chore: trigger release process #993
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Feb 13, 2025
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…990) * build(deps): update scipy requirementV --------- Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: project-defiant <[email protected]>
* feat: add LOF curation ingestion step and config * feat: add LOF annotations path to variant_index step * feat: add parser for LOF data * feat: add function to normalise LOF assessments * feat: add function for creating LOF description * feat: enable annotation of VariantIndex with the variantDescription field * refactor: rename inSilicoPredictors to variantEffect * refactor: rename inSilicoPredictors to variantEffect in merged files * chore: consolidate variant annotation paths into list
* Update _ukb_ppp_eur.md Original page hosted broken link for synapse and uninformative title for page * chore: pre-commit auto fixes [...] --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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* feat(prediction): add `model` as instance attribute * feat: added `convert_map_type_to_columns` spark util * feat(prediction): new method `explain` returns shapley values * feat(prediction): `explain` returns predictions with shapley values * chore: compute `shapleyValues` in the l2g step * refactor: use pandas udf instead * refactor: forget about udfs and get shaps single threaded * chore: remove reference to chromatin interaction data in HF card * fix(l2g_prediction): methods that return new instance preserve attribute * feat(dataset): `filter` method preserves all instance attributes * feat(l2gmodel): add features_list as model attribute and load it from the hub metadata * fix: pass correct order of features to shapley explainer * feat(l2g): predict mode to extract feature list from model, not from config * feat(l2g): pass default features list if model is loaded from a path * feat(l2gmodel): add features_list as model attribute and load it from the hub metadata * feat(l2g): predict mode to extract feature list from model, not from config * feat(l2gprediction): add `model` as attribute * feat(l2gmodel): add features_list as model attribute and load it from the hub metadata * feat(l2g): predict mode to extract feature list from model, not from config * feat(l2gprediction): add `model` as attribute * chore: fix typo * chore: remove `convert_map_type_to_columns` * feat(l2gprediction): refactor feature annotation and change schema * chore: pre-commit auto fixes [...] * feat: report as log odds * feat: calculate scaled probabilities * chore(l2gprediction): remove shapBaseProbability * chore: correct typo in add_features and make schemas non nullable * fix: rename columns in pandas df after pivoting * fix: add raw shap contributions * fix(model): when saving create directory if not exists * feat(l2g): bundle model and training data in hf * feat(model): include data when loading model * feat: final version of shap explanations * fix: do not infer features_list from df * fix: get_features_list_from_metadata returned cols that were not features * refactor(model): read training data in the local filesystem w pandas * chore: successful run, remove test
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