core point. A core points is defined as an observation which has more than the specified **minpts** points (including the point itself) within the specified **radius** around the point. - - -```{r, include = FALSE} -library(dbscan) -eps <- 0.35 -minpts <- 10 -dbscan_fit <- dbscan(penguins_std, eps = eps, minPts = minpts) - -``` - -```{r, echo = FALSE} -library(ggforce) -penguins_std %>% - ggplot() + - geom_point(aes(x = bill_length_std, y = bill_depth_std)) + - theme_minimal() + - geom_circle(tibble(x = c(-0.79162259), y = c(1.79706436), radius = rep(eps,1)), - mapping = aes(x0 = x, y0 = y, r = eps), - color = "gray", linewidth = 0.8) + - geom_point(tibble(x = c(-0.79162259), y = c(1.79706436)), mapping = aes(x = x, y = y, color = c("1"))) + - coord_fixed() + - theme(legend.position = "none") + - scale_color_manual(values = c("red")) -``` - - -```{r, echo = FALSE} -penguins_new <- penguins_std -penguins_new$cluster <- factor(dbscan_fit$cluster) -penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = minpts)) == 1, "Yes", "No"), levels = c("Yes", "No")) - -penguins_new %>% - ggplot(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + - geom_point() + - theme_minimal() + - coord_fixed() + - labs(color = "Is a core point?") + - scale_color_manual(values = c("red", "black")) + - theme(legend.position = "none") -``` - -2. For each core point, assign it to a cluster as well as all the points within the specified **radius** of the point. - -```{r, echo = FALSE, include = FALSE} -penguins_new %>% - ggplot() + - geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + - theme_minimal() + - geom_circle(tibble(x = c(-1.10300165), y = c(0.73366185), radius = rep(eps, 1)), - mapping = aes(x0 = x, y0 = y, r = eps), - color = "gray", linewidth = 0.8) + - geom_point(tibble(x = c(-1.10300165), y = c(0.73366185)), mapping = aes(x = x, y = y, color = c("1"))) + - coord_fixed() + - theme(legend.position = "none") + - scale_color_manual(values = c("red", "black", "#619CFF")) -``` - - - -```{r, echo = FALSE} -penguins_new %>% - mutate(cp = if_else(sqrt((bill_length_std - -1.10300165)^2 + (bill_depth_std - 0.73366185)^2) <= eps, "1", cp)) %>% - ggplot() + - geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + - theme_minimal() + - geom_circle(tibble(x = c(-1.10300165), y = c(0.73366185), radius = rep(eps, 1)), - mapping = aes(x0 = x, y0 = y, r = eps), - color = "gray", linewidth = 0.8) + - geom_point(tibble(x = c(-1.10300165), y = c(0.73366185)), mapping = aes(x = x, y = y, color = c("1"))) + - coord_fixed() + - theme(legend.position = "none") + - scale_color_manual(values = c("#619CFF", "black", "red")) -``` - -3. For all the points added to the cluster, check if a point added is a core point. If a point is a core point recursively add all other points wihtin that core point's **radius** to the current cluster. - -```{r, echo = FALSE} -penguins_new %>% - mutate(cluster_temp = if_else(cluster == 1, "temp", cp)) %>% - ggplot() + - geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster_temp)) + - theme_minimal() + - geom_circle(tibble(x = c(-1.25), y = c(-0.32), radius = rep(eps, 1)), - mapping = aes(x0 = x, y0 = y, r = eps), - color = "gray", linewidth = 0.8) + - geom_point(tibble(x = c(-1.25), y = c(-0.32)), mapping = aes(x = x, y = y, color = c("1"))) + - coord_fixed() + - theme(legend.position = "none") + - scale_color_manual(values = c("#619CFF", "black", "#619CFF", "red")) -``` - -4. Repeat until all core points have been assigned clusters - -```{r, echo = FALSE} -penguins_new %>% - mutate(cluster_temp = case_when(cluster == 1 ~ "temp", - sqrt((bill_length_std - 1.2)^2 + (bill_depth_std - 1.05)^2) <= eps ~ "1", - TRUE ~ cp)) %>% - ggplot() + - geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster_temp)) + - theme_minimal() + - geom_circle(tibble(x = c(1.2), y = c(1.05), radius = rep(eps, 1)), - mapping = aes(x0 = x, y0 = y, r = eps), - color = "gray", linewidth = 0.8) + - geom_point(tibble(x = c(1.2), y = c(1.05)), mapping = aes(x = x, y = y, color = c("1")), size = 1.8) + - coord_fixed() + - theme(legend.position = "none") + - scale_color_manual(values = c("#00BA38", "black", "#619CFF", "red")) -``` - -5. Points not assigned to a cluster are considered outliers by the algorithm. - -```{r, echo = FALSE} -penguins_new %>% - ggplot() + - geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster)) + - theme_minimal() + - coord_fixed() + - scale_color_manual(values = c("black", "#619CFF", "#F8766D", "#00BA38", "#C77CFF")) + - theme(legend.position = "none") -``` +## `db_clust` specification in {`tidyclust`} -## db clust specification in {`tidyclust`} +To specify a DBSCAN model in `tidyclust`, simply set the values for `radius` and `min_pts`: ```{r} db_clust_spec <- db_clust(radius = 0.35, minpts = 10) @@ -203,13 +67,21 @@ db_clust_spec <- db_clust(radius = 0.35, minpts = 10) db_clust_spec ``` -There is currently one engine: `dbscan::dbscan` (default) +There is currently one engine: `dbscan::dbscan` (default) ## Fitting db_clust models -We fit the model to data in the usual way: +After specifying the model specification, we fit the model to data in the usual way: + +Note that I have standardized bill length and bill depth since DBSCAN uses euclidean distance to fit clusters and these variables are on different scales. ```{r} + +penguins_std <- penguins %>% + mutate(bill_length_std = scale(bill_length_mm)[,1], + bill_depth_std = scale(bill_depth_mm)[,1]) %>% + select(bill_length_std, bill_depth_std) + db_clust_fit <- db_clust_spec %>% fit( ~ bill_length_std + bill_depth_std, data = penguins_std @@ -221,60 +93,36 @@ db_clust_fit %>% We can also extract the standard `tidyclust` summary list: - ```{r} -# no extract fit summary method for db_clust object yet +db_clust_summary <- db_clust_fit %>% extract_fit_summary() -# db_clust_summary <- db_clust_fit %>% extract_fit_summary() +db_clust_summary %>% str() -# db_clust_summary %>% str() ``` -```{r} -db_clust_fit %>% extract_cluster_assignment() -``` +## Cluster assignments and centers -```{r} -penguins_new %>% - mutate(cluster2 = (db_clust_fit %>% extract_cluster_assignment())$.cluster) %>% - ggplot() + - geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster2)) -``` +The cluster assignments for the training data can be accessed using the `extract_cluster_assignment()` function. +Note that the DBSCAN algorithm allows for some points to not be assigned clusters. These points are labeled as outliers. ```{r} -#| include: false -penguins_new %>% - mutate(cluster2 = (db_clust_fit %>% extract_cluster_assignment())$.cluster) %>% - ggplot() + - geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster2)) + - geom_point(aes(x = bill_length_std, y = bill_depth_std, color = "green", size = 2), data = penguins_new %>% .[144,]) + - theme_minimal() + - coord_fixed() + - scale_color_manual(values = c("#619CFF", "#F8766D", "#00BA38", "black", "green")) + - theme(legend.position = "none") +db_clust_fit %>% extract_cluster_assignment() ``` +### Centroids -If you have not yet read the `k_means` vignette, we recommend reading that first; -functions that are used in this vignette are explained in more detail there. +While the centroids produced by a db_clust fit may not be of primary interest, they can still be accessed via the `extract_fit_summary()` object. +```{r} +db_clust_summary$centroids +``` ## Prediction - - -Since $DBSCAN$ algorithm ultimately assigns training observations to the -clusters based on proximity to core points, it is natural to "predict" that test -observations belong to a cluster if they are within $radius$ distance to a core point. - -The `predict()` function behaves as expected, producing cluster assignment -predictions on new data based on distance to the core points from the fitted model. - - -*Need to make sure it predicts cluster of CLOSEST core point* +Since $DBSCAN$ algorithm ultimately assigns training observations to the clusters based on proximity to core points, it is natural to "predict" that test observations belong to a cluster if they are within `radius` distance to a core point. To reconcile points being within the radius of two core points that belong to different clusters, the `predict()` function assigns new observations to the cluster of the closest core point. If a point is not within the `radius` of any core points, it is predicted to be an outlier. ```{r} @@ -287,144 +135,87 @@ db_clust_fit %>% predict(new_penguin) ``` -```{r, include=FALSE} -# set.seed(80) - -dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) -is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) -pred_data <- penguins_std[is_core,] - -pred <- dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) -``` - - -```{r} -dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) -is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) -pred_data <- penguins_std[is_core,] - -pred <- dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) - -pred[144] -``` - - +## A brief introduction to density-based clustering -```{r, include = FALSE} - +Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a method of unsupervised learning that groups observations into clusters based on their density in multidimensional space. Unlike methods such as k-means, DBSCAN does not require specifying the number of clusters beforehand and can identify clusters of arbitrary shapes. Additionally, it can classify points that do not belong to any cluster as outliers, allowing for greater flexibility in handling real-world data. -dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) -is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) -pred_data <- penguins_std[is_core,] +In DBSCAN, observations are considered as locations in multidimensional space. The algorithm works by defining a cluster as a dense region of connected points. During the fitting process, points are classified as core points, border points, or noise based on their proximity to other points (**radius**) and a density threshold (**min_pts**). -pred <- dbscan:::.predict_frNN(newdata = penguins_std, data = pred_data, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) +The fitting process can be described as follows: -test <- db_clust_fit %>% - predict(penguins_std) %>% - mutate(.fit_cluster = (db_clust_fit %>% extract_cluster_assignment())$.cluster, - .bc = pred, - .bc = case_when(.bc == 0 ~ "Outlier", - TRUE ~ paste0("Cluster_", .bc))) +```{r, echo = FALSE, include = FALSE} +penguins_std %>% +ggplot() + +geom_point(aes(x = bill_length_std, y = bill_depth_std)) + +theme_minimal() + +coord_fixed() -which(test$.fit_cluster != test$.bc) ``` +1. For each observation in the data, determine whether each point is a core point. A core point is defined as an observation which has more than the specified **minpts** points (including the point itself) within the specified **radius** around the point. -```{r} -dbscan_adj <- dbscan(penguins_std, eps = eps, minPts = minpts) -is_core <- is.corepoint(penguins_std, eps = eps, minPts = minpts) -core_points <- penguins_std[is_core,] -non_core_points <- penguins_std[!is_core,] +```{r, include = FALSE} +library(dbscan) +eps <- 0.35 +minpts <- 10 +dbscan_fit <- dbscan(penguins_std, eps = eps, minPts = minpts) -non_core_point_fits <- dbscan:::.predict_frNN(newdata = non_core_points, data = core_points, dbscan_adj$cluster[is_core], eps = dbscan_adj$eps) -core_point_fits <- dbscan_adj$cluster[is_core] ``` +```{r, echo = FALSE} +par(mfrow=c(1,2)) -```{r} -penguins_temp <- penguins_std -ncps <- data.frame(non_core_point_fits) -cps <- data.frame(core_point_fits) - -core_point_fits <- dbscan_adj$cluster[is_core] - -penguins_std$is_core <- as.character(as.numeric(is_core)) - -penguins_std$id <- ave(penguins_std$is_core, penguins_std$is_core, FUN = seq_along) -ncps$id <- 1:nrow(ncps) -ncps$is_core <- "0" -cps$id <- 1:nrow(cps) -cps$is_core <- "1" - -penguins_temp <- merge(x = penguins_temp, y = ncps, by = c("id", "is_core"), all.x = TRUE) -penguins_temp <- merge(x = penguins_temp, y = cps, by = c("id", "is_core"), all.x = TRUE) - -penguins_temp$cluster <- ifelse(!is.na(penguins_temp$non_core_point_fits), penguins_temp$non_core_point_fits, penguins_temp$core_point_fits) +penguins_std %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std)) + + theme_minimal() + + geom_circle(tibble(x = c(-0.79162259), y = c(0.2), radius = rep(eps,1)), + mapping = aes(x0 = x, y0 = y, r = eps), + color = "gray", linewidth = 0.8) + + geom_point(tibble(x = c(-0.79162259), y = c(0.2)), mapping = aes(x = x, y = y, color = c("1"))) + + coord_fixed() + + theme(legend.position = "none") + + scale_color_manual(values = c("red")) +penguins_new <- penguins_std +penguins_new$cluster <- factor(dbscan_fit$cluster) +penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = minpts)) == 1, "Yes", "No"), levels = c("Yes", "No")) +penguins_new %>% + ggplot(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + + geom_point() + + theme_minimal() + + coord_fixed() + + scale_color_manual(values = c("red", "black")) + + theme(legend.position = "none") ``` +2. For each core point, assign it to a cluster as well as all other core points within the specified **radius** of the point. Do until all core points have been assigned to a cluster. -```{r} - - -dbscan_helper <- function(object, - ...) { - - is_core <- attr(object, "is_core") - print(length(is_core)) - training_data <- data.frame(attr(object, "training_data")) - cp <- training_data[is_core,] - non_cp <- training_data[!is_core,] - cp_clusters <- object$cluster[is_core] - eps <- attr(object, "radius") - - # get fit values according to closest core point - non_cp_clusters <- dbscan:::.predict_frNN(newdata = non_cp, data = cp, cp_clusters, eps = eps) - - # join back separated fits into proper order in training data - non_cp_clusters <- data.frame(non_cp_clusters) - cp_clusters <- data.frame(cp_clusters) - - # create vars to join back results in proper order - training_data$is_core <- ifelse(is_core, "cp", "non cp") - non_cp_clusters$is_core <- "non cp" - cp_clusters$is_core <- "cp" - - training_data$overall_order <- 1:nrow(training_data) - training_data$id <- ave(training_data$is_core, training_data$is_core, FUN = seq_along) - non_cp_clusters$id <- 1:nrow(non_cp_clusters) - cp_clusters$id <- 1:nrow(cp_clusters) - - training_data <- merge(x = training_data, y = non_cp_clusters, by = c("id", "is_core"), all.x = TRUE) - training_data <- merge(x = training_data, y = cp_clusters, by = c("id", "is_core"), all.x = TRUE) - - training_data$cluster <- ifelse(!is.na(training_data$non_cp_clusters), training_data$non_cp_clusters, training_data$cp_clusters) - - training_data$cluster[order(training_data$overall_order)] - -} - +```{r, echo = FALSE} +penguins_new %>% + mutate(cp_color = if_else(cp == "Yes", cluster, "0")) %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cp_color)) + + theme_minimal() + + coord_fixed() + + scale_color_manual(values = c("black", "#619CFF", "#F8766D", "#00BA38")) + + theme(legend.position = "none") ``` +3. For the remaining points not assigned to a cluster, check whether the point is within the radius of a core point. For points not in the radius of any core points, assign the point as an outlier. Otherwise, assign the point to the cluster of the closest core point. -```{r} -dbscan_helper(db_clust_fit$fit) -``` - -```{r} +```{r, echo = FALSE} penguins_new %>% - mutate(cluster2 = factor(dbscan_helper(db_clust_fit$fit))) %>% ggplot() + - geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster2)) - - -dbscan_helper(db_clust_fit$fit) + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster)) + + theme_minimal() + + coord_fixed() + + scale_color_manual(values = c("black", "#619CFF", "#F8766D", "#00BA38")) + + theme(legend.position = "none") ``` - - diff --git a/vignettes/articles/gm_clust.Rmd b/vignettes/articles/gm_clust.Rmd index bece292..5315dee 100644 --- a/vignettes/articles/gm_clust.Rmd +++ b/vignettes/articles/gm_clust.Rmd @@ -10,7 +10,9 @@ vignette: > ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, - comment = "#>" + comment = "#>", + message = FALSE, + warning = FALSE ) ``` @@ -19,17 +21,21 @@ knitr::opts_chunk$set( ```{r} library(workflows) library(parsnip) -library(mclust) ``` +Load Libraries: + ```{r} library(tidyclust) library(tidyverse) library(tidymodels) -set.seed(838383) ``` +```{r setup_secret, echo = FALSE} +set.seed(822) +``` + Load and clean a dataset: ```{r} @@ -51,7 +57,9 @@ At the end of this vignette, you will find a brief overview of the GMM algorithm -## gm clust specification in {`tidyclust`} +## `gm_clust` specification in {`tidyclust`} + +To specify a GMM model in `tidyclust`, simply set the value for `num_clusters`: ```{r} gm_clust_spec <- gm_clust(num_clusters = 3) @@ -63,7 +71,7 @@ There is currently one engine: `mclust::Mclust` (default) ## Fitting gm_clust models -We fit the model to data in the usual way: +After specifying the model specification, we fit the model to data in the usual way: ```{r} gm_clust_fit <- gm_clust_spec %>% @@ -74,33 +82,46 @@ gm_clust_fit <- gm_clust_spec %>% gm_clust_fit %>% summary() ``` -We can also extract the standard `tidyclust` summary list: +We can also extract the standard `tidyclust` summary list: ```{r} -# gm_clust_summary <- gm_clust_fit %>% extract_fit_summary() +gm_clust_summary <- gm_clust_fit %>% extract_fit_summary() -# gm_clust_summary %>% str() +gm_clust_summary %>% str() ``` +## Cluster assignments and centers +The cluster assignments for the training data can be accessed using the `extract_cluster_assignment()` function. ```{r} gm_clust_fit %>% extract_cluster_assignment() +``` +**Should probably make some way to extract probabilities of belonging to each group** - +```{r} +gm_clust_fit$fit$z %>% head() ``` + + If you have not yet read the `k_means` vignette, we recommend reading that first; functions that are used in this vignette are explained in more detail there. +### Centroids + +The centroids for the fitted clusters can be accessed via `extract_centroids()`: + +```{r} +gm_clust_fit %>% extract_centroids() +``` + ## Prediction - - -write stuff here +For GMMs each cluster is modeled as a multivariate normal distribution. This makes prediction easy since we can calculate the probability that each point belongs to each of the fitted clusters. Therefore, it is natural for the `predict()` function to assign new observations to the cluster in which they have the highest probability of belonging to. ```{r} new_penguin <- tibble( @@ -116,7 +137,6 @@ gm_clust_fit %>% ## A brief introduction to density-based clustering - Gaussian Mixture Models (GMM) is a probabilistic unsupervised learning method that models data as a mixture of multiple Gaussian distributions. Unlike clustering methods such as k-means, GMM provides a soft clustering approach, where each observation has a probability of belonging to multiple clusters. This allows for more flexibility in capturing complex data distributions. In GMM, observations are assumed to be generated from a combination of Gaussian distributions, each with its own mean and covariance. The algorithm works by iteratively estimating the parameters of these distributions using the Expectation-Maximization (EM) algorithm. During the fitting process, observations are assigned to clusters based on their probability of belonging to each Gaussian component (**num_clusters**), making GMM effective for identifying overlapping or elliptical clusters in data. @@ -126,22 +146,108 @@ In GMM, observations are assumed to be generated from a combination of Gaussian ```{r} -penguins %>% - mutate(cluster = paste0("Cluster_", sample(c(1,2,3), size = nrow(penguins), replace = TRUE))) %>% - ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = cluster)) + - geom_point() + - stat_ellipse(level = .50, type = "norm", linetype = 1) + - stat_ellipse(level = .75, type = "norm", linetype = 1) + - stat_ellipse(level = .95, type = "norm", linetype = 1) + - theme_minimal() + - theme(legend.position = "none") - # scale_color_manual(values = c("red")) +#| include: false +plot_state <- function(obj) { + + data.grid <- expand.grid(bill_length_mm = seq(20, 80, length.out=200), bill_depth_mm = seq(10, 25, length.out=200)) + + mean1 <- obj$mean1 + mean2 <- obj$mean2 + mean3 <- obj$mean3 + + sigma1 <- obj$sigma1 + sigma2 <- obj$sigma2 + sigma3 <- obj$sigma3 + + q.samp1 <- cbind(data.grid, prob = mvtnorm::dmvnorm(data.grid, mean = mean1, sigma = sigma1)) + q.samp2 <- cbind(data.grid, prob = mvtnorm::dmvnorm(data.grid, mean = mean2, sigma = sigma2)) + q.samp3 <- cbind(data.grid, prob = mvtnorm::dmvnorm(data.grid, mean = mean3, sigma = sigma3)) + + penguins_temp <- penguins %>% + mutate(p1 = mvtnorm::dmvnorm(cbind(bill_length_mm, bill_depth_mm), mean = mean1, sigma = sigma1), + p2 = mvtnorm::dmvnorm(cbind(bill_length_mm, bill_depth_mm), mean = mean2, sigma = sigma2), + p3 = mvtnorm::dmvnorm(cbind(bill_length_mm, bill_depth_mm), mean = mean3, sigma = sigma3), + class = case_when(p1 > p2 & p1 > p3 ~ "1", + p2 > p3 ~ "2", + TRUE ~ "3")) + + ggplot() + + geom_point(aes(x = bill_length_mm, y = bill_depth_mm, color = class), data = penguins_temp) + + geom_contour(aes(x=bill_length_mm, y=bill_depth_mm, z=prob), data = q.samp1, color = "#F8766D", bins = 5) + + geom_contour(aes(x=bill_length_mm, y=bill_depth_mm, z=prob), data = q.samp2, color = "#00BA38", bins = 5) + + geom_contour(aes(x=bill_length_mm, y=bill_depth_mm, z=prob), data = q.samp3, color = "#619CFF", bins = 5) + + theme_minimal() + + scale_fill_manual() + + theme(legend.position = "none") + +} +``` + + +```{r} +#| include: false +param_init <- list( + mean1 = c(45,20), + mean2 = c(45, 17.5), + mean3 = c(45, 15), + sigma1 = matrix(c(15,0,0,0.4), nrow = 2, byrow = TRUE), + sigma2 = matrix(c(15,0,0,0.4), nrow = 2, byrow = TRUE), + sigma3 = matrix(c(15,0,0,0.4), nrow = 2, byrow = TRUE) +) + +param_iter1 <- list( + mean1 = c(45.73339, 19.27432), + mean2 = c(40.12257, 17.81372), + mean3 = c(47.44993, 14.93927), + sigma1 = matrix(c(37.408656, -1.623795, -1.623795, 1.079872), nrow = 2), + sigma2 = matrix(c(16.9103292, 0.3527712, 0.3527712, 0.8637195), nrow = 2), + sigma3 = matrix(c(8.093719, 1.797110, 1.797110, 0.874565), nrow = 2) +) + +param_iter2 <- list( + mean1 = c(48.15322, 18.25719), + mean2 = c(38.52801, 18.28852), + mean3 = c(47.28095, 14.84609), + sigma1 = matrix(c(17.177953, 1.247011, 1.247011, 1.643133), nrow = 2), + sigma2 = matrix(c(5.9677874, 0.9537252, 0.9537252, 1.4710272), nrow = 2), + sigma3 = matrix(c(7.584198, 1.613411, 1.613411, 0.766815), nrow = 2) +) + +param_conv <- list( + mean1 = c(49.17829, 18.46058), + mean2 = c(39.0444, 18.3100), + mean3 = c(47.69776, 14.98856), + sigma1 = matrix(c(7.626905, 2.329064, 2.329064, 1.346795), nrow = 2), + sigma2 = matrix(c(7.837458, 1.077226, 1.077226, 1.477878), nrow = 2), + sigma3 = matrix(c(10.3399995, 2.3100534, 2.3100534, 0.9769847), nrow = 2) +) + +``` + + +```{r} +#| echo: false +plot_state(param_init) +``` +```{r} +#| echo: false +plot_state(param_iter1) ``` +```{r} +#| echo: false +plot_state(param_iter2) +``` +```{r} +#| echo: false +plot_state(param_conv) +``` + ```{r} +#| include: false penguins %>% mutate(cluster = (gm_clust_fit %>% extract_cluster_assignment())$.cluster) %>% ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = cluster)) + @@ -156,16 +262,4 @@ penguins %>% -```{r} -gm_clust_engine <- gm_clust_fit %>% extract_fit_engine() -``` - - -```{r} -gm_clust_engine$parameters -``` - -```{r} -plot(gm_clust_engine) -``` From 1b109fe0d5774b65b670a9330c313fbab4b611a7 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 15 Mar 2025 10:52:10 -0700 Subject: [PATCH 09/44] updated db_clust to have outlier as last level of .cluster factor. also adjusted extract_cluster_summary to return NA vals for selected metrics for outlier cluster --- R/extract_cluster_assignment.R | 11 +++++++---- R/extract_fit_summary.R | 15 +++++++++++---- R/predict_helpers.R | 5 +++-- vignettes/articles/db_clust.Rmd | 9 ++++++--- vignettes/articles/gm_clust.Rmd | 5 +++++ 5 files changed, 32 insertions(+), 13 deletions(-) diff --git a/R/extract_cluster_assignment.R b/R/extract_cluster_assignment.R index e989190..79cd94b 100644 --- a/R/extract_cluster_assignment.R +++ b/R/extract_cluster_assignment.R @@ -189,10 +189,13 @@ cluster_assignment_tibble_w_outliers <- function(clusters, n_clusters, ..., prefix = "Cluster_") { - reorder_clusts <- order(union(unique(clusters), 0:(n_clusters-1))) - names <- paste0(prefix, 0:(n_clusters-1)) - res <- names[reorder_clusts][clusters+1] - res[res == paste0(prefix, "0")] <- "Outlier" + # move outlier from cluster 0 to cluster n_clusters + clusters <- ifelse(clusters == 0, n_clusters, clusters) + + reorder_clusts <- order(union(unique(clusters), seq_len(n_clusters))) + names <- paste0(prefix, seq_len(n_clusters)) + names[n_clusters] <- "Outlier" + res <- names[reorder_clusts][clusters] tibble::tibble(.cluster = factor(res)) } diff --git a/R/extract_fit_summary.R b/R/extract_fit_summary.R index 71f11d4..01333d2 100644 --- a/R/extract_fit_summary.R +++ b/R/extract_fit_summary.R @@ -183,8 +183,7 @@ extract_fit_summary.hclust <- function(object, ...) { #' @export extract_fit_summary.dbscan <- function(object, ...) { clusts <- extract_cluster_assignment(object, ...)