Update lifelines fork #1
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…ive-to-ctv Adding baseline cumulative to ctv
…v0.14.4 * 'v0.14.4' of github.com:CamDavidsonPilon/lifelines: more docs and tests clean up adding baseline cumlative hazard and survival to ctv
Properly forward fill cumulative sums when making covariate timeline
…v0.14.4 * 'v0.14.4' of github.com:CamDavidsonPilon/lifelines: Properly forward fill cumulative sums when making covariate timeline
Finally got around to checking KM for propensity scores/inverse probability weights. When running the KM fitter, if weights=df['weights'] is not converted to a np.array, then unexpected behaviors occur. I have rewritten survival_table_from_events to convert the specified weights to a np.asarray(). Without this change the user would have to specify weights=np.asarray(df['weights']). Results were compared to SAS and consistent results were obtained with this fix
Added a test for KM survival estimate with weights and an unaligned index
Fixed issues (removed redundant pandas dataframe connection and called KaplanMeierFitter correctly
Fix error for IPW Kaplan Meier
* 'master' of github.com:CamDavidsonPilon/lifelines: Update test_estimation.py Update test_estimation.py Fix error for IPW Kaplan Meier
* tests pass, though things feel slow * interval inference with right censored data works * initial tests against icenreg look positive * okay I think univariate models can do interval censoring now * univariate models now do interval * starting to add tests against R * mo-mo-monster PR * need to add some more tests * adding the diabetes dataset * renam start and stop to lower bound and upper bound * bump version * fix left censoring * fix plotting
* removing axes from errorbar_kwargs if provided * removing pyplot calls if axes is present
…er support for interval in survival_table_from_events
* Doc improvements * Adding piecwise regression fitter (no tests yet though) * New default behaviour for `predict` in KaplanMeier and NelsonAalen: use the previous value, interpolate with interpolate flag. * docs and changelog * Adding some tests * clean tests * only tests /tests * some cleanup * change how I internally track how censoring is kept * bump version * fix tests
* Fixed scipy version to <=1.2.1 Fixed scipy version to <= 1.2.1 until autograd HIPS/autograd#490 is released. After that we could fix autograd to >= whatever version they release with that specific PR and return scipy to >=1.0. * Fixed scipy to >=1.0,<=1.2.1, changed base-requirements.txt for travis
The warning text refers to `lifelines.utils.datetime_to_durations` but should refer to `lifelines.utils.datetimes_to_durations` (notice "s" after datetime).
…ting sklearn models (#736) * Added an adapter allowing to evaluate plug lifelines models to the stuff expecting sklearn models * Some minor improvements in adapter
* allowing users to plot HR instead of log(HR) * allow users to subset columns to test ph assuptions * make covariates_from_event_matrix handle nulls better * fix this * update some docs " * bump version * Sklearn adapter v0.1 (#738) * Fixing reference in warning text for "unexpected datetype" (#729) The warning text refers to `lifelines.utils.datetime_to_durations` but should refer to `lifelines.utils.datetimes_to_durations` (notice "s" after datetime). * Added an adapter allowing to plug lifelines models to the stuff expecting sklearn models (#736) * Added an adapter allowing to evaluate plug lifelines models to the stuff expecting sklearn models * Some minor improvements in adapter * Adding new sklearn adapter + docs * well this works, but I hate the globals() hack * lots of updates to sklean adapter * lots of updates to sklean adapter * change _fitter to lifelines_model * some linting
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