case weights (#692)

* initial case weight work for xy models

* make the env arg of eval_tidy more explicit

* conversion of weights class to some numeric type

* changes for formula call formation

* check to see if the model can use case weights

* change envir vector name to weights

* update model defs for non-standard case weight arg names

* could it possibly be this easy?

* better approach to handling model.frame() issues in lm()

* no case weights for LiblinearR (they are class weights)

* add and update unit tests

* version updates and remotes

* Apply suggestions from code review

Co-authored-by: Davis Vaughan <[email protected]>

* function for grouped binomial data

* changes based on reviewer feedback

* re-export hardhat functions

* changes based on reviewer feedback

* test non-standard argument names

* temp bypass for r-devel

* pass case weights to xgboost

* update tests for xgboost/boost_tree args

* add case weight summary to show_model_info()

* added glm_grouped to pkgdown

* more unit tests

* spark support for case weights

* updates to documentation for case weights

* add missing topic

* more engine doc updates

* added more notes in engine docs

* added more notes in engine docs

* gam weights

* doc update

* revert nnet case weights

* S3 method to convert hardhat format to numeric

* Ensure that `fit_xy()` patches the formula environment with weights (#705)

* Prefix everywhere we use `new_quosure()` or `empty_env()`

We don't import these, so we have to do this. Tests were only working by chance because we have `library(rlang)` in some of the test files!

* Ensure that `fit_xy()` patches the formula environment with weights

* missing roxygen tag

* avoid deprecated tests

Co-authored-by: Max Kuhn <[email protected]>

* updated for latest roxygen2

* get xgb to stop being so chatty

* update snapshots

* doc update

* missing doc entry

* remove convert_case_weights

Co-authored-by: Davis Vaughan <[email protected]>

Author: topepo

DESCRIPTION

@@ -21,11 +21,11 @@ Depends:
 Imports: 
     cli,
     dplyr (>= 0.8.0.1),
-    generics (>= 0.1.0.9000),
+    generics (>= 0.1.2),
     ggplot2,
     globals,
     glue,
-    hardhat (>= 0.1.6.9001),
+    hardhat (>= 0.2.0.9000),
     lifecycle,
     magrittr,
     prettyunits,
@@ -40,9 +40,8 @@ Imports:
 Suggests: 
     C50,
     covr,
-    dials (>= 0.0.10.9001),
+    dials (>= 0.1.0),
     earth,
-    tensorflow,
     ggrepel,
     keras,
     kernlab,
@@ -60,30 +59,17 @@ Suggests:
     rpart,
     sparklyr (>= 1.0.0),
     survival,
+    tensorflow,
     testthat (>= 3.0.0),
     xgboost (>= 1.5.0.1)
+Remotes:
+    tidymodels/hardhat
 VignetteBuilder: 
     knitr
 ByteCompile: true
-Config/Needs/website: 
-    C50, 
-    dbarts, 
-    earth, 
-    glmnet, 
-    keras, 
-    kernlab, 
-    kknn,
-    LiblineaR, 
-    mgcv, 
-    nnet, 
-    parsnip, 
-    randomForest, 
-    ranger, 
-    rpart, 
-    rstanarm,
-    tidymodels/tidymodels, 
-    tidyverse/tidytemplate, 
-    rstudio/reticulate,
+Config/Needs/website: C50, dbarts, earth, glmnet, keras, kernlab, kknn,
+    LiblineaR, mgcv, nnet, parsnip, randomForest, ranger, rpart, rstanarm,
+    tidymodels/tidymodels, tidyverse/tidytemplate, rstudio/reticulate,
     xgboost
 Config/rcmdcheck/ignore-inconsequential-notes: true
 Encoding: UTF-8

NAMESPACE

@@ -205,6 +205,7 @@ export(format_linear_pred)
 export(format_num)
 export(format_survival)
 export(format_time)
+export(frequency_weights)
 export(gen_additive_mod)
 export(get_dependency)
 export(get_encoding)
@@ -213,7 +214,9 @@ export(get_from_env)
 export(get_model_env)
 export(get_pred_type)
 export(glance)
+export(glm_grouped)
 export(has_multi_predict)
+export(importance_weights)
 export(is_varying)
 export(keras_mlp)
 export(keras_predict_classes)
@@ -333,6 +336,8 @@ importFrom(hardhat,extract_fit_engine)
 importFrom(hardhat,extract_parameter_dials)
 importFrom(hardhat,extract_parameter_set_dials)
 importFrom(hardhat,extract_spec_parsnip)
+importFrom(hardhat,frequency_weights)
+importFrom(hardhat,importance_weights)
 importFrom(hardhat,tune)
 importFrom(magrittr,"%>%")
 importFrom(purrr,"%||%")

NEWS.md

@@ -1,5 +1,12 @@
 # parsnip (development version)
 
+
+* Enable the use of case weights for models that support them. 
+
+* Added a `glm_grouped()` function to convert long data to the grouped format required by `glm()` for logistic regression. 
+
+* `show_model_info()` now indicates which models can utilize case weights. 
+
 * `xgb_train()` now allows for case weights 
 
 * Added `ctree_train()` and `cforest_train()` wrappers for the functions in the partykit package. Engines for these will be added to other parsnip extension packages.  
@@ -14,6 +21,7 @@
 
 * Model type functions will now message informatively if a needed parsnip extension package is not loaded (#731).
 
