registration.md
In parsnip: A Common API to Modeling and Analysis Functions

adding a new model

Code
  set_new_model()
Condition
  Error in `set_new_model()`:
  ! `model` must be a single string, not absent.

Code
  set_new_model(2)
Condition
  Error in `set_new_model()`:
  ! `model` must be a single string, not the number 2.

Code
  set_new_model(letters[1:2])
Condition
  Error in `set_new_model()`:
  ! `model` must be a single string, not a character vector.

existing modes

Code
  get_from_env("modes")
Output
  [1] "classification"      "regression"          "censored regression"
  [4] "quantile regression" "unknown"

adding a new mode

Code
  set_model_mode("sponge")
Condition
  Error in `set_model_mode()`:
  ! `mode` must be a single string, not absent.

adding a new engine

Code
  set_model_engine("sponge", eng = "gum")
Condition
  Error in `set_model_engine()`:
  ! `mode` must be a single string, not absent.

Code
  set_model_engine("sponge", mode = "classification")
Condition
  Error in `set_model_engine()`:
  ! `eng` must be a single string, not absent.

Code
  set_model_engine("sponge", mode = "regression", eng = "gum")
Condition
  Error in `set_model_engine()`:
  ! "regression" is not a known mode for model `sponge()`.

adding a new package

Code
  set_dependency("sponge", "gum", letters[1:2])
Condition
  Error in `set_dependency()`:
  ! `pkg` must be a single string, not a character vector.

Code
  set_dependency("sponge", "gummies", "trident")
Condition
  Error in `set_dependency()`:
  ! The engine "gummies" has not been registered for model "sponge".

Code
  set_dependency("sponge", "gum", "trident", mode = "regression")
Condition
  Error in `set_dependency()`:
  ! mode "regression" is not a valid mode for "sponge".

adding a new argument

Code
  set_model_arg(model = "lunchroom", eng = "gum", parsnip = "modeling", original = "modelling",
    func = list(pkg = "foo", fun = "bar"), has_submodel = FALSE)
Condition
  Error in `set_model_arg()`:
  ! Model "lunchroom" has not been registered.

Code
  set_model_arg(model = "sponge", eng = "gum", parsnip = "modeling", func = list(
    pkg = "foo", fun = "bar"), has_submodel = FALSE)
Condition
  Error in `set_model_arg()`:
  ! `original` must be a single string, not absent.

Code
  set_model_arg(model = "sponge", eng = "gum", original = "modelling", func = list(
    pkg = "foo", fun = "bar"), has_submodel = FALSE)
Condition
  Error in `set_model_arg()`:
  ! `parsnip` must be a single string, not absent.

Code
  set_model_arg(model = "sponge", eng = "gum", parsnip = "modeling", original = "modelling",
    func = "foo::bar", has_submodel = FALSE)
Condition
  Error in `set_model_arg()`:
  ! `func` should be a named vector with element fun and the optional elements pkg, range, trans, and values. func and pkg should both be single character strings.

Code
  set_model_arg(model = "sponge", eng = "gum", parsnip = "modeling", original = "modelling",
    func = list(pkg = "foo", fun = "bar"), has_submodel = 2)
Condition
  Error in `set_model_arg()`:
  ! `has_submodel` must be `TRUE` or `FALSE`, not the number 2.

Code
  set_model_arg(model = "sponge", eng = "gum", parsnip = "modeling", original = "modelling",
    func = list(pkg = "foo", fun = "bar"))
Condition
  Error in `set_model_arg()`:
  ! `has_submodel` must be `TRUE` or `FALSE`, not absent.

Code
  set_model_arg(model = "sponge", eng = "gum", parsnip = "yodeling", original = "yodelling",
    func = c(foo = "a", bar = "b"), has_submodel = FALSE)
Condition
  Error in `set_model_arg()`:
  ! `func` should be a named vector with element fun and the optional elements pkg, range, trans, and values. func and pkg should both be single character strings.

