Description Usage Arguments Value
When large-scale experiments are performed, e.g., in machine learning studies
or benchmarking of multi-objective optimization algorithms, usually the results of experimental
runs are somehow stored in files in the file system. These files are not stored arbitrarily, but
rather the way they are stored follows some folder structure. Imagine we run a stochastic algorithm
“myalgo” in total for 10 replications on problem instances “instance-1” and “instance-2”.
The algorithm has parameters. Here, we assume the logical parameter a and the numeric parameter
b to be varied. Moreover, assume that the root folder of results files is “/results/”.
Then, a possible folder structure below “/results/” is
“/results/instance-1/myalgo_TRUE_0.5/1/res.csv”. In order to evaluate the data one usually needs
to gather all or partial results in a single, e.g., data.frame
and append the instance, algorithm
parameter and replication. The following import
function does exactly this in a convenient
manner. Here, we pass a character vector of full files paths and an essential format string. The latter
is the very nice part of the function and lets the user specify the names and atomic data types of the
algorithm parameters with a nice notation. For the upper example we would set param.format.string
to “/results/instancec/algorithmc_al_bn/replicationi/filename.csv”. All fragments in the
format string with an “{.}” appended are interpreted as parameter which should be imported from
the file(s) paths. The values inside the curly braces specify the data type, e.g., “i” for “integer”.
1 2 3 |
files |
[ |
param.sep |
[ |
param.format.string |
[ |
append.params |
[ |
parser |
[ |
combiner |
[ |
continue.on.error |
[ |
... |
[any] |
any
Reduced results (data.frame
by default).
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