ENH: Minimize duplication of data when preparing datasets for frame classification models

[NickleDave]

Currently running vak prep results in a lot of duplicated data, especially for learning curves. For the TweetyNet canary dataset, this means prepared datasets can balloon to ~700 GB of data and quickly use up space on a drive.

There's a couple of ways that duplication happens:

• We prepare the spectrograms as npz files, that include the frequencies and times vectors
• Then we extract just the spectrogram matrixes from these files, and we save these as additional files in the split directories, with the frames.npy extension
• We also leave the spectrogram files untouched, the logic being that they contain a kind of data we might want to use
• Additionally when we create splits for learncurves, we duplicate the training data again, in a separate directory for each split

I think we should fix this in the following ways

• Get rid of the spectrogram files after we get the "frames" out of them.
  • I would prefer to keep intermediate steps of data processing for reproducibility but I think we just need to conceive of "prep" as giving us the final transformed data from whatever the input is, so we don't end up using up all of people's storage space
• No longer make separate splits directories for each subset of the training set we use in learncurve
• Instead we will achieve the learncurve functionality as follows:
  • Represent "splits" separately from "subsets" of a split, like the subsets of the training data split that we make for learning curves:
  • We will add the learning curve subset names (e.g. "duration 300s, replicate 9") to a 'subset' column in the csv representing the dataset -- so some samples will get repeated in multiple rows of the dataset csv, but we won't have multiple copies of the same file
  • We will add a subset parameter to dataset classes
  • If specified, this parameter overrides the split, specifically when it is used to grab the rows of the dataframe (loaded from the csv) that correspond to the specified split. We instead grab all the rows that correspond to a subset when it is specified. If None is specified, we use the whole split.

[NickleDave]

• Add this subset parameter for ParametricUMAP datasets too even though we haven't implemented the learning curve yet