Get the trained model summaries info for an agent

This function retrieves the final summarized model info (hyperparameters, recipe steps, feature importance, etc.) after an agent completes its run. Built-in variable-importance rows are included when the optional vip package was available while summaries were generated; all other summary sections remain available without it. Custom models expose their exact version, confirmed interpretation and representation without source text or arbitrary fitted-state introspection. Driver importance is the mean signed increase in historical holdout RMSE under ten deterministic cyclic permutations, conditional on supplied drivers. It is not causal attribution or a percentage. Generating these summaries refits trusted custom source once per fitted workflow on earlier history and predicts complete holdout cohorts; no-driver models need no diagnostic fit. Non-estimable cases are disclosed through warnings and method/status rows.

get_summarized_models(agent_info)

Arguments

agent_info

Agent info from set_agent_info()

Value

A tibble containing the summarized models for the agent. Built-in importance sections may be omitted without vip. Custom importance does not require vip. Existing saved summaries are not migrated; newly generated summaries can contain additional importance metadata rows.

Examples

if (FALSE) { # \dontrun{
# load example data
hist_data <- timetk::m4_monthly %>%
  dplyr::filter(date >= "2013-01-01") %>%
  dplyr::rename(Date = date) %>%
  dplyr::mutate(id = as.character(id))

# set up Finn project
project <- set_project_info(
  project_name = "Demo_Project",
  combo_variables = c("id"),
  target_variable = "value",
  date_type = "month"
)

# set up LLM
llm <- ellmer::chat_azure_openai(model = "gpt-4o-mini")

# set up agent info
agent_info <- set_agent_info(
  project_info = project,
  llm = llm,
  input_data = hist_data,
  forecast_horizon = 6
)

# run the forecast iteration process
iterate_forecast(
  agent_info = agent_info,
  max_iter = 3,
  weighted_mape_goal = 0.03
)

# get the final model summaries for an agent
model_summary <- get_summarized_models(agent_info = agent_info)
} # }