scikit-survival 0.3 released

  • 1 August 2017
  • sebp

Today, I released a new version of scikit-survival, a Python module for survival analysis built on top of scikit-learn.

This release adds predict_survival_function and predict_cumulative_hazard_function to sksurv.linear_model.CoxPHSurvivalAnalysis, which return the survival function and cumulative hazard function using Breslow's estimator.

Moreover, it fixes a build error on Windows (#3) and adds the sksurv.preprocessing.OneHotEncoder class, which can be used in a scikit-learn pipeline.

Announcing scikit-survival – a Python library for survival analysis build on top of scikit-learn

  • 29 December 2016
  • sebp

I've meant to do this release for quite a while now and last week I finally had some time to package everything and update the dependencies. scikit-survival contains the majority of code I developed during my Ph.D.

About Survival Analysis

Using IPython for parallel computing on an MPI cluster using SLURM

  • 31 May 2015
  • sebp

IPython and IPython notebook (or Jupyter as it is now called) are great tools that make interactive Python work incredibly easy. If you want to do large-scale computations, like nested cross-validation I mentioned in a previous post, you want to automatically distribute your work across multiple compute nodes instead of interactively looking at results. Thankfully, the folks behind IPython provide IPython.parallel, which is a library for parallel computing. It is versatile in the sense that you can choose whether you want to distribute your work locally, via SSH or MPI by simply adjusting config files.

MPI-based Nested Cross-Validation for scikit-learn

  • 29 March 2015
  • sebp

If you are working with machine learning, at some point you have to choose hyper-parameters for your model of choice and do cross-validation to estimate how well the model generalizes to unseen data. Usually, you want to avoid over-fitting on your data when selecting hyper-parameters to get a less biased estimate of the model's true performance. Therefore, the data you do hyper-parameter search on has to be independent from data you use to assess a model's performance.

GitLab 6 + Apache + Phusion Passenger

  • 28 September 2013
  • sebp

I've successfully been running an instance of GitLab for almost a year now. The same server is running Redmine, hence both GitLab and Redmine are running in their respective sub-directories. Phusion Passenger is my application server of choice. Unfortunately, it became increasingly difficult to keep this setup running with newer versions of GitLab. First, GitLab officially is not supporting running it out of a sub-directory, second, by default it uses Unicorn. Here, I want to detail my setup how you still can achieve the GitLab + Apache + Phusion Passenger combo, because I could only find slightly outdated guides online.

PyGObject 3.1.0 released

  • 6 February 2012
  • sebp

I am pleased to announce version 3.1.0 of the Python bindings for GObject. This is the first release of the unstable branch, which will eventually result in the stable 3.2.x series.

It is important to point out that this release reverts the change "Convert all strings to utf-8 encoding when retrieving from TreeModel" to restore backwards compatibility. If you are using Python 2 it is recommended to always use the byte-representation of UTF-8 encoded strings (str class) instead of the unicode objects. I'm going to add a section to the Python GTK+ 3 tutorial that will explain this issue and how to deal with it in more detail.

Download

http://download.gnome.org/sources/pygobject/3.1/pygobject-3.1.0.tar.xz

sha256sum: a5b36eff7c4b14f161bc9ba2ae09a03ddb47d9f2c769589b8f389ae3a92cc92e

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