Merge pull request #196 from tidymodels/cran-0-0-3-changes

changes for CRAN 0.0.3 release

Author: topepo

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: parsnip
-Version: 0.0.2.9000
+Version: 0.0.3
 Title: A Common API to Modeling and Analysis Functions
 Description: A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', etc). 
 Authors@R: c(

NAMESPACE

@@ -5,6 +5,12 @@ S3method(fit_xy,model_spec)
 S3method(has_multi_predict,default)
 S3method(has_multi_predict,model_fit)
 S3method(has_multi_predict,workflow)
+S3method(min_grid,boost_tree)
+S3method(min_grid,linear_reg)
+S3method(min_grid,logistic_reg)
+S3method(min_grid,mars)
+S3method(min_grid,multinom_reg)
+S3method(min_grid,nearest_neighbor)
 S3method(multi_predict,"_C5.0")
 S3method(multi_predict,"_earth")
 S3method(multi_predict,"_elnet")
@@ -50,8 +56,11 @@ S3method(print,svm_rbf)
 S3method(translate,boost_tree)
 S3method(translate,decision_tree)
 S3method(translate,default)
+S3method(translate,linear_reg)
+S3method(translate,logistic_reg)
 S3method(translate,mars)
 S3method(translate,mlp)
+S3method(translate,multinom_reg)
 S3method(translate,nearest_neighbor)
 S3method(translate,rand_forest)
 S3method(translate,surv_reg)
@@ -104,6 +113,13 @@ export(linear_reg)
 export(logistic_reg)
 export(make_classes)
 export(mars)
+export(min_grid)
+export(min_grid.boost_tree)
+export(min_grid.linear_reg)
+export(min_grid.logistic_reg)
+export(min_grid.mars)
+export(min_grid.multinom_reg)
+export(min_grid.nearest_neighbor)
 export(mlp)
 export(model_printer)
 export(multi_predict)

NEWS.md

@@ -1,17 +1,26 @@
-# parsnip 0.0.2.9000
+# parsnip 0.0.3
+
+Unplanned release based on CRAN requirements for Solaris.
 
 ## Breaking Changes
 
- * The method that `parsnip` stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models. The methods are detailed in `?get_model_env()` and the [package vignette for adding models](https://tidymodels.github.io/parsnip/articles/articles/Scratch.html).
- * The mode need to be declared for models that can be used for more than one mode prior to fitting and/or translation). 
+ * The method that `parsnip` stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models. The methods are detailed in `?get_model_env` and the [package vignette for adding models](https://tidymodels.github.io/parsnip/articles/articles/Scratch.html).
+
+ * The mode needs to be declared for models that can be used for more than one mode prior to fitting and/or translation. 
+
  * For `surv_reg()`, the engine that uses the `survival` package is now called `survival` instead of `survreg`.  
 
+ * For `glmnet` models, the full regularization path is always fit regardless of the value given to `penalty`. Previously, the model was fit with passing `penalty` to `glmnet`'s `lambda` argument and the model could only make predictions at those specific values. [(#195)](https://github.com/tidymodels/parsnip/issues/195)
+
 ## New Features
 
  * `add_rowindex()` can create a column called `.row` to a data frame. 
 
  * If a computational engine is not explicitly set, a default will be used. Each default is documented on the corresponding model page. A warning is issued at fit time unless verbosity is zero.  
- * `nearest_neighbor` gained a `multi_predict` method. The `multi_predict()` documentation is a little better organized.  
+
+ * `nearest_neighbor()` gained a `multi_predict` method. The `multi_predict()` documentation is a little better organized.  
+ 
+ * A suite of internal functions were added to help with upcoming model tuning features.  
 
 
 # parsnip 0.0.2

R/aaa.R

@@ -19,10 +19,102 @@ convert_stan_interval <- function(x, level = 0.95, lower = TRUE) {
 }
 