Code
  set_model_arg(model = "sponge", eng = "gum", parsnip = "yodeling", original = "yodelling",
    func = c(foo = "a"), has_submodel = FALSE)
Condition
  Error in `set_model_arg()`:
  ! `func` should be a named vector with element fun and the optional elements pkg, range, trans, and values. func and pkg should both be single character strings.

Code
  set_model_arg(model = "sponge", eng = "gum", parsnip = "yodeling", original = "yodelling",
    func = c(fun = 2, pkg = 1), has_submodel = FALSE)
Condition
  Error in `set_model_arg()`:
  ! The `fun` element of `func` must be a single string, not the number 2.

adding a new fit

Code
  set_fit(model = "cactus", eng = "gum", mode = "classification", value = fit_vals)
Condition
  Error in `set_fit()`:
  ! Model "cactus" has not been registered.

Code
  set_fit(model = "sponge", eng = "nose", mode = "classification", value = fit_vals)
Condition
  Error in `set_fit()`:
  ! The combination of engine `nose` and mode `classification` has not been registered for model `sponge`.

Code
  set_fit(model = "sponge", eng = "gum", mode = "frog", value = fit_vals)
Condition
  Error in `set_fit()`:
  ! "frog" is not a known mode for model `sponge()`.

Code
  set_fit(model = "sponge", eng = "gum", mode = "classification", value = fit_vals[
    -i])
Condition
  Error in `set_fit()`:
  ! The `value` argument should have elements: defaults, func, interface, and protect.

Code
  set_fit(model = "sponge", eng = "gum", mode = "classification", value = fit_vals[
    -i])
Condition
  Error in `set_fit()`:
  ! The `value` argument should have elements: defaults, func, interface, and protect.

Code
  set_fit(model = "sponge", eng = "gum", mode = "classification", value = fit_vals[
    -i])
Condition
  Error in `set_fit()`:
  ! The `value` argument should have elements: defaults, func, interface, and protect.

Code
  set_fit(model = "sponge", eng = "gum", mode = "classification", value = fit_vals[
    -i])
Condition
  Error in `set_fit()`:
  ! The `value` argument should have elements: defaults, func, interface, and protect.

Code
  set_fit(model = "sponge", eng = "gum", mode = "classification", value = fit_vals_0)
Condition
  Error in `check_interface_val()`:
  ! The interface element should have a single of: data.frame, formula, and matrix.

Code
  set_fit(model = "sponge", eng = "gum", mode = "classification", value = fit_vals_1)
Condition
  Error in `set_fit()`:
  ! The defaults element should be a list.

Code
  set_fit(model = "sponge", eng = "gum", mode = "classification", value = fit_vals_2)
Condition
  Error in `check_fit_info()`:
  ! `func` should be a named vector with element fun and the optional elements pkg, range, trans, and values. func and pkg should both be single character strings.

Code
  set_fit(model = "sponge", eng = "gum", mode = "classification", value = fit_vals_3)
Condition
  Error in `check_interface_val()`:
  ! The interface element should have a single of: data.frame, formula, and matrix.

adding a new predict method

Code
  set_pred(model = "cactus", eng = "gum", mode = "classification", type = "class",
    value = class_vals)
Condition
  Error in `set_pred()`:
  ! Model "cactus" has not been registered.

Code
  set_pred(model = "sponge", eng = "nose", mode = "classification", type = "class",
    value = class_vals)
Condition
  Error in `set_pred()`:
  ! The combination of engine `nose` and mode `classification` has not been registered for model `sponge`.

Code
  set_pred(model = "sponge", eng = "gum", mode = "classification", type = "eggs",
    value = class_vals)
Condition
  Error in `set_pred()`:
  ! The prediction type should be one of: "raw", "numeric", "class", "prob", "conf_int", "pred_int", "quantile", "time", "survival", "linear_pred", and "hazard".

Code
  set_pred(model = "sponge", eng = "gum", mode = "frog", type = "class", value = class_vals)
Condition
  Error in `set_pred()`:
  ! "frog" is not a known mode for model `sponge()`.

Code
  set_pred(model = "sponge", eng = "gum", mode = "classification", type = "class",
    value = class_vals[-i])
Condition
  Error in `set_pred()`:
  ! The predict module should have elements: "args", "func", "post", and "pre".

