Need to provide a "skip model" or "skip sample" functionality

[PeterDSteinberg]

In the process of semi-random sample formation from file iterators, it happens that one may select a file unsuitable for learning. There are currently ways of filtering files on metadata and file name, but if all or some of the sample_pipeline has to be run in order to determine whether to skip the sample, then we have no system of dealing with this.

• [ ] We should provide a way in run_sample_pipeline to end the sample pipeline early on a statistical filter (such removing an image of all-night conditions with a zero-variance filter).
• [ ] Also a fit in fit.py should be able to return an unfitted model and info about why fitting could not happen.

[PeterDSteinberg]

Converting this to a documentation issue:

• [ ] In an evolutionary search, if an invalid sample / fit operation occurs, then the objective function should return a Inf (minimization objective) or -Inf (maximization objective). If this type of functionality is needed, then use scikit-learn make_scorer on a custom function to return Inf/-Inf as needed.
• [ ] In other cases, see the dask-searchcv pattern of raising and handling fitting exceptions.