 # ------------------------------------------------------------------------------
+# min_grid generic - put here so that the generic shows up first in the man file
+
+#' Determine the minimum set of model fits
+#'
+#' `min_grid` determines exactly what models should be fit in order to
+#'  evaluate the entire set of tuning parameter combinations. This is for
+#'  internal use only and the API may change in the near future.
+#' @param x A model specification.
+#' @param grid A tibble with tuning parameter combinations.
+#' @param ... Not currently used.
+#' @return A tibble with the minimum tuning parameters to fit and an additional
+#' list column with the parameter combinations used for prediction.
+#' @keywords internal
+#' @export
+min_grid <- function(x, grid, ...) {
+  # x is a `model_spec` object from parsnip
+  # grid is a tibble of tuning parameter values with names
+  #  matching the parameter names.
+  UseMethod("min_grid")
+}
+
+# As an example, if we fit a boosted tree  model and tune over
+# trees = 1:20 and min_n = c(20, 30)
+# we should only have to fit two models:
+#
+#   trees = 20 & min_n = 20
+#   trees = 20 & min_n = 30
+#
+# The logic related to how this "mini grid" gets made is model-specific.
+#
+# To get the full set of predictions, we need to know, for each of these two
+# models, what values of num_terms to give to the multi_predict() function.
+#
+# The current idea is to have a list column of the extra models for prediction.
+# For the example above:
+#
+#   # A tibble: 2 x 3
+#     trees min_n .submodels
+#     <dbl> <dbl> <list>
+#   1    20    20 <named list [1]>
+#   2    20    30 <named list [1]>
+#
+# and the .submodels would both be
+#
+#  list(trees = 1:19)
+#
+# There are a lot of other things to consider in future versions like grids
+# where there are multiple columns with the same name (maybe the results of
+# a recipe) and so on.
+
+# ------------------------------------------------------------------------------
+# helper functions
+
+# Template for model results that do no have the sub-model feature
+blank_submodels <- function(grid) {
+  grid %>%
+    dplyr::mutate(.submodels = map(1:nrow(grid), ~ list()))
+}
+
+get_fixed_args <- function(info) {
+  # Get non-sub-model columns to iterate over
+  fixed_args <- info$name[!info$has_submodel]
+}
+
+get_submodel_info <- function(spec, grid) {
+  param_info <-
+    get_from_env(paste0(class(spec)[1], "_args")) %>%
+    dplyr::filter(engine == spec$engine) %>%
+    dplyr::select(name = parsnip, has_submodel)
+
+  # In case a recipe or other activity has grid parameter columns,
+  # add those to the results
+  grid_names <- names(grid)
+  is_mod_param <- grid_names %in% param_info$name
+  if (any(!is_mod_param)) {
+    param_info <-
+      param_info %>%
+      dplyr::bind_rows(
+        tibble::tibble(name = grid_names[!is_mod_param],
+                       has_submodel = FALSE)
+      )
+  }
+  param_info %>% dplyr::filter(name %in% grid_names)
+}
+
+
+# ------------------------------------------------------------------------------
+# nocov
 
 #' @importFrom utils globalVariables
 utils::globalVariables(
   c('.', '.label', '.pred', '.row', 'data', 'engine', 'engine2', 'group',
     'lab', 'original', 'predicted_label', 'prediction', 'value', 'type',
-    "neighbors")
+    "neighbors", ".submodels", "has_submodel", "max_neighbor", "max_penalty",
+    "max_terms", "max_tree", "name", "num_terms", "penalty", "trees",
+    "sub_neighbors")
   )
+
+# nocov end

R/boost_tree.R

@@ -514,3 +514,41 @@ C50_by_tree <- function(tree, object, new_data, type, ...) {
   pred[, c(".row", "trees", nms)]
 }
 
+# ------------------------------------------------------------------------------
+
+#' @export
+#' @export min_grid.boost_tree
+#' @rdname min_grid
+min_grid.boost_tree <- function(x, grid, ...) {
+  grid_names <- names(grid)
+  param_info <- get_submodel_info(x, grid)
+
+  # No ability to do submodels? Finish here:
+  if (!any(param_info$has_submodel)) {
+    return(blank_submodels(grid))
+  }
+
+  fixed_args <- get_fixed_args(param_info)
+
+  # For boosted trees, fit the model with the most trees (conditional on the
+  # other parameters) so that you can do predictions on the smaller models.
+  fit_only <-
+    grid %>%
+    dplyr::group_by(!!!rlang::syms(fixed_args)) %>%
+    dplyr::summarize(trees = max(trees, na.rm = TRUE)) %>%
+    dplyr::ungroup()
+
+  # Add a column .submodels that is a list with what should be predicted
+  # by `multi_predict()` (assuming `predict()` has already been executed
+  # on the original value of 'trees')
+  min_grid_df <-
+    dplyr::full_join(fit_only %>% rename(max_tree = trees), grid, by = fixed_args) %>%
+    dplyr::filter(trees != max_tree) %>%
+    dplyr::group_by(!!!rlang::syms(fixed_args)) %>%
+    dplyr::summarize(.submodels = list(list(trees = trees))) %>%
+    dplyr::ungroup() %>%
+    dplyr::full_join(fit_only, grid, by = fixed_args)
+
+  min_grid_df  %>% dplyr::select(dplyr::one_of(grid_names), .submodels)
+}
+

