As in #612, this is a to-do list issue that organizes what to do for version 1.0

Edit: released 1.0.0 2024/05/12 since I'm using it for experiments and want a pinned version that I don't need to install with extra pip commands. Will continue working with this to-do list though

Add model abstractions

• Remove entry points for models, metrics, etc. #601
  • I think we can do this right after merging Add abstractions to make it easier to declare and instantiate models #605, more or less?
  • Models are already there without entry points, what we need is a replacement for the whole system of loading them through entry points and then mapping to a config
• CLN: Remove unused 'multiple model' functionality #538
  • this will require removing the logic to load a config file again with an array (list) of model names taken from another table in the file and looping over them
  • question is, what replaces that? Basically just a dict lookup on the models sub-package? getattr(vak.models, 'SomeModel)`?
  • as noted in this issue, changing config option models to model singular is kind of a breaking change
  • so we might as well just introduce the new config format
• ENH: Add timing of training #2 need this for WIP so adding it here

Add dataset abstractions, to set us up to add actual datasets

• ENH: Have prep create a directory with standardized format for each prepared dataset #650
• ENH: Have prep generate learncurve splits ahead of time #651
• ENH: Run validate at end of prep, not start of train/learncurve, and include as "metadata" in dataset #649
• ENH: Add ability for prep to create datasets where the source for training samples $x$ is audio #652

Add models

• ENH: Add ability to run eval with and without post-processing transforms #621
• ENH: rename parameter csv_path -> dataset_path #549
  • do this in version 1.0 since it's a breaking change
• ENH: Convert post-processing to Transform classes that can be specified in config  #537
  • I think this can help with next issue, ENH: Make it as easy as possible to get predictions from a windowed frame classification model #604, as well as related issue ENH: eval should have option compute metrics with and without clean-ups #472
• ENH: Add UMAP models and datasets #631

Do these all at once:

• DEV/ENH: Switch to using tomlkit #685 (do this before the next one)
• change config file format to leverage toml tables #345 -- vak.train.prep, vak.train.model.TweetyNet, vak.eval.prep + vak.eval.model.TweetyNet <-- actually write these up and test them
• ENH: Add dataset sub-table to config file, remove other dataset/transform param keys #748

Then fix this bug:

• ENH: Add TrainerConfig to train/learncurve/eval/predict #691

Then do these in one fell swoop:

• Refactor frame classification models to use single WindowedFramesDatapipe #574
• ENH: Rename datasets to pipes, that have built-in transforms #724

Then finally do this:

• BUG/CLN: Refactor model abstraction so we don't subclass LightningModel, to fix loss logging #737

• ENH: Add AVA models and datasets #674 (finish!)

Add pre-trained models, improve fine tuning functionality:

• Be able to refer to a model in config files with model_path option instead of needing to specify checkpoint_path, labelmap_path, etc., ENH: Add model_path option to config for eval + predict, use in place of checkpoint_path, spect_scaler, etc. #672 and Declare/formalize a model file format #673

• ENH: Add pre-trained model checkpoints  #756

• ENH: Make it possible to specify different splits for datasets #749 -- we are set up to do this with built-in static dataset(s) but should also do it with datapipes for prep'd datasets

• DOC: Finish documenting new model + family abstractions #616 -- we need to actually document this!

• ENH: Add config / params dataclasses for high-level functions #679

• add cli command that generates an empty config file #366 we want to do this after we make changes to API and add "params" classes

• ENH: Add TCN models + DAS datasets #630 -- bumping these up the to-do list since they move us towards actually being a framework and force us to generalize functionality

• ENH: Add object detection model family, models, and datasets #635

• Refactor frame classification models to use single WindowedFramesDatapipe #574

  • It feels like we need to revisit this to decouple VocalizationDataset from transforms
  • Will need to do this for ENH: Add TCN models + DAS datasets #630 anyway

• ENH: Add from_config_toml_path classmethod to vak.models.base.Model #615 (tentative)

• ENH: Make it as easy as possible to get predictions from a windowed frame classification model #604

  • this is part of the reason for doing Add abstractions to make it easier to declare and instantiate models #605, to get to fixing this issue

• ENH: Have default spect_output_dir be output_dir not data_dir #573

  • Need to revisit if this is worth doing, if so it's a breaking change so should be new in 1.0

• rename 'unlabeled_label' -> 'unlabeled_class'; define as constant and use constant for default args #408

• ENH: Add decorator to register models, in vak.models #623

• DOC: Fix tutorial config files after version 1.0.0 gets out of alpha #647

• CLN: Ensure learncurve runs in ascending order of training set size #648

moved here from #612

• ENH: add dataclasses to represents results directories, e.g. TrainResults and LearncurveResults #449
  • do this before next issue since it will make that one easier: we can use TrainResults to help automagically generate a config file for eval and predict
• add cli command that generates an empty config file #366
• DEP: use crowsetta 5.0.0 (post pyOpenSci review) #526
• ENH: Add LabelMap class #582
• TST: Add unit tests for transforms sub-package #585
• ENH: Add type annotations to all functions and classes #548
• ENH: Add app / logging with rich + textualize #613
• move labeled_timebins module to a transforms sub-package, provide class versions #374
  • need to think about how doable this one is without breaking changes
  • done in ENH: Add ability to run eval with and without post-processing transforms #621
• ENH: Rewrite core.prep to use vocles + vocalpy, remove io module #558
  • in theory can do this w/out it being a breaking change, but will require actually developing these libraries more. so putting it near the end