start antolini implementation

[RaphaelS1]

No description provided.

[RaphaelS1]

Hi @bblodfon this starts an Antolini implementation. I've used a new method for the Cindex just using R vectorisation. To demonstrate it I've shown how you can also use it with Harrell's C and it produces identical results (within 1e-6) at significantly faster time. The current Antolini implementation might be broken though as frequently seeing C<0.5, I suspect the bug is in lines 6-7 when calling the distr6 function, as you did this recently with several models/measures please could you take a look at that line? Once happy I'll code it into an actual measure

[bblodfon]

with the updated code, the use of the distr6 function is correct now, but still we get Antolini C-indexes < 0.5 so there is probably something wrong in the logic of the code itself?

What doesn't make sense to me is that scores coming from S(T) from Antolini's and the crank from Harrell's are "treated" the same way in the code but haven't checked into more details yet...

I did a small benchmark to check the output values:

set.seed(42)
bmr = benchmark(benchmark_grid(
  tasks = tsks(c("rats", "gbcs", "grace")),
  learners = lrn("surv.coxph"),
  resamplings = rsmp("cv", folds = 3)
))
bmr$score()$surv.cindex 

for (i in 1:3) {
  for (p in bmr$resample_results$resample_result[[i]]$predictions()) {
    print(cindex(pred = p, meth = "A")) # "H" => checking Harrell's C is the same as above (YES)
  }
}

[RaphaelS1]

with the updated code, the use of the distr6 function is correct now, but still we get Antolini C-indexes < 0.5 so there is probably something wrong in the logic of the code itself?

What doesn't make sense to me is that scores coming from S(T) from Antolini's and the crank from Harrell's are "treated" the same way in the code but haven't checked into more details yet...

I did a small benchmark to check the output values:

set.seed(42)
bmr = benchmark(benchmark_grid(
  tasks = tsks(c("rats", "gbcs", "grace")),
  learners = lrn("surv.coxph"),
  resamplings = rsmp("cv", folds = 3)
))
bmr$score()$surv.cindex 

for (i in 1:3) {
  for (p in bmr$resample_results$resample_result[[i]]$predictions()) {
    print(cindex(pred = p, meth = "A")) # "H" => checking Harrell's C is the same as above (YES)
  }
}

Well not treated the same, the inequality is reversed https://github.com/mlr-org/mlr3proba/pull/343/files#diff-033f1cd099ed95e401392d6763a5b44330ec434215f754990b0c0215220a7538R32-R36