$.cluster - n_clust <- dplyr::n_distinct(clusts)-1 - n_outliers <- length(clusts == "Outlier") + n_clust <- dplyr::n_distinct(clusts) training_data <- attr(object, "training_data") overall_centroid <- colMeans(training_data) @@ -194,7 +193,6 @@ extract_fit_summary.dbscan <- function(object, ...) { dplyr::mutate( .cluster = clusts ) %>% - dplyr::filter(.cluster != "Outlier") %>% dplyr::group_by(.cluster) %>% tidyr::nest() @@ -202,12 +200,21 @@ extract_fit_summary.dbscan <- function(object, ...) { map(dplyr::summarize_all, mean) %>% dplyr::bind_rows() + centroids[n_clust, ] <- NA + + sse_within_total_total <- map2_dbl( + by_clust$data, + seq_len(n_clust), + ~ sum(Rfast::dista(centroids[.y, ], .x)) + ) + list( cluster_names = unique(clusts), centroids = centroids, n_members = unname(as.integer(table(clusts))), - n_outliers = n_outliers, + sse_within_total_total = sse_within_total_total, + sse_total = sum(Rfast::dista(t(overall_centroid), training_data)), orig_labels = NULL, cluster_assignments = clusts ) diff --git a/R/predict_helpers.R b/R/predict_helpers.R index 23dd272..9494ea9 100644 --- a/R/predict_helpers.R +++ b/R/predict_helpers.R @@ -4,9 +4,10 @@ make_predictions <- function(x, prefix, n_clusters) { } make_predictions_w_outliers <- function(x, prefix, n_clusters) { - levels <- seq_len(n_clusters)-1 + x <- ifelse(x == 0, n_clusters, x) + levels <- seq_len(n_clusters) labels <- paste0(prefix, levels) - labels[labels == paste0(prefix, "0")] <- "Outlier" + labels[n_clusters] <- "Outlier" factor(x, levels = levels, labels = labels) } diff --git a/vignettes/articles/db_clust.Rmd b/vignettes/articles/db_clust.Rmd index 405d198..97894b0 100644 --- a/vignettes/articles/db_clust.Rmd +++ b/vignettes/articles/db_clust.Rmd @@ -89,6 +89,7 @@ db_clust_fit <- db_clust_spec %>% db_clust_fit %>% summary() + ``` We can also extract the standard `tidyclust` summary list: @@ -181,7 +182,8 @@ penguins_std %>% scale_color_manual(values = c("red")) penguins_new <- penguins_std -penguins_new$cluster <- factor(dbscan_fit$cluster) +penguins_new$cluster <- (db_clust_fit %>% extract_cluster_assignment())$.cluster +# factor(dbscan_fit$cluster) penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = minpts)) == 1, "Yes", "No"), levels = c("Yes", "No")) penguins_new %>% @@ -203,7 +205,7 @@ penguins_new %>% geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cp_color)) + theme_minimal() + coord_fixed() + - scale_color_manual(values = c("black", "#619CFF", "#F8766D", "#00BA38")) + + scale_color_manual(values = c("black", "#F8766D", "#00BA38", "#619CFF")) + theme(legend.position = "none") ``` @@ -216,6 +218,7 @@ penguins_new %>% geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster)) + theme_minimal() + coord_fixed() + - scale_color_manual(values = c("black", "#619CFF", "#F8766D", "#00BA38")) + + scale_color_manual(values = c("#F8766D", "#00BA38", "#619CFF", "black")) + theme(legend.position = "none") ``` + diff --git a/vignettes/articles/gm_clust.Rmd b/vignettes/articles/gm_clust.Rmd index 5315dee..d0dc4a2 100644 --- a/vignettes/articles/gm_clust.Rmd +++ b/vignettes/articles/gm_clust.Rmd @@ -262,4 +262,9 @@ penguins %>% +```{r} +gmm_engine <- gm_clust_fit %>% extract_fit_engine() + +mclust::plot.Mclust(gmm_engine) +``` From 47b8e69ba99050a765610abd3f5bf3cbd14783f0 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 22 Mar 2025 09:30:45 -0700 Subject: [PATCH 10/44] minpts -> min_points --- R/db_clust.R | 28 ++++++++++++++-------------- R/db_clust_data.R | 6 +++--- vignettes/articles/db_clust.Rmd | 10 +++++----- 3 files changed, 22 insertions(+), 22 deletions(-) diff --git a/R/db_clust.R b/R/db_clust.R index 0728d1e..3894dd1 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -16,7 +16,7 @@ #' @param engine A single character string specifying what computational engine #' to use for fitting. The engine for this model is `"dbscan"`. #' @param radius Positive integer, Radius used to determine core-points and cluster points together (required). -#' @param minpts Positive double, Minimum number of points needed to form a cluster (required) +#' @param min_points Positive double, Minimum number of points needed to form a cluster (required) #' #' #' @details @@ -40,10 +40,10 @@ db_clust <- function(mode = "partition", engine = "dbscan", radius = NULL, - minpts = NULL) { + min_points = NULL) { args <- list( radius = enquo(radius), - minpts = enquo(minpts) + min_points = enquo(min_points) ) new_cluster_spec( @@ -77,7 +77,7 @@ print.db_clust <- function(x, ...) { update.db_clust <- function(object, parameters = NULL, radius = NULL, - minpts = NULL, + min_points = NULL, fresh = FALSE, ...) { eng_args <- parsnip::update_engine_parameters( object$eng_args, @@ -89,7 +89,7 @@ update.db_clust <- function(object, } args <- list( radius = enquo(radius), - minpts = enquo(minpts), + min_points = enquo(min_points), ) args <- parsnip::update_main_parameters(args, parameters) @@ -126,7 +126,7 @@ update.db_clust <- function(object, check_args.db_clust <- function(object) { args <- lapply(object$args, rlang::eval_tidy) - if (all(is.numeric(args$minpts)) && any(args$minpts < 0)) { + if (all(is.numeric(args$min_points)) && any(args$min_points < 0)) { rlang::abort("The number of points in a cluster should be > 0.") } @@ -148,19 +148,19 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { #' Simple Wrapper around dbscan function #' #' This wrapper prepares the data into a distance matrix to send to -#' `dbscan::dbscan` and retains the parameters `radius` or `minpts` as an +#' `dbscan::dbscan` and retains the parameters `radius` or `min_points` as an #' attribute. #' #' @param x matrix or data frame #' @param radius Radius used to determine core-points and cluster points together -#' @param minpts Minimum number of points needed to form a cluster +#' @param min_points Minimum number of points needed to form a cluster #' #' @return dbscan object #' @keywords internal #' @export .db_clust_fit_dbscan <- function(x, eps = NULL, - minPts = NULL, + min_points = NULL, ...) { if (is.null(eps)) { rlang::abort( @@ -169,18 +169,18 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { ) } - if (is.null(minPts)) { + if (is.null(min_points)) { rlang::abort( - "Please specify `minpts` to be able to fit specification.", + "Please specify `min_points` to be able to fit specification.", call = call("fit") ) } - res <- dbscan::dbscan(x, eps = eps, minPts = minPts) + res <- dbscan::dbscan(x, eps = eps, min_points = min_points) attr(res, "radius") <- eps - attr(res, "minpts") <- minPts + attr(res, "min_points") <- min_points attr(res, "training_data") <- x - is_core <- dbscan::is.corepoint(x, eps = eps, minPts = minPts) + is_core <- dbscan::is.corepoint(x, eps = eps, min_points = min_points) attr(res, "is_core") <- is_core res diff --git a/R/db_clust_data.R b/R/db_clust_data.R index 1330f5b..eede5aa 100644 --- a/R/db_clust_data.R +++ b/R/db_clust_data.R @@ -50,16 +50,16 @@ make_db_clust <- function() { eng = "dbscan", exposed = "radius", original = "eps", - func = list(pkg = "dials", fun = "raidus"), + func = list(pkg = "dials", fun = "radius"), has_submodel = TRUE ) modelenv::set_model_arg( model = "db_clust", eng = "dbscan", - exposed = "minpts", + exposed = "min_points", original = "minPts", - func = list(pkg = "dials", fun = "minpts"), + func = list(pkg = "dials", fun = "min_points"), has_submodel = TRUE ) diff --git a/vignettes/articles/db_clust.Rmd b/vignettes/articles/db_clust.Rmd index 97894b0..aceee24 100644 --- a/vignettes/articles/db_clust.Rmd +++ b/vignettes/articles/db_clust.Rmd @@ -62,7 +62,7 @@ At the end of this vignette, you will find a brief overview of the DBSCAN algori To specify a DBSCAN model in `tidyclust`, simply set the values for `radius` and `min_pts`: ```{r} -db_clust_spec <- db_clust(radius = 0.35, minpts = 10) +db_clust_spec <- db_clust(radius = 0.35, min_points = 10) db_clust_spec ``` @@ -155,14 +155,14 @@ coord_fixed() ``` -1. For each observation in the data, determine whether each point is a core point. A core point is defined as an observation which has more than the specified **minpts** points (including the point itself) within the specified **radius** around the point. +1. For each observation in the data, determine whether each point is a core point. A core point is defined as an observation which has more than the specified **min_points** points (including the point itself) within the specified **radius** around the point. ```{r, include = FALSE} library(dbscan) eps <- 0.35 -minpts <- 10 -dbscan_fit <- dbscan(penguins_std, eps = eps, minPts = minpts) +min_points <- 10 +dbscan_fit <- dbscan(penguins_std, eps = eps, min_points = min_points) ``` @@ -184,7 +184,7 @@ penguins_std %>% penguins_new <- penguins_std penguins_new$cluster <- (db_clust_fit %>% extract_cluster_assignment())$.cluster # factor(dbscan_fit$cluster) -penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = minpts)) == 1, "Yes", "No"), levels = c("Yes", "No")) +penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, min_points = min_points)) == 1, "Yes", "No"), levels = c("Yes", "No")) penguins_new %>% ggplot(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + From 54130bf08022075db9db2fc05d3bf391a5d43738 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 22 Mar 2025 09:39:32 -0700 Subject: [PATCH 11/44] fixed bugs in transition from minpts to min_points --- R/db_clust.R | 10 +++++----- vignettes/articles/db_clust.Rmd | 6 +++--- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/R/db_clust.R b/R/db_clust.R index 3894dd1..07666d6 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -160,7 +160,7 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { #' @export .db_clust_fit_dbscan <- function(x, eps = NULL, - min_points = NULL, + minPts = NULL, ...) { if (is.null(eps)) { rlang::abort( @@ -169,18 +169,18 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { ) } - if (is.null(min_points)) { + if (is.null(minPts)) { rlang::abort( "Please specify `min_points` to be able to fit specification.", call = call("fit") ) } - res <- dbscan::dbscan(x, eps = eps, min_points = min_points) + res <- dbscan::dbscan(x, eps = eps, minPts = minPts) attr(res, "radius") <- eps - attr(res, "min_points") <- min_points + attr(res, "min_points") <- minPts attr(res, "training_data") <- x - is_core <- dbscan::is.corepoint(x, eps = eps, min_points = min_points) + is_core <- dbscan::is.corepoint(x, eps = eps, minPts = minPts) attr(res, "is_core") <- is_core res diff --git a/vignettes/articles/db_clust.Rmd b/vignettes/articles/db_clust.Rmd index aceee24..14a0cc2 100644 --- a/vignettes/articles/db_clust.Rmd +++ b/vignettes/articles/db_clust.Rmd @@ -141,7 +141,7 @@ db_clust_fit %>% Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a method of unsupervised learning that groups observations into clusters based on their density in multidimensional space. Unlike methods such as k-means, DBSCAN does not require specifying the number of clusters beforehand and can identify clusters of arbitrary shapes. Additionally, it can classify points that do not belong to any cluster as outliers, allowing for greater flexibility in handling real-world data. -In DBSCAN, observations are considered as locations in multidimensional space. The algorithm works by defining a cluster as a dense region of connected points. During the fitting process, points are classified as core points, border points, or noise based on their proximity to other points (**radius**) and a density threshold (**min_pts**). +In DBSCAN, observations are considered as locations in multidimensional space. The algorithm works by defining a cluster as a dense region of connected points. During the fitting process, points are classified as core points, border points, or noise based on their proximity to other points (**radius**) and a density threshold (**min_points**). The fitting process can be described as follows: @@ -162,7 +162,7 @@ coord_fixed() library(dbscan) eps <- 0.35 min_points <- 10 -dbscan_fit <- dbscan(penguins_std, eps = eps, min_points = min_points) +dbscan_fit <- dbscan(penguins_std, eps = eps, minPts = min_points) ``` @@ -184,7 +184,7 @@ penguins_std %>% penguins_new <- penguins_std penguins_new$cluster <- (db_clust_fit %>% extract_cluster_assignment())$.cluster # factor(dbscan_fit$cluster) -penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, min_points = min_points)) == 1, "Yes", "No"), levels = c("Yes", "No")) +penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = min_points)) == 1, "Yes", "No"), levels = c("Yes", "No")) penguins_new %>% ggplot(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + From 5b7e9d18e5a71050ad79491da746fbb76fdb4ebb Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 22 Mar 2025 09:40:58 -0700 Subject: [PATCH 12/44] min_ponits documentation update --- man/db_clust.Rd | 9 +++++++-- man/dot-db_clust_fit_dbscan.Rd | 4 ++-- man/tidyclust_update.Rd | 2 +- 3 files changed, 10 insertions(+), 5 deletions(-) diff --git a/man/db_clust.Rd b/man/db_clust.Rd index c352b9c..9c2a764 100644 --- a/man/db_clust.Rd +++ b/man/db_clust.Rd @@ -4,7 +4,12 @@ \alias{db_clust} \title{Density-Based Spatial Clustering of Applications with Noise (DBSCAN)} \usage{ -db_clust(mode = "partition", engine = "dbscan", radius = NULL, minpts = NULL) +db_clust( + mode = "partition", + engine = "dbscan", + radius = NULL, + min_points = NULL +) } \arguments{ \item{mode}{A single character string for the type of model. The only @@ -15,7 +20,7 @@ to use for fitting. The engine for this model is \code{"dbscan"}.} \item{radius}{Positive integer, Radius used to determine core-points and cluster points together (required).} -\item{minpts}{Positive double, Minimum number of points needed to form a cluster (required)} +\item{min_points}{Positive double, Minimum number of points needed to form a cluster (required)} } \value{ A \code{db_clust} cluster specification. diff --git a/man/dot-db_clust_fit_dbscan.Rd b/man/dot-db_clust_fit_dbscan.Rd index 4c2f6d9..d6012ca 100644 --- a/man/dot-db_clust_fit_dbscan.Rd +++ b/man/dot-db_clust_fit_dbscan.Rd @@ -11,14 +11,14 @@ \item{radius}{Radius used to determine core-points and cluster points together} -\item{minpts}{Minimum number of points needed to form a cluster} +\item{min_points}{Minimum number of points needed to form a cluster} } \value{ dbscan object } \description{ This wrapper prepares the data into a distance matrix to send to -\code{dbscan::dbscan} and retains the parameters \code{radius} or \code{minpts} as an +\code{dbscan::dbscan} and retains the parameters \code{radius} or \code{min_points} as an attribute. } \keyword{internal} diff --git a/man/tidyclust_update.Rd b/man/tidyclust_update.Rd index 2b6c906..673bdd6 100644 --- a/man/tidyclust_update.Rd +++ b/man/tidyclust_update.Rd @@ -13,7 +13,7 @@ object, parameters = NULL, radius = NULL, - minpts = NULL, + min_points = NULL, fresh = FALSE, ... ) From 034b9955516c1c7a1da3c07c9ed7723eb46cdc6c Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 29 Mar 2025 11:10:24 -0700 Subject: [PATCH 13/44] add tunable for db_clust --- R/tunable.R | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/R/tunable.R b/R/tunable.R index da49bd9..cdd7790 100644 --- a/R/tunable.R +++ b/R/tunable.R @@ -67,6 +67,8 @@ tunable.k_means <- function(x, ...) { res } + + stats_k_means_engine_args <- tibble::tibble( name = c( @@ -79,3 +81,29 @@ stats_k_means_engine_args <- component = "k_means", component_id = "engine" ) + + +# #' @export +# tunable.dbscan <- function(x, ...) { +# res <- NextMethod() +# if (x$engine == "dbscan") { +# res <- add_engine_parameters(res, stats_k_means_engine_args) +# } +# res +# } +# +# dbscan_db_clust_engine_args <- +# tibble::tibble( +# name = c( +# "", +# "core_points" +# ), +# call_info = list( +# list(pkg = "dials", fun = "radius"), +# list(pkg = "dials", fun = "core_points"), +# ), +# source = "cluster_spec", +# component = "db_scan", +# component_id = "engine" +# ) + From 0797be979d0c479e61606e6933d2df78380ed417 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Sat, 29 Mar 2025 11:10:39 -0700 Subject: [PATCH 14/44] add tunable for dbscan --- R/tunable.R | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/R/tunable.R b/R/tunable.R index cdd7790..d3ba7d0 100644 --- a/R/tunable.R +++ b/R/tunable.R @@ -83,27 +83,27 @@ stats_k_means_engine_args <- ) -# #' @export -# tunable.dbscan <- function(x, ...) { -# res <- NextMethod() -# if (x$engine == "dbscan") { -# res <- add_engine_parameters(res, stats_k_means_engine_args) -# } -# res -# } -# -# dbscan_db_clust_engine_args <- -# tibble::tibble( -# name = c( -# "", -# "core_points" -# ), -# call_info = list( -# list(pkg = "dials", fun = "radius"), -# list(pkg = "dials", fun = "core_points"), -# ), -# source = "cluster_spec", -# component = "db_scan", -# component_id = "engine" -# ) +#' @export +tunable.db_clust <- function(x, ...) { + res <- NextMethod() + if (x$engine == "dbscan") { + res <- add_engine_parameters(res, dbscan_db_clust_engine_args) + } + res +} + +dbscan_db_clust_engine_args <- + tibble::tibble( + name = c( + "eps", + "minPts" + ), + call_info = list( + list(pkg = "dials", fun = "radius"), + list(pkg = "dials", fun = "min_points"), + ), + source = "cluster_spec", + component = "dbscan", + component_id = "engine" + ) From 9238147d40a1345f963cacf4d21e75e4f5322891 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Thu, 3 Apr 2025 12:23:28 -0700 Subject: [PATCH 15/44] finalize cluster assignment tibble helper --- R/db_clust.R | 6 +++--- R/db_clust_data.R | 2 +- R/extract_cluster_assignment.R | 12 ++++++------ R/tunable.R | 2 +- 4 files changed, 11 insertions(+), 11 deletions(-) diff --git a/R/db_clust.R b/R/db_clust.R index 07666d6..37ada77 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -15,8 +15,8 @@ #' possible value for this model is "partition". #' @param engine A single character string specifying what computational engine #' to use for fitting. The engine for this model is `"dbscan"`. -#' @param radius Positive integer, Radius used to determine core-points and cluster points together (required). -#' @param min_points Positive double, Minimum number of points needed to form a cluster (required) +#' @param radius Positive double, Radius drawn around points to determine core-points and cluster assignments (required) +#' @param min_points Positive integer, Minimum number of points required to form a core-point (required) #' #' #' @details @@ -89,7 +89,7 @@ update.db_clust <- function(object, } args <- list( radius = enquo(radius), - min_points = enquo(min_points), + min_points = enquo(min_points) ) args <- parsnip::update_main_parameters(args, parameters) diff --git a/R/db_clust_data.R b/R/db_clust_data.R index eede5aa..9b6e0be 100644 --- a/R/db_clust_data.R +++ b/R/db_clust_data.R @@ -27,7 +27,7 @@ make_db_clust <- function() { mode = "partition", value = list( interface = "matrix", - protect = c("data"), + protect = c("x", "eps", "minPts"), func = c(pkg = "tidyclust", fun = ".db_clust_fit_dbscan"), defaults = list() ) diff --git a/R/extract_cluster_assignment.R b/R/extract_cluster_assignment.R index 79cd94b..94480d4 100644 --- a/R/extract_cluster_assignment.R +++ b/R/extract_cluster_assignment.R @@ -189,13 +189,13 @@ cluster_assignment_tibble_w_outliers <- function(clusters, n_clusters, ..., prefix = "Cluster_") { - # move outlier from cluster 0 to cluster n_clusters - clusters <- ifelse(clusters == 0, n_clusters, clusters) - reorder_clusts <- order(union(unique(clusters), seq_len(n_clusters))) - names <- paste0(prefix, seq_len(n_clusters)) - names[n_clusters] <- "Outlier" - res <- names[reorder_clusts][clusters] + no_outliers <- clusters[clusters != 0] + new_mappings <- c(0,order(union(unique(no_outliers), seq_len(n_clusters-1)))) + + names <- paste0(prefix, 0:(n_clusters)) + names[1] <- "Outlier" + res <- names[new_mappings[(clusters+1)]+1] tibble::tibble(.cluster = factor(res)) } diff --git a/R/tunable.R b/R/tunable.R index d3ba7d0..cc64103 100644 --- a/R/tunable.R +++ b/R/tunable.R @@ -100,7 +100,7 @@ dbscan_db_clust_engine_args <- ), call_info = list( list(pkg = "dials", fun = "radius"), - list(pkg = "dials", fun = "min_points"), + list(pkg = "dials", fun = "min_points") ), source = "cluster_spec", component = "dbscan", From d1b3d5537928b73daabb3b6f3acef9bae54b2300 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Tue, 8 Apr 2025 19:48:17 -0700 Subject: [PATCH 16/44] test-db_clust done --- R/extract_fit_summary.R | 3 +- R/predict_helpers.R | 15 ++- tests/testthat/_snaps/db_clust.md | 104 +++++++++++++++ tests/testthat/test-db_clust.R | 184 ++++++++++++++++++++++++++ tests/testthat/test-gm_clust.R | 207 ++++++++++++++++++++++++++++++ 5 files changed, 508 insertions(+), 5 deletions(-) create mode 100644 tests/testthat/_snaps/db_clust.md create mode 100644 tests/testthat/test-db_clust.R create mode 100644 tests/testthat/test-gm_clust.R diff --git a/R/extract_fit_summary.R b/R/extract_fit_summary.R index 01333d2..555eaf9 100644 --- a/R/extract_fit_summary.R +++ b/R/extract_fit_summary.R @@ -200,7 +200,8 @@ extract_fit_summary.dbscan <- function(object, ...) { map(dplyr::summarize_all, mean) %>% dplyr::bind_rows() - centroids[n_clust, ] <- NA + outlier_idx <- which(unique(clusts) == "Outlier") + centroids[outlier_idx, ] <- rep(NA, ncol(centroids)) sse_within_total_total <- map2_dbl( by_clust$data, diff --git a/R/predict_helpers.R b/R/predict_helpers.R index 9494ea9..8162a2d 100644 --- a/R/predict_helpers.R +++ b/R/predict_helpers.R @@ -4,13 +4,20 @@ make_predictions <- function(x, prefix, n_clusters) { } make_predictions_w_outliers <- function(x, prefix, n_clusters) { - x <- ifelse(x == 0, n_clusters, x) - levels <- seq_len(n_clusters) - labels <- paste0(prefix, levels) - labels[n_clusters] <- "Outlier" + + if (sum(x == 0) > 0) { + levels <- 0:(n_clusters-1) + labels <- paste0(prefix, levels) + labels[1] <- "Outlier" + + } else { + levels <- 1:(n_clusters) + labels <- paste0(prefix, levels) + } factor(x, levels = levels, labels = labels) } + .k_means_predict_stats <- function(object, new_data, prefix = "Cluster_") { res <- object$centers res <- flexclust::dist2(res, new_data) diff --git a/tests/testthat/_snaps/db_clust.md b/tests/testthat/_snaps/db_clust.md new file mode 100644 index 0000000..40d8e03 --- /dev/null +++ b/tests/testthat/_snaps/db_clust.md @@ -0,0 +1,104 @@ +# bad input + + Code + db_clust(mode = "bogus") + Condition + Error in `modelenv::check_spec_mode_engine_val()`: + ! 'bogus' is not a known mode for model `db_clust()`. + +--- + + Code + bt <- db_clust(radius = -1) %>% set_engine("dbscan") + fit(bt, mpg ~ ., mtcars) + Condition + Error in `check_args()`: + ! The radius used to create a cluster should be > 0. + +--- + + Code + bt <- db_clust(min_points = -1) %>% set_engine("dbscan") + fit(bt, mpg ~ ., mtcars) + Condition + Error in `check_args()`: + ! The number of points in a cluster should be > 0. + +--- + + Code + translate_tidyclust(db_clust(), engine = NULL) + Condition + Error in `translate_tidyclust.default()`: + ! Please set an engine. + +--- + + Code + translate_tidyclust(db_clust(formula = ~x)) + Condition + Error in `db_clust()`: + ! unused argument (formula = ~x) + +# printing + + Code + db_clust() + Output + DBSCAN Clustering Specification (partition) + + Computational engine: dbscan + + +--- + + Code + db_clust(radius = 20, min_points = 3) + Output + DBSCAN Clustering Specification (partition) + + Main Arguments: + radius = 20 + min_points = 3 + + Computational engine: dbscan + + +# updating + + Code + db_clust(radius = 20, min_points = 5) %>% update(radius = tune()) + Output + DBSCAN Clustering Specification (partition) + + Main Arguments: + radius = tune() + min_points = 5 + + Computational engine: dbscan + + +# errors if `radius` and `min_points` aren't specified + + Code + db_clust() %>% set_engine("dbscan") %>% fit(~., data = mtcars) + Condition + Error in `fit()`: + ! Please specify `radius` to be able to fit specification. + +# errors if `radius` isn't specified + + Code + db_clust(min_points = 10) %>% set_engine("dbscan") %>% fit(~., data = mtcars) + Condition + Error in `fit()`: + ! Please specify `radius` to be able to fit specification. + +# errors if `min_points` isn't specified + + Code + db_clust(radius = 20) %>% set_engine("dbscan") %>% fit(~., data = mtcars) + Condition + Error in `fit()`: + ! Please specify `min_points` to be able to fit specification. + diff --git a/tests/testthat/test-db_clust.R b/tests/testthat/test-db_clust.R new file mode 100644 index 0000000..f4a3b50 --- /dev/null +++ b/tests/testthat/test-db_clust.R @@ -0,0 +1,184 @@ +test_that("primary arguments", { + basic <- db_clust(mode = "partition") + basic_dbscan <- translate_tidyclust(basic %>% set_engine("dbscan")) + expect_equal( + basic_dbscan$method$fit$args, + list( + x = rlang::expr(missing_arg()), + eps = rlang::expr(missing_arg()), + minPts = rlang::expr(missing_arg()) + ) + ) + + db <- db_clust(radius = 2, min_points = 4, mode = "partition") + db_dbscan <- translate_tidyclust(db %>% set_engine("dbscan")) + expect_equal( + db_dbscan$method$fit$args, + list( + x = rlang::expr(missing_arg()), + eps = rlang::expr(missing_arg()), + minPts = rlang::expr(missing_arg()), + eps = new_empty_quosure(2), + minPts = new_empty_quosure(4) + ) + ) +}) + + +test_that("bad input", { + expect_snapshot(error = TRUE, db_clust(mode = "bogus")) + expect_snapshot(error = TRUE, { + bt <- db_clust(radius = -1) %>% set_engine("dbscan") + fit(bt, mpg ~ ., mtcars) + }) + expect_snapshot(error = TRUE, { + bt <- db_clust(min_points = -1) %>% set_engine("dbscan") + fit(bt, mpg ~ ., mtcars) + }) + expect_snapshot(error = TRUE, translate_tidyclust(db_clust(), engine = NULL)) + expect_snapshot(error = TRUE, translate_tidyclust(db_clust(formula = ~x))) +}) + +test_that("predictions", { + set.seed(1234) + db_clust_fit <- db_clust(radius = 60, min_points = 5) %>% + set_engine("dbscan") %>% + fit(~., mtcars) + + set.seed(1234) + ref_res <- dbscan::dbscan(mtcars, eps = 60, minPts = 5)$cluster + + ref_clusts <- ref_res[ref_res != 0] + ref_outliers <- which(ref_res == 0) + + relevel_preds <- function(x) { + factor(unname(x), unique(unname(x))) %>% as.numeric() + } + + expect_equal( + ref_clusts, + predict(db_clust_fit, mtcars)$.pred_cluster %>% .