- First release on PyPI.
- Minor documentation changes
- Renamed some internal variables
- Introduced new feature: regress_out_feat
- Major renaming of variables for concistency
- Added L-BFGS optimizer in addition to Adam. Use optimizer="lbfgs" in Concise()
- New function: :code:
best_kmers
for motif efficient initialization
- refactor: Removed regress_out feature
- feature: multi-task learning
- bugfix: multi-task learning
- same as 0.4.1 (pypi upload failed for 0.4.1)
- feat: added early_stop_patience argument
- fix: When X_feat had 0 columns, loading its weights from file was failing.
- feat: When training the global model in ConciseCV, use the average number of epochs yielding the best validation-set accuracy.
- fix: Update tensorflow function (tf.op_scope -> tf.name_scope, initialize_all_variables -> tf.global_variables_initializer)
- Complete re-write. Now moved completely to Keras from pure TensorFlow.
- fix: required version keras>=2.0.2
- added more documentation
- ipynb -> docs compilation
- hocomoco motif database added to
concise.data.hocomoco
- added utility function for position extraction
- fix: hyopt and memoization of data() now works
- implemented seqlogo plot
- Added more documentation
- hyopt.CompileFN: loss_metric -> optim_metric
- eval_metrics: y,z -> y_true, y_pred arguments
- speedup encodeSequences by 4x
- Added
gtfparse
dependency for parsing the gtf files
- cosmetic changes to get rid of warnings due to external package updates
- https://github.com/gagneurlab/concise/commit/cf9ec50238e5ef3a9fee4a6bcb57ea24bfaca09d
- https://github.com/gagneurlab/concise/commit/761571772050ea1b892c6c3d89c1beebd85430ff
- https://github.com/gagneurlab/concise/commit/9277d15b6f97d923ecc0297c5b40f80dba382a54
- https://github.com/gagneurlab/concise/commit/2c704312203c388e1b2bde165c26ea647011d600