+
 # parsnip 0.2.1
 
 * Fixed a major bug in spark models induced in the previous version (#671).

R/aaa_models.R

@@ -927,8 +927,24 @@ show_model_info <- function(model) {
   engines <- get_from_env(model)
   if (nrow(engines) > 0) {
     cat(" engines: \n")
-    engines %>%
+
+    weight_info <-
+      purrr::map_df(
+        model,
+        ~ get_from_env(paste0(.x, "_fit")) %>% mutate(model = .x)
+      ) %>%
+      dplyr::mutate(protect = map(value, ~ .x$protect)) %>%
+      dplyr::select(-value) %>%
       dplyr::mutate(
+        has_wts = purrr::map_lgl(protect, ~ any(grepl("^weight", .x))),
+        has_wts = ifelse(has_wts, cli::symbol$sup_1, "")
+      ) %>%
+      dplyr::select(engine, mode, has_wts)
+
+      engines %>%
+        dplyr::left_join(weight_info, by = c("engine", "mode")) %>%
+      dplyr::mutate(
+        engine = paste0(engine, has_wts),
         mode = format(paste0(mode, ": "))
       ) %>%
       dplyr::group_by(mode) %>%
@@ -941,7 +957,7 @@ show_model_info <- function(model) {
       dplyr::ungroup() %>%
       dplyr::pull(lab) %>%
       cat(sep = "")
-    cat("\n")
+    cat("\n", cli::symbol$sup_1, "The model can use case weights.\n\n", sep = "")
   } else {
     cat(" no registered engines.\n\n")
   }

R/arguments.R

@@ -150,11 +150,35 @@ make_call <- function(fun, ns, args, ...) {
 
 make_form_call <- function(object, env = NULL) {
   fit_args <- object$method$fit$args
+  uses_weights <- has_weights(env)
 
-  # Get the arguments related to data:
+  # In model specification code using `set_fit()`, there are two main arguments
+  # that dictate the data-related model arguments (e.g. 'formula', 'data', 'x',
+  # etc).
+  # The 'protect' element specifies which data arguments should not be modifiable
+  # by the user (as an engine argument). These have standardized names that
+  # follow the usual R conventions. For example, `foo(formula, data, weights)`
+  # and so on.
+  # However, some packages do not follow these naming conventions. The 'data'
+  # element in `set_fit()` allows use to have non-standard argument names by
+  # providing a named list. If function `bar(f, dat, wts)` was being used, the
+  # 'data' element would be `c(formula = "f", data = "dat", weights = "wts)`.
+  # If conventional names are used, there is no 'data' element since the values
+  # in 'protect' suffice.
+
+  # Get the arguments related to data arguments to insert into the model call
+
+  # Do we have conventional argument names?
   if (is.null(object$method$fit$data)) {
-    data_args <- c(formula = "formula", data = "data")
+    # Set the minimum arguments for formula methods.
+    data_args <- object$method$fit$protect
+    names(data_args) <- data_args
+    # Case weights _could_ be used but remove the arg if they are not given:
+    if (!uses_weights) {
+      data_args <- data_args[data_args != "weights"]
+    }
   } else {
+    # What are the non-conventional names?
     data_args <- object$method$fit$data
   }
 
@@ -166,6 +190,7 @@ make_form_call <- function(object, env = NULL) {
   # sub in actual formula
   fit_args[[ unname(data_args["formula"]) ]]  <- env$formula
 
+  # TODO remove weights col from data?
   if (object$engine == "spark") {
     env$x <- env$data
   }
@@ -178,12 +203,20 @@ make_form_call <- function(object, env = NULL) {
   fit_call
 }
 
-make_xy_call <- function(object, target) {
+# TODO we need something to indicate that case weights are being used.
+make_xy_call <- function(object, target, env) {
   fit_args <- object$method$fit$args
+  uses_weights <- has_weights(env)
+
+  # See the comments above in make_form_call()
 