[. != "Outlier"] %>% as.numeric() - 1 + ) + + expect_equal( + relevel_preds(ref_clusts), + extract_cluster_assignment(db_clust_fit)$.cluster %>% .[. != "Outlier"] %>% as.numeric() + ) + + expect_equal( + ref_outliers, + which(predict(db_clust_fit, mtcars)$.pred_cluster == "Outlier") + ) +}) + + +test_that("extract_centroids work", { + set.seed(1234) + dbscan_fit <- db_clust(radius = 20, min_points = 3) %>% + set_engine("dbscan") %>% + fit(~., mtcars) + + set.seed(1234) + ref_res <- dbscan::dbscan(mtcars, eps = 20, minPts = 3) + + centers <- bind_cols(mtcars, .cluster = ref_res$cluster) %>% + dplyr::group_by(.cluster) %>% + dplyr::summarize_all(mean) %>% + dplyr::filter(.cluster != 0) %>% + dplyr::select(-.cluster) + + ref_centroids <- dplyr::bind_cols( + .cluster = c(1L, 2L, 4L, 5L, 3L), + centers + ) %>% + dplyr::arrange(.cluster) %>% + dplyr::select(-.cluster) + + expect_identical( + extract_centroids(dbscan_fit) %>% + dplyr::filter(.cluster != "Outlier") %>% + dplyr::select(-.cluster), + ref_centroids + ) +}) + +test_that("Right classes", { + expect_equal( + class(db_clust()), + c("db_clust", "cluster_spec", "unsupervised_spec") + ) +}) + +test_that("printing", { + expect_snapshot( + db_clust() + ) + expect_snapshot( + db_clust(radius = 20, min_points = 3) + ) +}) + +test_that("updating", { + expect_snapshot( + db_clust(radius = 20, min_points = 5) %>% + update(radius = tune()) + ) +}) + + +test_that("reordering is done correctly for stats k_means", { + set.seed(42) + + kmeans_fit <- k_means(num_clusters = 6) %>% + set_engine("stats") %>% + fit(~., data = mtcars) + + summ <- extract_fit_summary(kmeans_fit) + + expect_identical( + summ$n_members, + unname(as.integer(table(summ$cluster_assignments))) + ) +}) +# +# test_that("reordering is done correctly for ClusterR k_means", { +# set.seed(42) +# +# kmeans_fit <- k_means(num_clusters = 6) %>% +# set_engine("ClusterR") %>% +# fit(~., data = mtcars) +# +# summ <- extract_fit_summary(kmeans_fit) +# +# expect_identical( +# summ$n_members, +# unname(as.integer(table(summ$cluster_assignments))) +# ) +# }) + +test_that("errors if `radius` and `min_points` aren't specified", { + expect_snapshot( + error = TRUE, + db_clust() %>% + set_engine("dbscan") %>% + fit(~ ., data = mtcars) + ) +}) + +test_that("errors if `radius` isn't specified", { + expect_snapshot( + error = TRUE, + db_clust(min_points = 10) %>% + set_engine("dbscan") %>% + fit(~ ., data = mtcars) + ) +}) + +test_that("errors if `min_points` isn't specified", { + expect_snapshot( + error = TRUE, + db_clust(radius = 20) %>% + set_engine("dbscan") %>% + fit(~ ., data = mtcars) + ) +}) diff --git a/tests/testthat/test-gm_clust.R b/tests/testthat/test-gm_clust.R new file mode 100644 index 0000000..f855e8c --- /dev/null +++ b/tests/testthat/test-gm_clust.R @@ -0,0 +1,207 @@ +test_that("primary arguments", { + basic <- hier_clust(mode = "partition") + basic_stats <- translate_tidyclust(basic %>% set_engine("stats")) + expect_equal( + basic_stats$method$fit$args, + list( + data = rlang::expr(missing_arg()), + linkage_method = new_empty_quosure("complete") + ) + ) +}) + +test_that("engine arguments", { + stats_print <- hier_clust(mode = "partition") + expect_equal( + translate_tidyclust( + stats_print %>% + set_engine("stats", members = NULL) + )$method$fit$args, + list( + data = rlang::expr(missing_arg()), + linkage_method = new_empty_quosure("complete"), + members = new_empty_quosure(NULL) + ) + ) +}) + +test_that("bad input", { + expect_snapshot( + error = TRUE, + hier_clust(mode = "bogus") + ) + expect_snapshot( + error = TRUE, + { + bt <- hier_clust(linkage_method = "bogus") %>% set_engine("stats") + fit(bt, mpg ~ ., mtcars) + } + ) + expect_snapshot( + error = TRUE, + translate_tidyclust(hier_clust(), engine = NULL) + ) + expect_snapshot( + error = TRUE, + translate_tidyclust(hier_clust(formula = ~x)) + ) +}) + +test_that("predictions", { + set.seed(1234) + hclust_fit <- hier_clust(num_clusters = 4) %>% + set_engine("stats") %>% + fit(~., mtcars) + + set.seed(1234) + ref_res <- cutree(hclust(dist(mtcars)), k = 4) + + ref_predictions <- ref_res %>% unname() + + relevel_preds <- function(x) { + factor(unname(x), unique(unname(x))) %>% as.numeric() + } + + expect_equal( + relevel_preds(predict(hclust_fit, mtcars)$.pred_cluster), + predict(hclust_fit, mtcars)$.pred_cluster %>% as.numeric() + ) + + expect_equal( + relevel_preds(ref_predictions), + extract_cluster_assignment(hclust_fit)$.cluster %>% as.numeric() + ) +}) + +test_that("extract_cluster_assignment works if you don't set num_clusters", { + set.seed(1234) + hclust_fit <- hier_clust(num_clusters = 4) %>% + set_engine("stats") %>% + fit(~., mtcars) + + set.seed(1234) + hclust_fit_no_args <- hier_clust() %>% + set_engine("stats") %>% + fit(~., mtcars) + + expect_identical( + extract_cluster_assignment(hclust_fit, mtcars), + extract_cluster_assignment(hclust_fit_no_args, mtcars, num_clusters = 4) + ) +}) + +test_that("predict works if you don't set num_clusters", { + set.seed(1234) + hclust_fit <- hier_clust(num_clusters = 4) %>% + set_engine("stats") %>% + fit(~., mtcars) + + set.seed(1234) + hclust_fit_no_args <- hier_clust() %>% + set_engine("stats") %>% + fit(~., mtcars) + + expect_identical( + predict(hclust_fit, mtcars), + predict(hclust_fit_no_args, mtcars, num_clusters = 4) + ) +}) + +test_that("extract_centroids work", { + set.seed(1234) + hclust_fit <- hier_clust(num_clusters = 4) %>% + set_engine("stats") %>% + fit(~., mtcars) + + set.seed(1234) + ref_res <- cutree(hclust(dist(mtcars)), k = 4) + + ref_predictions <- ref_res %>% unname() + + expect_identical( + extract_centroids(hclust_fit) %>% + dplyr::mutate(.cluster = as.integer(.cluster)), + mtcars %>% + dplyr::group_by(.cluster = ref_predictions) %>% + dplyr::summarize(dplyr::across(dplyr::everything(), mean)) + ) +}) + +test_that("extract_centroids work if you don't set num_clusters", { + set.seed(1234) + hclust_fit <- hier_clust() %>% + set_engine("stats") %>% + fit(~., mtcars) + + set.seed(1234) + ref_res <- cutree(hclust(dist(mtcars)), k = 4) + + ref_predictions <- ref_res %>% unname() + + expect_identical( + extract_centroids(hclust_fit, num_clusters = 4) %>% + dplyr::mutate(.cluster = as.integer(.cluster)), + mtcars %>% + dplyr::group_by(.cluster = ref_predictions) %>% + dplyr::summarize(dplyr::across(dplyr::everything(), mean)) + ) +}) + +test_that("predictions with new data", { + set.seed(1234) + hclust_fit <- hier_clust(num_clusters = 4) %>% + set_engine("stats") %>% + fit(~., mtcars) + + set.seed(1234) + ref_res <- cutree(hclust(dist(mtcars)), k = 4) + + ref_predictions <- ref_res %>% unname() + + relevel_preds <- function(x) { + factor(unname(x), unique(unname(x))) %>% as.numeric() + } + + expect_equal( + relevel_preds(predict(hclust_fit, mtcars[1:10, ])$.pred_cluster), + predict(hclust_fit, mtcars[1:10, ])$.pred_cluster %>% as.numeric() + ) +}) + +test_that("Right classes", { + expect_equal( + class(hier_clust()), + c("hier_clust", "cluster_spec", "unsupervised_spec") + ) +}) + +test_that("printing", { + expect_snapshot( + hier_clust() + ) + expect_snapshot( + hier_clust(num_clusters = 10) + ) +}) + +test_that("updating", { + expect_snapshot( + hier_clust(num_clusters = 5) %>% + update(num_clusters = tune()) + ) +}) + +test_that("reordering is done correctly for stats hier_clust", { + set.seed(42) + + kmeans_fit <- hier_clust(num_clusters = 6) %>% + set_engine("stats") %>% + fit(~., data = mtcars) + + summ <- extract_fit_summary(kmeans_fit) + + expect_identical( + summ$n_members, + unname(as.integer(table(summ$cluster_assignments))) + ) +}) From 3d7f2d4da1ce4771ae9687d718382bdcf267d15d Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Wed, 9 Apr 2025 17:12:13 -0700 Subject: [PATCH 17/44] db_clust dbscan engine testing complete --- R/db_clust.R | 10 +++ R/extract_cluster_assignment.R | 18 ++++-- R/predict_helpers.R | 23 ++++--- tests/testthat/test-db_clust-dbscan.R | 93 +++++++++++++++++++++++++++ tests/testthat/test-db_clust.R | 2 +- 5 files changed, 126 insertions(+), 20 deletions(-) create mode 100644 tests/testthat/test-db_clust-dbscan.R diff --git a/R/db_clust.R b/R/db_clust.R index 37ada77..a82edd2 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -204,6 +204,16 @@ dbscan_helper <- function(object, cp_clusters <- object$cluster[is_core] eps <- attr(object, "radius") + # if all points are core points then no border points / outliers to fit + if (sum(is_core) == nrow(training_data)) { + return(cp_clusters) + } + + # if there are no core points, all points are considered outliers + if (sum(is_core) == 0) { + return(rep(0, nrow(training_data))) + } + # get fit values according to closest core point non_cp_clusters <- dbscan:::.predict_frNN(newdata = non_cp, data = cp, cp_clusters, eps = eps) diff --git a/R/extract_cluster_assignment.R b/R/extract_cluster_assignment.R index 94480d4..74332a6 100644 --- a/R/extract_cluster_assignment.R +++ b/R/extract_cluster_assignment.R @@ -160,7 +160,8 @@ extract_cluster_assignment.hclust <- function(object, #' @export extract_cluster_assignment.dbscan <- function(object, ...) { clusters <- dbscan_helper(object) - n_clusters <- length(unique(clusters)) + + n_clusters <- length(unique(clusters[clusters != 0])) + 1 cluster_assignment_tibble_w_outliers(clusters, n_clusters, ...) } @@ -191,11 +192,14 @@ cluster_assignment_tibble_w_outliers <- function(clusters, prefix = "Cluster_") { no_outliers <- clusters[clusters != 0] - new_mappings <- c(0,order(union(unique(no_outliers), seq_len(n_clusters-1)))) - - names <- paste0(prefix, 0:(n_clusters)) - names[1] <- "Outlier" + if (n_clusters == 1) { + res <- rep("Outlier", length(clusters)) + } else { + new_mappings <- c(0,order(union(unique(no_outliers), seq_len(n_clusters-1)))) + names <- paste0(prefix, 0:(n_clusters-1)) + names[1] <- "Outlier" + res <- names[new_mappings[(clusters+1)]+1] + } - res <- names[new_mappings[(clusters+1)]+1] - tibble::tibble(.cluster = factor(res)) + tibble::tibble(.cluster = factor(res, levels = names)) } diff --git a/R/predict_helpers.R b/R/predict_helpers.R index 8162a2d..566cbaa 100644 --- a/R/predict_helpers.R +++ b/R/predict_helpers.R @@ -4,16 +4,9 @@ make_predictions <- function(x, prefix, n_clusters) { } make_predictions_w_outliers <- function(x, prefix, n_clusters) { - - if (sum(x == 0) > 0) { - levels <- 0:(n_clusters-1) - labels <- paste0(prefix, levels) - labels[1] <- "Outlier" - - } else { - levels <- 1:(n_clusters) - labels <- paste0(prefix, levels) - } + levels <- 0:(n_clusters-1) + labels <- paste0(prefix, levels) + labels[1] <- "Outlier" factor(x, levels = levels, labels = labels) } @@ -176,10 +169,16 @@ make_predictions_w_outliers <- function(x, prefix, n_clusters) { cp_clusters <- object$cluster[is_core] eps <- attr(object, "radius") - clusters <- dbscan:::.predict_frNN(newdata = new_data, data = cp, cp_clusters, eps = eps) - n_clusters <- length(unique(object$cluster)) + if (sum(is_core) == 0) { + clusters <- (rep(0, nrow(new_data))) + n_clusters <- 1 + } else { + clusters <- dbscan:::.predict_frNN(newdata = new_data, data = cp, cp_clusters, eps = eps) + n_clusters <- length(unique(object$cluster[object$cluster != 0])) + 1 + } make_predictions_w_outliers(clusters, prefix, n_clusters) + } .gm_clust_predict_mclust <- function(object, new_data, prefix = "Cluster_") { diff --git a/tests/testthat/test-db_clust-dbscan.R b/tests/testthat/test-db_clust-dbscan.R new file mode 100644 index 0000000..8ec0966 --- /dev/null +++ b/tests/testthat/test-db_clust-dbscan.R @@ -0,0 +1,93 @@ +test_that("fitting", { + set.seed(1234) + spec <- db_clust(radius = 10, min_points = 5) %>% + set_engine("dbscan") + + expect_no_error( + res <- fit(spec, ~., mtcars) + ) + + expect_no_error( + res <- fit_xy(spec, mtcars) + ) +}) + +test_that("predicting", { + set.seed(1234) + spec <- db_clust(radius = .42, min_points = 5) %>% + set_engine("dbscan") + + iris_temp <- iris %>% dplyr::select(-Species) + res <- fit(spec, ~., iris_temp) + + + preds <- predict(res, iris_temp[c(58 ,25, 75, 125), ]) + exp <- factor(c("Outlier", paste0("Cluster_", 1:3)), levels = c("Outlier", paste0("Cluster_", 1:3))) + + expect_identical( + preds, + tibble::tibble(.pred_cluster = exp) + ) +}) + + + +test_that("all levels are preserved with 1 row predictions", { + set.seed(1234) + spec <- db_clust(radius = 50, min_points = 5) %>% + set_engine("dbscan") + + res <- fit(spec, ~., mtcars) + + preds <- predict(res, mtcars[1, ]) + + expect_identical( + levels(preds$.pred_cluster), + c("Outlier", paste0("Cluster_", 1:2)) + ) +}) + +# test_that("extract_centroids() works", { +# set.seed(1234) +# spec <- db_clust(radius = .42, min_points = 5) %>% +# set_engine("dbscan") +# +# iris_temp <- iris %>% select(-Species) +# res <- fit(spec, ~., iris) +# +# centroids <- extract_centroids(res) +# +# expected <- vctrs::vec_cbind( +# tibble::tibble(.cluster = factor(paste0("Cluster_", 1:4))), +# tibble::as_tibble(res$fit$centers) +# ) +# +# expect_identical( +# centroids, +# expected +# ) +# }) + +test_that("extract_cluster_assignment() works", { + set.seed(1234) + spec <- db_clust(radius = .42, min_points = 5) %>% + set_engine("dbscan") + + iris_temp <- iris %>% dplyr::select(-Species) + res <- fit(spec, ~., iris_temp) + + clusters <- extract_cluster_assignment(res) + + expected <- vctrs::vec_cbind( + tibble::tibble(.cluster = factor(paste0("Cluster_", res$fit$cluster)) %>% + forcats::fct_recode("Outlier" = "Cluster_0") %>% + relevel(ref = "Outlier")) + + ) + + + expect_identical( + clusters, + expected + ) +}) diff --git a/tests/testthat/test-db_clust.R b/tests/testthat/test-db_clust.R index f4a3b50..a62c0a9 100644 --- a/tests/testthat/test-db_clust.R +++ b/tests/testthat/test-db_clust.R @@ -62,7 +62,7 @@ test_that("predictions", { expect_equal( relevel_preds(ref_clusts), - extract_cluster_assignment(db_clust_fit)$.cluster %>% .[. != "Outlier"] %>% as.numeric() + extract_cluster_assignment(db_clust_fit)$.cluster %>% .[. != "Outlier"] %>% as.numeric() - 1 ) expect_equal( From a4a3ea80bbe716334e1ca1e6d4f48aa292499bfa Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Wed, 9 Apr 2025 21:35:58 -0700 Subject: [PATCH 18/44] gm_clust true/false args for specifying different models --- R/db_clust.R | 7 ++- R/gm_clust.R | 142 ++++++++++++++++++++++++++++++++++++++++++++-- R/gm_clust_data.R | 47 ++++++++++++++- 3 files changed, 188 insertions(+), 8 deletions(-) diff --git a/R/db_clust.R b/R/db_clust.R index a82edd2..098e2a4 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -188,11 +188,12 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { #' dbscan fit helper function #' -#' This function... +#' This function returns the cluster assignments for the training data +#' based on their distance to the CLOSEST core point in the data #' -#' @param x matrix or data frame +#' @param object db_clust object #' -#' @return dbscan object +#' @return numeric vector #' @keywords internal dbscan_helper <- function(object, ...) { diff --git a/R/gm_clust.R b/R/gm_clust.R index 3b645e0..ddb8eab 100644 --- a/R/gm_clust.R +++ b/R/gm_clust.R @@ -38,9 +38,20 @@ gm_clust <- function(mode = "partition", engine = "mclust", - num_clusters = NULL) { + num_clusters = NULL, + circular = FALSE, + zero_covariance = FALSE, + shared_orientation = FALSE, + shared_shape = FALSE, + shared_size = FALSE + ) { args <- list( - num_clusters = enquo(num_clusters) + num_clusters = enquo(num_clusters), + circular = enquo(circular), + zero_covariance = enquo(zero_covariance), + shared_orientation = enquo(shared_orientation), + shared_shape = enquo(shared_shape), + shared_size = enquo(shared_size) ) new_cluster_spec( @@ -74,6 +85,11 @@ print.gm_clust <- function(x, ...) { update.gm_clust <- function(object, parameters = NULL, num_clusters = NULL, + circular = NULL, + zero_covariance = NULL, + shared_orientation = NULL, + shared_shape = NULL, + shared_size = NULL, fresh = FALSE, ...) { eng_args <- parsnip::update_engine_parameters( object$eng_args, @@ -84,7 +100,12 @@ update.gm_clust <- function(object, parameters <- parsnip::check_final_param(parameters) } args <- list( - num_clusters = enquo(num_clusters) + num_clusters = enquo(num_clusters), + circular = enquo(circular), + zero_covariance = enquo(zero_covariance), + shared_orientation = enquo(shared_orientation), + shared_shape = enquo(shared_shape), + shared_size = enquo(shared_size) ) args <- parsnip::update_main_parameters(args, parameters) @@ -125,6 +146,26 @@ check_args.gm_clust <- function(object) { rlang::abort("The number of clusters should be > 0.") } + if (all(!is.logical(args$circular))) { + rlang::abort("The circular cluster shape argument should be TRUE or FALSE.") + } + + if (all(!is.logical(args$zero_covariance))) { + rlang::abort("The zero covariance argument should be TRUE or FALSE.") + } + + if (all(!is.logical(args$shared_orientation))) { + rlang::abort("The shared cluster orientation argument should be TRUE or FALSE.") + } + + if (all(!is.logical(args$shared_shape))) { + rlang::abort("The shared cluster shape argument should be TRUE or FALSE.") + } + + if (all(!is.logical(args$shared_size))) { + rlang::abort("The shared cluster size argument should be TRUE or FALSE.") + } + invisible(object) } @@ -150,6 +191,11 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { #' @export .gm_clust_fit_mclust <- function(x, G = NULL, + circular = NULL, + zero_covariance = NULL, + shared_orientation = NULL, + shared_shape = NULL, + shared_size = NULL, ...) { if (is.null(G)) { rlang::abort( @@ -157,9 +203,97 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { call = call("fit") ) } + if (is.null(circular)) { + rlang::abort( + "Please specify `circular` to be able to fit specification.", + call = call("fit") + ) + } + if (is.null(shared_size)) { + rlang::abort( + "Please specify `shared_size` to be able to fit specification.", + call = call("fit") + ) + } + if (!circular) { - res <- mclust::Mclust(x, G = G) + if (is.null(zero_covariance)) { + rlang::abort( + "Please specify `zero_covariance` to be able to fit specification.", + call = call("fit") + ) + } + + if (is.null(shared_shape)) { + rlang::abort( + "Please specify `shared_shape` to be able to fit specification.", + call = call("fit") + ) + } + + if (!zero_covariance) { + + if (is.null(shared_orientation)) { + rlang::abort( + "Please specify `shared_orientation` to be able to fit specification.", + call = call("fit") + ) + } + } + } + + model_name <- mclust_helper(circular, + zero_covariance, + shared_orientation, + shared_shape, + shared_size) + + + + res <- mclust::Mclust(x, G = G, modelNames = model_name) attr(res, "num_clusters") <- G + attr(res, "circular") <- circular + attr(res, "zero_covariance") <- zero_covariance + attr(res, "shared_orientation") <- shared_orientation + attr(res, "shared_shape") <- shared_shape + attr(res, "shared_size") <- shared_size attr(res, "training_data") <- x res } + + +#' mclust fit helper function +#' +#' This function returns the mclust model name based on the specified +#' TRUE/FALSE model arguments +#' +#' @param object gm_clust object +#' +#' @return string containing mclust model name +#' @keywords internal +mclust_helper <- function(circular, + zero_covariance, + shared_orientation, + shared_shape, + shared_size) { + model_name <- case_when( + circular & shared_size ~ "EII", + circular & !shared_size ~ "VII", + !circular & zero_covariance & shared_shape & shared_size ~ "EEI", + !circular & zero_covariance & !shared_shape & shared_size ~ "EVI", + !circular & zero_covariance & shared_shape & !shared_size ~ "VEI", + !circular & zero_covariance & !shared_shape & !shared_size ~ "VVI", + !circular & !zero_covariance & shared_orientation & shared_shape & shared_size ~ "EEE", + !circular & !zero_covariance & shared_orientation & !shared_shape & shared_size ~ "EVE", + !circular & !zero_covariance & shared_orientation & shared_shape & !shared_size ~ "VEE", + !circular & !zero_covariance & shared_orientation & !shared_shape & !shared_size ~ "VVE", + !circular & !zero_covariance & !shared_orientation & shared_shape & shared_size ~ "EEV", + !circular & !zero_covariance & !shared_orientation & shared_shape & !shared_size ~ "EVV", + !circular & !zero_covariance & !shared_orientation & !shared_shape & shared_size ~ "VEV", + !circular & !zero_covariance & !shared_orientation & !shared_shape & !shared_size ~ "VVV" + + + ) + +} + diff --git a/R/gm_clust_data.R b/R/gm_clust_data.R index 08efe84..b61ce6b 100644 --- a/R/gm_clust_data.R +++ b/R/gm_clust_data.R @@ -27,12 +27,13 @@ make_gm_clust <- function() { mode = "partition", value = list( interface = "matrix", - protect = c("data"), + protect = c("x", "G", "circular", "shared_orientation", "shared_shape", "shared_size"), func = c(pkg = "tidyclust", fun = ".gm_clust_fit_mclust"), defaults = list() ) ) + modelenv::set_encoding( model = "gm_clust", eng = "mclust", @@ -54,6 +55,50 @@ make_gm_clust <- function() { has_submodel = TRUE ) + modelenv::set_model_arg( + model = "gm_clust", + eng = "mclust", + exposed = "circular", + original = "circular", + func = list(pkg = "dials", fun = "circular"), + has_submodel = TRUE + ) + + modelenv::set_model_arg( + model = "gm_clust", + eng = "mclust", + exposed = "zero_covariance", + original = "zero_covariance", + func = list(pkg = "dials", fun = "zero_covariance"), + has_submodel = TRUE + ) + + modelenv::set_model_arg( + model = "gm_clust", + eng = "mclust", + exposed = "shared_orientation", + original = "shared_orientation", + func = list(pkg = "dials", fun = "shared_orientation"), + has_submodel = TRUE + ) + + modelenv::set_model_arg( + model = "gm_clust", + eng = "mclust", + exposed = "shared_shape", + original = "shared_shape", + func = list(pkg = "dials", fun = "shared_shape"), + has_submodel = TRUE + ) + + modelenv::set_model_arg( + model = "gm_clust", + eng = "mclust", + exposed = "shared_size", + original = "shared_size", + func = list(pkg = "dials", fun = "shared_size"), + has_submodel = TRUE + ) modelenv::set_pred( model = "gm_clust", From cfca66fe175ed90c422c2f6e6b8e4c364e30700b Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Thu, 17 Apr 2025 09:13:15 -0700 Subject: [PATCH 19/44] testing done --- NAMESPACE | 1 + R/gm_clust.R | 29 +-- R/gm_clust_data.R | 2 +- R/tunable.R | 33 ++++ man/db_clust.Rd | 4 +- man/dbscan_helper.Rd | 7 +- man/dot-gm_clust_fit_mclust.Rd | 11 +- man/gm_clust.Rd | 11 +- man/mclust_helper.Rd | 25 +++ man/tidyclust_update.Rd | 13 +- tests/testthat/_snaps/gm_clust.md | 133 +++++++++++++ tests/testthat/test-db_clust.R | 24 +-- tests/testthat/test-gm_clust-mclust.R | 85 +++++++++ tests/testthat/test-gm_clust.R | 265 +++++++++++++++++--------- 14 files changed, 512 insertions(+), 131 deletions(-) create mode 100644 man/mclust_helper.Rd create mode 100644 tests/testthat/_snaps/gm_clust.md create mode 100644 tests/testthat/test-gm_clust-mclust.R diff --git a/NAMESPACE b/NAMESPACE index 46eacc1..091b875 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -71,6 +71,7 @@ S3method(translate_tidyclust,gm_clust) S3method(translate_tidyclust,hier_clust) S3method(translate_tidyclust,k_means) S3method(tunable,cluster_spec) +S3method(tunable,db_clust) S3method(tunable,k_means) S3method(tune_args,cluster_spec) S3method(tune_cluster,cluster_spec) diff --git a/R/gm_clust.R b/R/gm_clust.R index ddb8eab..2089c0d 100644 --- a/R/gm_clust.R +++ b/R/gm_clust.R @@ -39,11 +39,11 @@ gm_clust <- function(mode = "partition", engine = "mclust", num_clusters = NULL, - circular = FALSE, - zero_covariance = FALSE, - shared_orientation = FALSE, - shared_shape = FALSE, - shared_size = FALSE + circular = TRUE, + shared_size = TRUE, + zero_covariance = TRUE, + shared_orientation = TRUE, + shared_shape = TRUE ) { args <- list( num_clusters = enquo(num_clusters), @@ -251,12 +251,21 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { res <- mclust::Mclust(x, G = G, modelNames = model_name) + + if (is.null(res)) { + rlang::abort( + "Model cannot be estimated. Please specify a model specification with less parameters by setting some model arguments to TRUE", + call = call("fit") + ) + } + attr(res, "num_clusters") <- G attr(res, "circular") <- circular attr(res, "zero_covariance") <- zero_covariance attr(res, "shared_orientation") <- shared_orientation attr(res, "shared_shape") <- shared_shape attr(res, "shared_size") <- shared_size + attr(res, "model_name") <- model_name attr(res, "training_data") <- x res } @@ -276,7 +285,7 @@ mclust_helper <- function(circular, shared_orientation, shared_shape, shared_size) { - model_name <- case_when( + model_name <- dplyr::case_when( circular & shared_size ~ "EII", circular & !shared_size ~ "VII", !circular & zero_covariance & shared_shape & shared_size ~ "EEI", @@ -288,12 +297,10 @@ mclust_helper <- function(circular, !circular & !zero_covariance & shared_orientation & shared_shape & !shared_size ~ "VEE", !circular & !zero_covariance & shared_orientation & !shared_shape & !shared_size ~ "VVE", !circular & !zero_covariance & !shared_orientation & shared_shape & shared_size ~ "EEV", - !circular & !zero_covariance & !shared_orientation & shared_shape & !shared_size ~ "EVV", - !circular & !zero_covariance & !shared_orientation & !shared_shape & shared_size ~ "VEV", + !circular & !zero_covariance & !shared_orientation & !shared_shape & shared_size ~ "EVV", + !circular & !zero_covariance & !shared_orientation & shared_shape & !shared_size ~ "VEV", !circular & !zero_covariance & !shared_orientation & !shared_shape & !shared_size ~ "VVV" - - ) - + model_name } diff --git a/R/gm_clust_data.R b/R/gm_clust_data.R index b61ce6b..d58e820 100644 --- a/R/gm_clust_data.R +++ b/R/gm_clust_data.R @@ -27,7 +27,7 @@ make_gm_clust <- function() { mode = "partition", value = list( interface = "matrix", - protect = c("x", "G", "circular", "shared_orientation", "shared_shape", "shared_size"), + protect = c("x", "G", "circular", "zero_covariance", "shared_orientation", "shared_shape", "shared_size"), func = c(pkg = "tidyclust", fun = ".gm_clust_fit_mclust"), defaults = list() ) diff --git a/R/tunable.R b/R/tunable.R index cc64103..9d86ca5 100644 --- a/R/tunable.R +++ b/R/tunable.R @@ -107,3 +107,36 @@ dbscan_db_clust_engine_args <- component_id = "engine" ) + +#' @export +tunable.gm_clust <- function(x, ...) { + res <- NextMethod() + if (x$engine == "mclust") { + res <- add_engine_parameters(res, mclust_gm_clust_engine_args) + } + res +} + +mclust_gm_clust_engine_args <- + tibble::tibble( + name = c( + "G", + "circular", + "zero_covariance", + "shared_orientation", + "shared_shape", + "shared_size" + ), + call_info = list( + list(pkg = "dials", fun = "num_clusters"), + list(pkg = "dials", fun = "circular"), + list(pkg = "dials", fun = "zero_covariance"), + list(pkg = "dials", fun = "shared_orientation"), + list(pkg = "dials", fun = "shared_shape"), + list(pkg = "dials", fun = "shared_size") + ), + source = "cluster_spec", + component = "mclust", + component_id = "engine" + ) + diff --git a/man/db_clust.Rd b/man/db_clust.Rd index 9c2a764..d085cfb 100644 --- a/man/db_clust.Rd +++ b/man/db_clust.Rd @@ -18,9 +18,9 @@ possible value for this model is "partition".