Collect candidate parameters and stacking coefficients

A function to help situate candidates within a stack. Takes in a data stack or model stack and candidate name and returns a tibble mapping the candidate/member names to their hyperparameters (and, if a model stack, to their stacking coefficients as well).

Usage

collect_parameters(stack, candidates, ...)

# Default S3 method
collect_parameters(stack, candidates, ...)

# S3 method for class 'data_stack'
collect_parameters(stack, candidates, ...)

# S3 method for class 'model_stack'
collect_parameters(stack, candidates, ...)

Arguments

stack: A data_stack or model_stack object.
candidates: The name of the candidates to collect parameters on. This will either be the name argument supplied to add_candidates() or, if not supplied, the name of the object supplied to the candidates argument in add_candidates().
...: Additional arguments. Currently ignored.

Value

A tibble::tbl_df with information on member names and hyperparameters.

Example Data

This package provides some resampling objects and datasets for use in examples and vignettes derived from a study on 1212 red-eyed tree frog embryos!

Red-eyed tree frog (RETF) embryos can hatch earlier than their normal 7ish days if they detect potential predator threat. Researchers wanted to determine how, and when, these tree frog embryos were able to detect stimulus from their environment. To do so, they subjected the embryos at varying developmental stages to "predator stimulus" by jiggling the embryos with a blunt probe. Beforehand, though some of the embryos were treated with gentamicin, a compound that knocks out their lateral line (a sensory organ.) Researcher Julie Jung and her crew found that these factors inform whether an embryo hatches prematurely or not!

Note that the data included with the stacks package is not necessarily a representative or unbiased subset of the complete dataset, and is only for demonstrative purposes.

reg_folds and class_folds are rset cross-fold validation objects from rsample, splitting the training data into for the regression and classification model objects, respectively. tree_frogs_reg_test and tree_frogs_class_test are the analogous testing sets.

reg_res_lr, reg_res_svm, and reg_res_sp contain regression tuning results for a linear regression, support vector machine, and spline model, respectively, fitting latency (i.e. how long the embryos took to hatch in response to the jiggle) in the tree_frogs data, using most all of the other variables as predictors. Note that the data underlying these models is filtered to include data only from embryos that hatched in response to the stimulus.

class_res_rf and class_res_nn contain multiclass classification tuning results for a random forest and neural network classification model, respectively, fitting reflex (a measure of ear function) in the data using most all of the other variables as predictors.

log_res_rf and log_res_nn, contain binary classification tuning results for a random forest and neural network classification model, respectively, fitting hatched (whether or not the embryos hatched in response to the stimulus) using most all of the other variables as predictors.

See ?example_data to learn more about these objects, as well as browse the source code that generated them.

Examples

# see the "Example Data" section above for
# clarification on the objects used in these examples!

# put together a data stack using
# tuning results for regression models
reg_st <- 
  stacks() %>%
  add_candidates(reg_res_lr) %>%
  add_candidates(reg_res_svm) %>%
  add_candidates(reg_res_sp, "spline")
  
reg_st
#> # A data stack with 3 model definitions and 16 candidate members:
#> #   reg_res_lr: 1 model configuration
#> #   reg_res_svm: 5 model configurations
#> #   spline: 10 model configurations
#> # Outcome: latency (numeric)
  
# check out the hyperparameters for some of the candidates
collect_parameters(reg_st, "reg_res_svm")
#> # A tibble: 5 × 3
#>   member             cost     rbf_sigma
#>   <chr>             <dbl>         <dbl>
#> 1 reg_res_svm_1_1 3.09    0.000000465  
#> 2 reg_res_svm_1_2 9.56    0.00346      
#> 3 reg_res_svm_1_3 0.321   0.0256       
#> 4 reg_res_svm_1_4 0.00486 0.00000251   
#> 5 reg_res_svm_1_5 0.0146  0.00000000959

collect_parameters(reg_st, "spline")
#> # A tibble: 10 × 2
#>    member        age
#>    <chr>       <int>
#>  1 spline_01_1    12
#>  2 spline_02_1     6
#>  3 spline_03_1    15
#>  4 spline_04_1     5
#>  5 spline_05_1     3
#>  6 spline_06_1    13
#>  7 spline_07_1     9
#>  8 spline_08_1    10
#>  9 spline_09_1     7
#> 10 spline_10_1     2

# blend the data stack to view the hyperparameters 
# along with the stacking coefficients!
collect_parameters(
  reg_st %>% blend_predictions(), 
  "spline"
)
#> # A tibble: 10 × 3
#>    member        age   coef
#>    <chr>       <int>  <dbl>
#>  1 spline_01_1    12 0     
#>  2 spline_02_1     6 0     
#>  3 spline_03_1    15 0.484 
#>  4 spline_04_1     5 0     
#>  5 spline_05_1     3 0     
#>  6 spline_06_1    13 0     
#>  7 spline_07_1     9 0     
#>  8 spline_08_1    10 0     
#>  9 spline_09_1     7 0     
#> 10 spline_10_1     2 0.0496