API brainstorming

[kushalkolar]

From reading the sklearn design decisions paper:

• Transformers
  • motion correction
  • with scoring function if we can find good metrics
  • high pass filtering
  • any other arbitrary function that makes the data easier to use for a sucessive step:
  • percentile filtering
  • min frame substraction

All transformers must be symmetric in their input and output. Arbitrarily pass the output of one transformer into another.

• Predictors

  • spatial initialization, unsupervised soft clustering

• Estimators

  • noise estimation
  • signal extraction

Any post processing has to be done with the output of an estimator, NOT by the estimator itself. Following the sklearn design principles, a model/estimator should be a discrete unit. It takes parameters and inputs, and fits a model. That is it. Any curration of the model must be done elsewhere, for example with CNMF filtering components based on other parameters would be done by another object, perhaps a predictor or another estimator? -> This would also make unit testing much much easier! And easier to build visualization.

Any matrices or vectors uses for initializing a model must be a parameter to the constructor. For example: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparsePCA.html#sklearn.decomposition.SparsePCA

Must support multiple backends (cpu, gpu) and lazy compute, especially for visualization. Perhaps dask. Keep WGPU for compute in mind from the beginning. Follow sklearn development on array-API stuff.

[kushalkolar]

OK I've read through the 2013 design doc paper, need to go through this next: https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator