casimir (version 0.3.3)

create_comparison: Join gold standard and predicted results

Description

Join the gold standard and the predicted results in one table based on the document id and the label id.

Usage

create_comparison(
  predicted,
  gold_standard,
  doc_groups = NULL,
  label_groups = NULL,
  graded_relevance = FALSE,
  propensity_scored = FALSE,
  label_distribution = NULL,
  ignore_inconsistencies = options::opt("ignore_inconsistencies")
)

Value

A data.frame with columns "label_id", "doc_id", "suggested", "gold".

Arguments

predicted

Multi-label prediction results. Expects a data.frame with columns "label_id", "doc_id".

gold_standard

Expects a data.frame with columns "label_id", "doc_id".

doc_groups

A two-column data.frame with a column "doc_id" and a second column defining groups of documents to stratify results by. It is recommended that groups are of type factor so that levels are not implicitly dropped during bootstrap replications.

label_groups

A two-column data.frame with a column "label_id" and a second column defining groups of labels to stratify results by. Results in each stratum will restrict gold standard and predictions to the specified label groups as if the vocabulary was consisting of the label group only. All modes "doc-avg", "subj-avg", "micro" are supported within label strata. Nevertheless, mixing mode = "doc-avg" with fine-grained label strata can result in many missing values on document-level results. Also rank-based thresholding (e.g. top 5) will result in inhomogeneous numbers of labels per document within the defined label strata. mode = "subj-avg" or mode = "micro" can be more appropriate in these circumstances.

graded_relevance

A logical indicator for graded relevance. Defaults to FALSE for binary relevance. If set to TRUE, the predicted data.frame should contain a numeric column "relevance" with values in the range of \([0, 1]\).

propensity_scored

Logical, whether to use propensity scores as weights.

label_distribution

Expects a data.frame with columns "label_id", "label_freq", "n_docs". label_freq corresponds to the number of occurences a label has in the gold standard. n_docs corresponds to the total number of documents in the gold standard.

ignore_inconsistencies

Warnings about data inconsistencies will be silenced. (Defaults to FALSE, overwritable using option 'casimir.ignore_inconsistencies' or environment variable 'R_CASIMIR_IGNORE_INCONSISTENCIES')

Examples

library(casimir)

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

pred <- tibble::tribble(
  ~doc_id, ~label_id,
  "A", "a",
  "A", "d",
  "A", "f",
  "B", "a",
  "B", "e",
  "C", "f"
)

create_comparison(pred, gold)