Code
  set_pred(model = "sponge", eng = "gum", mode = "classification", type = "class",
    value = class_vals[-i])
Condition
  Error in `set_pred()`:
  ! The predict module should have elements: "args", "func", "post", and "pre".

Code
  set_pred(model = "sponge", eng = "gum", mode = "classification", type = "class",
    value = class_vals[-i])
Condition
  Error in `set_pred()`:
  ! The predict module should have elements: "args", "func", "post", and "pre".

Code
  set_pred(model = "sponge", eng = "gum", mode = "classification", type = "class",
    value = class_vals[-i])
Condition
  Error in `set_pred()`:
  ! The predict module should have elements: "args", "func", "post", and "pre".

Code
  set_pred(model = "sponge", eng = "gum", mode = "classification", type = "class",
    value = class_vals_0)
Condition
  Error in `set_pred()`:
  ! The `pre` element of `pred_obj` must be a function or `NULL`, not the string "I".

Code
  set_pred(model = "sponge", eng = "gum", mode = "classification", type = "class",
    value = class_vals_1)
Condition
  Error in `set_pred()`:
  ! The `post` element of `pred_obj` must be a function or `NULL`, not the string "I".

Code
  set_pred(model = "sponge", eng = "gum", mode = "classification", type = "class",
    value = class_vals_2)
Condition
  Error in `check_pred_info()`:
  ! `func` should be a named vector with element fun and the optional elements pkg, range, trans, and values. func and pkg should both be single character strings.

showing model info

Code
  show_model_info("rand_forest")
Output
  Information for `rand_forest`
   modes: unknown, classification, regression, censored regression

   engines: 
     classification: randomForest, ranger1, spark
     regression:     randomForest, ranger1, spark

  1The model can use case weights.

   arguments: 
     ranger:       
        mtry  --> mtry
        trees --> num.trees
        min_n --> min.node.size
     randomForest: 
        mtry  --> mtry
        trees --> ntree
        min_n --> nodesize
     spark:        
        mtry  --> feature_subset_strategy
        trees --> num_trees
        min_n --> min_instances_per_node

   fit modules:
           engine           mode
           ranger classification
           ranger     regression
     randomForest classification
     randomForest     regression
            spark classification
            spark     regression

   prediction modules:
               mode       engine                    methods
     classification randomForest           class, prob, raw
     classification       ranger class, conf_int, prob, raw
     classification        spark                class, prob
         regression randomForest               numeric, raw
         regression       ranger     conf_int, numeric, raw
         regression        spark                    numeric

Code
  show_model_info("mlp")
Output
  Information for `mlp`
   modes: unknown, classification, regression

   engines: 
     classification: brulee, brulee_two_layer, keras, nnet
     regression:     brulee, brulee_two_layer, keras, nnet


   arguments: 
     keras:            
        hidden_units --> hidden_units
        penalty      --> penalty
        dropout      --> dropout
        epochs       --> epochs
        activation   --> activation
     nnet:             
        hidden_units --> size
        penalty      --> decay
        epochs       --> maxit
     brulee:           
        hidden_units --> hidden_units
        penalty      --> penalty
        epochs       --> epochs
        dropout      --> dropout
        learn_rate   --> learn_rate
        activation   --> activation
     brulee_two_layer: 
        hidden_units --> hidden_units
        penalty      --> penalty
        epochs       --> epochs
        dropout      --> dropout
        learn_rate   --> learn_rate
        activation   --> activation

   fit modules:
               engine           mode
                keras     regression
                keras classification
                 nnet     regression
                 nnet classification
               brulee     regression
               brulee classification
     brulee_two_layer     regression
     brulee_two_layer classification

   prediction modules:
               mode           engine          methods
     classification           brulee      class, prob
     classification brulee_two_layer      class, prob
     classification            keras class, prob, raw
     classification             nnet class, prob, raw
         regression           brulee          numeric
         regression brulee_two_layer          numeric
         regression            keras     numeric, raw
         regression             nnet     numeric, raw