we regret:

[GiadaLalli]

this comment has been removed

[GiadaLalli]

ok, so: there's a ton of sh*t that fell from above during this weekend, and here it is a clearer explaination:

• we always went blabbering that the library is PyTorch based but never actually used PyTorch implementation, so all the metrics needs to be "translated" into torch-runnable metrics; this was already possible for 3 of them (dot prod was already implemented with torch + pearson :

  def __pearson_metric_t(first, second):
  if (first.dim(), second.dim()) == (1, 1):
      return t.corrcoef(t.tensor(first,second))[0,1]
  
  combined = t.cat([first, second], axis=1)
  return t.corrcoef(combined.T)[: first.shape[1] - 1, first.shape[1] :]

  • spearman :

    
    def __spearman_metric_t(first, second):
    if (first.dim(), second.dim()) == (1, 1):
    X = t.argsort(first)
    Y = t.argsort(second)
    combined = t.cat([X, Y], axis=1)
    return t.corrcoef(combined.T)[0,1]

  X = t.argsort(first, dim=0)
  Y = t.argsort(second, dim=0) combined = t.cat([X, Y], axis=1) return t.corrcoef(combined.T)[: first.shape[1] - 1, first.shape[1] :]

• we also still have the problem of not having LD for SNP-data (which means the unmapped, discrete); both LD and mutual_info should be usable only for that data type - moreover, I cannot find a way to "translate mutual_info" from sklearn to torch, it's basically too much work and it's not worth it, so I wanted to try and use these 2 metrics as "external metrics" but it doesn't work, so we'll have to figure out why

• Daniele requested me to benchmark all the metrics (this shitty paper will be the end of me, I'll never be able to run all the things in a suitable time), which means: running Pearson/Spearman Scipy version + Mutual_info Sklearn version + Dot Prod Numpy version, LD Scikit-allel version VS a) all the metrics I have in torch (so Pearson, Spearman and Dot Prod) CPU speed b) all the metrics I have in torch (so Pearson, Spearman and Dot Prod) GPU speed - and this is a problem already moreover he want this done in a very specific way which will take me forever

• cuda activation should be added as a parameter of the isn computation functions (I have no idea how to say this better, basically for activating the GPU for the metrics I need to add every damn time these lines that I post here as an example:

  def dot_metric(first, second):
  if torch.cuda.is_available(): 
      dev = "cuda:0" 
  else: 
      dev = "cpu" 
  
  device = torch.device(dev) 
  first = first.to(device)
  second = second.to(device)
  return torch.matmul(first.permute(*torch.arange(first.ndim - 1, -1, -1)), second)

• another problem worth mentioning: tractor doesn't work anymore; or better said: what last week took 5 seconds for computation, now takes 145 secs, which means that the benchmark analysis is completely f*cked up and I cannot say anymore that "tractor is the fastest bs for computing ISNs" as it would not be true

[MaybeJustJames]

Closed by #30