catch upon News

NEWS.md

@@ -1,10 +1,21 @@
+# sl3 1.4.4
+* Updates to `Lrnr_nnls` to support binary outcomes, including support for
+  convexity of the resultant model fit and warnings on prediction quality.
+* Refined, clearer documentation for `Lrnr_define_interactions`
+* Tweaks to `Lrnr_bound` to better support more flexible bounding for
+  continuous outcomes (automatically setting a maximum of infinity).
+* Changes to `Lrnr_cv_selector` to support improved computation of the CV-risk,
+  averaging the risk strictly across validation/holdout sets.
+* Bug fixes for `Lrnr_earth` (improving formals recognition), `Lrnr_glmnet`
+  (allowing offsets), and `Lrnr_caret` (reformatting of arguments).
+
 # sl3 1.4.3
 * Additional arguments for 'Keras' learners `Lrnr_lstm_keras` and
   `Lrnr_gru_keras` provide support for callback functions list and 2-layer
   networks. Default `callbacks` list provides early stopping criteria with
-  respect to 'Keras' defaults and `patience` of 10 epochs. Also, these two 
-  'Keras' learners now call `args_to_list` upon initialization, and set 
-  verbose argument according to `options("keras.fit_verbose")` or 
+  respect to 'Keras' defaults and `patience` of 10 epochs. Also, these two
+  'Keras' learners now call `args_to_list` upon initialization, and set
+  verbose argument according to `options("keras.fit_verbose")` or
   `options("sl3.verbose")`.
 * Update `Lrnr_xgboost` to support prediction tasks consisting of one
   observation (e.g., leave-one-out cross-validation).
@@ -13,63 +24,63 @@
   method (the `.cv_risk` slot is populated on the first `print` call, and only
   ever re-printed thereafter).
 * Update documentation of `default_metalearner` to use native markdown tables.
-* Fix `Lrnr_screener_importance`'s pairing of (a) covariates returned by the 
-  importance function with (b) covariates as they are defined in the task. This 
-  issue only arose when discrete covariates were automatically one-hot encoded 
+* Fix `Lrnr_screener_importance`'s pairing of (a) covariates returned by the
+  importance function with (b) covariates as they are defined in the task. This
+  issue only arose when discrete covariates were automatically one-hot encoded
   upon task initiation (i.e., when `colnames(task$X) != task$nodes$covariates`).
-* Reformat `importance_plot` to plot variables in decreasing order of 
-  importance, so most important variables are placed at the top of the dotchart. 
-* Enhanced functionality in `sl3` task's `add_interactions` method to support 
-  interactions that involve factors. This method is most commonly used by 
-  `Lrnr_define_interactions`, which is intended for use with another learner 
+* Reformat `importance_plot` to plot variables in decreasing order of
+  importance, so most important variables are placed at the top of the dotchart.
+* Enhanced functionality in `sl3` task's `add_interactions` method to support
+  interactions that involve factors. This method is most commonly used by
+  `Lrnr_define_interactions`, which is intended for use with another learner
   (e.g., `Lrnr_glmnet` or `Lrnr_glm`) in a `Pipeline`.
-* Modified `Lrnr_gam` formula (if not specified by user) to not use `mgcv`'s 
-  default `k=10` degrees of freedom for each smooth `s` term when there are 
-  less than `k=10` degrees of freedom. This bypasses an `mgcv::gam` error, and 
-  tends to be relevant only for small n. 
-* Added `options(java.parameters = "-Xmx2500m")` and warning message when 
-  `Lrnr_bartMachine` is initialized, if this option has not already been set. 
-  This option was incorporated since the default RAM of 500MB for a Java 
+* Modified `Lrnr_gam` formula (if not specified by user) to not use `mgcv`'s
+  default `k=10` degrees of freedom for each smooth `s` term when there are
+  less than `k=10` degrees of freedom. This bypasses an `mgcv::gam` error, and
+  tends to be relevant only for small n.
+* Added `options(java.parameters = "-Xmx2500m")` and warning message when
+  `Lrnr_bartMachine` is initialized, if this option has not already been set.
+  This option was incorporated since the default RAM of 500MB for a Java
   virtual machine often errors due to memory issues with `Lrnr_bartMachine`.
-* Incorporated `stratify_cv` argument in `Lrnr_glmnet`, which stratifies 
-  internal cross-validation folds such that binary outcome prevalence in 
-  training and validation folds roughly matches the prevalence in the training 
-  task. 
-* Incorporated `min_screen` argument `Lrnr_screener_coefs`, which tries to 
-  ensure that at least `min_screen` number of covariates are selected. If this 
-  argument is specified and the `learner` argument in `Lrnr_screener_coefs` is 
-  a `Lrnr_glmnet`, then `lambda` is increased until `min_screen` number of 
-  covariates are selected and a warning is produced. If `min_screen` is 
