Plot pareto front from an galgo.Obj

plot_pareto(output)

Arguments

output	An object of class `galgo.Obj`

Value

This function returns a scatterplot showing the solutions found by Galgo accross all generations in the solution space, where the Silhouette Fitness is in the x-axis and the survival fitness in the y-axis. A line is drawn over all non-dominated solutions showing the estimated Pareto front

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.3 8 0.008032105 393.5728        1      Inf
#> result.4 2 0.107028979 159.7093        1      Inf
#> result.9 3 0.062615068 190.5994        1 1.485737
#> Generation 2 Non-dominated solutions:
#>            k                      rnkIndex     CrowD
#> result.3   8 0.008032105 393.5728        1       Inf
#> result.4   2 0.107028979 159.7093        1       Inf
#> result.15  4 0.039845463 277.2249        1 0.7459856
#> result.13 10 0.013840014 311.1867        1 0.5847601
#> result.9   3 0.062615068 190.5994        1 0.4983246
#> result.7   3 0.068259214 182.5032        1 0.4737165
#> Generation 3 Non-dominated solutions:
#>            k                      rnkIndex     CrowD
#> result.3   8 0.008032105 393.5728        1       Inf
#>            2 0.112051394 163.1679        1       Inf
#> result.15  4 0.039845463 277.2249        1 0.7213814
#> result.13 10 0.013840014 311.1867        1 0.5667686
#> result.7   3 0.068259214 182.5032        1 0.4891259
#> result.9   3 0.062615068 190.5994        1 0.4827297
#> Generation 4 Non-dominated solutions:
#>           k                      rnkIndex     CrowD
#> result.3  8 0.008032105 393.5728        1       Inf
#>           2 0.114401326 153.2837        1       Inf
#> result.15 4 0.039845463 277.2249        1 0.6030124
#>           3 0.064355189 194.0444        1 0.4787716
#> result.7  3 0.068259214 182.5032        1 0.4688120
#>           2 0.112492725 167.2178        1 0.4583558
#>           8 0.016415191 314.0912        1 0.3761686
#>           7 0.024964997 301.6537        1 0.2891682
#> Generation 5 Non-dominated solutions:
#>           k                      rnkIndex     CrowD
#> result.3  8 0.008032105 393.5728        1       Inf
#>           2 0.114401326 153.2837        1       Inf
#>           2 0.087283673 207.8368        1 0.8643172
#> result.15 4 0.039845463 277.2249        1 0.7930920
#>           8 0.016415191 314.0912        1 0.3852060
#>           2 0.111798727 189.0180        1 0.3276373
#>           7 0.024964997 301.6537        1 0.3022539
#>           2 0.112492725 167.2178        1 0.1144067
plot_pareto(output)