IR metrics for R

This is a small library for implementing several standard "test collection" or "offline" evaluation measures for search systems. It uses the C/W/L framework described by Moffat et al. and implements:

• Precision at k (P)
• Average precision (AP)
• Reciprocal rank (RR)
• Expected reciprocal rank (ERR)
• Rank-biased precision (RBP)
• Scaled discounted cumulative gain (SDCG)
• INST

Install with

devtools::install_github("Microsoft/irmetrics-r")
library(irmetrics)

Example:

# a gain or relevance vector: how good each retrieved thing is, in the order they appear
gains <- c(1, 0, 0, 0.5, 1)

# precision at 1 is perfect
P(gains, k=1) #> 1
# precision at 3 is one-third
P(gains, k=3) #> 0.33

# rank-biased precision, with persistence 0.7
RBP(gains, p=0.7) #> 0.42

# Investigate the weight vector, see the metric accumulate, ...
str(INST(gains, T=1))
#> List of 8
#>  $ metric    : num 0.678
#>  $ C         : num [1:5] 0.25 0.444 0.562 0.605 0.605
#>  $ W         : num [1:5] 0.6345 0.1586 0.0705 0.0397 0.024
#>  $ gain      : num [1:5] 1 0 0 0.5 1
#>  $ cum.metric: num [1:5] 0.634 0.634 0.634 0.654 0.678
#>  $ i         : int [1:5] 1 2 3 4 5
#>  $ residual  : num 0.0728

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.