llama (version 0.6)

misc: Convenience functions

Description

Convenience functions for computing predictions based on input data.

Usage

vbs(data = NULL)
singleBest(data = NULL)
singleBestByCount(data = NULL)
singleBestByPar(data = NULL, factor = 10)
singleBestBySuccesses(data = NULL)

Arguments

data

the data to use. The structure returned by input.

factor

the penalization factor to use for non-successful choices. Default 10.

Value

• A list of predictions, that is, data frames with columns algorithm and score. The data frames are sorted such that the most preferred algorithm is first. The score is 1 if the respective algorithm is chosen for the instance, 0 otherwise.

Details

vbs and singleBest take a data frame of input data and return predictions that correspond to the virtual best and the single best algorithm, respectively. The virtual best picks the best algorithm for each instance. The single best picks the algorithm that has the best cumulative performance over the entire data set.

singleBestByCount returns the algorithm that has the best performance the highest number of times over the entire data set. Only whether or not an algorithm is the best matters for this, not the difference to other algorithms.

singleBestByPar aggregates the PAR score over the entire data set and returns the algorithm with the lowest overall PAR score. singleBestBySuccesses counts the number of successes over the data set and returns the algorithm with the highest overall number of successes.

Examples

data(satsolvers)

# number of total successes for virtual best solver
print(sum(successes(satsolvers, vbs)))
# number of total successes for single best solver by count
print(sum(successes(satsolvers, singleBestByCount)))

# sum of PAR10 scores for single best solver by PAR10 score
print(sum(parscores(satsolvers, singleBestByPar)))

# number of total successes for single best solver by successes
print(sum(successes(satsolvers, singleBestBySuccesses)))