R: Ranking Metrics for Top-K Items

metrics {rsparse} | R Documentation

## Ranking Metrics for Top-K Items

### Description

`ap_k` calculates Average Precision at K (`ap@k`). Please refer to Information retrieval wikipedia article

`ndcg_k()` calculates Normalized Discounted Cumulative Gain at K (`ndcg@k`). Please refer to Discounted cumulative gain

### Usage

    ap_k(predictions, actual, ...)

    ndcg_k(predictions, actual, ...)

### Arguments

`predictions` | matrix of predictions. Predctions can be defined 2 ways: `predictions` = `integer` matrix with item indices (correspond to column numbers in `actual`) `predictions` = `character` matrix with item identifiers (characters which correspond to `colnames(actual)`) which has attribute "indices" (`integer` matrix with item indices which correspond to column numbers in `actual`).
`actual` | sparse Matrix of relevant items. Each non-zero entry considered as relevant item. Value of the each non-zero entry considered as relevance for calculation of `ndcg@k`. It should inherit from `Matrix::sparseMatrix`. Internally `Matrix::RsparseMatrix` is used.
`...` | other arguments (not used at the moment)

### Examples

    predictions = matrix(
      c(5L, 7L, 9L, 2L),
      nrow = 1
    )
    actual = matrix(
      c(0, 0, 0, 0, 1, 0, 1, 0, 1, 0),
      nrow = 1
    )
    actual = as(actual, "RsparseMatrix")
    identical(rsparse::ap_k(predictions, actual), 1)

[Package rsparse version 0.3.3.4 Index]