-  # Get the arguments related to data:
   if (is.null(object$method$fit$data)) {
-    data_args <- c(x = "x", y = "y")
+    data_args <- object$method$fit$protect
+    names(data_args) <- data_args
+    # Case weights _could_ be used but remove the arg if they are not given:
+    if (!uses_weights) {
+      data_args <- data_args[data_args != "weights"]
+    }
   } else {
     data_args <- object$method$fit$data
   }
@@ -197,6 +230,9 @@ make_xy_call <- function(object, target) {
       matrix = rlang::expr(maybe_matrix(x)),
       rlang::abort(glue::glue("Invalid data type target: {target}."))
     )
+  if (uses_weights) {
+    object$method$fit$args[[ unname(data_args["weights"]) ]] <- rlang::expr(weights)
+  }
 
   fit_call <- make_call(
     fun = object$method$fit$func["fun"],
@@ -269,3 +305,4 @@ min_rows <- function(num_rows, source, offset = 0) {
 
   as.integer(num_rows)
 }
+

R/boost_tree_data.R

@@ -82,7 +82,7 @@ set_fit(
   mode = "regression",
   value = list(
     interface = "matrix",
-    protect = c("x", "y"),
+    protect = c("x", "y", "weights"),
     func = c(pkg = "parsnip", fun = "xgb_train"),
     defaults = list(nthread = 1, verbose = 0)
   )
@@ -132,7 +132,7 @@ set_fit(
   mode = "classification",
   value = list(
     interface = "matrix",
-    protect = c("x", "y"),
+    protect = c("x", "y", "weights"),
     func = c(pkg = "parsnip", fun = "xgb_train"),
     defaults = list(nthread = 1, verbose = 0)
   )

R/case_weights.R

@@ -0,0 +1,93 @@
+#' Using case weights with parsnip
+#'
+#' Case weights are positive numeric values that influence how much each data
+#' point has during the model fitting process. There are a variety of situations
+#' where case weights can be used.
+#'
+#' tidymodels packages differentiate _how_ different types of case weights
+#' should be used during the entire data analysis process, including
+#' preprocessing data, model fitting, performance calculations, etc.
+#'
+#' The tidymodels packages require users to convert their numeric vectors to a
+#' vector class that reflects how these should be used. For example, there are
+#' some situations where the weights should not affect operations such as
+#' centering and scaling or other preprocessing operations.
+#'
+#' The types of weights allowed in tidymodels are:
+#'
+#' * Frequency weights via [hardhat::frequency_weights()]
+#' * Importance weights via [hardhat::importance_weights()]
+#'
+#' More types can be added by request.
+#'
+#' For parsnip, the [fit()] and [fit_xy] functions contain a `case_weight`
+#' argument that takes these data. For Spark models, the argument value should
+#' be a character value.
+#'
+#' @name case_weights
+#' @seealso [frequency_weights()], [importance_weights()], [fit()], [fit_xy]
+NULL
+
+# ------------------------------------------------------------------------------
+
+weights_to_numeric <- function(x, spec) {
+  if (is.null(x)) {
+    return(NULL)
+  } else if (spec$engine == "spark") {
+    # Spark wants a column name
+    return(x)
+  }
+
+  to_int <- c("hardhat_frequency_weights")
+  if (inherits(x, to_int)) {
+    x <- as.integer(x)
+  } else {
+    x <- as.numeric(x)
+  }
+  x
+}
+
+patch_formula_environment_with_case_weights <- function(formula,
+                                                        data,
+                                                        case_weights) {
+  # `lm()` and `glm()` and others use the original model function call to
+  # construct a call for `model.frame()`. That will normally fail because the
+  # formula has its own environment attached (usually the global environment)
+  # and it will look there for a vector named 'weights'. To account
+  # for this, we create a child of the `formula`'s environment and
+  # stash the `weights` there with the expected name and then
+  # reassign this as the `formula`'s environment
+  environment(formula) <- rlang::new_environment(
+    data = list(data = data, weights = case_weights),
+    parent = environment(formula)
+  )
+
+  formula
+}
+
+# ------------------------------------------------------------------------------
+
+case_weights_allowed <- function(spec) {
+  mod_type <- class(spec)[1]
+  mod_eng <- spec$engine
+  mod_mode <- spec$mode
+
+  model_info <-
+    get_from_env(paste0(mod_type, "_fit")) %>%
+    dplyr::filter(engine == mod_eng & mode == mod_mode)
+  if (nrow(model_info) != 1) {
+    rlang::abort(
+      glue::glue(
+        "Error in geting model information for model {mod_type} with engine {mod_eng} and mode {mod_mode}."
+      )
+    )
+  }
+  # If weights are used, they are protected data arguments with the canonical
+  # name 'weights' (although this may not be the model function's argument name).
+  data_args <- model_info$value[[1]]$protect
+  any(data_args == "weights")
+}
+
+has_weights <- function(env) {
+  !is.null(env$weights)
+}

R/convert_data.R

@@ -252,6 +252,12 @@
     if (length(weights) != nrow(x)) {
       rlang::abort(glue::glue("`weights` should have {nrow(x)} elements"))
     }
+
+    form <- patch_formula_environment_with_case_weights(
+      formula = form,
+      data = x,
+      case_weights = weights
+    )
   }
 
   res <- list(