- Adds
lionheart --version
command to CLI.
- Fixes package specification in pyproject.toml
Future note: An upcoming version will contain completely recomputed resource files with changed bin-coordinates to reduce RAM usage of the mosdepth
coverage extraction. At the same time, we will be updating the exclusion bin index files to fix a small discrepency between the shared features and the features extracted with the current lionheart
version. Stay tuned for updates in the coming month(s).
- Adds project URLs to package to list them on the
pypi
site.
- Fixes writing of README in
lionheart predict_sample
. Thanks to @LauraAndersen for detecting the problem. - Improvements to installation guide in repository README.
- Workflow example improvements.
- Improves CLI documentation for some commands (in
--help
pages).
This release adds multiple CLI commands that:
- allow reproducing results from the article and seeing the effect of adding your own datasets:
- Adds
lionheart cross_validate
command. Perform nested leave-one-dataset-out cross-validation on your dataset(s) and/or the included features. - Adds
lionheart validate
command. Validate a model on the included external dataset or a custom dataset. - Adds
lionheart evaluate_univariates
command. Evaluate each feature (cell-type) separately on your dataset(s) and/or the included features.
- expands what you can do with your own data:
- Adds
lionheart customize_thresholds
command. Calculate the ROC curve and probability densities (for deciding probability thresholds) on your data and/or the included features for a custom model or an included model. Allows using probability thresholds suited to your own data when usinglionheart predict_sample
andlionheart validate
. - Adds
--custom_threshold_dirs
argument inlionheart predict_sample
. Allows passing the ROC curves and probability densities extracted withlionheart customize_thresholds
.
Also:
- Adds
matplotlib
as dependency. - Bumps
generalize
dependency requirement to0.2.1
. - Bumps
utipy
dependency requirement to1.0.3
.
- Fixes bug when training model on a single dataset.
- Adds tests for a subset of the CLI tools.
- Fixed model name.