} \item{engine}{A single character string specifying what computational engine to use for fitting. The engine for this model is \code{"dbscan"}.} -\item{radius}{Positive integer, Radius used to determine core-points and cluster points together (required).} +\item{radius}{Positive double, Radius drawn around points to determine core-points and cluster assignments (required)} -\item{min_points}{Positive double, Minimum number of points needed to form a cluster (required)} +\item{min_points}{Positive integer, Minimum number of points required to form a core-point (required)} } \value{ A \code{db_clust} cluster specification. diff --git a/man/dbscan_helper.Rd b/man/dbscan_helper.Rd index 7a557ac..d6dc43a 100644 --- a/man/dbscan_helper.Rd +++ b/man/dbscan_helper.Rd @@ -7,12 +7,13 @@ dbscan_helper(object, ...) } \arguments{ -\item{x}{matrix or data frame} +\item{object}{db_clust object} } \value{ -dbscan object +numeric vector } \description{ -This function... +This function returns the cluster assignments for the training data +based on their distance to the CLOSEST core point in the data } \keyword{internal} diff --git a/man/dot-gm_clust_fit_mclust.Rd b/man/dot-gm_clust_fit_mclust.Rd index aad9416..c8f252b 100644 --- a/man/dot-gm_clust_fit_mclust.Rd +++ b/man/dot-gm_clust_fit_mclust.Rd @@ -4,7 +4,16 @@ \alias{.gm_clust_fit_mclust} \title{Simple Wrapper around Mclust function} \usage{ -.gm_clust_fit_mclust(x, G = NULL, ...) +.gm_clust_fit_mclust( + x, + G = NULL, + circular = NULL, + zero_covariance = NULL, + shared_orientation = NULL, + shared_shape = NULL, + shared_size = NULL, + ... +) } \arguments{ \item{x}{matrix or data frame} diff --git a/man/gm_clust.Rd b/man/gm_clust.Rd index 9daf6ac..cc5d972 100644 --- a/man/gm_clust.Rd +++ b/man/gm_clust.Rd @@ -4,7 +4,16 @@ \alias{gm_clust} \title{Gaussian Mixture Models (GMM)} \usage{ -gm_clust(mode = "partition", engine = "mclust", num_clusters = NULL) +gm_clust( + mode = "partition", + engine = "mclust", + num_clusters = NULL, + circular = FALSE, + zero_covariance = FALSE, + shared_orientation = FALSE, + shared_shape = FALSE, + shared_size = FALSE +) } \arguments{ \item{mode}{A single character string for the type of model. The only diff --git a/man/mclust_helper.Rd b/man/mclust_helper.Rd new file mode 100644 index 0000000..d42adb1 --- /dev/null +++ b/man/mclust_helper.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/gm_clust.R +\name{mclust_helper} +\alias{mclust_helper} +\title{mclust fit helper function} +\usage{ +mclust_helper( + circular, + zero_covariance, + shared_orientation, + shared_shape, + shared_size +) +} +\arguments{ +\item{object}{gm_clust object} +} +\value{ +string containing mclust model name +} +\description{ +This function returns the mclust model name based on the specified +TRUE/FALSE model arguments +} +\keyword{internal} diff --git a/man/tidyclust_update.Rd b/man/tidyclust_update.Rd index 673bdd6..e1cda51 100644 --- a/man/tidyclust_update.Rd +++ b/man/tidyclust_update.Rd @@ -18,7 +18,18 @@ ... ) -\method{update}{gm_clust}(object, parameters = NULL, num_clusters = NULL, fresh = FALSE, ...) +\method{update}{gm_clust}( + object, + parameters = NULL, + num_clusters = NULL, + circular = NULL, + zero_covariance = NULL, + shared_orientation = NULL, + shared_shape = NULL, + shared_size = NULL, + fresh = FALSE, + ... +) \method{update}{hier_clust}( object, diff --git a/tests/testthat/_snaps/gm_clust.md b/tests/testthat/_snaps/gm_clust.md new file mode 100644 index 0000000..5a89562 --- /dev/null +++ b/tests/testthat/_snaps/gm_clust.md @@ -0,0 +1,133 @@ +# bad input + + Code + gm_clust(mode = "bogus") + Condition + Error in `modelenv::check_spec_mode_engine_val()`: + ! 'bogus' is not a known mode for model `gm_clust()`. + +--- + + Code + bt <- gm_clust(circular = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + Condition + Error in `check_args()`: + ! The circular cluster shape argument should be TRUE or FALSE. + +--- + + Code + bt <- gm_clust(zero_covariance = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + Condition + Error in `check_args()`: + ! The zero covariance argument should be TRUE or FALSE. + +--- + + Code + bt <- gm_clust(shared_orientation = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + Condition + Error in `check_args()`: + ! The shared cluster orientation argument should be TRUE or FALSE. + +--- + + Code + bt <- gm_clust(shared_shape = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + Condition + Error in `check_args()`: + ! The shared cluster shape argument should be TRUE or FALSE. + +--- + + Code + bt <- gm_clust(shared_size = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + Condition + Error in `check_args()`: + ! The shared cluster size argument should be TRUE or FALSE. + +--- + + Code + bt <- gm_clust(num_clusters = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + Condition + Warning in `sort()`: + NAs introduced by coercion + Error in `if (G[1] == 1) ...`: + ! missing value where TRUE/FALSE needed + +--- + + Code + translate_tidyclust(gm_clust(), engine = NULL) + Condition + Error in `translate_tidyclust.default()`: + ! Please set an engine. + +--- + + Code + translate_tidyclust(gm_clust(formula = ~x)) + Condition + Error in `gm_clust()`: + ! unused argument (formula = ~x) + +# printing + + Code + gm_clust() + Output + GMM Clustering Specification (partition) + + Main Arguments: + circular = TRUE + zero_covariance = TRUE + shared_orientation = TRUE + shared_shape = TRUE + shared_size = TRUE + + Computational engine: mclust + + +--- + + Code + gm_clust(num_clusters = 3) + Output + GMM Clustering Specification (partition) + + Main Arguments: + num_clusters = 3 + circular = TRUE + zero_covariance = TRUE + shared_orientation = TRUE + shared_shape = TRUE + shared_size = TRUE + + Computational engine: mclust + + +# updating + + Code + gm_clust(num_clusters = 5) %>% update(num_clusters = tune()) + Output + GMM Clustering Specification (partition) + + Main Arguments: + num_clusters = tune() + circular = TRUE + zero_covariance = TRUE + shared_orientation = TRUE + shared_shape = TRUE + shared_size = TRUE + + Computational engine: mclust + + diff --git a/tests/testthat/test-db_clust.R b/tests/testthat/test-db_clust.R index a62c0a9..d7e0317 100644 --- a/tests/testthat/test-db_clust.R +++ b/tests/testthat/test-db_clust.R @@ -126,35 +126,21 @@ test_that("updating", { }) -test_that("reordering is done correctly for stats k_means", { +test_that("reordering is done correctly for dbscan db_clust", { set.seed(42) - kmeans_fit <- k_means(num_clusters = 6) %>% - set_engine("stats") %>% + dbscan_fit <- db_clust(radius = 20, min_points = 3) %>% + set_engine("dbscan") %>% fit(~., data = mtcars) - summ <- extract_fit_summary(kmeans_fit) + summ <- extract_fit_summary(dbscan_fit) expect_identical( summ$n_members, unname(as.integer(table(summ$cluster_assignments))) ) }) -# -# test_that("reordering is done correctly for ClusterR k_means", { -# set.seed(42) -# -# kmeans_fit <- k_means(num_clusters = 6) %>% -# set_engine("ClusterR") %>% -# fit(~., data = mtcars) -# -# summ <- extract_fit_summary(kmeans_fit) -# -# expect_identical( -# summ$n_members, -# unname(as.integer(table(summ$cluster_assignments))) -# ) -# }) + test_that("errors if `radius` and `min_points` aren't specified", { expect_snapshot( diff --git a/tests/testthat/test-gm_clust-mclust.R b/tests/testthat/test-gm_clust-mclust.R new file mode 100644 index 0000000..12e499d --- /dev/null +++ b/tests/testthat/test-gm_clust-mclust.R @@ -0,0 +1,85 @@ +test_that("fitting", { + set.seed(1234) + spec <- gm_clust(num_clusters = 3) %>% + set_engine("mclust") + + expect_no_error( + res <- fit(spec, ~., mtcars) + ) + + expect_no_error( + res <- fit_xy(spec, mtcars) + ) +}) + +test_that("predicting", { + set.seed(1234) + spec <- gm_clust(num_clusters = 3) %>% + set_engine("mclust") + + res <- fit(spec, ~., iris) + + preds <- predict(res, iris[c(25, 75, 125), ]) + + expect_identical( + preds, + tibble::tibble(.pred_cluster = factor(paste0("Cluster_", 1:3))) + ) +}) + +test_that("all levels are preserved with 1 row predictions", { + set.seed(1234) + spec <- gm_clust(num_clusters = 3) %>% + set_engine("mclust") + + res <- fit(spec, ~., mtcars) + + preds <- predict(res, mtcars[1, ]) + + expect_identical( + levels(preds$.pred_cluster), + paste0("Cluster_", 1:3) + ) +}) + +test_that("extract_centroids() works", { + set.seed(1234) + spec <- gm_clust(num_clusters = 3) %>% + set_engine("mclust") + + res <- fit(spec, ~., iris) + + centroids <- extract_centroids(res) + + expect_identical( + colnames(centroids), + c(".cluster", "Sepal.Length", "Sepal.Width", "Petal.Length", + "Petal.Width", "Speciesversicolor", "Speciesvirginica") + ) + + expect_identical( + centroids$.cluster, + factor(c("Cluster_1", "Cluster_2", "Cluster_3")) + ) +}) + +test_that("extract_cluster_assignment() works", { + set.seed(1234) + spec <- gm_clust(num_clusters = 3) %>% + set_engine("mclust") + + res <- fit(spec, ~., iris) + + clusters <- extract_cluster_assignment(res) + + res$fit$classification + + expected <- vctrs::vec_cbind( + tibble::tibble(.cluster = factor(paste0("Cluster_", res$fit$classification))) + ) + + expect_identical( + clusters, + expected + ) +}) diff --git a/tests/testthat/test-gm_clust.R b/tests/testthat/test-gm_clust.R index f855e8c..eea9a20 100644 --- a/tests/testthat/test-gm_clust.R +++ b/tests/testthat/test-gm_clust.R @@ -1,26 +1,49 @@ test_that("primary arguments", { - basic <- hier_clust(mode = "partition") - basic_stats <- translate_tidyclust(basic %>% set_engine("stats")) + basic <- gm_clust(mode = "partition") + basic_mclust <- translate_tidyclust(basic %>% set_engine("mclust")) expect_equal( - basic_stats$method$fit$args, + basic_mclust$method$fit$args, list( - data = rlang::expr(missing_arg()), - linkage_method = new_empty_quosure("complete") + x = rlang::expr(missing_arg()), + + G= rlang::expr(missing_arg()), + circular = rlang::expr(missing_arg()), + zero_covariance = rlang::expr(missing_arg()), + shared_orientation = rlang::expr(missing_arg()), + shared_shape = rlang::expr(missing_arg()), + shared_size = rlang::expr(missing_arg()), + + circular = new_empty_quosure(TRUE), + zero_covariance = new_empty_quosure(TRUE), + shared_orientation = new_empty_quosure(TRUE), + shared_shape = new_empty_quosure(TRUE), + shared_size = new_empty_quosure(TRUE) ) ) }) test_that("engine arguments", { - stats_print <- hier_clust(mode = "partition") + mclust_print <- gm_clust(mode = "partition") expect_equal( translate_tidyclust( - stats_print %>% - set_engine("stats", members = NULL) + mclust_print %>% + set_engine("mclust") )$method$fit$args, list( - data = rlang::expr(missing_arg()), - linkage_method = new_empty_quosure("complete"), - members = new_empty_quosure(NULL) + x = rlang::expr(missing_arg()), + + G = rlang::expr(missing_arg()), + circular = rlang::expr(missing_arg()), + zero_covariance = rlang::expr(missing_arg()), + shared_orientation = rlang::expr(missing_arg()), + shared_shape = rlang::expr(missing_arg()), + shared_size = rlang::expr(missing_arg()), + + circular = new_empty_quosure(TRUE), + zero_covariance = new_empty_quosure(TRUE), + shared_orientation = new_empty_quosure(TRUE), + shared_shape = new_empty_quosure(TRUE), + shared_size = new_empty_quosure(TRUE) ) ) }) @@ -28,33 +51,70 @@ test_that("engine arguments", { test_that("bad input", { expect_snapshot( error = TRUE, - hier_clust(mode = "bogus") + gm_clust(mode = "bogus") ) expect_snapshot( error = TRUE, { - bt <- hier_clust(linkage_method = "bogus") %>% set_engine("stats") + bt <- gm_clust(circular = "bogus") %>% set_engine("mclust") fit(bt, mpg ~ ., mtcars) } ) expect_snapshot( error = TRUE, - translate_tidyclust(hier_clust(), engine = NULL) + { + bt <- gm_clust(zero_covariance = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + } + ) + expect_snapshot( + error = TRUE, + { + bt <- gm_clust(shared_orientation = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + } + ) + expect_snapshot( + error = TRUE, + { + bt <- gm_clust(shared_shape = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + } + ) + expect_snapshot( + error = TRUE, + { + bt <- gm_clust(shared_size = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + } + ) + expect_snapshot( + error = TRUE, + { + bt <- gm_clust(num_clusters = "bogus") %>% set_engine("mclust") + fit(bt, mpg ~ ., mtcars) + } ) expect_snapshot( error = TRUE, - translate_tidyclust(hier_clust(formula = ~x)) + translate_tidyclust(gm_clust(), engine = NULL) + ) + expect_snapshot( + error = TRUE, + translate_tidyclust(gm_clust(formula = ~x)) ) }) test_that("predictions", { set.seed(1234) - hclust_fit <- hier_clust(num_clusters = 4) %>% - set_engine("stats") %>% + + mclust_fit <- gm_clust(num_clusters = 4) %>% + set_engine("mclust") %>% fit(~., mtcars) set.seed(1234) - ref_res <- cutree(hclust(dist(mtcars)), k = 4) + mclustBIC <- mclust::mclustBIC + ref_res <- mclust::Mclust(mtcars, G = 4, modelNames = "EII")$classification ref_predictions <- ref_res %>% unname() @@ -63,98 +123,48 @@ test_that("predictions", { } expect_equal( - relevel_preds(predict(hclust_fit, mtcars)$.pred_cluster), - predict(hclust_fit, mtcars)$.pred_cluster %>% as.numeric() + relevel_preds(predict(mclust_fit, mtcars)$.pred_cluster), + predict(mclust_fit, mtcars)$.pred_cluster %>% as.numeric() ) expect_equal( relevel_preds(ref_predictions), - extract_cluster_assignment(hclust_fit)$.cluster %>% as.numeric() + extract_cluster_assignment(mclust_fit)$.cluster %>% as.numeric() ) }) -test_that("extract_cluster_assignment works if you don't set num_clusters", { - set.seed(1234) - hclust_fit <- hier_clust(num_clusters = 4) %>% - set_engine("stats") %>% - fit(~., mtcars) - - set.seed(1234) - hclust_fit_no_args <- hier_clust() %>% - set_engine("stats") %>% - fit(~., mtcars) - - expect_identical( - extract_cluster_assignment(hclust_fit, mtcars), - extract_cluster_assignment(hclust_fit_no_args, mtcars, num_clusters = 4) - ) -}) - -test_that("predict works if you don't set num_clusters", { - set.seed(1234) - hclust_fit <- hier_clust(num_clusters = 4) %>% - set_engine("stats") %>% - fit(~., mtcars) - - set.seed(1234) - hclust_fit_no_args <- hier_clust() %>% - set_engine("stats") %>% - fit(~., mtcars) - - expect_identical( - predict(hclust_fit, mtcars), - predict(hclust_fit_no_args, mtcars, num_clusters = 4) - ) -}) test_that("extract_centroids work", { set.seed(1234) - hclust_fit <- hier_clust(num_clusters = 4) %>% - set_engine("stats") %>% + mclust_fit <- gm_clust(num_clusters = 4) %>% + set_engine("mclust") %>% fit(~., mtcars) set.seed(1234) - ref_res <- cutree(hclust(dist(mtcars)), k = 4) + mclustBIC <- mclust::mclustBIC + ref_res <- mclust::Mclust(mtcars, G = 4, modelNames = "EII")$classification ref_predictions <- ref_res %>% unname() expect_identical( - extract_centroids(hclust_fit) %>% - dplyr::mutate(.cluster = as.integer(.cluster)), + extract_centroids(mclust_fit) %>% + dplyr::mutate(.cluster = as.double(.cluster)), mtcars %>% dplyr::group_by(.cluster = ref_predictions) %>% dplyr::summarize(dplyr::across(dplyr::everything(), mean)) ) }) -test_that("extract_centroids work if you don't set num_clusters", { - set.seed(1234) - hclust_fit <- hier_clust() %>% - set_engine("stats") %>% - fit(~., mtcars) - - set.seed(1234) - ref_res <- cutree(hclust(dist(mtcars)), k = 4) - - ref_predictions <- ref_res %>% unname() - - expect_identical( - extract_centroids(hclust_fit, num_clusters = 4) %>% - dplyr::mutate(.cluster = as.integer(.cluster)), - mtcars %>% - dplyr::group_by(.cluster = ref_predictions) %>% - dplyr::summarize(dplyr::across(dplyr::everything(), mean)) - ) -}) test_that("predictions with new data", { set.seed(1234) - hclust_fit <- hier_clust(num_clusters = 4) %>% - set_engine("stats") %>% + mclust_fit <- gm_clust(num_clusters = 4) %>% + set_engine("mclust") %>% fit(~., mtcars) set.seed(1234) - ref_res <- cutree(hclust(dist(mtcars)), k = 4) + mclustBIC <- mclust::mclustBIC + ref_res <- mclust::Mclust(mtcars, G = 4, modelNames = "EII")$classification ref_predictions <- ref_res %>% unname() @@ -163,45 +173,116 @@ test_that("predictions with new data", { } expect_equal( - relevel_preds(predict(hclust_fit, mtcars[1:10, ])$.pred_cluster), - predict(hclust_fit, mtcars[1:10, ])$.pred_cluster %>% as.numeric() + relevel_preds(predict(mclust_fit, mtcars[1:10, ])$.pred_cluster), + predict(mclust_fit, mtcars[1:10, ])$.pred_cluster %>% as.numeric() ) }) test_that("Right classes", { expect_equal( - class(hier_clust()), - c("hier_clust", "cluster_spec", "unsupervised_spec") + class(gm_clust()), + c("gm_clust", "cluster_spec", "unsupervised_spec") ) }) test_that("printing", { expect_snapshot( - hier_clust() + gm_clust() ) expect_snapshot( - hier_clust(num_clusters = 10) + gm_clust(num_clusters = 3) ) }) test_that("updating", { expect_snapshot( - hier_clust(num_clusters = 5) %>% + gm_clust(num_clusters = 5) %>% update(num_clusters = tune()) ) }) -test_that("reordering is done correctly for stats hier_clust", { +test_that("reordering is done correctly for gm_clust", { set.seed(42) - kmeans_fit <- hier_clust(num_clusters = 6) %>% - set_engine("stats") %>% + gm_fit <- gm_clust(num_clusters = 6) %>% + set_engine("mclust") %>% fit(~., data = mtcars) - summ <- extract_fit_summary(kmeans_fit) + summ <- extract_fit_summary(gm_fit) expect_identical( summ$n_members, unname(as.integer(table(summ$cluster_assignments))) ) }) + +test_that("model errors when parameters cannot be estimated", { + set.seed(42) + + expect_error( + gm_clust(num_clusters = 10, circular=F,zero_covariance=F,shared_orientation=F,shared_shape=F,shared_size=F) %>% + set_engine("mclust") %>% + fit(~., data = mtcars) + ) + +}) + + +test_that("mappings to different model names are correct", { + expect_identical( + tidyclust:::mclust_helper(T, NA, NA, NA, T), + "EII" + ) + expect_identical( + tidyclust:::mclust_helper(T, NA, NA, NA, F), + "VII" + ) + expect_identical( + tidyclust:::mclust_helper(F, T, NA, T, T), + "EEI" + ) + expect_identical( + tidyclust:::mclust_helper(F, T, NA, F, T), + "EVI" + ) + expect_identical( + tidyclust:::mclust_helper(F, T, NA, T, F), + "VEI" + ) + expect_identical( + tidyclust:::mclust_helper(F, T, NA, F, F), + "VVI" + ) + expect_identical( + tidyclust:::mclust_helper(F, F, T, T, T), + "EEE" + ) + expect_identical( + tidyclust:::mclust_helper(F, F, T, F, T), + "EVE" + ) + expect_identical( + tidyclust:::mclust_helper(F, F, T, T, F), + "VEE" + ) + expect_identical( + tidyclust:::mclust_helper(F, F, T, F, F), + "VVE" + ) + expect_identical( + tidyclust:::mclust_helper(F, F, F, T, T), + "EEV" + ) + expect_identical( + tidyclust:::mclust_helper(F, F, F, F, T), + "EVV" + ) + expect_identical( + tidyclust:::mclust_helper(F, F, F, T, F), + "VEV" + ) + expect_identical( + tidyclust:::mclust_helper(F, F, F, F, F), + "VVV" + ) +}) From 0b563d8cd47e26c41c00d83f78e99ac94b275828 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Wed, 4 Jun 2025 12:45:19 -0700 Subject: [PATCH 20/44] updated documentation --- NAMESPACE | 1 + R/db_clust.R | 4 +- R/extract_fit_summary.R | 15 ++- R/gm_clust.R | 9 +- man/db_clust.Rd | 4 +- man/gm_clust.Rd | 24 ++-- man/rmd/db_clust_dbscan.Rmd | 58 +++++++++ man/rmd/gm_clust_mclust.Rmd | 57 +++++++++ tests/testthat/_snaps/k_means.new.md | 85 -------------- tests/testthat/test-gm_clust.R | 162 ++++++++++++++++++++++++++ vignettes/articles/db_clust.Rmd | 103 ++++++++++++---- vignettes/articles/gm_clust.Rmd | 100 ++++++++++------ vignettes/articles/gmm_models/eee.jpg | Bin 0 -> 50421 bytes vignettes/articles/gmm_models/eei.jpg | Bin 0 -> 46980 bytes vignettes/articles/gmm_models/eev.jpg | Bin 0 -> 50337 bytes vignettes/articles/gmm_models/eii.jpg | Bin 0 -> 45380 bytes vignettes/articles/gmm_models/eve.jpg | Bin 0 -> 51770 bytes vignettes/articles/gmm_models/evi.jpg | Bin 0 -> 46695 bytes vignettes/articles/gmm_models/evv.jpg | Bin 0 -> 50580 bytes vignettes/articles/gmm_models/vee.jpg | Bin 0 -> 46283 bytes vignettes/articles/gmm_models/vei.jpg | Bin 0 -> 46214 bytes vignettes/articles/gmm_models/vev.jpg | Bin 0 -> 43934 bytes vignettes/articles/gmm_models/vii.jpg | Bin 0 -> 44938 bytes vignettes/articles/gmm_models/vve.jpg | Bin 0 -> 48193 bytes vignettes/articles/gmm_models/vvi.jpg | Bin 0 -> 43963 bytes vignettes/articles/gmm_models/vvv.jpg | Bin 0 -> 46505 bytes 26 files changed, 461 insertions(+), 161 deletions(-) create mode 100644 man/rmd/db_clust_dbscan.Rmd create mode 100644 man/rmd/gm_clust_mclust.Rmd delete mode 100644 tests/testthat/_snaps/k_means.new.md create mode 100644 vignettes/articles/gmm_models/eee.jpg create mode 100644 vignettes/articles/gmm_models/eei.jpg create mode 100644 vignettes/articles/gmm_models/eev.jpg create mode 100644 vignettes/articles/gmm_models/eii.jpg create mode 100644 vignettes/articles/gmm_models/eve.jpg create mode 100644 vignettes/articles/gmm_models/evi.jpg create mode 100644 vignettes/articles/gmm_models/evv.jpg create mode 100644 vignettes/articles/gmm_models/vee.jpg create mode 100644 vignettes/articles/gmm_models/vei.jpg create mode 100644 vignettes/articles/gmm_models/vev.jpg create mode 100644 vignettes/articles/gmm_models/vii.jpg create mode 100644 vignettes/articles/gmm_models/vve.jpg create mode 100644 vignettes/articles/gmm_models/vvi.jpg create mode 100644 vignettes/articles/gmm_models/vvv.jpg diff --git a/NAMESPACE b/NAMESPACE index 091b875..5c8bac1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -72,6 +72,7 @@ S3method(translate_tidyclust,hier_clust) S3method(translate_tidyclust,k_means) S3method(tunable,cluster_spec) S3method(tunable,db_clust) +S3method(tunable,gm_clust) S3method(tunable,k_means) S3method(tune_args,cluster_spec) S3method(tune_cluster,cluster_spec) diff --git a/R/db_clust.R b/R/db_clust.R index 098e2a4..85b9860 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -3,9 +3,9 @@ #' @description #' #' `db_clust` defines a model that fits clusters based on areas with observations -#' that are densely packed together +#' that are densely packed together using the DBSCAN algorithm #' -#' There are different ways to fit this model, and the method of estimation is +#' There are multiple implementations for this model, and the implementation is #' chosen by setting the model engine. The engine-specific pages for this model #' are listed below. #' diff --git a/R/extract_fit_summary.R b/R/extract_fit_summary.R index 555eaf9..c8e22c2 100644 --- a/R/extract_fit_summary.R +++ b/R/extract_fit_summary.R @@ -203,6 +203,10 @@ extract_fit_summary.dbscan <- function(object, ...) { outlier_idx <- which(unique(clusts) == "Outlier") centroids[outlier_idx, ] <- rep(NA, ncol(centroids)) + + # reorder centroids + centroids <- centroids[c(outlier_idx, setdiff(1:n_clust, outlier_idx)), ] + sse_within_total_total <- map2_dbl( by_clust$data, seq_len(n_clust), @@ -210,8 +214,9 @@ extract_fit_summary.dbscan <- function(object, ...) { ) + clust_names <- unique(clusts)[c(outlier_idx, setdiff(1:n_clust, outlier_idx))] list( - cluster_names = unique(clusts), + cluster_names = clust_names, centroids = centroids, n_members = unname(as.integer(table(clusts))), sse_within_total_total = sse_within_total_total, @@ -242,11 +247,19 @@ extract_fit_summary.Mclust <- function(object, ...) { map(dplyr::summarize_all, mean) %>% dplyr::bind_rows() + sse_within_total_total <- map2_dbl( + by_clust$data, + seq_len(n_clust), + ~ sum(Rfast::dista(centroids[.y, ], .x)) + ) + list( cluster_names = unique(clusts), centroids = centroids, n_members = unname(as.integer(table(clusts))), + sse_within_total_total = sse_within_total_total, + sse_total = sum(Rfast::dista(t(overall_centroid), training_data)), orig_labels = NULL, cluster_assignments = clusts ) diff --git a/R/gm_clust.R b/R/gm_clust.R index 2089c0d..1301ae9 100644 --- a/R/gm_clust.R +++ b/R/gm_clust.R @@ -3,9 +3,9 @@ #' @description #' #' `gm_clust` defines a model that fits clusters based on fitting a specified number of -#' multivariate normal distributions to the data. +#' multivariate Gaussian distributions (MVG) to the data. #' -#' There are different ways to fit this model, and the method of estimation is +#' There are multiple implementations for this model, and the implementation is #' chosen by setting the model engine. The engine-specific pages for this model #' are listed below. #' @@ -16,6 +16,11 @@ #' @param engine A single character string specifying what computational engine #' to use for fitting. The engine for this model is `"mclust"`. #' @param num_clusters Positive integer, number of clusters in model (required). +#' @param circular Boolean, whether or not to fit circular MVG distributions for each cluster +#' @param zero_covariance Boolean, whether or not to assign covariances of 0 for each MVG +#' @param shared_orientation Boolean, whether each cluster MVG should have the same orientation +#' @param shared_shape Boolean, whether each cluster MVG should have the same shape +#' @param shared_size Boolean, whether each cluster MVG should have the same size/volume #' #' @importFrom mclust mclustBIC #' diff --git a/man/db_clust.Rd b/man/db_clust.Rd index d085cfb..db44a06 100644 --- a/man/db_clust.Rd +++ b/man/db_clust.Rd @@ -27,9 +27,9 @@ A \code{db_clust} cluster specification. } \description{ \code{db_clust} defines a model that fits clusters based on areas with observations -that are densely packed together +that are densely packed together using the DBSCAN algorithm -There are different ways to fit this model, and the method of estimation is +There are multiple implementations for this model, and the implementation is chosen by setting the model engine. The engine-specific pages for this model are listed below. \itemize{ diff --git a/man/gm_clust.Rd b/man/gm_clust.Rd index cc5d972..385ada3 100644 --- a/man/gm_clust.Rd +++ b/man/gm_clust.Rd @@ -8,11 +8,11 @@ gm_clust( mode = "partition", engine = "mclust", num_clusters = NULL, - circular = FALSE, - zero_covariance = FALSE, - shared_orientation = FALSE, - shared_shape = FALSE, - shared_size = FALSE + circular = TRUE, + shared_size = TRUE, + zero_covariance = TRUE, + shared_orientation = TRUE, + shared_shape = TRUE ) } \arguments{ @@ -23,15 +23,25 @@ possible value for this model is "partition".