-  specified and the `learner` argument in `Lrnr_screener_coefs` is not a 
-  `Lrnr_glmnet` then it will error. 
+* Incorporated `stratify_cv` argument in `Lrnr_glmnet`, which stratifies
+  internal cross-validation folds such that binary outcome prevalence in
+  training and validation folds roughly matches the prevalence in the training
+  task.
+* Incorporated `min_screen` argument `Lrnr_screener_coefs`, which tries to
+  ensure that at least `min_screen` number of covariates are selected. If this
+  argument is specified and the `learner` argument in `Lrnr_screener_coefs` is
+  a `Lrnr_glmnet`, then `lambda` is increased until `min_screen` number of
+  covariates are selected and a warning is produced. If `min_screen` is
+  specified and the `learner` argument in `Lrnr_screener_coefs` is not a
+  `Lrnr_glmnet` then it will error.
 * Updated `Lrnr_hal9001` to work with v0.4.0 of the `hal9001` package.
-* Added `formula` parameter and `process_formula` function to the base 
-  learner, `Lrnr_base`, whose methods carry over to all other learners. When 
-  a `formula` is supplied as a learner parameter, the `process_formula` 
-  function constructs a design matrix by supplying the `formula` to 
-  `model.matrix`. This implementation allows `formula` to be supplied to all 
-  learners, even those without native `formula` support. The `formula` should 
-  be an object of class "`formula`", or a character string that can be coerced 
+* Added `formula` parameter and `process_formula` function to the base
+  learner, `Lrnr_base`, whose methods carry over to all other learners. When
+  a `formula` is supplied as a learner parameter, the `process_formula
+  function constructs a design matrix by supplying the `formula` to
+  `model.matrix`. This implementation allows `formula` to be supplied to all
+  learners, even those without native `formula` support. The `formula` should
+  be an object of class "`formula`", or a character string that can be coerced
   to that class.
-* Added factory function for performance-based risks for binary outcomes with 
-  `ROCR` performance measures `custom_ROCR_risk`. Supports cutoff-dependent and 
-  scalar `ROCR` performance measures. The risk is defined as 1 - performance, 
-  and is transformed back to the performance measure in `cv_risk` and 
-  `importance` functions. This change prompted the revision of argument name 
-  `loss_fun` and `loss_function` to `eval_fun` and `eval_function`, 
-  respectively, since the evaluation of predictions relative to the observations 
-  can be either a risk or a loss function. This argument name change impacted 
-  the following: `Lrnr_solnp`, `Lrnr_optim`, `Lrnr_cv_selector`, `cv_risk`, 
-  `importance`, and `CV_Lrnr_sl`. 
+* Added factory function for performance-based risks for binary outcomes with
+  `ROCR` performance measures `custom_ROCR_risk`. Supports cutoff-dependent and
+  scalar `ROCR` performance measures. The risk is defined as 1 - performance,
+  and is transformed back to the performance measure in `cv_risk` and
+  `importance` functions. This change prompted the revision of argument name
+  `loss_fun` and `loss_function` to `eval_fun` and `eval_function`,
+  respectively, since the evaluation of predictions relative to the observations
+  can be either a risk or a loss function. This argument name change impacted
+  the following: `Lrnr_solnp`, `Lrnr_optim`, `Lrnr_cv_selector`, `cv_risk`,
+  `importance`, and `CV_Lrnr_sl`.
 * Added name attribute to all loss functions, where naming was defined in terms
-  of the risk implied by each loss function (i.e., the common name for the 
-  expected loss). The names in `cv_risk` and `importance` tables now swap "risk" 
+  of the risk implied by each loss function (i.e., the common name for the
+  expected loss). The names in `cv_risk` and `importance` tables now swap "risk"
   with this name attribute.
-* Incorporated stratified cross-validation when `folds` are not supplied to the 
-  `sl3_Task` and the outcome is a discrete (i.e., binary or categorical) 
-  variable. 
-* Added to the `importance` method the option to evaluate importance over 
-  `covariate_groups`, by removing/permuting all covariates in the same group 
+* Incorporated stratified cross-validation when `folds` are not supplied to the
+  `sl3_Task` and the outcome is a discrete (i.e., binary or categorical)
+  variable.
+* Added to the `importance` method the option to evaluate importance over
+  `covariate_groups`, by removing/permuting all covariates in the same group
   together.
 * Added `Lrnr_ga` as another metalearner.
 
@@ -82,13 +93,13 @@
   and prediction on a new task using previously trained learners, respectively.
 
 # sl3 1.4.1
-* [TODO]
+[missing]
 
 # sl3 1.4.0
-* [TODO]
+[missing]
 
 # sl3 1.3.9
-* [TODO]
+[missing]
 
 # sl3 1.3.8
 * Updates to variable importance functionality, including use of risk ratios.