pycox - Update python version

- Python
Published by havakv over 1 year ago

pycox - Fix breaking updates in sklearn

- Python
Published by havakv about 4 years ago

pycox - Bug fixes and dedicated data storage

- Python
Published by havakv about 5 years ago

pycox - Fix numba changes and update python and pytorch version

- Python
Published by havakv almost 6 years ago

pycox - Update to work with torchtuples v0.2.0

Release notes

Features

• Administrative Brier score and Binomial log-likelihood for evaluation of data sets with administrative censoring.

• BCESurv which is a method that disregards censoring and does not enforce monotone survival functions. It is meant to represent a set of binary classifiers that disregards censored observations.

• Improved kkbox data sets with administrative censoring times and more covariates.

• sac_admin5 simulated data set with administrative censoring.

• More simulations studies with covariate dependent censoring times and administrative censoring.

Changes

• Updated to work with torchtuples v.0.2.0

• CoxPH now use a regular data set, instead of the durations sorted. The old method is renamed CoxPHSorted but will be removed.

- Python
Published by havakv about 6 years ago

pycox - PyPI package

Minor bug fixes and release to PyPI.

- Python
Published by havakv about 6 years ago

pycox - v0.1.0

Release notes v0.1.0

These note mainly focus on the changes to existing code and not new functionality.

Evaluation criteria

• Fixed wrong index in IPCW.
• EvalSurv now has a steps argument determining how the survival curve should behave between estimated times. Previously set to 'pre', but now 'post' is default. This will affect the concordance for the discrete-time methods the most. Set ev.step = 'pre' to obtain old results. Or use some reasonable interpolation scheme.
• Moved pycox.evaluation.utils to pycox.utils.
• Replaced the binomial log-likelihood mbll with the negative binomial log-likelihood nbll. I.e. only the sign is different.

Models

• Replaced predict_survival_function with predict_surv and predict_surv_df.
• More stable version of CoxCC and CoxTime loss for single control.
• Restructured the locations of the Cox models.

Preprocessing

• Added quantiles discretization for methods.

- Python
Published by havakv over 6 years ago