This PR optionally (and by default) replaces "mutability parsimony" with a poisson S5F context-based likelihood, described in https://github.com/matsengrp/poisson-subs-models/blob/main/main.tex
This likelihood assumes MLE branch lengths, and doesn't require any parameter fitting on the parsimony forest.
The PR also adds two new command line arguments to gctree infer:
--use_old_mut_parsimony allows the user to use the old version of mutability parsimony, instead of the new context-based likelihood.
--branching_process_ranking_coeff allows the user to specify the ranking coefficient for branching process likelihood, in particular allowing branching process likelihood to be ignored altogether. The default value remains -1 as before, but if the provided value is zero, branching process parameter fitting is skipped entirely
The CollapsedForest.filter_trees method was largely rewritten. It should be somewhat faster and provides much better log messages (if verbose flag is set) indicating how tree ranking is performed, which is reassuring when you want to be sure that the ranking coefficients line up with the proper weights. It also warns the user if the sign of a ranking coefficient doesn't match the appropriate optimization function for that weight (e.g. coefficients for likelihoods should be negative, since higher likelihoods are better)
More test cases are now included in tests/smalltest.sh, testing more of the possible cli parameter combinations. There is also a new test in tests/test_likelihoods.py comparing the new context-based likelihood to a couple of hand-computed simple cases.
This PR optionally (and by default) replaces "mutability parsimony" with a poisson S5F context-based likelihood, described in https://github.com/matsengrp/poisson-subs-models/blob/main/main.tex This likelihood assumes MLE branch lengths, and doesn't require any parameter fitting on the parsimony forest.
The PR also adds two new command line arguments to
gctree infer:--use_old_mut_parsimonyallows the user to use the old version of mutability parsimony, instead of the new context-based likelihood.--branching_process_ranking_coeffallows the user to specify the ranking coefficient for branching process likelihood, in particular allowing branching process likelihood to be ignored altogether. The default value remains-1as before, but if the provided value is zero, branching process parameter fitting is skipped entirelyThe
CollapsedForest.filter_treesmethod was largely rewritten. It should be somewhat faster and provides much better log messages (ifverboseflag is set) indicating how tree ranking is performed, which is reassuring when you want to be sure that the ranking coefficients line up with the proper weights. It also warns the user if the sign of a ranking coefficient doesn't match the appropriate optimization function for that weight (e.g. coefficients for likelihoods should be negative, since higher likelihoods are better)More test cases are now included in
tests/smalltest.sh, testing more of the possible cli parameter combinations. There is also a new test intests/test_likelihoods.pycomparing the new context-based likelihood to a couple of hand-computed simple cases.