} to use for fitting. The engine for this model is \code{"mclust"}.} \item{num_clusters}{Positive integer, number of clusters in model (required).} + +\item{circular}{Boolean, whether or not to fit circular MVG distributions for each cluster} + +\item{shared_size}{Boolean, whether each cluster MVG should have the same size/volume} + +\item{zero_covariance}{Boolean, whether or not to assign covariances of 0 for each MVG} + +\item{shared_orientation}{Boolean, whether each cluster MVG should have the same orientation} + +\item{shared_shape}{Boolean, whether each cluster MVG should have the same shape} } \value{ A \code{gm_clust} cluster specification. } \description{ \code{gm_clust} defines a model that fits clusters based on fitting a specified number of -multivariate normal distributions to the data. +multivariate Gaussian distributions (MVG) to the data. -There are different ways to fit this model, and the method of estimation is +There are multiple implementations for this model, and the implementation is chosen by setting the model engine. The engine-specific pages for this model are listed below. \itemize{ diff --git a/man/rmd/db_clust_dbscan.Rmd b/man/rmd/db_clust_dbscan.Rmd new file mode 100644 index 0000000..3934928 --- /dev/null +++ b/man/rmd/db_clust_dbscan.Rmd @@ -0,0 +1,58 @@ +```{r, child = "aaa.Rmd", include = FALSE} +``` + +`r descr_models("db_clust", "dbscan")` + +## Tuning Parameters + +```{r stats-param-info, echo = FALSE} +defaults <- + tibble::tibble(tidyclust = c("radius", "min_points"), + default = c("no default", "no_default")) + +param <- + db_clust() %>% + set_engine("dbscan") %>% + set_mode("partition") %>% + make_parameter_list(defaults) +``` + +This model has `r nrow(param)` tuning parameters: + +```{r dbscan-param-list, echo = FALSE, results = "asis"} +param$item +``` + +## Translation from tidyclust to the original package (partition) + +```{r stats-cls} +db_clust(radius = 0.5, min_points = 5)%>% + set_engine("dbscan") %>% + set_mode("partition") %>% + translate_tidyclust() +``` + +## Preprocessing requirements + +```{r child = "template-makes-dummies.Rmd"} +``` + +```{r child = "template-same-scale.Rmd"} +``` + + +## References + +- Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. + +- Hahsler, M., Piekenbrock, M., & Doran, D. (2019a). Dbscan : Fast Density-Based Clustering with r. Journal of Statistical Software, 91(1). https://doi.org/10.18637/jss.v091.i01 + +- Hahsler, M., Piekenbrock, M., & Doran, D. (2019b). dbscan: Fast density-based clustering +with R. Journal of Statistical Software, 91(1), 1–30. https://doi.org/10.18637/jss.v091. +i01 + +- Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011). Density-based clustering. WIREs Data Mining and Knowledge Discovery, 1(3), 231–240. https://doi.org/10.1002/widm. 30 + +- Tran, T. N., Drab, K., & Daszykowski, M. (2013). Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometrics and Intelligent Laboratory Systems, 49 120, 92–96. https://doi.org/10.1016/j.chemolab.2012.11.006 + + diff --git a/man/rmd/gm_clust_mclust.Rmd b/man/rmd/gm_clust_mclust.Rmd new file mode 100644 index 0000000..0cef423 --- /dev/null +++ b/man/rmd/gm_clust_mclust.Rmd @@ -0,0 +1,57 @@ +```{r, child = "aaa.Rmd", include = FALSE} +``` + +`r descr_models("gm_clust", "mclust")` + +## Tuning Parameters + +```{r stats-param-info, echo = FALSE} +defaults <- + tibble::tibble(tidyclust = c("num_clusters", "circular", "zero_covariance", "shared_orientation", "shared_shape", "shared_size"), + default = c("no default", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE")) + +param <- + gm_clust() %>% + set_engine("mclust") %>% + set_mode("partition") %>% + make_parameter_list(defaults) +``` + +This model has `r nrow(param)` tuning parameters: + +```{r dbscan-param-list, echo = FALSE, results = "asis"} +param$item +``` + +## Translation from tidyclust to the original package (partition) + +```{r stats-cls} +gm_clust(num_clusters = 3, circular = FALSE, zero_covariance = FALSE) %>% + set_engine("mclust") %>% + set_mode("partition") %>% + translate_tidyclust() +``` + +## Preprocessing requirements + +Gaussian Mixture Models should be fit with only quantitative predictors and without any categorical predictors. No scaling is required since the variance-covariance matrices of the Gaussian distributions account for the unequal variances between predictors and their covariances. + +## References + +- Banfield, J. D., & Raftery, A. E. (1993). Model-Based Gaussian and Non-Gaussian Clustering. Biometrics, 49(3), 803. https://doi.org/10.2307/2532201 + +- Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition, 28(5), 781–793. https://doi.org/10.1016/0031-3203(94)00125-6 + +- Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. + +- McNicholas, P. D. (2016). Model-Based clustering. Journal of Classification, 33(3), 331–373. https://doi.org/10.1007/s00357-016-9211-9 + +- Scrucca, L., Fop, M., Murphy, T., Brendan, & Raftery, A., E. (2016). Mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. The R Journal, 8(1), 289. https://doi.org/10.32614/RJ-2016-021 + +- Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Model-based clustering, +classification, and density estimation using mclust in R. Chapman; Hall/CRC. https: +//doi.org/10.1201/9781003277965 + + + + diff --git a/tests/testthat/_snaps/k_means.new.md b/tests/testthat/_snaps/k_means.new.md deleted file mode 100644 index 51be933..0000000 --- a/tests/testthat/_snaps/k_means.new.md +++ /dev/null @@ -1,85 +0,0 @@ -# bad input - - Code - k_means(mode = "bogus") - Condition - Error in `modelenv::check_spec_mode_engine_val()`: - ! 'bogus' is not a known mode for model `k_means()`. - ---- - - Code - bt <- k_means(num_clusters = -1) %>% set_engine("stats") - fit(bt, mpg ~ ., mtcars) - Condition - Error in `check_args()`: - ! The number of centers should be >= 0. - ---- - - Code - translate_tidyclust(k_means(), engine = NULL) - Condition - Error in `translate_tidyclust.default()`: - ! Please set an engine. - ---- - - Code - translate_tidyclust(k_means(formula = ~x)) - Condition - Error in `k_means()`: - ! unused argument (formula = ~x) - -# printing - - Code - k_means() - Output - K Means Cluster Specification (partition) - - Computational engine: stats - - ---- - - Code - k_means(num_clusters = 10) - Output - K Means Cluster Specification (partition) - - Main Arguments: - num_clusters = 10 - - Computational engine: stats - - -# updating - - Code - k_means(num_clusters = 5) %>% update(num_clusters = tune()) - Output - K Means Cluster Specification (partition) - - Main Arguments: - num_clusters = tune() - - Computational engine: stats - - -# errors if `num_clust` isn't specified - - Code - k_means() %>% set_engine("stats") %>% fit(~., data = mtcars) - Condition - Error in `fit()`: - ! Please specify `num_clust` to be able to fit specification. - ---- - - Code - k_means() %>% set_engine("ClusterR") %>% fit(~., data = mtcars) - Condition - Error in `fit()`: - ! This engine requires some package installs: 'ClusterR' - diff --git a/tests/testthat/test-gm_clust.R b/tests/testthat/test-gm_clust.R index eea9a20..5a25f9d 100644 --- a/tests/testthat/test-gm_clust.R +++ b/tests/testthat/test-gm_clust.R @@ -285,4 +285,166 @@ test_that("mappings to different model names are correct", { tidyclust:::mclust_helper(F, F, F, F, F), "VVV" ) +}) + + + test_that("mappings to different model names are correct", { + set.seed(42) + + iris_sub <- iris %>% dplyr::select(Sepal.Length, Sepal.Width) + + gm_fit <- gm_clust(num_clusters = 3) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "EII" + ) + + gm_fit <- gm_clust(num_clusters = 3, + shared_size = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "VII" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "EEI" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + shared_shape = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "EVI" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + shared_size = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "VEI" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + zero_covariance = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "EEE" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + zero_covariance = FALSE, + shared_shape = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "EVE" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + zero_covariance = FALSE, + shared_size = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "VEE" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + zero_covariance = FALSE, + shared_shape = FALSE, + shared_size = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "VVE" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + zero_covariance = FALSE, + shared_orientation = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "EEV" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + zero_covariance = FALSE, + shared_orientation = FALSE, + shared_shape = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "EVV" + ) + + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + zero_covariance = FALSE, + shared_orientation = FALSE, + shared_size = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "VEV" + ) + + gm_fit <- gm_clust(num_clusters = 3, + circular = FALSE, + zero_covariance = FALSE, + shared_orientation = FALSE, + shared_shape = FALSE, + shared_size = FALSE) %>% + set_engine("mclust") %>% + fit(~., data = iris_sub) + + expect_identical( + gm_fit %>% extract_fit_engine() %>% .$modelName, + "VVV" + ) + + + }) diff --git a/vignettes/articles/db_clust.Rmd b/vignettes/articles/db_clust.Rmd index 14a0cc2..575694e 100644 --- a/vignettes/articles/db_clust.Rmd +++ b/vignettes/articles/db_clust.Rmd @@ -77,15 +77,15 @@ Note that I have standardized bill length and bill depth since DBSCAN uses eucli ```{r} -penguins_std <- penguins %>% - mutate(bill_length_std = scale(bill_length_mm)[,1], - bill_depth_std = scale(bill_depth_mm)[,1]) %>% - select(bill_length_std, bill_depth_std) +penguins_recipe <- recipe(~bill_length_mm + bill_depth_mm, data = penguins) %>% + step_normalize(all_numeric_predictors()) + +db_workflow <- workflow() %>% + add_model(db_clust_spec) %>% + add_recipe(penguins_recipe) -db_clust_fit <- db_clust_spec %>% - fit( ~ bill_length_std + bill_depth_std, - data = penguins_std - ) +db_clust_fit <- db_workflow %>% + fit(data = penguins) db_clust_fit %>% summary() @@ -117,7 +117,7 @@ db_clust_fit %>% extract_cluster_assignment() While the centroids produced by a db_clust fit may not be of primary interest, they can still be accessed via the `extract_fit_summary()` object. ```{r} -db_clust_summary$centroids +db_clust_fit %>% extract_centroids() ``` ## Prediction @@ -128,8 +128,8 @@ Since $DBSCAN$ algorithm ultimately assigns training observations to the cluster ```{r} new_penguin <- tibble( - bill_length_std = -1.2454235, - bill_depth_std = 0.5012132 + bill_length_mm = 45, + bill_depth_mm = 15 ) db_clust_fit %>% @@ -143,6 +143,14 @@ Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a method In DBSCAN, observations are considered as locations in multidimensional space. The algorithm works by defining a cluster as a dense region of connected points. During the fitting process, points are classified as core points, border points, or noise based on their proximity to other points (**radius**) and a density threshold (**min_points**). +```{r, include = FALSE} +penguins_std <- penguins %>% + mutate(bill_length_std = scale(bill_length_mm)[,1], + bill_depth_std = scale(bill_depth_mm)[,1]) %>% + select(bill_length_std, bill_depth_std) +``` + + The fitting process can be described as follows: ```{r, echo = FALSE, include = FALSE} @@ -151,11 +159,12 @@ penguins_std %>% ggplot() + geom_point(aes(x = bill_length_std, y = bill_depth_std)) + theme_minimal() + -coord_fixed() +coord_fixed() + +labs(x = "Standardized Bill Length", y = "Standardized Bill Depth") ``` -1. For each observation in the data, determine whether each point is a core point. A core point is defined as an observation which has more than the specified **min_points** points (including the point itself) within the specified **radius** around the point. +1. Begin scanning the dataset for core points. A core point is defined as an observation which has more than the specified **min_points** points (including the point itself) within the specified **radius** around the point. ```{r, include = FALSE} @@ -167,7 +176,6 @@ dbscan_fit <- dbscan(penguins_std, eps = eps, minPts = min_points) ``` ```{r, echo = FALSE} -par(mfrow=c(1,2)) penguins_std %>% ggplot() + @@ -179,37 +187,81 @@ penguins_std %>% geom_point(tibble(x = c(-0.79162259), y = c(0.2)), mapping = aes(x = x, y = y, color = c("1"))) + coord_fixed() + theme(legend.position = "none") + - scale_color_manual(values = c("red")) + scale_color_manual(values = c("red"))+ +labs(x = "Standardized Bill Length", y = "Standardized Bill Depth") + + +``` + + +2. If a core point is found, form a cluster with the core point and begin recursively building out the cluster to nearby core points. + + +```{r} + +penguins_std %>% + ggplot() + + geom_point(aes(x = bill_length_std, y = bill_depth_std)) + + theme_minimal() + + geom_circle(tibble(x = c(-0.79162259), y = c(0.2), radius = rep(eps,1)), + mapping = aes(x0 = x, y0 = y, r = eps), + color = "gray", linewidth = 0.8) + + geom_point(tibble(x = c(-0.79162259), y = c(0.2)), mapping = aes(x = x, y = y, color = c("1")), size = 2) + + coord_fixed() + + theme(legend.position = "none") + + scale_color_manual(values = c("#619CFF"))+ +labs(x = "Standardized Bill Length", y = "Standardized Bill Depth") + + +``` + + +3. For every other point within the specified **radius** of the core point, search for other core points to add to the cluster. For every core point added, recursively search for core points within **radius** distance of any core point added until core points for the single cluster have been found. +```{r} penguins_new <- penguins_std penguins_new$cluster <- (db_clust_fit %>% extract_cluster_assignment())$.cluster # factor(dbscan_fit$cluster) -penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = min_points)) == 1, "Yes", "No"), levels = c("Yes", "No")) +penguins_new$cp2 <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = min_points)) == 1 & penguins_new$cluster == "Cluster_2", "Yes", "No"), levels = c("Yes", "No")) + +penguins_new$radius <- if_else(penguins_new$cp == "Yes", eps, 0) + +penguins_new_sub <- penguins_new %>% filter(cp == "Yes") %>% + .[c(2,3,4,5,7,9,16),] penguins_new %>% ggplot(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + geom_point() + theme_minimal() + + geom_circle(mapping = aes(x0 = bill_length_std, y0 = bill_depth_std, r = radius), color = "gray", linewidth = 0.8, data = penguins_new_sub) + coord_fixed() + - scale_color_manual(values = c("red", "black")) + - theme(legend.position = "none") + scale_color_manual(values = c("#619CFF", "black")) + + theme(legend.position = "none")+ +labs(x = "Standardized Bill Length", y = "Standardized Bill Depth") + + + ``` -2. For each core point, assign it to a cluster as well as all other core points within the specified **radius** of the point. Do until all core points have been assigned to a cluster. +4. Repeat the previous steps until all core points for every cluster have been identified. ```{r, echo = FALSE} +penguins_new$cp <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = min_points)) == 1, "Yes", "No"), levels = c("Yes", "No")) + penguins_new %>% mutate(cp_color = if_else(cp == "Yes", cluster, "0")) %>% ggplot() + geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cp_color)) + theme_minimal() + coord_fixed() + - scale_color_manual(values = c("black", "#F8766D", "#00BA38", "#619CFF")) + - theme(legend.position = "none") + scale_color_manual(values = c("black", "#F8766D", "#619CFF", "#00BA38")) + + theme(legend.position = "none")+ +labs(x = "Standardized Bill Length", y = "Standardized Bill Depth") + ``` -3. For the remaining points not assigned to a cluster, check whether the point is within the radius of a core point. For points not in the radius of any core points, assign the point as an outlier. Otherwise, assign the point to the cluster of the closest core point. +5. For the remaining points not assigned to a cluster, check whether the point is within the radius of a core point and assign to cluster corresponding to nearest core point. Points not in the radius of any core points, are considered outliers. ```{r, echo = FALSE} @@ -218,7 +270,10 @@ penguins_new %>% geom_point(aes(x = bill_length_std, y = bill_depth_std, color = cluster)) + theme_minimal() + coord_fixed() + - scale_color_manual(values = c("#F8766D", "#00BA38", "#619CFF", "black")) + - theme(legend.position = "none") + scale_color_manual(values = c("black", "#F8766D", "#619CFF", "#00BA38")) + + theme(legend.position = "none")+ +labs(x = "Standardized Bill Length", y = "Standardized Bill Depth") + ``` + diff --git a/vignettes/articles/gm_clust.Rmd b/vignettes/articles/gm_clust.Rmd index d0dc4a2..06cfaca 100644 --- a/vignettes/articles/gm_clust.Rmd +++ b/vignettes/articles/gm_clust.Rmd @@ -29,6 +29,7 @@ Load Libraries: library(tidyclust) library(tidyverse) library(tidymodels) +library(mclust) ``` @@ -53,16 +54,23 @@ penguins <- penguins %>% -At the end of this vignette, you will find a brief overview of the GMM algorithm. +At the end of this vignette, you will find a brief overview of the GMM algorithm and examples of each GMM model specification. ## `gm_clust` specification in {`tidyclust`} -To specify a GMM model in `tidyclust`, simply set the value for `num_clusters`: +To specify a GMM model in `tidyclust`, set the value for `num_clusters` and use the TRUE/FALSE parameters to select which model specification to use: ```{r} -gm_clust_spec <- gm_clust(num_clusters = 3) +gm_clust_spec <- gm_clust( + num_clusters = 3, + circular = FALSE, + zero_covariance = FALSE, + shared_orientation = TRUE, + shared_shape = FALSE, + shared_size = FALSE +) gm_clust_spec ``` @@ -98,13 +106,6 @@ The cluster assignments for the training data can be accessed using the `extract gm_clust_fit %>% extract_cluster_assignment() ``` -**Should probably make some way to extract probabilities of belonging to each group** - -```{r} -gm_clust_fit$fit$z %>% head() -``` - - If you have not yet read the `k_means` vignette, we recommend reading that first; functions that are used in this vignette are explained in more detail there. @@ -134,17 +135,6 @@ gm_clust_fit %>% ``` -## A brief introduction to density-based clustering - - -Gaussian Mixture Models (GMM) is a probabilistic unsupervised learning method that models data as a mixture of multiple Gaussian distributions. Unlike clustering methods such as k-means, GMM provides a soft clustering approach, where each observation has a probability of belonging to multiple clusters. This allows for more flexibility in capturing complex data distributions. - -In GMM, observations are assumed to be generated from a combination of Gaussian distributions, each with its own mean and covariance. The algorithm works by iteratively estimating the parameters of these distributions using the Expectation-Maximization (EM) algorithm. During the fitting process, observations are assigned to clusters based on their probability of belonging to each Gaussian component (**num_clusters**), making GMM effective for identifying overlapping or elliptical clusters in data. - - - - - ```{r} #| include: false plot_state <- function(obj) { @@ -181,9 +171,53 @@ plot_state <- function(obj) { theme(legend.position = "none") } + + +get_state <- function(params) { + + list(mean1 = unname(params$mean[,1]), + mean2 = unname(params$mean[,2]), + mean3 = unname(params$mean[,3]), + sigma1 = unname(params$variance$sigma)[,,1], + sigma2 = unname(params$variance$sigma)[,,2], + sigma3 = unname(params$variance$sigma)[,,3]) +} + ``` + +| Model Name | Circular Clusters? | Zero Covariance? | Shared Orientation? | Shared Shape? | Shared Size? | Example | +|-----------|-----------|-----------|-----------|-----------|-----------|-----------| +| EII | TRUE | -- | -- | -- | TRUE | ![](gmm_models/eii.jpg) | +| VII | TRUE | -- | -- | -- | FALSE | ![](gmm_models/vii.jpg) | +| EEI | FALSE | TRUE | -- | TRUE | TRUE | ![](gmm_models/eei.jpg) | +| EVI | FALSE | TRUE | -- | FALSE | TRUE | ![](gmm_models/evi.jpg) | +| VEI | FALSE | TRUE | -- | TRUE | FALSE | ![](gmm_models/vei.jpg) | +| VVI | FALSE | TRUE | -- | FALSE | FALSE | ![](gmm_models/vvi.jpg) | +| EEE | FALSE | FALSE | TRUE | TRUE | TRUE | ![](gmm_models/eee.jpg) | +| EVE | FALSE | FALSE | TRUE | FALSE | TRUE | ![](gmm_models/eve.jpg) | +| VEE | FALSE | FALSE | TRUE | TRUE | FALSE | ![](gmm_models/vee.jpg) | +| VVE | FALSE | FALSE | TRUE | FALSE | FALSE | ![](gmm_models/vve.jpg) | +| EEV | FALSE | FALSE | FALSE | TRUE | TRUE | ![](gmm_models/eev.jpg) | +| EVV | FALSE | FALSE | FALSE | FALSE | TRUE | ![](gmm_models/evv.jpg) | +| VEV | FALSE | FALSE | FALSE | TRUE | FALSE | ![](gmm_models/vev.jpg) | +| VVV | FALSE | FALSE | FALSE | FALSE | FALSE | ![](gmm_models/vvv.jpg) | + +: GMM Model Specifications with gm_clust() + + + + +## A brief introduction to density-based clustering + + +Gaussian Mixture Models (GMM) is a probabilistic unsupervised learning method that models data as a mixture of multiple Gaussian distributions. Unlike clustering methods such as k-means, GMM provides a soft clustering approach, where each observation has a probability of belonging to multiple clusters. This allows for more flexibility in capturing complex data distributions. + +In GMM, observations are assumed to be generated from a combination of Gaussian distributions, each with some mean vector and variance-covariance matrix. The algorithm works by iteratively estimating these parameters for each cluster using the Expectation-Maximization (EM) algorithm. After estimating these parameters observations are assigned to clusters based on their probability of belonging to each Gaussian component. + + + ```{r} #| include: false param_init <- list( @@ -229,42 +263,32 @@ param_conv <- list( #| echo: false plot_state(param_init) ``` + + ```{r} #| echo: false plot_state(param_iter1) ``` + + ```{r} #| echo: false plot_state(param_iter2) ``` + + ```{r} #| echo: false plot_state(param_conv) ``` -```{r} -#| include: false -penguins %>% - mutate(cluster = (gm_clust_fit %>% extract_cluster_assignment())$.cluster) %>% - ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = cluster)) + - geom_point() + - stat_ellipse(level = .50, type = "norm", linetype = 1) + - stat_ellipse(level = .75, type = "norm", linetype = 1) + - stat_ellipse(level = .95, type = "norm", linetype = 1) + - theme_minimal() + - scale_color_manual(values = c("#F8766D", "#00BA38", "#619CFF")) + - theme(legend.position = "none") -``` -```{r} -gmm_engine <- gm_clust_fit %>% extract_fit_engine() -mclust::plot.Mclust(gmm_engine) -``` + diff --git a/vignettes/articles/gmm_models/eee.jpg b/vignettes/articles/gmm_models/eee.jpg new file mode 100644 index 0000000000000000000000000000000000000000..7b5c0dc69643260c308436b1e057736c06288467 GIT binary patch literal 50421 zcmd432Ut^E(?1$TL1_^LQCbj?Dos!jArYlY?;R8bq=WPt0i{DIQbZy2UPO8m>8SLo z^bVmT0YV7jZt$GwaqQ9ZS5VMU%w3u4h@ft{`fgIJ2$_uxU{^2THX1zi{9Hmz#L*v#z3S5Y9JZV zBM=A%I>4U(<46zv>oh0uOo!xBR!21R_+X-P$^}M0gzs3OLGg&#licr0N zbA$bSR8$7*jM~6JzZ8|~-+vXgwT}wP#X0j=Pe3ky9a(n`jW<7vpmTul`o}oA>hpvi z;(5ZLhrR3-Y%`Z`EbM1xW&NF}^ib)i8MhxYqQje{bzX_1Zup0ng1q`JJ&StDae=?{ zKGpf_&*?+;^z{D8A}P6pQ)IcHUk|zCNEHA5rXJCOf9F!DHm~U^IP0Gm(PX86KK(N2 z`8EgZx!5nMQH09pUrF3xAOrc|5v4tM;kmQWKi=i<@BjN=lMW9H8iI4>PVeBL8HbwE zCm_V^9^_lmaBbB;MpIMwVgqroWGN?nS|fN_)!+n_Yu|D>l#!YzBcT@Q|F<e>GM`n)&~jiCiQeYKo-|IRVWt!P5Td#b@Qc_{VFe)}4UV zF+())RXDBx*0J)ua=`JUW6o`Ou5lI=yhno0<9ZVF4=%Vkd~uCi8Y;(!