- save xgboost model as json file for transparency
- set connectionDetails to NULL in getPlpData
- updated andromeda functions - restrict to pop and tidy covs for speed
- quick fix for GBM survival predicting negative values
- fixed occasional demoSum error for survival models
- updated index creation to use Andromeda function
- fixed bug when normalize data is false
- fixed bugs when single feature (gbm + python)
- updated GBM
- updated calibration slope
- fixed missing age/gender in prediction
- fixed shiny intercept bug
- fixed diagnostic
- fixed missing covariateSettings in load cvs plp
- Removed plpData from evaluation
- Added recalibration into externalVal
- Updated shiny app for recalibration
- Added population creation setting to use cohortEndDate as timeAtRisk end
- fixed tests
- Reduced imports by adding code to install some dependencies when used
- fixed csv result saving bug when no model param
- fixed r check vignette issues
- added conda install to test
- finalised permutation feature importance
- fixed deepNN index issue (reported on github - thanks dapritchard)
- add compression to python pickles
- removed requirement to have outcomeCount for prediction with python models
- cleaned all checks
- fixed bug in python toSparseMatrix
- fixed warning in studyPop
- fixed bug (identified by Chungsoo) in covariateSummary
- fixed bug with thresholdSummary
- edited threshold summary function to make it cleaner
- added to ensemble where you can combine multiple models into an ensemble
- cleaned up the notes and tests
- updated simulated data covariateId in tests to use integer64
- fixed description imports (and sorted them)
- fixed Cox model calibration plots
- fixed int64 conversion bug
- added baseline risk to Cox model
- updated shiny: added attrition and hyper-parameter grid search into settings
- updated shiny app added 95% CI to AUC in summary, size is now complete data size and there is a column valPercent that tells what percentage of the data were used for validation
- updated GBMsurvival to use survival metrics and c-stat
- added survival metrics
- added updates and fixes into master from development branch
- fixed bug with pdw data extraction due to multiple person_id columns
- fixed bug in shiny app converting covariate values due to tibble
- added calibration updates: cal-in-large, weak cal
- updated smooth cal plot (sample for speed in big data)
- defaulted to 100 values in calibrationSummary + updated cal plot
- fixed backwards compat with normalization
- fixed python joblib dependancy
- fixed bug in preprocessing
- added cross validation aucs to LR, GBM, RF and MLP
- added more settings into MLP
- added threads option in LR
- fixed minor bug with shiny dependency
- fixed some tests
- added standardizedMeanDiff to covariatesummary
- updated createStudyPopulation to make it cleaner to read and count outcome per TAR
- Andromeda replaced ff data objects
- added age/gender into cohort
- fixed python warnings
- updated shiny plp viewer
- Fixed bug when running multiple analyses using a data extraction sample with multiple covariate settings
- improved shiny PLP viewer
- added diagnostic shiny viewer
- updated external validate code to enable custom covariates using ATLAS cohorts
- fixed issues with startAnchor and endAnchor
- Deprecating addExposureDaysToStart and addExposureDaysToEnd arguments in createStudyPopulation, adding new arguments called startAnchor and endAnchor. The hope is this is less confusing.
- fixed transfer learning code (can now transfer or fine-tune model)
- made view plp shiny apps work when some results are missing
- set up testing
- fixed build warnings
- added tests to get >70% coverage (keras tests too slow for travis)
- Fixed minor bugs
- Fixed deep learning code and removed pythonInR dependancy
- combined shiny into one file with one interface
- added recalibration using 25% sample in existing models
- added option to provide score to probabilities for existing models
- fixed warnings with some plots
Small bug fixes:
- added analysisId into model saving/loading
- made external validation saving recursive
- added removal of patients with negative TAR when creating population
- added option to apply model without preprocessing settings (make them NULL)
- updated create study population to remove patients with negative time-at-risk
Changes:
- merged in bug fix from Martijn - fixed AUC bug causing crash with big data
- update SQL code to be compatible with v6.0 OMOP CDM
- added save option to external validate PLP
Changes:
- Updated splitting functions to include a splitby subject and renamed personSplitter to randomSplitter
- Cast indices to integer in python functions to fix bug with non integer sparse matrix indices
Changes:
- Added GLM status to log (will now inform about any fitting issue in log)
- Added GBM survival model (still under development)
- Added RF quantile regression (still under development)
- Updated viewMultiplePlp() to match PLP skeleton package app
- Updated single plp vignette with additional example
- Merge in deep learning updates from Chan
Changes:
- Updated website
Changes:
- Added more tests
- test files now match R files
Changes:
- Fixed ensemble stacker
Changes:
- Using reticulate for python interface
- Speed improvements
- Bug fixes