R/linear_reg.R

@@ -68,9 +68,9 @@
 #'
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "keras")}
 #'
-#' When using `glmnet` models, there is the option to pass
-#'  multiple values (or no values) to the `penalty` argument. This
-#'  can have an effect on the model object results. When using the
+#' For `glmnet` models, the full regularization path is always fit regardless
+#' of the value given to `penalty`. Also, there is the option to pass
+#'  multiple values (or no values) to the `penalty` argument. When using the
 #'  `predict()` method in these cases, the return value depends on
 #'  the value of `penalty`. When using `predict()`, only a single
 #'  value of the penalty can be used. When predicting on multiple
@@ -138,6 +138,23 @@ print.linear_reg <- function(x, ...) {
   invisible(x)
 }
 
+
+#' @export
+translate.linear_reg <- function(x, engine = x$engine, ...) {
+  x <- translate.default(x, engine, ...)
+
+  if (engine == "glmnet") {
+    # See discussion in https://github.com/tidymodels/parsnip/issues/195
+    x$method$fit$args$lambda <- NULL
+    # Since the `fit` infomration is gone for the penalty, we need to have an
+    # evaludated value for the parameter.
+    x$args$penalty <- rlang::eval_tidy(x$args$penalty)
+  }
+
+  x
+}
+
+
 # ------------------------------------------------------------------------------
 
 #' @inheritParams update.boost_tree
@@ -274,6 +291,11 @@ predict._elnet <-
     if (any(names(enquos(...)) == "newdata"))
       stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
 
+    # See discussion in https://github.com/tidymodels/parsnip/issues/195
+    if (is.null(penalty) & !is.null(object$spec$args$penalty)) {
+      penalty <- object$spec$args$penalty
+    }
+
     object$spec$args$penalty <- check_penalty(penalty, object, multi)
 
     object$spec <- eval_args(object$spec)
@@ -314,7 +336,12 @@ multi_predict._elnet <-
     object$spec <- eval_args(object$spec)
 
     if (is.null(penalty)) {
-      penalty <- object$fit$lambda
+      # See discussion in https://github.com/tidymodels/parsnip/issues/195
+      if (!is.null(object$spec$args$penalty)) {
+        penalty <- object$spec$args$penalty
+      } else {
+        penalty <- object$fit$lambda
+      }
     }
 
     pred <- predict._elnet(object, new_data = new_data, type = "raw",
@@ -332,3 +359,37 @@ multi_predict._elnet <-
     names(pred) <- NULL
     tibble(.pred = pred)
   }
+
+
+# ------------------------------------------------------------------------------
+
+#' @export
+#' @export min_grid.linear_reg
+#' @rdname min_grid
+min_grid.linear_reg <- function(x, grid, ...) {
+
+  grid_names <- names(grid)
+  param_info <- get_submodel_info(x, grid)
+
+  if (!any(param_info$has_submodel)) {
+    return(blank_submodels(grid))
+  }
+
+  fixed_args <- get_fixed_args(param_info)
+
+  fit_only <-
+    grid %>%
+    dplyr::group_by(!!!rlang::syms(fixed_args)) %>%
+    dplyr::summarize(penalty = max(penalty, na.rm = TRUE)) %>%
+    dplyr::ungroup()
+
+  min_grid_df <-
+    dplyr::full_join(fit_only %>% rename(max_penalty = penalty), grid, by = fixed_args) %>%
+    dplyr::filter(penalty != max_penalty) %>%
+    dplyr::group_by(!!!rlang::syms(fixed_args)) %>%
+    dplyr::summarize(.submodels = list(list(penalty = penalty))) %>%
+    dplyr::ungroup() %>%
+    dplyr::full_join(fit_only, grid, by = fixed_args)
+
+  min_grid_df  %>% dplyr::select(dplyr::one_of(grid_names), .submodels)
+}