I’m an AI researcher in the computational pathology, Oncology R&D team at AstraZeneca and an open-source enthusiast working on machine learning for biomedical applications. My research interests are time-to-event analysis (survival analysis) and causal inference. Previously, I worked at the lab for Artificial Intelligence in Medical Imaging at the Technical University of Munich and The Institute of Cancer Research, London. I’m the author of scikit-survival, a machine learning library for survival analysis built on top of scikit-learn.
PhD in Computer Science, 2016
Technische Universität München
MSc in Bioinformatics, 2011
Ludwig-Maximilians-Universität & Technische Universität München
BSc in Bioinformatics, 2008
Ludwig-Maximilians-Universität & Technische Universität München
Today marks the release of scikit-survival 0.21.0. This release features some exciting new features and significant performance improvements:
I’m pleased to announce the release of scikit-survival 0.18.0, which adds support for scikit-learn 1.1.
In addition, this release adds the return_array
argument to all models providing
predict_survival_function
and predict_cumulative_hazard_function
.
That means you can now choose, whether you want to have the survival
(cumulative hazard function) automatically evaluated at the unique event
times. This is particular useful for plotting. Previously, you would have
to evaluate each survival function before plotting:
I recently wanted to create a development container for VS Code to develop applications using SYCL based on the CUDA backend of the oneAPI DPC++ (Data Parallel C++) compiler. As I’m running Fedora, it seemed natural to use Podman’s rootless containers instead of Docker for this. This turned out to be more challenging than expected, so I’m going to summarize my setup in this post. I’m using Fedora Linux 36 with Podman version 4.1.0.
I’m pleased to announce the release of scikit-survival 0.17.2. This release fixes several small issues with packaging scikit-survival and the documentation. For a full list of changes in scikit-survival 0.17.2, please see the release notes.
Most notably, binary wheels are now available for Linux, Windows, and macOS (Intel).
This has been possible thanks to the cibuildwheel
build tool, which makes it incredible easy to use GitHub Actions for building
those wheels for multiple versions of Python.
Therefore, you can now use pip
without building everything from source by
simply running
pip install scikit-survival
As before, pre-built conda packages are available too, by running
conda install -c sebp scikit-survival
This release adds support for scikit-learn 1.0, which includes support for feature names. If you pass a pandas dataframe to fit
, the estimator will set a feature_names_in_
attribute containing the feature names. When a dataframe is passed to predict
, it is checked that the column names are consistent with those passed to fit
.
The
example below
illustrates this feature.
For a full list of changes in scikit-survival 0.17.0, please see the release notes.
scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while …