@TuIiqhBYW z2DPCIo_~meraS_3sN8Zm{O{L^ICgmq(NQmAS<1jrTd6MO 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zQ%-L!!jO=Jx8_B^6GQ50hy^_k;ROB(AKX4r^0y%?m-1shHG?JLHrEbD#jWP&_Noh0 z^&8xdNAWLkE15+f2iQQcqw>P+@=#XJ3+@h}E40~<-rLO_XTW)>O?aHP!5V0~d+KQ0Kv}g{3mZrGGZ&DLs3C zEPyHf7L5+^eNJj0I+kYGS#3^fi;`^b-&VAP()V^?(HPs+8{2JNVAqj+t)R1fH% zuWS~if^*#!BnbH2cXP#ybg>Pw)*O5B6uq~dUY~;va%>cQ@Sa<(JqO!>;nlfcwA^uZ zc{K}|+JPu#Bu>V#jZo7KG1a26AN!v%jR>kRYrQV#oocK$wAUd9ei2fRn48Aa8HnlS zc2NMGxyO{wcfRYS?0t}T?&)_+|J!0d8ltHVv@<;fR$Te*w5!c}9 zs@{W7fkpk%@x#N27V=eO@0{gYMTsvIGi~ac%1$hgRZU@Uj{2jWHb z^sdoBsV$<9JTfCdrZ~08Mmq2O0^l9Y*VYKLFO$&r!GZlzdtfEov4$!813lPb@=k?`O1@17=y2? zA72I$IoM)<==4!oTVKZ&`Q+q2aRX_c?z>J~-XcAm=lI#Rr>R4^$bgQ>y1D5Jsl8*1 zlQPvO=YzkJr)|7=!c)q-TV6>1E=(O+9=A8=7X~FEb*M!hWAIJQ1PAJBrRG&juY_0HbYw0gd_U%3Y|Fr+(HGuu}<{$i%9BKdn literal 0 HcmV?d00001 From 342ca2703e34ddafc6498c287d175f724ed5bd16 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Thu, 12 Jun 2025 08:54:30 -0700 Subject: [PATCH 21/44] added redundant gmm model specification warnings --- R/gm_clust.R | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/R/gm_clust.R b/R/gm_clust.R index 1301ae9..907836c 100644 --- a/R/gm_clust.R +++ b/R/gm_clust.R @@ -151,6 +151,10 @@ check_args.gm_clust <- function(object) { rlang::abort("The number of clusters should be > 0.") } + if (length(args$num_clusters) > 1) { + rlang::abort("The number of clusters should be a single number.") + } + if (all(!is.logical(args$circular))) { rlang::abort("The circular cluster shape argument should be TRUE or FALSE.") } @@ -247,6 +251,27 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { } } + if (circular) { + if (!zero_covariance) { + warning("circular = TRUE so zero_covariance = FALSE has no effect on the fitted model specification") + } + if (!shared_orientation) { + warning("circular = TRUE so shared_orientation = FALSE has no effect on the fitted model specification") + } + if (!shared_shape) { + warning("circular = TRUE so shared_shape = FALSE has no effect on the fitted model specification") + } + + } else { + if (zero_covariance) { + if (!shared_orientation) { + warning("zero_covariance = TRUE so shared_orientation = FALSE has no effect on the fitted model specification") + } + } + } + + + model_name <- mclust_helper(circular, zero_covariance, shared_orientation, From 26ca1e906a7887755617f0045692d45336ada63b Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Thu, 12 Jun 2025 09:55:26 -0700 Subject: [PATCH 22/44] fix tiny error in hier_clust that makes tests fail --- R/predict_helpers.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/predict_helpers.R b/R/predict_helpers.R index d2541e9..2c8242e 100644 --- a/R/predict_helpers.R +++ b/R/predict_helpers.R @@ -183,7 +183,7 @@ make_predictions_w_outliers <- function(x, prefix, n_clusters) { } pred_clusts <- unique(clusters$.cluster)[pred_clusts_num] - pred_clust + pred_clusts } .db_clust_predict_dbscan <- function(object, new_data, prefix = "Cluster_") { From 207185d17e09817c20e372a48cd42e072591df96 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Thu, 12 Jun 2025 09:56:49 -0700 Subject: [PATCH 23/44] tests/documentation updates --- man/details_db_clust_dbscan.Rd | 65 +++++++++++++++++++++++++ man/details_gm_clust_mclust.Rd | 78 +++++++++++++++++++++++++++++ man/rmd/db_clust_dbscan.md | 62 +++++++++++++++++++++++ man/rmd/gm_clust_mclust.md | 81 +++++++++++++++++++++++++++++++ man/set_args.cluster_spec.Rd | 2 +- man/set_engine.cluster_spec.Rd | 2 +- man/set_mode.cluster_spec.Rd | 2 +- tests/testthat/_snaps/db_clust.md | 4 +- tidyclust.Rproj | 1 - 9 files changed, 291 insertions(+), 6 deletions(-) create mode 100644 man/rmd/db_clust_dbscan.md create mode 100644 man/rmd/gm_clust_mclust.md diff --git a/man/details_db_clust_dbscan.Rd b/man/details_db_clust_dbscan.Rd index 3ac9204..97fd9c5 100644 --- a/man/details_db_clust_dbscan.Rd +++ b/man/details_db_clust_dbscan.Rd @@ -6,4 +6,69 @@ \description{ \code{\link[=db_clust]{db_clust()}} creates DBSCAN model. } +\details{ +For this engine, there is a single mode: partition +\subsection{Tuning Parameters}{ + +This model has 2 tuning parameters: +\itemize{ +\item \code{radius}: Radius (type: double, default: no default) +\item \code{min_points}: Min Points (type: integer, default: no_default) +} +} + +\subsection{Translation from tidyclust to the original package (partition)}{ + +\if{html}{\out{

}}\preformatted{db_clust(radius = 0.5, min_points = 5)\%>\% + set_engine("dbscan") \%>\% + set_mode("partition") \%>\% + translate_tidyclust() + +## DBSCAN Clustering Specification (partition) +## +## Main Arguments: +## radius = 0.5 +## min_points = 5 +## +## Computational engine: dbscan +## +## Model fit template: +## tidyclust::.db_clust_fit_dbscan(x = missing_arg(), eps = missing_arg(), +## minPts = missing_arg(), eps = 0.5, minPts = 5) +}\if{html}{\out{
}} +} + +\subsection{Preprocessing requirements}{ + +Factor/categorical predictors need to be converted to numeric values +(e.g., dummy or indicator variables) for this engine. When using the +formula method via , tidyclust will convert factor columns to +indicators. + +Predictors should have the same scale. One way to achieve this is to +center and scale each so that each predictor has mean zero and a +variance of one. +} + +\subsection{References}{ +\itemize{ +\item Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A +Density-Based Algorithm for Discovering Clusters in Large Spatial +Databases with Noise. +\item Hahsler, M., Piekenbrock, M., & Doran, D. (2019a). Dbscan : Fast +Density-Based Clustering with r. Journal of Statistical Software, +91(1). \url{https://doi.org/10.18637/jss.v091.i01} +\item Hahsler, M., Piekenbrock, M., & Doran, D. (2019b). dbscan: Fast +density-based clustering with R. Journal of Statistical Software, +91(1), 1–30. \url{https://doi.org/10.18637/jss.v091}. i01 +\item Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011). Density-based +clustering. WIREs Data Mining and Knowledge Discovery, 1(3), 231–240. +\url{https://doi.org/10.1002/widm}. 30 +\item Tran, T. N., Drab, K., & Daszykowski, M. (2013). Revised DBSCAN +algorithm to cluster data with dense adjacent clusters. Chemometrics +and Intelligent Laboratory Systems, 49 120, 92–96. +\url{https://doi.org/10.1016/j.chemolab.2012.11.006} +} +} +} \keyword{internal} diff --git a/man/details_gm_clust_mclust.Rd b/man/details_gm_clust_mclust.Rd index 385a563..a662e3a 100644 --- a/man/details_gm_clust_mclust.Rd +++ b/man/details_gm_clust_mclust.Rd @@ -6,4 +6,82 @@ \description{ \code{\link[=gm_clust]{gm_clust()}} creates GMM model. } +\details{ +For this engine, there is a single mode: partition +\subsection{Tuning Parameters}{ + +This model has 6 tuning parameters: +\itemize{ +\item \code{num_clusters}: # Clusters (type: integer, default: no default) +\item \code{circular}: Circular cluster shapes? (type: logical, default: TRUE) +\item \code{zero_covariance}: Zero covariance between predictors? (type: logical, +default: TRUE) +\item \code{shared_orientation}: Shared orientation between clusters? (type: +logical, default: TRUE) +\item \code{shared_shape}: Shared shape between clusters? (type: logical, +default: TRUE) +\item \code{shared_size}: Shared size between clusters? (type: logical, default: +TRUE) +} +} + +\subsection{Translation from tidyclust to the original package (partition)}{ + +\if{html}{\out{
}}\preformatted{gm_clust(num_clusters = 3, circular = FALSE, zero_covariance = FALSE) \%>\% + set_engine("mclust") \%>\% + set_mode("partition") \%>\% + translate_tidyclust() + +## GMM Clustering Specification (partition) +## +## Main Arguments: +## num_clusters = 3 +## circular = FALSE +## zero_covariance = FALSE +## shared_orientation = TRUE +## shared_shape = TRUE +## shared_size = TRUE +## +## Computational engine: mclust +## +## Model fit template: +## tidyclust::.gm_clust_fit_mclust(x = missing_arg(), G = missing_arg(), +## circular = missing_arg(), zero_covariance = missing_arg(), +## shared_orientation = missing_arg(), shared_shape = missing_arg(), +## shared_size = missing_arg(), G = 3, circular = FALSE, zero_covariance = FALSE, +## shared_orientation = TRUE, shared_shape = TRUE, shared_size = TRUE) +}\if{html}{\out{
}} +} + +\subsection{Preprocessing requirements}{ + +Gaussian Mixture Models should be fit with only quantitative predictors +and without any categorical predictors. No scaling is required since the +variance-covariance matrices of the Gaussian distributions account for +the unequal variances between predictors and their covariances. +} + +\subsection{References}{ +\itemize{ +\item Banfield, J. D., & Raftery, A. E. (1993). Model-Based Gaussian and +Non-Gaussian Clustering. Biometrics, 49(3), 803. +\url{https://doi.org/10.2307/2532201} +\item Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering +models. Pattern Recognition, 28(5), 781–793. +\url{https://doi.org/10.1016/0031-3203(94)00125-6} +\item Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum +Likelihood from Incomplete Data via the EM Algorithm. +\item McNicholas, P. D. (2016). Model-Based clustering. Journal of +Classification, 33(3), 331–373. +\url{https://doi.org/10.1007/s00357-016-9211-9} +\item Scrucca, L., Fop, M., Murphy, T., Brendan, & Raftery, A., E. (2016). +Mclust 5: Clustering, Classification and Density Estimation Using +Gaussian Finite Mixture Models. The R Journal, 8(1), 289. +\url{https://doi.org/10.32614/RJ-2016-021} +\item Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). +Model-based clustering, classification, and density estimation using +mclust in R. Chapman; Hall/CRC. https: //doi.org/10.1201/9781003277965 +} +} +} \keyword{internal} diff --git a/man/rmd/db_clust_dbscan.md b/man/rmd/db_clust_dbscan.md new file mode 100644 index 0000000..e5655a4 --- /dev/null +++ b/man/rmd/db_clust_dbscan.md @@ -0,0 +1,62 @@ +For this engine, there is a single mode: partition + +## Tuning Parameters + +This model has 2 tuning parameters: + +- `radius`: Radius (type: double, default: no default) + +- `min_points`: Min Points (type: integer, default: no\_default) + +## Translation from tidyclust to the original package (partition) + + db_clust(radius = 0.5, min_points = 5)%>% + set_engine("dbscan") %>% + set_mode("partition") %>% + translate_tidyclust() + + ## DBSCAN Clustering Specification (partition) + ## + ## Main Arguments: + ## radius = 0.5 + ## min_points = 5 + ## + ## Computational engine: dbscan + ## + ## Model fit template: + ## tidyclust::.db_clust_fit_dbscan(x = missing_arg(), eps = missing_arg(), + ## minPts = missing_arg(), eps = 0.5, minPts = 5) + +## Preprocessing requirements + +Factor/categorical predictors need to be converted to numeric values +(e.g., dummy or indicator variables) for this engine. When using the +formula method via \code{\link\[=fit.cluster\_spec\]{fit()}}, tidyclust +will convert factor columns to indicators. + +Predictors should have the same scale. One way to achieve this is to +center and scale each so that each predictor has mean zero and a +variance of one. + +## References + +- Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A + Density-Based Algorithm for Discovering Clusters in Large Spatial + Databases with Noise. + +- Hahsler, M., Piekenbrock, M., & Doran, D. (2019a). Dbscan : Fast + Density-Based Clustering with r. Journal of Statistical Software, + 91(1). + +- Hahsler, M., Piekenbrock, M., & Doran, D. (2019b). dbscan: Fast + density-based clustering with R. Journal of Statistical Software, + 91(1), 1–30. . i01 + +- Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011). + Density-based clustering. WIREs Data Mining and Knowledge Discovery, + 1(3), 231–240. . 30 + +- Tran, T. N., Drab, K., & Daszykowski, M. (2013). Revised DBSCAN + algorithm to cluster data with dense adjacent clusters. Chemometrics + and Intelligent Laboratory Systems, 49 120, 92–96. + diff --git a/man/rmd/gm_clust_mclust.md b/man/rmd/gm_clust_mclust.md new file mode 100644 index 0000000..be85739 --- /dev/null +++ b/man/rmd/gm_clust_mclust.md @@ -0,0 +1,81 @@ +For this engine, there is a single mode: partition + +## Tuning Parameters + +This model has 6 tuning parameters: + +- `num_clusters`: \# Clusters (type: integer, default: no default) + +- `circular`: Circular cluster shapes? (type: logical, default: TRUE) + +- `zero_covariance`: Zero covariance between predictors? (type: + logical, default: TRUE) + +- `shared_orientation`: Shared orientation between clusters? (type: + logical, default: TRUE) + +- `shared_shape`: Shared shape between clusters? (type: logical, + default: TRUE) + +- `shared_size`: Shared size between clusters? (type: logical, + default: TRUE) + +## Translation from tidyclust to the original package (partition) + + gm_clust(num_clusters = 3, circular = FALSE, zero_covariance = FALSE) %>% + set_engine("mclust") %>% + set_mode("partition") %>% + translate_tidyclust() + + ## GMM Clustering Specification (partition) + ## + ## Main Arguments: + ## num_clusters = 3 + ## circular = FALSE + ## zero_covariance = FALSE + ## shared_orientation = TRUE + ## shared_shape = TRUE + ## shared_size = TRUE + ## + ## Computational engine: mclust + ## + ## Model fit template: + ## tidyclust::.gm_clust_fit_mclust(x = missing_arg(), G = missing_arg(), + ## circular = missing_arg(), zero_covariance = missing_arg(), + ## shared_orientation = missing_arg(), shared_shape = missing_arg(), + ## shared_size = missing_arg(), G = 3, circular = FALSE, zero_covariance = FALSE, + ## shared_orientation = TRUE, shared_shape = TRUE, shared_size = TRUE) + +## Preprocessing requirements + +Gaussian Mixture Models should be fit with only quantitative predictors +and without any categorical predictors. No scaling is required since the +variance-covariance matrices of the Gaussian distributions account for +the unequal variances between predictors and their covariances. + +## References + +- Banfield, J. D., & Raftery, A. E. (1993). Model-Based Gaussian and + Non-Gaussian Clustering. Biometrics, 49(3), 803. + + +- Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering + models. Pattern Recognition, 28(5), 781–793. + + +- Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum + Likelihood from Incomplete Data via the EM Algorithm. + +- McNicholas, P. D. (2016). Model-Based clustering. Journal of + Classification, 33(3), 331–373. + + +- Scrucca, L., Fop, M., Murphy, T., Brendan, & Raftery, A., E. (2016). + Mclust 5: Clustering, Classification and Density Estimation Using + Gaussian Finite Mixture Models. The R Journal, 8(1), 289. + + +- Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). + Model-based clustering, classification, and density estimation using + mclust in R. Chapman; Hall/CRC. https: + //doi.org/10.1201/9781003277965 diff --git a/man/set_args.cluster_spec.Rd b/man/set_args.cluster_spec.Rd index d36e8a9..a9143d2 100644 --- a/man/set_args.cluster_spec.Rd +++ b/man/set_args.cluster_spec.Rd @@ -7,7 +7,7 @@ \method{set_args}{cluster_spec}(object, ...) } \arguments{ -\item{object}{A \link[parsnip:model_spec]{model specification}.} +\item{object}{A model specification.} \item{...}{One or more named model arguments.} } diff --git a/man/set_engine.cluster_spec.Rd b/man/set_engine.cluster_spec.Rd index f0600ff..cfd1412 100644 --- a/man/set_engine.cluster_spec.Rd +++ b/man/set_engine.cluster_spec.Rd @@ -7,7 +7,7 @@ \method{set_engine}{cluster_spec}(object, engine, ...) } \arguments{ -\item{object}{A \link[parsnip:model_spec]{model specification}.} +\item{object}{A model specification.} \item{engine}{A character string for the software that should be used to fit the model. This is highly dependent on the type diff --git a/man/set_mode.cluster_spec.Rd b/man/set_mode.cluster_spec.Rd index 03d6b7d..226c64e 100644 --- a/man/set_mode.cluster_spec.Rd +++ b/man/set_mode.cluster_spec.Rd @@ -7,7 +7,7 @@ \method{set_mode}{cluster_spec}(object, mode, ...) } \arguments{ -\item{object}{A \link[parsnip:model_spec]{model specification}.} +\item{object}{A model specification.} \item{mode}{A character string for the model type (e.g. "classification" or "regression")} diff --git a/tests/testthat/_snaps/db_clust.md b/tests/testthat/_snaps/db_clust.md index 40d8e03..68ad4b5 100644 --- a/tests/testthat/_snaps/db_clust.md +++ b/tests/testthat/_snaps/db_clust.md @@ -3,8 +3,8 @@ Code db_clust(mode = "bogus") Condition - Error in `modelenv::check_spec_mode_engine_val()`: - ! 'bogus' is not a known mode for model `db_clust()`. + Error in `db_clust()`: + ! "bogus" is not a known mode for model `db_clust()`. --- diff --git a/tidyclust.Rproj b/tidyclust.Rproj index ee85d21..7f1b52b 100644 --- a/tidyclust.Rproj +++ b/tidyclust.Rproj @@ -1,5 +1,4 @@ Version: 1.0 -ProjectId: 7aa6e982-3f23-4734-8da7-2ab97b3d66a5 RestoreWorkspace: No SaveWorkspace: No From d784ab99fea0bd7c5b153e3475d5a4367c48f405 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Thu, 12 Jun 2025 09:57:14 -0700 Subject: [PATCH 24/44] tests/documentation updates --- tests/testthat/_snaps/gm_clust.md | 4 +- tests/testthat/_snaps/tune_cluster.new.md | 140 ++++++++++++++++++++++ 2 files changed, 142 insertions(+), 2 deletions(-) create mode 100644 tests/testthat/_snaps/tune_cluster.new.md diff --git a/tests/testthat/_snaps/gm_clust.md b/tests/testthat/_snaps/gm_clust.md index 5a89562..c6f6346 100644 --- a/tests/testthat/_snaps/gm_clust.md +++ b/tests/testthat/_snaps/gm_clust.md @@ -3,8 +3,8 @@ Code gm_clust(mode = "bogus") Condition - Error in `modelenv::check_spec_mode_engine_val()`: - ! 'bogus' is not a known mode for model `gm_clust()`. + Error in `gm_clust()`: + ! "bogus" is not a known mode for model `gm_clust()`. --- diff --git a/tests/testthat/_snaps/tune_cluster.new.md b/tests/testthat/_snaps/tune_cluster.new.md new file mode 100644 index 0000000..64be851 --- /dev/null +++ b/tests/testthat/_snaps/tune_cluster.new.md @@ -0,0 +1,140 @@ +# verbose argument works + + Code + res <- tune_cluster(wflow, resamples = folds, grid = grid, control = control, + metrics = metrics) + Message + i Fold1: preprocessor 1/3 + v Fold1: preprocessor 1/3 + i Fold1: preprocessor 1/3, model 1/3 + v Fold1: preprocessor 1/3, model 1/3 + i Fold1: preprocessor 1/3, model 1/3 (predictions) + i Fold1: preprocessor 1/3, model 2/3 + v Fold1: preprocessor 1/3, model 2/3 + i Fold1: preprocessor 1/3, model 2/3 (predictions) + i Fold1: preprocessor 1/3, model 3/3 + v Fold1: preprocessor 1/3, model 3/3 + i Fold1: preprocessor 1/3, model 3/3 (predictions) + i Fold1: preprocessor 2/3 + v Fold1: preprocessor 2/3 + i Fold1: preprocessor 2/3, model 1/3 + v Fold1: preprocessor 2/3, model 1/3 + i Fold1: preprocessor 2/3, model 1/3 (predictions) + i Fold1: preprocessor 2/3, model 2/3 + v Fold1: preprocessor 2/3, model 2/3 + i Fold1: preprocessor 2/3, model 2/3 (predictions) + i Fold1: preprocessor 2/3, model 3/3 + v Fold1: preprocessor 2/3, model 3/3 + i Fold1: preprocessor 2/3, model 3/3 (predictions) + i Fold1: preprocessor 3/3 + v Fold1: preprocessor 3/3 + i Fold1: preprocessor 3/3, model 1/3 + v Fold1: preprocessor 3/3, model 1/3 + i Fold1: preprocessor 3/3, model 1/3 (predictions) + i Fold1: preprocessor 3/3, model 2/3 + v Fold1: preprocessor 3/3, model 2/3 + i Fold1: preprocessor 3/3, model 2/3 (predictions) + i Fold1: preprocessor 3/3, model 3/3 + v Fold1: preprocessor 3/3, model 3/3 + i Fold1: preprocessor 3/3, model 3/3 (predictions) + i Fold2: preprocessor 1/3 + v Fold2: preprocessor 1/3 + i Fold2: preprocessor 1/3, model 1/3 + v Fold2: preprocessor 1/3, model 1/3 + i Fold2: preprocessor 1/3, model 1/3 (predictions) + i Fold2: preprocessor 1/3, model 2/3 + v Fold2: preprocessor 1/3, model 2/3 + i Fold2: preprocessor 1/3, model 2/3 (predictions) + i Fold2: preprocessor 1/3, model 3/3 + v Fold2: preprocessor 1/3, model 3/3 + i Fold2: preprocessor 1/3, model 3/3 (predictions) + i Fold2: preprocessor 2/3 + v Fold2: preprocessor 2/3 + i Fold2: preprocessor 2/3, model 1/3 + v Fold2: preprocessor 2/3, model 1/3 + i Fold2: preprocessor 2/3, model 1/3 (predictions) + i Fold2: preprocessor 2/3, model 2/3 + v Fold2: preprocessor 2/3, model 2/3 + i Fold2: preprocessor 2/3, model 2/3 (predictions) + i Fold2: preprocessor 2/3, model 3/3 + v Fold2: preprocessor 2/3, model 3/3 + i Fold2: preprocessor 2/3, model 3/3 (predictions) + i Fold2: preprocessor 3/3 + v Fold2: preprocessor 3/3 + i Fold2: preprocessor 3/3, model 1/3 + v Fold2: preprocessor 3/3, model 1/3 + i Fold2: preprocessor 3/3, model 1/3 (predictions) + i Fold2: preprocessor 3/3, model 2/3 + v Fold2: preprocessor 3/3, model 2/3 + i Fold2: preprocessor 3/3, model 2/3 (predictions) + i Fold2: preprocessor 3/3, model 3/3 + v Fold2: preprocessor 3/3, model 3/3 + i Fold2: preprocessor 3/3, model 3/3 (predictions) + +# tune model only - failure in formula is caught elegantly + + Code + cars_res <- tune_cluster(helper_objects$kmeans_mod, ~z, resamples = data_folds, + grid = cars_grid, control = tune::control_grid(extract = function(x) { + 1 + }, save_pred = TRUE)) + Message + x Fold1: preprocessor 1/1: Error in `get_all_predictors()`: + ! The following predi... + x Fold2: preprocessor 1/1: Error in `get_all_predictors()`: + ! The following predi... + Condition + Warning: + All models failed. + i See the `.notes` column. + +# argument order gives errors for recipes + + Code + tune_cluster(helper_objects$rec_tune_1, helper_objects$kmeans_mod_no_tune, + rsample::vfold_cv(mtcars, v = 2)) + Condition + Error in `tune_cluster()`: + ! The first argument to `tune_cluster()` should be either a model or workflow. + +# argument order gives errors for formula + + Code + tune_cluster(mpg ~ ., helper_objects$kmeans_mod_no_tune, rsample::vfold_cv( + mtcars, v = 2)) + Condition + Error in `tune_cluster()`: + ! The first argument to `tune_cluster()` should be either a model or workflow. + +# ellipses with tune_cluster + + Code + tune_cluster(wflow, resamples = folds, grid = 3, something = "wrong") + Condition + Warning: + The `...` are not used in this function but one or more objects were passed: 'something' + Output + # Tuning results + # 2-fold cross-validation + # A tibble: 2 x 4 + splits id .metrics .notes + + 1 Fold1 + 2 Fold2 + +# select_best() and show_best() works + + Code + tmp <- tune::show_best(res) + Condition + Warning in `tune::show_best()`: + No value of `metric` was given; "sse_within_total" will be used. + +--- + + Code + tmp <- tune::select_best(res) + Condition + Warning in `tune::select_best()`: + No value of `metric` was given; "sse_within_total" will be used. + From 3ba190f1e5b7744802476e2d5af8ad3c493d835c Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Thu, 12 Jun 2025 10:16:44 -0700 Subject: [PATCH 25/44] fixed errors where RcppHungarian was not installed --- .../_snaps/reconcile_clusterings.new.md | 17 --- tests/testthat/_snaps/tune_cluster.md | 8 +- tests/testthat/_snaps/tune_cluster.new.md | 140 ------------------ 3 files changed, 4 insertions(+), 161 deletions(-) delete mode 100644 tests/testthat/_snaps/reconcile_clusterings.new.md delete mode 100644 tests/testthat/_snaps/tune_cluster.new.md diff --git a/tests/testthat/_snaps/reconcile_clusterings.new.md b/tests/testthat/_snaps/reconcile_clusterings.new.md deleted file mode 100644 index 8bb2168..0000000 --- a/tests/testthat/_snaps/reconcile_clusterings.new.md +++ /dev/null @@ -1,17 +0,0 @@ -# reconciliation works with uneven numbers - - Code - reconcile_clusterings_mapping(primary_cluster_assignment, - alt_cluster_assignment, one_to_one = TRUE) - Condition - Error in `reconcile_clusterings_mapping()`: - ! The package "RcppHungarian" is required. - -# reconciliation errors for uneven lengths - - Code - reconcile_clusterings_mapping(letters, letters[1:10]) - Condition - Error in `reconcile_clusterings_mapping()`: - ! The package "RcppHungarian" is required. - diff --git a/tests/testthat/_snaps/tune_cluster.md b/tests/testthat/_snaps/tune_cluster.md index 78f7054..64be851 100644 --- a/tests/testthat/_snaps/tune_cluster.md +++ b/tests/testthat/_snaps/tune_cluster.md @@ -79,10 +79,10 @@ 1 }, save_pred = TRUE)) Message - x Fold1: preprocessor 1/1: Error in `hardhat::mold()`: - ! The following predictor ... - x Fold2: preprocessor 1/1: Error in `hardhat::mold()`: - ! The following predictor ... + x Fold1: preprocessor 1/1: Error in `get_all_predictors()`: + ! The following predi... + x Fold2: preprocessor 1/1: Error in `get_all_predictors()`: + ! The following predi... Condition Warning: All models failed. diff --git a/tests/testthat/_snaps/tune_cluster.new.md b/tests/testthat/_snaps/tune_cluster.new.md deleted file mode 100644 index 64be851..0000000 --- a/tests/testthat/_snaps/tune_cluster.new.md +++ /dev/null @@ -1,140 +0,0 @@ -# verbose argument works - - Code - res <- tune_cluster(wflow, resamples = folds, grid = grid, control = control, - metrics = metrics) - Message - i Fold1: preprocessor 1/3 - v Fold1: preprocessor 1/3 - i Fold1: preprocessor 1/3, model 1/3 - v Fold1: preprocessor 1/3, model 1/3 - i Fold1: preprocessor 1/3, model 1/3 (predictions) - i Fold1: preprocessor 1/3, model 2/3 - v Fold1: preprocessor 1/3, model 2/3 - i Fold1: preprocessor 1/3, model 2/3 (predictions) - i Fold1: preprocessor 1/3, model 3/3 - v Fold1: preprocessor 1/3, model 3/3 - i Fold1: preprocessor 1/3, model 3/3 (predictions) - i Fold1: preprocessor 2/3 - v Fold1: preprocessor 2/3 - i Fold1: preprocessor 2/3, model 1/3 - v Fold1: preprocessor 2/3, model 1/3 - i Fold1: preprocessor 2/3, model 1/3 (predictions) - i Fold1: preprocessor 2/3, model 2/3 - v Fold1: preprocessor 2/3, model 2/3 - i Fold1: preprocessor 2/3, model 2/3 (predictions) - i Fold1: preprocessor 2/3, model 3/3 - v Fold1: preprocessor 2/3, model 3/3 - i Fold1: preprocessor 2/3, model 3/3 (predictions) - i Fold1: preprocessor 3/3 - v Fold1: preprocessor 3/3 - i Fold1: preprocessor 3/3, model 1/3 - v Fold1: preprocessor 3/3, model 1/3 - i Fold1: preprocessor 3/3, model 1/3 (predictions) - i Fold1: preprocessor 3/3, model 2/3 - v Fold1: preprocessor 3/3, model 2/3 - i Fold1: preprocessor 3/3, model 2/3 (predictions) - i Fold1: preprocessor 3/3, model 3/3 - v Fold1: preprocessor 3/3, model 3/3 - i Fold1: preprocessor 3/3, model 3/3 (predictions) - i Fold2: preprocessor 1/3 - v Fold2: preprocessor 1/3 - i Fold2: preprocessor 1/3, model 1/3 - v Fold2: preprocessor 1/3, model 1/3 - i Fold2: preprocessor 1/3, model 1/3 (predictions) - i Fold2: preprocessor 1/3, model 2/3 - v Fold2: preprocessor 1/3, model 2/3 - i Fold2: preprocessor 1/3, model 2/3 (predictions) - i Fold2: preprocessor 1/3, model 3/3 - v Fold2: preprocessor 1/3, model 3/3 - i Fold2: preprocessor 1/3, model 3/3 (predictions) - i Fold2: preprocessor 2/3 - v Fold2: preprocessor 2/3 - i Fold2: preprocessor 2/3, model 1/3 - v Fold2: preprocessor 2/3, model 1/3 - i Fold2: preprocessor 2/3, model 1/3 (predictions) - i Fold2: preprocessor 2/3, model 2/3 - v Fold2: preprocessor 2/3, model 2/3 - i Fold2: preprocessor 2/3, model 2/3 (predictions) - i Fold2: preprocessor 2/3, model 3/3 - v Fold2: preprocessor 2/3, model 3/3 - i Fold2: preprocessor 2/3, model 3/3 (predictions) - i Fold2: preprocessor 3/3 - v Fold2: preprocessor 3/3 - i Fold2: preprocessor 3/3, model 1/3 - v Fold2: preprocessor 3/3, model 1/3 - i Fold2: preprocessor 3/3, model 1/3 (predictions) - i Fold2: preprocessor 3/3, model 2/3 - v Fold2: preprocessor 3/3, model 2/3 - i Fold2: preprocessor 3/3, model 2/3 (predictions) - i Fold2: preprocessor 3/3, model 3/3 - v Fold2: preprocessor 3/3, model 3/3 - i Fold2: preprocessor 3/3, model 3/3 (predictions) - -# tune model only - failure in formula is caught elegantly - - Code - cars_res <- tune_cluster(helper_objects$kmeans_mod, ~z, resamples = data_folds, - grid = cars_grid, control = tune::control_grid(extract = function(x) { - 1 - }, save_pred = TRUE)) - Message - x Fold1: preprocessor 1/1: Error in `get_all_predictors()`: - ! The following predi... - x Fold2: preprocessor 1/1: Error in `get_all_predictors()`: - ! The following predi... - Condition - Warning: - All models failed. - i See the `.notes` column. - -# argument order gives errors for recipes - - Code - tune_cluster(helper_objects$rec_tune_1, helper_objects$kmeans_mod_no_tune, - rsample::vfold_cv(mtcars, v = 2)) - Condition - Error in `tune_cluster()`: - ! The first argument to `tune_cluster()` should be either a model or workflow. - -# argument order gives errors for formula - - Code - tune_cluster(mpg ~ ., helper_objects$kmeans_mod_no_tune, rsample::vfold_cv( - mtcars, v = 2)) - Condition - Error in `tune_cluster()`: - ! The first argument to `tune_cluster()` should be either a model or workflow. - -# ellipses with tune_cluster - - Code - tune_cluster(wflow, resamples = folds, grid = 3, something = "wrong") - Condition - Warning: - The `...