casimir (version 0.3.3)

compute_intermediate_results_rr: Compute intermediate ranked retrieval results per group

Description

Compute intermediate ranked retrieval results per group such as Discounted Cumulative Gain (DCG), Ideal Discounted Cumulative Gain (IDCG), Normalised Discounted Cumulative Gain (NDCG) and Label Ranking Average Precision (LRAP).

Usage

compute_intermediate_results_rr(
  gold_vs_pred,
  grouping_var,
  drop_empty_groups = options::opt("drop_empty_groups")
)

Value

A data.frame with columns "dcg", "idcg", "ndcg", "lrap".

Arguments

gold_vs_pred

A data.frame as generated by create_comparison, additionally containing a column "score".

grouping_var

A character vector of grouping variables that must be present in gold_vs_pred.

drop_empty_groups

Should empty levels of factor variables be dropped in grouped set retrieval computation? (Defaults to TRUE, overwritable using option 'casimir.drop_empty_groups' or environment variable 'R_CASIMIR_DROP_EMPTY_GROUPS')

Examples

library(casimir)

gold <- tibble::tribble(
  ~doc_id, ~label_id,
  "A", "a",
  "A", "b",
  "A", "c",
  "A", "d",
  "A", "e",
)

pred <- tibble::tribble(
  ~doc_id, ~label_id, ~score,
  "A", "f", 0.3277,
  "A", "e", 0.32172,
  "A", "b", 0.13517,
  "A", "g", 0.10134,
  "A", "h", 0.09152,
  "A", "a", 0.07483,
  "A", "i", 0.03649,
  "A", "j", 0.03551,
  "A", "k", 0.03397,
  "A", "c", 0.03364
)

gold_vs_pred <- create_comparison(gold, pred)

compute_intermediate_results_rr(
  gold_vs_pred,
  rlang::syms(c("doc_id"))
)