Convert galgo.Obj to list

Source: R/convert-functions.R

The current function transforms a galgo.Obj to a list

to_list(output)

Arguments

output

An object of class galgo.Obj

Value

The current function restructurates a galgo.Obj to a more easy to understand an use list. This output is particularly useful if one wants to select a given solution and use its outputs in a new classifier. The output of type list has a length equals to the number of solutions obtained by the galgo algorithm.

Basically this output is a list of lists, where each element of the output is named after the solution's name (solution.n, where n is the number assigned to that solution), and inside of it, it has all the constituents for that given solution with the following structure:

  1. output$solution.n$Genes: A vector of the features included in the solution

  2. output$solution.n$k: The number of partitions found in that solution

  3. output$solution.n$SC.Fit: The average silhouette coefficient of the partitions found

  4. output$solution.n$Surv.Fit: The survival fitness value

  5. output$solution.n$Rank: The solution rank

  6. CrowD: The solution crowding distance related to the rest of the solutions

Examples

# load example dataset
library(breastCancerTRANSBIG)
data(transbig)
Train <- transbig
rm(transbig)
#> Warning: object 'transbig' not found

expression <- Biobase::exprs(Train)
clinical <- Biobase::pData(Train)
OS <- survival::Surv(time = clinical$t.rfs, event = clinical$e.rfs)

# We will use a reduced dataset for the example
expression <- expression[sample(1:nrow(expression), 100), ]

# Now we scale the expression matrix
expression <- t(scale(t(expression)))

# Run galgo
output <- GSgalgoR::galgo(generations = 5, population = 15,
prob_matrix = expression, OS = OS)
#> Using CPU for computing pearson distance
#> Generation 1 Non-dominated solutions:
#>           k                     rnkIndex     CrowD
#> result.1  2 0.12942719 122.4747        1       Inf
#> result.2  4 0.07083137 195.2294        1 1.0371665
#> result.4  5 0.05675895 221.9400        1 0.5267322
#> result.12 6 0.02857908 236.1127        1       Inf
#> Generation 2 Non-dominated solutions:
#>           k                     rnkIndex     CrowD
#> result.1  2 0.12942719 122.4747        1       Inf
#> result.15 8 0.03818634 316.2620        1       Inf
#> result.2  4 0.07083137 195.2294        1 0.9097067
#> result.4  5 0.05675895 221.9400        1 0.6546047
#> Generation 3 Non-dominated solutions:
#>           k                     rnkIndex     CrowD
#> result.1  2 0.12942719 122.4747        1       Inf
#> result.15 8 0.03818634 316.2620        1       Inf
#> result.2  4 0.07083137 195.2294        1 0.8462842
#> result.4  5 0.05675895 221.9400        1 0.6323392
#> Generation 4 Non-dominated solutions:
#>           k                     rnkIndex     CrowD
#> result.1  2 0.12942719 122.4747        1       Inf
#> result.15 8 0.03818634 316.2620        1       Inf
#>           3 0.07897683 183.1320        1 0.6502882
#> result.4  5 0.05675895 221.9400        1 0.6198473
#> result.2  4 0.07083137 195.2294        1 0.2824740
#> Generation 5 Non-dominated solutions:
#>           k                     rnkIndex     CrowD
#> result.1  2 0.12942719 122.4747        1       Inf
#> result.15 8 0.03818634 316.2620        1       Inf
#>           2 0.08123501 224.5801        1 1.0435306
#>           2 0.09975443 130.1886        1 0.6752811
outputDF <- to_dataframe(output)
outputList <- to_list(output)