` are not used in this function but one or more objects were passed: 'something' - Output - # Tuning results - # 2-fold cross-validation - # A tibble: 2 x 4 - splits id .metrics .notes - - 1 Fold1 - 2 Fold2 - -# select_best() and show_best() works - - Code - tmp <- tune::show_best(res) - Condition - Warning in `tune::show_best()`: - No value of `metric` was given; "sse_within_total" will be used. - ---- - - Code - tmp <- tune::select_best(res) - Condition - Warning in `tune::select_best()`: - No value of `metric` was given; "sse_within_total" will be used. - From 9c867a1416728d00d9b7d6ad21d0953d680de954 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Tue, 17 Jun 2025 15:39:04 -0700 Subject: [PATCH 26/44] documentation --- DESCRIPTION | 3 +++ vignettes/articles/gm_clust.Rmd | 2 ++ 2 files changed, 5 insertions(+) diff --git a/DESCRIPTION b/DESCRIPTION index 2a4a273..656d160 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -22,14 +22,17 @@ Imports: dials (>= 1.3.0), dplyr (>= 1.0.9), flexclust (>= 1.3-6), + forcats, foreach, generics (>= 0.1.2), glue (>= 1.6.2), hardhat (>= 1.0.0), + mclust, modelenv (>= 0.2.0), parsnip (>= 1.0.2), philentropy (>= 0.9.0), prettyunits (>= 1.1.0), + Rfast, rlang (>= 1.0.6), rsample (>= 1.0.0), stats, diff --git a/vignettes/articles/gm_clust.Rmd b/vignettes/articles/gm_clust.Rmd index 06cfaca..73a16f2 100644 --- a/vignettes/articles/gm_clust.Rmd +++ b/vignettes/articles/gm_clust.Rmd @@ -135,6 +135,8 @@ gm_clust_fit %>% ``` +## Gaussian Mixture Model Specifications + ```{r} #| include: false plot_state <- function(obj) { From b9e38c487580e40f62b3d5250ae2fd871ddedf2f Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Tue, 17 Jun 2025 18:34:23 -0700 Subject: [PATCH 27/44] use philentropy distance functions --- R/extract_fit_summary.R | 33 ++++++++++++++++++++++++++++----- 1 file changed, 28 insertions(+), 5 deletions(-) diff --git a/R/extract_fit_summary.R b/R/extract_fit_summary.R index ad8db00..dccd7ba 100644 --- a/R/extract_fit_summary.R +++ b/R/extract_fit_summary.R @@ -223,7 +223,13 @@ extract_fit_summary.dbscan <- function(object, ...) { sse_within_total_total <- map2_dbl( by_clust$data, seq_len(n_clust), - ~ sum(Rfast::dista(centroids[.y, ], .x)) + ~sum( + philentropy::dist_many_many( + as.matrix(centroids[.y, ]), + as.matrix(.x), + method = "euclidean" + ) + ) ) @@ -233,7 +239,13 @@ extract_fit_summary.dbscan <- function(object, ...) { centroids = centroids, n_members = unname(as.integer(table(clusts))), sse_within_total_total = sse_within_total_total, - sse_total = sum(Rfast::dista(t(overall_centroid), training_data)), + sse_total = sse_total = sum( + philentropy::dist_many_many( + t(overall_centroid), + as.matrix(training_data), + method = "euclidean" + ) + ), orig_labels = NULL, cluster_assignments = clusts ) @@ -263,16 +275,27 @@ extract_fit_summary.Mclust <- function(object, ...) { sse_within_total_total <- map2_dbl( by_clust$data, seq_len(n_clust), - ~ sum(Rfast::dista(centroids[.y, ], .x)) + ~sum( + philentropy::dist_many_many( + as.matrix(centroids[.y, ]), + as.matrix(.x), + method = "euclidean" + ) + ) ) - list( cluster_names = unique(clusts), centroids = centroids, n_members = unname(as.integer(table(clusts))), sse_within_total_total = sse_within_total_total, - sse_total = sum(Rfast::dista(t(overall_centroid), training_data)), + sse_total = sse_total = sum( + philentropy::dist_many_many( + t(overall_centroid), + as.matrix(training_data), + method = "euclidean" + ) + ), orig_labels = NULL, cluster_assignments = clusts ) From b392a1d08cfb38d8a0e72808fa7576b4abe2f371 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Tue, 17 Jun 2025 18:36:11 -0700 Subject: [PATCH 28/44] fix typo --- R/extract_fit_summary.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/extract_fit_summary.R b/R/extract_fit_summary.R index dccd7ba..05186ae 100644 --- a/R/extract_fit_summary.R +++ b/R/extract_fit_summary.R @@ -239,7 +239,7 @@ extract_fit_summary.dbscan <- function(object, ...) { centroids = centroids, n_members = unname(as.integer(table(clusts))), sse_within_total_total = sse_within_total_total, - sse_total = sse_total = sum( + sse_total = sum( philentropy::dist_many_many( t(overall_centroid), as.matrix(training_data), @@ -289,7 +289,7 @@ extract_fit_summary.Mclust <- function(object, ...) { centroids = centroids, n_members = unname(as.integer(table(clusts))), sse_within_total_total = sse_within_total_total, - sse_total = sse_total = sum( + sse_total = sum( philentropy::dist_many_many( t(overall_centroid), as.matrix(training_data), From 82dbcb791a780916eeac7ef5dae79e28aaca3f7c Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Tue, 17 Jun 2025 18:53:03 -0700 Subject: [PATCH 29/44] assign NA to within sum squares for outlier group after philentropy call --- R/extract_fit_summary.R | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/R/extract_fit_summary.R b/R/extract_fit_summary.R index 05186ae..b58186f 100644 --- a/R/extract_fit_summary.R +++ b/R/extract_fit_summary.R @@ -214,12 +214,8 @@ extract_fit_summary.dbscan <- function(object, ...) { dplyr::bind_rows() outlier_idx <- which(unique(clusts) == "Outlier") - centroids[outlier_idx, ] <- rep(NA, ncol(centroids)) - # reorder centroids - centroids <- centroids[c(outlier_idx, setdiff(1:n_clust, outlier_idx)), ] - sse_within_total_total <- map2_dbl( by_clust$data, seq_len(n_clust), @@ -234,7 +230,7 @@ extract_fit_summary.dbscan <- function(object, ...) { clust_names <- unique(clusts)[c(outlier_idx, setdiff(1:n_clust, outlier_idx))] - list( + summary <- list( cluster_names = clust_names, centroids = centroids, n_members = unname(as.integer(table(clusts))), @@ -249,6 +245,14 @@ extract_fit_summary.dbscan <- function(object, ...) { orig_labels = NULL, cluster_assignments = clusts ) + + summary$centroids[outlier_idx, ] <- rep(NA, ncol(summary$centroids)) + summary$sse_within_total_total[outlier_idx] <- NA + + # reorder centroids + summary$centroids <- summary$centroids[c(outlier_idx, setdiff(1:n_clust, outlier_idx)), ] + + summary } #' @export From e2d9fc0e8ea7c8e9b4242f36453aa02a4cc1b6db Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Fri, 27 Jun 2025 18:10:05 -0700 Subject: [PATCH 30/44] remove rfast from imports and add dbscan to suggests --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 656d160..b1807ab 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -32,7 +32,6 @@ Imports: parsnip (>= 1.0.2), philentropy (>= 0.9.0), prettyunits (>= 1.1.0), - Rfast, rlang (>= 1.0.6), rsample (>= 1.0.0), stats, @@ -46,6 +45,7 @@ Suggests: ClusterR, clustMixType (>= 0.3-5), covr, + dbscan, klaR, knitr, modeldata (>= 1.0.0), From 670599c19073b55cd464c003c357b8e17ae80d7e Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Fri, 27 Jun 2025 18:32:47 -0700 Subject: [PATCH 31/44] add skip_if_not_installed for tests relating to dbscan engine and removing forcats use in tests --- tests/testthat/test-db_clust-dbscan.R | 15 +++++++++------ tests/testthat/test-db_clust.R | 10 ++++++++++ 2 files changed, 19 insertions(+), 6 deletions(-) diff --git a/tests/testthat/test-db_clust-dbscan.R b/tests/testthat/test-db_clust-dbscan.R index 8ec0966..4e6e3dd 100644 --- a/tests/testthat/test-db_clust-dbscan.R +++ b/tests/testthat/test-db_clust-dbscan.R @@ -1,4 +1,5 @@ test_that("fitting", { + skip_if_not_installed("dbscan") set.seed(1234) spec <- db_clust(radius = 10, min_points = 5) %>% set_engine("dbscan") @@ -13,6 +14,7 @@ test_that("fitting", { }) test_that("predicting", { + skip_if_not_installed("dbscan") set.seed(1234) spec <- db_clust(radius = .42, min_points = 5) %>% set_engine("dbscan") @@ -33,6 +35,7 @@ test_that("predicting", { test_that("all levels are preserved with 1 row predictions", { + skip_if_not_installed("dbscan") set.seed(1234) spec <- db_clust(radius = 50, min_points = 5) %>% set_engine("dbscan") @@ -69,6 +72,7 @@ test_that("all levels are preserved with 1 row predictions", { # }) test_that("extract_cluster_assignment() works", { + skip_if_not_installed("dbscan") set.seed(1234) spec <- db_clust(radius = .42, min_points = 5) %>% set_engine("dbscan") @@ -78,12 +82,11 @@ test_that("extract_cluster_assignment() works", { clusters <- extract_cluster_assignment(res) - expected <- vctrs::vec_cbind( - tibble::tibble(.cluster = factor(paste0("Cluster_", res$fit$cluster)) %>% - forcats::fct_recode("Outlier" = "Cluster_0") %>% - relevel(ref = "Outlier")) - - ) + expected <- paste0("Cluster_", res$fit$cluster) %>% + ifelse(. == "Cluster_0", "Outlier", .) %>% + as.factor() %>% + relevel(ref = "Outlier") %>% + tibble::tibble(.cluster = .) expect_identical( diff --git a/tests/testthat/test-db_clust.R b/tests/testthat/test-db_clust.R index d7e0317..1c6df8b 100644 --- a/tests/testthat/test-db_clust.R +++ b/tests/testthat/test-db_clust.R @@ -1,4 +1,5 @@ test_that("primary arguments", { + basic <- db_clust(mode = "partition") basic_dbscan <- translate_tidyclust(basic %>% set_engine("dbscan")) expect_equal( @@ -27,6 +28,9 @@ test_that("primary arguments", { test_that("bad input", { expect_snapshot(error = TRUE, db_clust(mode = "bogus")) + + skip_if_not_installed("dbscan") + expect_snapshot(error = TRUE, { bt <- db_clust(radius = -1) %>% set_engine("dbscan") fit(bt, mpg ~ ., mtcars) @@ -40,6 +44,7 @@ test_that("bad input", { }) test_that("predictions", { + skip_if_not_installed("dbscan") set.seed(1234) db_clust_fit <- db_clust(radius = 60, min_points = 5) %>% set_engine("dbscan") %>% @@ -73,6 +78,7 @@ test_that("predictions", { test_that("extract_centroids work", { + skip_if_not_installed("dbscan") set.seed(1234) dbscan_fit <- db_clust(radius = 20, min_points = 3) %>% set_engine("dbscan") %>% @@ -127,6 +133,7 @@ test_that("updating", { test_that("reordering is done correctly for dbscan db_clust", { + skip_if_not_installed("dbscan") set.seed(42) dbscan_fit <- db_clust(radius = 20, min_points = 3) %>% @@ -143,6 +150,7 @@ test_that("reordering is done correctly for dbscan db_clust", { test_that("errors if `radius` and `min_points` aren't specified", { + skip_if_not_installed("dbscan") expect_snapshot( error = TRUE, db_clust() %>% @@ -152,6 +160,7 @@ test_that("errors if `radius` and `min_points` aren't specified", { }) test_that("errors if `radius` isn't specified", { + skip_if_not_installed("dbscan") expect_snapshot( error = TRUE, db_clust(min_points = 10) %>% @@ -161,6 +170,7 @@ test_that("errors if `radius` isn't specified", { }) test_that("errors if `min_points` isn't specified", { + skip_if_not_installed("dbscan") expect_snapshot( error = TRUE, db_clust(radius = 20) %>% From d8c8368dc498bbd7f1a1503f22d1092e7f9c5da8 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Fri, 27 Jun 2025 18:45:55 -0700 Subject: [PATCH 32/44] db_clust minor documentation updates and rlang::abort() -> cli::cli_abort() --- R/db_clust.R | 14 +++++++------- R/gm_clust.R | 28 ++++++++++++++-------------- 2 files changed, 21 insertions(+), 21 deletions(-) diff --git a/R/db_clust.R b/R/db_clust.R index 85b9860..13efd92 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -12,7 +12,7 @@ #' - \link[=details_db_clust_dbscan]{dbscan} #' #' @param mode A single character string for the type of model. The only -#' possible value for this model is "partition". +#' possible value for this model is `"partition"`. #' @param engine A single character string specifying what computational engine #' to use for fitting. The engine for this model is `"dbscan"`. #' @param radius Positive double, Radius drawn around points to determine core-points and cluster assignments (required) @@ -127,11 +127,11 @@ check_args.db_clust <- function(object) { args <- lapply(object$args, rlang::eval_tidy) if (all(is.numeric(args$min_points)) && any(args$min_points < 0)) { - rlang::abort("The number of points in a cluster should be > 0.") + cli::cli_abort("The number of points in a cluster should be > 0.") } if (all(is.numeric(args$radius)) && any(args$radius < 0)) { - rlang::abort("The radius used to create a cluster should be > 0.") + cli::cli_abort("The radius used to create a cluster should be > 0.") } invisible(object) @@ -148,7 +148,7 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { #' Simple Wrapper around dbscan function #' #' This wrapper prepares the data into a distance matrix to send to -#' `dbscan::dbscan` and retains the parameters `radius` or `min_points` as an +#' `dbscan::dbscan()` and retains the parameters `radius` or `min_points` as an #' attribute. #' #' @param x matrix or data frame @@ -163,14 +163,14 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { minPts = NULL, ...) { if (is.null(eps)) { - rlang::abort( + cli::cli_abort( "Please specify `radius` to be able to fit specification.", call = call("fit") ) } if (is.null(minPts)) { - rlang::abort( + cli::cli_abort( "Please specify `min_points` to be able to fit specification.", call = call("fit") ) @@ -228,7 +228,7 @@ dbscan_helper <- function(object, non_cp_clusters$is_core <- "non cp" cp_clusters$is_core <- "cp" - training_data$id <- ave(training_data$is_core, training_data$is_core, FUN = seq_along) + training_data$id <- stats::ave(training_data$is_core, training_data$is_core, FUN = seq_along) non_cp_clusters$id <- 1:nrow(non_cp_clusters) cp_clusters$id <- 1:nrow(cp_clusters) diff --git a/R/gm_clust.R b/R/gm_clust.R index 907836c..582bdd3 100644 --- a/R/gm_clust.R +++ b/R/gm_clust.R @@ -148,31 +148,31 @@ check_args.gm_clust <- function(object) { args <- lapply(object$args, rlang::eval_tidy) if (all(is.numeric(args$num_clusters)) && any(args$num_clusters <= 0)) { - rlang::abort("The number of clusters should be > 0.") + cli::cli_abort("The number of clusters should be > 0.") } if (length(args$num_clusters) > 1) { - rlang::abort("The number of clusters should be a single number.") + cli::cli_abort("The number of clusters should be a single number.") } if (all(!is.logical(args$circular))) { - rlang::abort("The circular cluster shape argument should be TRUE or FALSE.") + cli::cli_abort("The circular cluster shape argument should be TRUE or FALSE.") } if (all(!is.logical(args$zero_covariance))) { - rlang::abort("The zero covariance argument should be TRUE or FALSE.") + cli::cli_abort("The zero covariance argument should be TRUE or FALSE.") } if (all(!is.logical(args$shared_orientation))) { - rlang::abort("The shared cluster orientation argument should be TRUE or FALSE.") + cli::cli_abort("The shared cluster orientation argument should be TRUE or FALSE.") } if (all(!is.logical(args$shared_shape))) { - rlang::abort("The shared cluster shape argument should be TRUE or FALSE.") + cli::cli_abort("The shared cluster shape argument should be TRUE or FALSE.") } if (all(!is.logical(args$shared_size))) { - rlang::abort("The shared cluster size argument should be TRUE or FALSE.") + cli::cli_abort("The shared cluster size argument should be TRUE or FALSE.") } invisible(object) @@ -207,19 +207,19 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { shared_size = NULL, ...) { if (is.null(G)) { - rlang::abort( + cli::cli_abort( "Please specify `num_clusters` to be able to fit specification.", call = call("fit") ) } if (is.null(circular)) { - rlang::abort( + cli::cli_abort( "Please specify `circular` to be able to fit specification.", call = call("fit") ) } if (is.null(shared_size)) { - rlang::abort( + cli::cli_abort( "Please specify `shared_size` to be able to fit specification.", call = call("fit") ) @@ -227,14 +227,14 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { if (!circular) { if (is.null(zero_covariance)) { - rlang::abort( + cli::cli_abort( "Please specify `zero_covariance` to be able to fit specification.", call = call("fit") ) } if (is.null(shared_shape)) { - rlang::abort( + cli::cli_abort( "Please specify `shared_shape` to be able to fit specification.", call = call("fit") ) @@ -243,7 +243,7 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { if (!zero_covariance) { if (is.null(shared_orientation)) { - rlang::abort( + cli::cli_abort( "Please specify `shared_orientation` to be able to fit specification.", call = call("fit") ) @@ -283,7 +283,7 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { res <- mclust::Mclust(x, G = G, modelNames = model_name) if (is.null(res)) { - rlang::abort( + cli::cli_abort( "Model cannot be estimated. Please specify a model specification with less parameters by setting some model arguments to TRUE", call = call("fit") ) From 624e53e80905e638615951c1ad6af36fdff00fd0 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Fri, 27 Jun 2025 18:49:44 -0700 Subject: [PATCH 33/44] dbscan helper args to match tidy names --- R/db_clust.R | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/R/db_clust.R b/R/db_clust.R index 13efd92..dde8005 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -159,28 +159,28 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { #' @keywords internal #' @export .db_clust_fit_dbscan <- function(x, - eps = NULL, - minPts = NULL, + radius = NULL, + min_points = NULL, ...) { - if (is.null(eps)) { + if (is.null(radius)) { cli::cli_abort( "Please specify `radius` to be able to fit specification.", call = call("fit") ) } - if (is.null(minPts)) { + if (is.null(min_points)) { cli::cli_abort( "Please specify `min_points` to be able to fit specification.", call = call("fit") ) } - res <- dbscan::dbscan(x, eps = eps, minPts = minPts) - attr(res, "radius") <- eps - attr(res, "min_points") <- minPts + res <- dbscan::dbscan(x, eps = radius, minPts = min_points) + attr(res, "radius") <- radius + attr(res, "min_points") <- min_points attr(res, "training_data") <- x - is_core <- dbscan::is.corepoint(x, eps = eps, minPts = minPts) + is_core <- dbscan::is.corepoint(x, eps = radius, minPts = min_points) attr(res, "is_core") <- is_core res From 5931da8f086a684a9622f1fdb4aca9239c495deb Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Fri, 27 Jun 2025 19:02:20 -0700 Subject: [PATCH 34/44] make db_clust helper args match tidy names (cont.) --- R/db_clust_data.R | 6 +++--- tests/testthat/test-db_clust.R | 12 ++++++------ 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/R/db_clust_data.R b/R/db_clust_data.R index 9b6e0be..e34367d 100644 --- a/R/db_clust_data.R +++ b/R/db_clust_data.R @@ -27,7 +27,7 @@ make_db_clust <- function() { mode = "partition", value = list( interface = "matrix", - protect = c("x", "eps", "minPts"), + protect = c("x", "radius", "min_points"), func = c(pkg = "tidyclust", fun = ".db_clust_fit_dbscan"), defaults = list() ) @@ -49,7 +49,7 @@ make_db_clust <- function() { model = "db_clust", eng = "dbscan", exposed = "radius", - original = "eps", + original = "radius", func = list(pkg = "dials", fun = "radius"), has_submodel = TRUE ) @@ -58,7 +58,7 @@ make_db_clust <- function() { model = "db_clust", eng = "dbscan", exposed = "min_points", - original = "minPts", + original = "min_points", func = list(pkg = "dials", fun = "min_points"), has_submodel = TRUE ) diff --git a/tests/testthat/test-db_clust.R b/tests/testthat/test-db_clust.R index 1c6df8b..1b39a02 100644 --- a/tests/testthat/test-db_clust.R +++ b/tests/testthat/test-db_clust.R @@ -6,8 +6,8 @@ test_that("primary arguments", { basic_dbscan$method$fit$args, list( x = rlang::expr(missing_arg()), - eps = rlang::expr(missing_arg()), - minPts = rlang::expr(missing_arg()) + radius = rlang::expr(missing_arg()), + min_points = rlang::expr(missing_arg()) ) ) @@ -17,10 +17,10 @@ test_that("primary arguments", { db_dbscan$method$fit$args, list( x = rlang::expr(missing_arg()), - eps = rlang::expr(missing_arg()), - minPts = rlang::expr(missing_arg()), - eps = new_empty_quosure(2), - minPts = new_empty_quosure(4) + radius = rlang::expr(missing_arg()), + min_points = rlang::expr(missing_arg()), + radius = new_empty_quosure(2), + min_points = new_empty_quosure(4) ) ) }) From 441c222d9f0b1dc520279aeabd01d7ba0d180672 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Fri, 27 Jun 2025 19:02:39 -0700 Subject: [PATCH 35/44] updated documentation --- man/db_clust.Rd | 2 +- man/dot-db_clust_fit_dbscan.Rd | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/man/db_clust.Rd b/man/db_clust.Rd index db44a06..764ac65 100644 --- a/man/db_clust.Rd +++ b/man/db_clust.Rd @@ -13,7 +13,7 @@ db_clust( } \arguments{ \item{mode}{A single character string for the type of model. The only -possible value for this model is "partition".} +possible value for this model is \code{"partition"}.} \item{engine}{A single character string specifying what computational engine to use for fitting. The engine for this model is \code{"dbscan"}.} diff --git a/man/dot-db_clust_fit_dbscan.Rd b/man/dot-db_clust_fit_dbscan.Rd index d6012ca..54fa451 100644 --- a/man/dot-db_clust_fit_dbscan.Rd +++ b/man/dot-db_clust_fit_dbscan.Rd @@ -4,7 +4,7 @@ \alias{.db_clust_fit_dbscan} \title{Simple Wrapper around dbscan function} \usage{ -.db_clust_fit_dbscan(x, eps = NULL, minPts = NULL, ...) +.db_clust_fit_dbscan(x, radius = NULL, min_points = NULL, ...) } \arguments{ \item{x}{matrix or data frame} @@ -18,7 +18,7 @@ dbscan object } \description{ This wrapper prepares the data into a distance matrix to send to -\code{dbscan::dbscan} and retains the parameters \code{radius} or \code{min_points} as an +\code{dbscan::dbscan()} and retains the parameters \code{radius} or \code{min_points} as an attribute. } \keyword{internal} From fdf7456be30abc7f217aad54e295be67d7017d19 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Mon, 30 Jun 2025 15:23:24 -0700 Subject: [PATCH 36/44] fix type in vignette --- vignettes/articles/db_clust.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/vignettes/articles/db_clust.Rmd b/vignettes/articles/db_clust.Rmd index 575694e..0d5b0ae 100644 --- a/vignettes/articles/db_clust.Rmd +++ b/vignettes/articles/db_clust.Rmd @@ -224,13 +224,13 @@ penguins_new$cluster <- (db_clust_fit %>% extract_cluster_assignment())$.cluster # factor(dbscan_fit$cluster) penguins_new$cp2 <- factor(if_else(as.numeric(is.corepoint(penguins_std, eps = eps, minPts = min_points)) == 1 & penguins_new$cluster == "Cluster_2", "Yes", "No"), levels = c("Yes", "No")) -penguins_new$radius <- if_else(penguins_new$cp == "Yes", eps, 0) +penguins_new$radius <- if_else(penguins_new$cp2 == "Yes", eps, 0) -penguins_new_sub <- penguins_new %>% filter(cp == "Yes") %>% +penguins_new_sub <- penguins_new %>% filter(cp2 == "Yes") %>% .