Inspect ensemble predictions

Inspect ensemble predictions

Usage

# S3 method for class 'pairwiseLLM_warm_predictions'
summary(object, ...)

# S3 method for class 'pairwiseLLM_warm_predictions'
print(x, ...)

Arguments

object, x

A pairwiseLLM_warm_predictions tibble.

...

Passed to tibble printing; summary arguments must be empty.

Value

summary() returns item count, component names, and summaries of the mean and diagnostic sample SD. print() invisibly returns its input.

See also

predict.pairwiseLLM_warm_ensemble(), make_warm_start_prior()

Other adaptive warm start: ensemble_warm_start_models(), extract_warm_start_features(), fit_warm_start_model(), make_warm_start_cv_plan(), make_warm_start_prior(), pairwiseLLM_warm_model, predict.pairwiseLLM_warm_ensemble(), predict.pairwiseLLM_warm_model(), prepare_warm_start_model(), register_warm_start_model(), save_warm_start_model(), summary.pairwiseLLM_warm_ensemble(), warm_start_coefficients(), warm_start_feature_schema(), warm_start_python_status()

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
    requireNamespace("withr", quietly = TRUE)) {
  local({
    # Synthetic features illustrate the interface, not predictive validity.
    example_features <- function(seed) {
      withr::local_seed(seed)
      fields <- warm_start_feature_schema()$feature
      x <- as.data.frame(matrix(runif(15 * length(fields)), nrow = 15))
      names(x) <- fields
      x$n_tokens <- 11:25
      x$token_length_mean <- 2 + 10 * x$token_length_mean
      x$token_length_std <- 0.2 + x$token_length_std
      x$dale_chall_readability_score <- 5 + 20 * x$dale_chall_readability_score
      x <- data.frame(item_id = as.character(1:15), x)
      attr(x, "warm_start_schema") <- "writing_features_v1"
      x
    }
    features <- example_features(3103)
    theta <- 10 + 0.4 * features$n_tokens - 2 * features$token_length_mean
    # A small alpha grid keeps this example fast; the default has 41 values.
    model <- fit_warm_start_model(features$item_id, theta, "synthetic-a",
      features = features, alpha_grid = c(0, 1))
    features_b <- example_features(3104)
    theta_b <- 30 + features_b$n_tokens - 3 * features_b$token_length_mean
    model_b <- fit_warm_start_model(features_b$item_id, theta_b, "synthetic-b",
      features = features_b, alpha_grid = c(0, 1))
    ensemble <- ensemble_warm_start_models(assessment_a = model, assessment_b = model_b)
    predictions <- predict(ensemble, features)
    summary(predictions)
    print(predictions)
  })
}
#> Warm-start ensemble predictions; sample SD is diagnostic, not Bayesian prior SD.
#> # A tibble: 15 × 5
#>    item_id component_assessment_a component_assessment_b ensemble_mean
#>    <chr>                    <dbl>                  <dbl>         <dbl>
#>  1 1                        0.553                  0.848        0.700 
#>  2 2                        0.199                  0.563        0.381 
#>  3 3                        0.134                  0.543        0.338 
#>  4 4                       -1.73                  -1.13        -1.43  
#>  5 5                        0.825                  1.26         1.04  
#>  6 6                       -1.82                  -1.13        -1.48  
#>  7 7                        0.896                  1.40         1.15  
#>  8 8                        0.874                  1.43         1.15  
#>  9 9                        0.961                  1.55         1.25  
#> 10 10                      -1.25                  -0.446       -0.847 
#> 11 11                      -0.198                  0.560        0.181 
#> 12 12                      -0.733                  0.108       -0.313 
#> 13 13                      -0.354                  0.498        0.0719
#> 14 14                       0.273                  1.11         0.693 
#> 15 15                       1.42                   2.21         1.81  
#> # ℹ 1 more variable: ensemble_sd <dbl>