Status of MLDataPattern porting

[CarloLucibello]

A list of what is currently exported from MLDataPattern.jl.

TO PORT

• getobs, getobs! and nobs. numobs and getobs #1
  • nobs is now numobs;
  • obsdim argument is dropped from the interface
• randobs numobs and getobs #1
• datasubset, DataSubset port datasubset #4
• shuffleobs port splitobs, shuffleobs, obsview #5
• splitobs port splitobs, shuffleobs, obsview #5
• DataView port splitobs, shuffleobs, obsview #5
  • Consider removal
• obsview, ObsView port splitobs, shuffleobs, obsview #5
• batchview, BatchView port batchview #6
• batchsize port batchview #6
• slidingwindow, SlidingWindow add slidingwindow #196
• stratifiedobs (now splitobs(..., stratified=y), add stratified options to splitobs #195)
• oversample, undersample add labelmap, oversample, undersample #10
• kfolds add eachobs, eachbatch, kfolds, leavepout #9
• leaveout add eachobs, eachbatch, kfolds, leavepout #9
• eachobs add eachobs, eachbatch, kfolds, leavepout #9
• eachbatch add eachobs, eachbatch, kfolds, leavepout #9

NOT TO BE PORTED

• BufferGetObs
• RandomObs, RandomBatches
• BalancedObs
• FoldView
• targets
• eachtarget

[CarloLucibello]

We can consider this essentially done

[rmkn85]

Hi, what about stratifiedobs and slidingwindow?
Were they explicitly excluded on purpose?
Thanks

[CarloLucibello]

not really, we just didn't port code that we weren't sure was going to be useful. I think stratifiedobs should go in, less sure of slidingwindow but didn't look much into it and alternatives in the ecosystem.

[rmkn85]

Just to clarify, I came here specifically for missing stratifiedobs.
It is needed to replicate the behaviour of Python's sklearn.model_selection.train_test_split([...] stratify=true)

Asked about slidingwindow on the way, since it was the only other one unchecked but not in the list of explicitly "not to be ported", but I don't have any use-case for it.

[kpa28-git]

I use slidingwindow often for time series data. Haven't looked too much for a replacement but the closest I've found is IterTools.jl partition. It has a similar interface but returns a tuple iterator

[kpa28-git]

Also found DSP.Periodograms.arraysplit which is similar to slidingwindow but you set the overlap instead of the stride. So far slidingwindow is the fastest of the three because it returns views.

[CarloLucibello]

stratified split has been implemented in #195