[c(2,3,4,5,7,9,16),] penguins_new %>% - ggplot(aes(x = bill_length_std, y = bill_depth_std, color = cp)) + + ggplot(aes(x = bill_length_std, y = bill_depth_std, color = cp2)) + geom_point() + theme_minimal() + geom_circle(mapping = aes(x0 = bill_length_std, y0 = bill_depth_std, r = radius), color = "gray", linewidth = 0.8, data = penguins_new_sub) + From 1ee9de4193d58a6add7c5ba3fb0e34931653aa36 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Mon, 30 Jun 2025 15:24:31 -0700 Subject: [PATCH 37/44] removed dependency on unexported dbscan:::predict_frNN() to use exported dbscan::frNN() --- R/db_clust.R | 10 +++++++++- R/predict_helpers.R | 10 +++++++++- 2 files changed, 18 insertions(+), 2 deletions(-) diff --git a/R/db_clust.R b/R/db_clust.R index dde8005..9b01610 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -216,7 +216,15 @@ dbscan_helper <- function(object, } # get fit values according to closest core point - non_cp_clusters <- dbscan:::.predict_frNN(newdata = non_cp, data = cp, cp_clusters, eps = eps) + nn <- dbscan::frNN(cp, + query = non_cp, + eps = eps, + sort = TRUE) + + non_cp_clusters <- vapply( + nn$id, function(nns) if (length(nns) == 0L) 0L else cp_clusters[nns[1L]], integer(1L) + ) + # join back separated fits into proper order in training data non_cp_clusters <- data.frame(non_cp_clusters) diff --git a/R/predict_helpers.R b/R/predict_helpers.R index 2c8242e..6f64276 100644 --- a/R/predict_helpers.R +++ b/R/predict_helpers.R @@ -198,7 +198,15 @@ make_predictions_w_outliers <- function(x, prefix, n_clusters) { clusters <- (rep(0, nrow(new_data))) n_clusters <- 1 } else { - clusters <- dbscan:::.predict_frNN(newdata = new_data, data = cp, cp_clusters, eps = eps) + nn <- dbscan::frNN(cp, + query = new_data, + eps = eps, + sort = TRUE) + + clusters <- vapply( + nn$id, function(nns) if (length(nns) == 0L) 0L else cp_clusters[nns[1L]], integer(1L) + ) + n_clusters <- length(unique(object$cluster[object$cluster != 0])) + 1 } From 0dae853e356adedadd59d8c4a56b385d17b793fb Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Mon, 30 Jun 2025 15:38:19 -0700 Subject: [PATCH 38/44] Added models to _pkgdown.yml along with bullet points for new models in NEWS.md --- NEWS.md | 6 ++++++ _pkgdown.yml | 2 ++ 2 files changed, 8 insertions(+) diff --git a/NEWS.md b/NEWS.md index b02a7a8..a32c544 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,11 @@ # tidyclust (development version) +## New Clustering Specifications + +* The `db_clust()` clustering specification has been added. This specification allows for the use of the DBSCAN algorithm using the dbscan engine. (#209) + +* The `gm_clust()` clustering specification has been added. This specification allows for the fitting of Gaussian mixture models using the mclust engine. (#209) + # tidyclust 0.2.4 * The philentropy package is now used to calculate distances rather than Rfast. (#199) diff --git a/_pkgdown.yml b/_pkgdown.yml index 7749c25..b295d99 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -22,6 +22,8 @@ reference: contents: - k_means - hier_clust + - db_clust + - gm_clust - cluster_spec - cluster_fit - title: Fit and Inspect From 142bf7ed37efc96bc377b5ca3149f5a8654347c1 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Mon, 30 Jun 2025 16:08:11 -0700 Subject: [PATCH 39/44] remove forcats from imports since its no longer used --- DESCRIPTION | 1 - 1 file changed, 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index b1807ab..53472cc 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -22,7 +22,6 @@ Imports: dials (>= 1.3.0), dplyr (>= 1.0.9), flexclust (>= 1.3-6), - forcats, foreach, generics (>= 0.1.2), glue (>= 1.6.2), From 6c7704bd5028923d8a19aa37b0679900defa0f67 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Mon, 30 Jun 2025 16:25:19 -0700 Subject: [PATCH 40/44] fix documentation for .gm_clust_fit_mclust and mclust_helper --- R/gm_clust.R | 19 ++++++++++++++----- R/gm_clust_data.R | 4 ++-- man/dot-gm_clust_fit_mclust.Rd | 12 +++++++++++- man/mclust_helper.Rd | 10 +++++++++- tests/testthat/test-gm_clust.R | 4 ++-- 5 files changed, 38 insertions(+), 11 deletions(-) diff --git a/R/gm_clust.R b/R/gm_clust.R index 582bdd3..8846500 100644 --- a/R/gm_clust.R +++ b/R/gm_clust.R @@ -194,19 +194,24 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { #' #' @param x matrix or data frame #' @param num_clusters Number of clusters +#' @param circular Whether or not to fit circular MVG distributions for each cluster +#' @param zero_covariance Whether or not to assign covariances of 0 for each MVG +#' @param shared_orientation Whether each cluster MVG should have the same orientation +#' @param shared_shape Whether each cluster MVG should have the same shape +#' @param shared_size Whether each cluster MVG should have the same size/volume #' #' @return mclust object #' @keywords internal #' @export .gm_clust_fit_mclust <- function(x, - G = NULL, + num_clusters = NULL, circular = NULL, zero_covariance = NULL, shared_orientation = NULL, shared_shape = NULL, shared_size = NULL, ...) { - if (is.null(G)) { + if (is.null(num_clusters)) { cli::cli_abort( "Please specify `num_clusters` to be able to fit specification.", call = call("fit") @@ -280,7 +285,7 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { - res <- mclust::Mclust(x, G = G, modelNames = model_name) + res <- mclust::Mclust(x, G = num_clusters, modelNames = model_name) if (is.null(res)) { cli::cli_abort( @@ -289,7 +294,7 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { ) } - attr(res, "num_clusters") <- G + attr(res, "num_clusters") <- num_clusters attr(res, "circular") <- circular attr(res, "zero_covariance") <- zero_covariance attr(res, "shared_orientation") <- shared_orientation @@ -306,7 +311,11 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { #' This function returns the mclust model name based on the specified #' TRUE/FALSE model arguments #' -#' @param object gm_clust object +#' @param circular Whether or not to fit circular MVG distributions for each cluster +#' @param zero_covariance Whether or not to assign covariances of 0 for each MVG +#' @param shared_orientation Whether each cluster MVG should have the same orientation +#' @param shared_shape Whether each cluster MVG should have the same shape +#' @param shared_size Whether each cluster MVG should have the same size/volume #' #' @return string containing mclust model name #' @keywords internal diff --git a/R/gm_clust_data.R b/R/gm_clust_data.R index d58e820..b69a22d 100644 --- a/R/gm_clust_data.R +++ b/R/gm_clust_data.R @@ -27,7 +27,7 @@ make_gm_clust <- function() { mode = "partition", value = list( interface = "matrix", - protect = c("x", "G", "circular", "zero_covariance", "shared_orientation", "shared_shape", "shared_size"), + protect = c("x", "num_clusters", "circular", "zero_covariance", "shared_orientation", "shared_shape", "shared_size"), func = c(pkg = "tidyclust", fun = ".gm_clust_fit_mclust"), defaults = list() ) @@ -50,7 +50,7 @@ make_gm_clust <- function() { model = "gm_clust", eng = "mclust", exposed = "num_clusters", - original = "G", + original = "num_clusters", func = list(pkg = "dials", fun = "num_clusters"), has_submodel = TRUE ) diff --git a/man/dot-gm_clust_fit_mclust.Rd b/man/dot-gm_clust_fit_mclust.Rd index c8f252b..2324620 100644 --- a/man/dot-gm_clust_fit_mclust.Rd +++ b/man/dot-gm_clust_fit_mclust.Rd @@ -6,7 +6,7 @@ \usage{ .gm_clust_fit_mclust( x, - G = NULL, + num_clusters = NULL, circular = NULL, zero_covariance = NULL, shared_orientation = NULL, @@ -19,6 +19,16 @@ \item{x}{matrix or data frame} \item{num_clusters}{Number of clusters} + +\item{circular}{Whether or not to fit circular MVG distributions for each cluster} + +\item{zero_covariance}{Whether or not to assign covariances of 0 for each MVG} + +\item{shared_orientation}{Whether each cluster MVG should have the same orientation} + +\item{shared_shape}{Whether each cluster MVG should have the same shape} + +\item{shared_size}{Whether each cluster MVG should have the same size/volume} } \value{ mclust object diff --git a/man/mclust_helper.Rd b/man/mclust_helper.Rd index d42adb1..68aac2e 100644 --- a/man/mclust_helper.Rd +++ b/man/mclust_helper.Rd @@ -13,7 +13,15 @@ mclust_helper( ) } \arguments{ -\item{object}{gm_clust object} +\item{circular}{Whether or not to fit circular MVG distributions for each cluster} + +\item{zero_covariance}{Whether or not to assign covariances of 0 for each MVG} + +\item{shared_orientation}{Whether each cluster MVG should have the same orientation} + +\item{shared_shape}{Whether each cluster MVG should have the same shape} + +\item{shared_size}{Whether each cluster MVG should have the same size/volume} } \value{ string containing mclust model name diff --git a/tests/testthat/test-gm_clust.R b/tests/testthat/test-gm_clust.R index 5a25f9d..345d23c 100644 --- a/tests/testthat/test-gm_clust.R +++ b/tests/testthat/test-gm_clust.R @@ -6,7 +6,7 @@ test_that("primary arguments", { list( x = rlang::expr(missing_arg()), - G= rlang::expr(missing_arg()), + num_clusters = rlang::expr(missing_arg()), circular = rlang::expr(missing_arg()), zero_covariance = rlang::expr(missing_arg()), shared_orientation = rlang::expr(missing_arg()), @@ -32,7 +32,7 @@ test_that("engine arguments", { list( x = rlang::expr(missing_arg()), - G = rlang::expr(missing_arg()), + num_clusters = rlang::expr(missing_arg()), circular = rlang::expr(missing_arg()), zero_covariance = rlang::expr(missing_arg()), shared_orientation = rlang::expr(missing_arg()), From d99b707fef3af6133ad6ed636e5670b4b10d09d0 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Mon, 30 Jun 2025 16:28:52 -0700 Subject: [PATCH 41/44] added skip_if_not_installed("mclust") for tests requiring mclust package --- tests/testthat/test-gm_clust-mclust.R | 12 ++++++++++++ tests/testthat/test-gm_clust.R | 21 +++++++++++++++++++++ 2 files changed, 33 insertions(+) diff --git a/tests/testthat/test-gm_clust-mclust.R b/tests/testthat/test-gm_clust-mclust.R index 12e499d..a9cae57 100644 --- a/tests/testthat/test-gm_clust-mclust.R +++ b/tests/testthat/test-gm_clust-mclust.R @@ -1,4 +1,7 @@ test_that("fitting", { + + skip_if_not_installed("mclust") + set.seed(1234) spec <- gm_clust(num_clusters = 3) %>% set_engine("mclust") @@ -13,6 +16,9 @@ test_that("fitting", { }) test_that("predicting", { + + skip_if_not_installed("mclust") + set.seed(1234) spec <- gm_clust(num_clusters = 3) %>% set_engine("mclust") @@ -28,6 +34,9 @@ test_that("predicting", { }) test_that("all levels are preserved with 1 row predictions", { + + skip_if_not_installed("mclust") + set.seed(1234) spec <- gm_clust(num_clusters = 3) %>% set_engine("mclust") @@ -43,6 +52,9 @@ test_that("all levels are preserved with 1 row predictions", { }) test_that("extract_centroids() works", { + + skip_if_not_installed("mclust") + set.seed(1234) spec <- gm_clust(num_clusters = 3) %>% set_engine("mclust") diff --git a/tests/testthat/test-gm_clust.R b/tests/testthat/test-gm_clust.R index 345d23c..6ae7eeb 100644 --- a/tests/testthat/test-gm_clust.R +++ b/tests/testthat/test-gm_clust.R @@ -53,6 +53,9 @@ test_that("bad input", { error = TRUE, gm_clust(mode = "bogus") ) + + skip_if_not_installed("mclust") + expect_snapshot( error = TRUE, { @@ -106,6 +109,9 @@ test_that("bad input", { }) test_that("predictions", { + + skip_if_not_installed("mclust") + set.seed(1234) mclust_fit <- gm_clust(num_clusters = 4) %>% @@ -135,6 +141,9 @@ test_that("predictions", { test_that("extract_centroids work", { + + skip_if_not_installed("mclust") + set.seed(1234) mclust_fit <- gm_clust(num_clusters = 4) %>% set_engine("mclust") %>% @@ -157,6 +166,9 @@ test_that("extract_centroids work", { test_that("predictions with new data", { + + skip_if_not_installed("mclust") + set.seed(1234) mclust_fit <- gm_clust(num_clusters = 4) %>% set_engine("mclust") %>% @@ -202,6 +214,9 @@ test_that("updating", { }) test_that("reordering is done correctly for gm_clust", { + + skip_if_not_installed("mclust") + set.seed(42) gm_fit <- gm_clust(num_clusters = 6) %>% @@ -217,6 +232,9 @@ test_that("reordering is done correctly for gm_clust", { }) test_that("model errors when parameters cannot be estimated", { + + skip_if_not_installed("mclust") + set.seed(42) expect_error( @@ -289,6 +307,9 @@ test_that("mappings to different model names are correct", { test_that("mappings to different model names are correct", { + + skip_if_not_installed("mclust") + set.seed(42) iris_sub <- iris %>% dplyr::select(Sepal.Length, Sepal.Width) From 75e829bc4b5faf4bf51abd87c0302d72ac5ae65b Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Tue, 1 Jul 2025 20:10:21 -0700 Subject: [PATCH 42/44] remove mclust import to tidyclust-package.R --- R/gm_clust.R | 2 -- R/tidyclust-package.R | 1 + 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/R/gm_clust.R b/R/gm_clust.R index 8846500..7e838fd 100644 --- a/R/gm_clust.R +++ b/R/gm_clust.R @@ -22,8 +22,6 @@ #' @param shared_shape Boolean, whether each cluster MVG should have the same shape #' @param shared_size Boolean, whether each cluster MVG should have the same size/volume #' -#' @importFrom mclust mclustBIC -#' #' @details #' #' ## What does it mean to predict? diff --git a/R/tidyclust-package.R b/R/tidyclust-package.R index 523796a..3a7c2f3 100644 --- a/R/tidyclust-package.R +++ b/R/tidyclust-package.R @@ -4,6 +4,7 @@ ## usethis namespace: start #' @importFrom dplyr bind_cols #' @importFrom generics tunable tune_args +#' @importFrom mclust mclustBIC #' @importFrom parsnip make_call #' @importFrom parsnip maybe_data_frame #' @importFrom parsnip maybe_matrix From 8315a059afd18245f979f23d2132ce50e33ef77b Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Tue, 1 Jul 2025 20:16:01 -0700 Subject: [PATCH 43/44] add periods to documentation for gm_clust and db_clust --- R/db_clust.R | 12 ++++++------ R/gm_clust.R | 36 +++++++++++++++++----------------- man/db_clust.Rd | 4 ++-- man/dbscan_helper.Rd | 2 +- man/dot-db_clust_fit_dbscan.Rd | 6 +++--- man/dot-gm_clust_fit_mclust.Rd | 14 ++++++------- man/gm_clust.Rd | 10 +++++----- man/mclust_helper.Rd | 12 ++++++------ 8 files changed, 48 insertions(+), 48 deletions(-) diff --git a/R/db_clust.R b/R/db_clust.R index 9b01610..9317d9a 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -15,8 +15,8 @@ #' possible value for this model is `"partition"`. #' @param engine A single character string specifying what computational engine #' to use for fitting. The engine for this model is `"dbscan"`. -#' @param radius Positive double, Radius drawn around points to determine core-points and cluster assignments (required) -#' @param min_points Positive integer, Minimum number of points required to form a core-point (required) +#' @param radius Positive double, Radius drawn around points to determine core-points and cluster assignments (required). +#' @param min_points Positive integer, Minimum number of points required to form a core-point (required). #' #' #' @details @@ -151,9 +151,9 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { #' `dbscan::dbscan()` and retains the parameters `radius` or `min_points` as an #' attribute. #' -#' @param x matrix or data frame -#' @param radius Radius used to determine core-points and cluster points together -#' @param min_points Minimum number of points needed to form a cluster +#' @param x matrix or data frame. +#' @param radius Radius used to determine core-points and cluster points together. +#' @param min_points Minimum number of points needed to form a cluster. #' #' @return dbscan object #' @keywords internal @@ -189,7 +189,7 @@ translate_tidyclust.db_clust <- function(x, engine = x$engine, ...) { #' dbscan fit helper function #' #' This function returns the cluster assignments for the training data -#' based on their distance to the CLOSEST core point in the data +#' based on their distance to the CLOSEST core point in the data. #' #' @param object db_clust object #' diff --git a/R/gm_clust.R b/R/gm_clust.R index 7e838fd..b02dbd5 100644 --- a/R/gm_clust.R +++ b/R/gm_clust.R @@ -16,11 +16,11 @@ #' @param engine A single character string specifying what computational engine #' to use for fitting. The engine for this model is `"mclust"`. #' @param num_clusters Positive integer, number of clusters in model (required). -#' @param circular Boolean, whether or not to fit circular MVG distributions for each cluster -#' @param zero_covariance Boolean, whether or not to assign covariances of 0 for each MVG -#' @param shared_orientation Boolean, whether each cluster MVG should have the same orientation -#' @param shared_shape Boolean, whether each cluster MVG should have the same shape -#' @param shared_size Boolean, whether each cluster MVG should have the same size/volume +#' @param circular Boolean, whether or not to fit circular MVG distributions for each cluster. Default `TRUE`. +#' @param zero_covariance Boolean, whether or not to assign covariances of 0 for each MVG. Default `TRUE`. +#' @param shared_orientation Boolean, whether each cluster MVG should have the same orientation. Default `TRUE`. +#' @param shared_shape Boolean, whether each cluster MVG should have the same shape. Default `TRUE`. +#' @param shared_size Boolean, whether each cluster MVG should have the same size/volume. Default `TRUE`. #' #' @details #' @@ -190,13 +190,13 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { #' `mclust::Mclust` and retains the parameters `num_clusters` as an #' attribute. #' -#' @param x matrix or data frame -#' @param num_clusters Number of clusters -#' @param circular Whether or not to fit circular MVG distributions for each cluster -#' @param zero_covariance Whether or not to assign covariances of 0 for each MVG -#' @param shared_orientation Whether each cluster MVG should have the same orientation -#' @param shared_shape Whether each cluster MVG should have the same shape -#' @param shared_size Whether each cluster MVG should have the same size/volume +#' @param x matrix or data frame. +#' @param num_clusters Number of clusters. +#' @param circular Whether or not to fit circular MVG distributions for each cluster. +#' @param zero_covariance Whether or not to assign covariances of 0 for each MVG. +#' @param shared_orientation Whether each cluster MVG should have the same orientation. +#' @param shared_shape Whether each cluster MVG should have the same shape. +#' @param shared_size Whether each cluster MVG should have the same size/volume. #' #' @return mclust object #' @keywords internal @@ -307,13 +307,13 @@ translate_tidyclust.gm_clust <- function(x, engine = x$engine, ...) { #' mclust fit helper function #' #' This function returns the mclust model name based on the specified -#' TRUE/FALSE model arguments +#' TRUE/FALSE model arguments. #' -#' @param circular Whether or not to fit circular MVG distributions for each cluster -#' @param zero_covariance Whether or not to assign covariances of 0 for each MVG -#' @param shared_orientation Whether each cluster MVG should have the same orientation -#' @param shared_shape Whether each cluster MVG should have the same shape -#' @param shared_size Whether each cluster MVG should have the same size/volume +#' @param circular Whether or not to fit circular MVG distributions for each cluster. +#' @param zero_covariance Whether or not to assign covariances of 0 for each MVG. +#' @param shared_orientation Whether each cluster MVG should have the same orientation. +#' @param shared_shape Whether each cluster MVG should have the same shape. +#' @param shared_size Whether each cluster MVG should have the same size/volume. #' #' @return string containing mclust model name #' @keywords internal diff --git a/man/db_clust.Rd b/man/db_clust.Rd index 764ac65..a99989f 100644 --- a/man/db_clust.Rd +++ b/man/db_clust.Rd @@ -18,9 +18,9 @@ possible value for this model is \code{"partition"}.} \item{engine}{A single character string specifying what computational engine to use for fitting. The engine for this model is \code{"dbscan"}.} -\item{radius}{Positive double, Radius drawn around points to determine core-points and cluster assignments (required)} +\item{radius}{Positive double, Radius drawn around points to determine core-points and cluster assignments (required).} -\item{min_points}{Positive integer, Minimum number of points required to form a core-point (required)} +\item{min_points}{Positive integer, Minimum number of points required to form a core-point (required).} } \value{ A \code{db_clust} cluster specification. diff --git a/man/dbscan_helper.Rd b/man/dbscan_helper.Rd index d6dc43a..df3503c 100644 --- a/man/dbscan_helper.Rd +++ b/man/dbscan_helper.Rd @@ -14,6 +14,6 @@ numeric vector } \description{ This function returns the cluster assignments for the training data -based on their distance to the CLOSEST core point in the data +based on their distance to the CLOSEST core point in the data. } \keyword{internal} diff --git a/man/dot-db_clust_fit_dbscan.Rd b/man/dot-db_clust_fit_dbscan.Rd index 54fa451..3fa4a41 100644 --- a/man/dot-db_clust_fit_dbscan.Rd +++ b/man/dot-db_clust_fit_dbscan.Rd @@ -7,11 +7,11 @@ .db_clust_fit_dbscan(x, radius = NULL, min_points = NULL, ...) } \arguments{ -\item{x}{matrix or data frame} +\item{x}{matrix or data frame.} -\item{radius}{Radius used to determine core-points and cluster points together} +\item{radius}{Radius used to determine core-points and cluster points together.} -\item{min_points}{Minimum number of points needed to form a cluster} +\item{min_points}{Minimum number of points needed to form a cluster.} } \value{ dbscan object diff --git a/man/dot-gm_clust_fit_mclust.Rd b/man/dot-gm_clust_fit_mclust.Rd index 2324620..777b0c9 100644 --- a/man/dot-gm_clust_fit_mclust.Rd +++ b/man/dot-gm_clust_fit_mclust.Rd @@ -16,19 +16,19 @@ ) } \arguments{ -\item{x}{matrix or data frame} +\item{x}{matrix or data frame.} -\item{num_clusters}{Number of clusters} +\item{num_clusters}{Number of clusters.} -\item{circular}{Whether or not to fit circular MVG distributions for each cluster} +\item{circular}{Whether or not to fit circular MVG distributions for each cluster.} -\item{zero_covariance}{Whether or not to assign covariances of 0 for each MVG} +\item{zero_covariance}{Whether or not to assign covariances of 0 for each MVG.} -\item{shared_orientation}{Whether each cluster MVG should have the same orientation} +\item{shared_orientation}{Whether each cluster MVG should have the same orientation.} -\item{shared_shape}{Whether each cluster MVG should have the same shape} +\item{shared_shape}{Whether each cluster MVG should have the same shape.} -\item{shared_size}{Whether each cluster MVG should have the same size/volume} +\item{shared_size}{Whether each cluster MVG should have the same size/volume.} } \value{ mclust object diff --git a/man/gm_clust.Rd b/man/gm_clust.Rd index 385ada3..4b313db 100644 --- a/man/gm_clust.Rd +++ b/man/gm_clust.Rd @@ -24,15 +24,15 @@ to use for fitting. The engine for this model is \code{"mclust"}.} \item{num_clusters}{Positive integer, number of clusters in model (required).} -\item{circular}{Boolean, whether or not to fit circular MVG distributions for each cluster} +\item{circular}{Boolean, whether or not to fit circular MVG distributions for each cluster. Default \code{TRUE}.} -\item{shared_size}{Boolean, whether each cluster MVG should have the same size/volume} +\item{shared_size}{Boolean, whether each cluster MVG should have the same size/volume. Default \code{TRUE}.} -\item{zero_covariance}{Boolean, whether or not to assign covariances of 0 for each MVG} +\item{zero_covariance}{Boolean, whether or not to assign covariances of 0 for each MVG. Default \code{TRUE}.} -\item{shared_orientation}{Boolean, whether each cluster MVG should have the same orientation} +\item{shared_orientation}{Boolean, whether each cluster MVG should have the same orientation. Default \code{TRUE}.} -\item{shared_shape}{Boolean, whether each cluster MVG should have the same shape} +\item{shared_shape}{Boolean, whether each cluster MVG should have the same shape. Default \code{TRUE}.} } \value{ A \code{gm_clust} cluster specification. diff --git a/man/mclust_helper.Rd b/man/mclust_helper.Rd index 68aac2e..1c950eb 100644 --- a/man/mclust_helper.Rd +++ b/man/mclust_helper.Rd @@ -13,21 +13,21 @@ mclust_helper( ) } \arguments{ -\item{circular}{Whether or not to fit circular MVG distributions for each cluster} +\item{circular}{Whether or not to fit circular MVG distributions for each cluster.} -\item{zero_covariance}{Whether or not to assign covariances of 0 for each MVG} +\item{zero_covariance}{Whether or not to assign covariances of 0 for each MVG.} -\item{shared_orientation}{Whether each cluster MVG should have the same orientation} +\item{shared_orientation}{Whether each cluster MVG should have the same orientation.} -\item{shared_shape}{Whether each cluster MVG should have the same shape} +\item{shared_shape}{Whether each cluster MVG should have the same shape.} -\item{shared_size}{Whether each cluster MVG should have the same size/volume} +\item{shared_size}{Whether each cluster MVG should have the same size/volume.} } \value{ string containing mclust model name } \description{ This function returns the mclust model name based on the specified -TRUE/FALSE model arguments +TRUE/FALSE model arguments. } \keyword{internal} From 2fa05fb0a4768c6e12bf77c910305143f4e45408 Mon Sep 17 00:00:00 2001 From: brendad8 <72055001+brendad8@users.noreply.github.com> Date: Fri, 4 Jul 2025 19:26:53 -0700 Subject: [PATCH 44/44] slight update to db_clust param description --- R/db_clust.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/db_clust.R b/R/db_clust.R index 9317d9a..d324755 100644 --- a/R/db_clust.R +++ b/R/db_clust.R @@ -16,7 +16,7 @@ #' @param engine A single character string specifying what computational engine #' to use for fitting. The engine for this model is `"dbscan"`. #' @param radius Positive double, Radius drawn around points to determine core-points and cluster assignments (required). -#' @param min_points Positive integer, Minimum number of points required to form a core-point (required). +#' @param min_points Positive integer, Minimum number of connected points required to form a core-point, including the point itself (required). #' #' #' @details