Inferelator

Latest version: v0.6.2

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0.6.2

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New Functionality:

- Generates & reports non-bootstrap model weights as part of results
- Saves full model information into an h5ad file
- Added new experimental prediction modules
- Added new preprocessing & normalization options

Code Refactoring:

- Logging messages now use logging module

Bug Fixes:

- Fixed several errors when sparse data was passed unexpectedly
- Corrected several deprecated numpy calls
- Updated calls and version requirement to anndata

0.6.1

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New Functionality:

- Extended support for mRNA velocity & decay calculations
- Added new experimental TFA modules

Code Refactoring:

- Workflow, ResultProcessor, and InferelatorData restructured for readability and clearer commenting

Bug Fixes:

- Slicing individual gene data returns numpy vector instead of anndata view
- Corrected several deprecated pandas calls to eliminate FutureWarnings

0.6.0

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New Functionality:

- Support for grouping arbitrary genes from multiple tasks into learning groups
- Workflow to learn homology groups together
- Workflow to explicitly incorporate velocity and decay into learning
- Added support for batching parallelization calls to reduce overhead when data is relatively small

Code Refactoring:

- Refactored multi-task learning to parameterize tfs and genes for each task
- Refactored parallelization around joblib & dask
- Removed pathos and replaced with joblib
- Optimized StARS-LASSO by replacing standalone LASSO with lasso_path

Bug Fixes:

- Fixed several messages to be more informative
- use_no_prior is appropriately applied in multitask learning

0.5.8

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Bug Fixes:

- Corrected combining multi-task gene and tf labels

0.5.7

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New Functionality:

- Added support for numba acceleration of AMuSR with ``.set_run_parameters(use_numba=True)`` (PR 46)

Code Refactoring:

- Updated example scripts
- Removed deprecated KVS multiprocessing and associated code

Bug Fixes:

- Gene labels are included as the first column of the produced confidences TSV file by default
- Matplotlib backend selection checks for non-interactive mode

0.5.6

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New Functionality:

- Added code to randomly generate noise in prior with ``.set_shuffle_parameters(add_prior_noise=None)``
- Added in-workflow benchmarks for CellOracle and pySCENIC


Code Refactoring:

- Minor changes to matplotlib interface
- Improved testing for multitask workflows
- Improved error messaging around prior and gold standard
- Switch from Travis.ci to GitHub Actions for continuous integration

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