clevr (version 0.1.2)

contingency_table_pairs: Binary Contingency Table for Linked Pairs

Description

Compute the binary contingency table for a set of predicted coreferent (linked) pairs given a set of ground truth coreferent pairs.

Usage

contingency_table_pairs(
  true_pairs,
  pred_pairs,
  num_pairs = NULL,
  ordered = FALSE
)

Value

Returns a \(2 \times 2\) contingency table of the form:

             Truth
   Prediction   TRUE  FALSE
        TRUE      TP     FP
        FALSE     FN     TN

Arguments

true_pairs

set of true coreferent pairs stored in a matrix or data.frame, where rows index pairs and columns index the ids of the constituents. Any pairs not included are assumed to be non-coreferent. Duplicate pairs (including equivalent pairs with reversed ids) are automatically removed.

pred_pairs

set of predicted coreferent pairs, following the same specification as true_pairs.

num_pairs

the total number of coreferent and non-coreferent pairs, excluding equivalent pairs with reversed ids. If not provided, the true negative cell will be set to NA.

ordered

whether to treat the element pairs as ordered---i.e. whether pair \((x, y)\) is distinct from pair \((y, x)\) for \(x \neq y\). Defaults to FALSE, which is appropriate for clustering, undirected link prediction, record linkage etc.

See Also

The membership_to_pairs and clusters_to_pairs functions can be used to transform other clustering representations into lists of pairs, as required by this function. The eval_report_pairs function computes common evaluation measures derived from binary contingency matrices, like the ones output by this function.

Examples

### Example where pairs/edges are undirected
# ground truth is 3-clique
true_pairs <- rbind(c(1,2), c(2,3), c(1,3))
# prediction misses one edge
pred_pairs <- rbind(c(1,2), c(2,3))
# total number of pairs assuming 3 elements
num_pairs <- 3 * (3 - 1) / 2
eval_report_pairs(true_pairs, pred_pairs, num_pairs)

### Example where pairs/edges are directed
# ground truth is a 3-star
true_pairs <- rbind(c(2,1), c(3,1), c(4,1))
# prediction gets direction of one edge incorrect
pred_pairs <- rbind(c(2,1), c(3,1), c(1,4))
# total number of pairs assuming 4 elements
num_pairs <- 4 * 4
eval_report_pairs(true_pairs, pred_pairs, num_pairs, ordered = TRUE)